The first three chapters of the book are targeted towards those who want to get a basic understanding of index funds. The first chapter talks about the massive growth of indexing and hence index funds & ETFs. The second chapter walks the reader through the history of various fund structures that came before index funds and ETFs. The third chapter gives a laundry list of entities that have benefited from the rise of index funds.

Chapters 4, 5 and 6 are different from the usual ETF marketing literature as they highlight the problems with index funds and ETF investing

Problems of Theory and Practice

The chapter highlights various problematic aspects of indexing

  • “In the long run, stocks always go up. Hence pick an index and stay invested”. The underlying assumption is that returns earned by business are ultimately translated in to returns earned by the stock market. This is the Efficient Market Hypothesis touted by academicians. However the market has always shown cyclical behavior and the average time that supposedly long term term investor stays in any fund in 3.5 years. Given these facts, the popular philosophy of “buy and index and forget it”, looks good on paper but ultimately fails to deliver
  • Risk = Reward Fallacy: The distribution of risk and fair returns is not a linear relationship. You might take on too much risk, not being aware of it, and pay for it in the long-term as markets slowly adjust price/risk anomalies, leaving you with years of unexpected under-performance.
  • Markets Always Rise Fallacy – This has been debunked in many markets across many time periods
  • Popular Indices are primarily Market Cap weighted, i.e. popular companies of the season make it to the index and make the index overpriced. Market cap-weighted indices will always over-represent the industries that cause a bubble in the first place. Index fund holders would always suffer the consequences of such market anomalies more than they actually deserved or even wanted.
  • Lot of Capital chasing passive industry might make market overvalued
  • Increased Margin Lending: To reduce operational costs, most funds are participants in the margin lending business, which in turn fuels short sellers activities
  • Companies outside of index face more difficult environments: In a world where market capitalization is the key factor of stock benchmarks today, companies with a larger market capitalization experience a disproportionate flow of trading activity in their shares, making it much easier for those companies to raise fresh capital at favorable conditions through stock issuance or bond finance. Keep in mind that this quirk goes back to the issues of market cap-weighted indices. Companies not included in a specific index don’t get buying attention from countless index funds. These companies have a much harder time raising capital from markets; it’s usually at less favorable financial terms, as there is less demand for bonds or stocks issued from those companies. The rich get richer.

Problem of Participation

The author highlights some of the key pain points associated with investor participation

  • Clients regularly underestimate the real fees involved even for index funds
  • Clients are confused by the product variety of today’s index fund universe
  • Clients have unreasonable return expectations induced by over optimistic advertising.

Another much touted wisdom is the application of asset allocation methodologies using index funds. This strategy has not delivered as expected. Target Date funds are a great example. Investors have realized that the so called “glide path” promised by the fund managers has been far from satisfactory. Most of the asset allocation strategies suggested by investment gurus is just a hogwash. The portfolio allocation – “Invest in ETFs in a specific proportion and forget it” hasn’t yielded good returns.

Problem of Narrative

The author talks about the various narratives that we hear about ETFs. He subsequently says that all those narratives never pan out. In reality, the following are the scenarios faced by index investors

  • Investors don’t achieve the buy-and-hold returns advertised by fund houses
  • Average holding period of a typical investor is 3.3 years
  • Index fund proponents often mention the intellectual superiority of index funds and those who buy them. They are supposedly more sophisticated and advanced than investors in funds with active management, just by association. Unfortunately, there is no evidence that buyers of index funds, especially ETFs, are more sophisticated than those buying traditional mutual funds or even simple stocks. Past statistics of inflows and outflows for index funds prove that the average index fund investor behaves the same as any other ordinary mutual fund investor.
  • Index investing is taking a massive bet on the market. Like any bet, it can go either ways
  • Buy and Hold doctrine narrative is hardly true if one analyzes the investor behavior of ETFs and Index Funds

The last chapter of the book tries to educate the reader on the way to go about index investing. I found this chapter very pretty cliched.

Trivia from the book

  • The big three index funds are Vanguard, Blackrock and StateStreet have 12 Trillion in AUM!
  • World’s first investment fund was floated in 1774 by a Adriaan van Ketwich
  • First index investment trust (Vanguard 500 Index Fund) was floated in 1976
  • 1924 saw the birth of first two open-end mutual funds
  • No one has profited more from the boom of index funds than the index originators themselves. It’s one of the best business models on the planet.
  • Bogle, the godfather of index investing, ironically made his money not out of index funds
  • According to SEC filings, MSCI boasts more than $1 billion in operating revenues for the year ending December 2015, operating income of over $400 million and net income of $220 million not so bad for a business that creates fictitious pools of securities in all imaginable sizes and forms. MSCI alone currently offers more than 160,000 indices.

I found nothing special about this book. May be the chapters that highlight the problems with index investing might be worth a quick read.



image Takeaway :

The book is definitely overpriced for what it delivers. Given that 50% of this book explains R basics, the title is not at all appropriate. Even the quant stuff that is covered in the remaining 50% of the book is laughably inadequate.


The central theme of this book is — major governments & central banks around the world have been waging a war on “gold” to keep its price low. The reason being, they want everyone to believe in their fiat currency, so that they can print away the money to solve their temporary problems.

The author strongly believes that by 2020, no amount of “managing gold price” tactics will work. Dollar will lose its supremacy and gold prices will skyrocket to represent its true value.

The book is organized as a set of 86 questions/topics spanning six different sections. The author answers each question with no more than a page of explanation,that can be quickly grasped. In this post, I will briefly summarize the answers for each of 86 questions.


  • What is the origin of money ?
    • A simple closed community never needed money. However when societies grew, the demand arose for a complex trading system. Desired products like cattle and dried meat were used more and more frequently as a method of payment. Bartering is still the most elementary system of trade and shows up whenever crisis situations arise.
  • How did gold become money ?
    • Typical Characteristics of anything to act as money are : easily divisible, portable, imperishable and scarce. Gold and Silver fit the bill and more over, they were enormously desirable all over the world. Out of the entire periodic table of elements, gold and silver are most suitable as a means of payment as they are impossible to copy. Gold became equivalent to money when people realized that its purchasing power remained same across time.
  • When did coins come into existence ?
    • The first western coins originated in Turkey, around 650 B.C. Many great kings built their empires around a monetary system based on gold.  Because gold was more rare, silver was used for coins with a low nominal value.
  • A short history of monetary gold
    • The gold coins existed from 491 A.D to 1453 A.D and were accepted as money from England to China. Gold coins called florins appeared in 1252. Dutch guilder came  next. Ducats were another form of gold coins that were introduced in Italy.
  • What are the advantages of a gold standard ?
    • Most important advantage is that it forces government to be disciplined in their fiscal policy because they cannot turn to printing money. Gold also acts as an inflation hedge. Due to the mounting silver shortages, the United Kingdom and many countries in the British Empire adopted a gold standard in 1816. They were soon followed by Canada (1853), the US (1873) and Germany, where the new gold mark was introduced in 1872. In the course of the 19th century, the gold standard became more and more popular
  • Why was the gold standard abandoned ?
    • Abandoning gold standard gives the government to print fiat money. This was the main reason why US abandoned gold standard in 1971. Many European countries ditched gold standard in 1914 to finance First World War.
  • What is fiat money ?
    • Money that is not backed by something substantial. Its value rests on the confidence that goods or services can be paid for
  • What is meant by fractional banking ?
    • The initial credit from the central bank to commercial banks is in turn used by commercial banks to generate a far higher credit level in the society.
  • Where was fiat money invented ?
    • China( Emperor Khan found a way of creating paper money that was pitched as something that is as valuable as gold and silver)
  • Other examples of fiat money throughout history
    • Louis XIV, king of France in 1716 set up a bank and issued bank notes. However it ended as a disaster
  • Other misfortunes with fiat money
    • Post French revolution, “assignats” were issued that could be later used as money. This exercise of Quantitative Easing 101 failed miserably. In 1796, hyperinflation hit France and paper money lost all its value
  • What is Quantitative Easing ?
    • One can easily understand this complicated and fancy term if one thinks it as equivalent to “ Operation Firing up the Printing Press”. Japan’s BOJ is seen as the inventor of QE. The original purpose of QE was to lower the interest rates. But from 2008 onwards, the Fed and other central banks started aggressively expanding their balance sheets by buying up assets such as Treasuries (US government  bonds) and mortgage-backed bonds in order to support the housing market and to finance the large fiscal deficits that arose as a result of the economic fallout from the credit crisis.
  • Do all central bankers agree on QE ?
    • No. Dallas Fed Reserve president owns a sizable portion of its portfolio as gold. Bank of England ED of Financial stability is also against QE
  • When did hyperinflation occur ?
    • Hyperinflation arises when the money loses its value and power. Some examples are Germany in 1923, Hungary in 1946, China in 1949, Yugoslavia in 1994, and Zimbabwe in 2008
  • Can we trust official inflation figures ?
    • In many countries, manipulation of key figures and economic indicators has been elevated to a work of art. CPI publication has become a con job
  • How is inflation calculated ?
    • Many tricks are used by statisticians, who are pressurized by politicians, to lower CPI – Replacement of cheaper alternatives, Geometric means, Hedonic adjustments etc.
  • Examples of the distortion of inflation figures
    • WSJ articles illustrating cost price of a TV model
  • Do central banks combat or cause inflation ?
    • Even though they advertise that their main job is to control inflation, they are the ones causing inflation by printing more and more money. Through excessive growth of debt, most Western currencies have lost over 95% of their spending power in the last century alone.
  • Does anybody really understand this financial system ?
    • In most countries, governments and banks have worked together to monopolize the creation of money. The fact that our money is backed by nothing but hope and trust must be kept hidden from ordinary people. Even most economists do not fully understand money. Only those who have studied monetary economics know the inner workings of our financial system. And most of them
      end up working for their government or central bank, so they are bound to keep their mouths shut.



  • When did the first form of banking emerge ?
    • The first recorded debt systems were in Sumer civilization around 3500 B.C. European banks appeared in early Middle ages. The word ‘bank’ comes from the Italian word ‘banca’, the name used for
      the marble tabletop upon which Italian goldsmiths dropped foreign coins. From the sound of the coins being dropped, they could assess whether a coin contained a lot of copper or nickel.  Banks as we know them today were first set up during the Renaissance in the Italian cities of Florence, Venice and Genoa.  The most famous amongst them is Medici Bank.
  • How did central banking start ?
    • European Royalty needed money to fight wars. This financing was provided by so-called moneychangers. These moneychangers  understood pretty quickly that lending to powerful entities such as kings and churches carried less risk because of the continual stream of income. The German Rothschild family established an international banking business and dynasty, becoming one of the most powerful families in the 19th century
  • The first central bank
    • Amsterdam Wisselbank founded in 1609.  Most central banks in the past 400 years were initiated by rich businessmen who understood quite well that (central) banks, which owned the monopoly on creating money and were backed by government tax revenue, had a wonderful business model.
  • Who created the first government bonds ?
    • Scotsman William Paterson.  Paterson was backed by a group of rich traders from the City of London who would generate the starting capital. He was also supported by Charles Montagu, one of the most important officials within the Ministry of Finance. Together, they persuaded the government to create a bill so that the Bank of England could be established. The Royal Charter was granted on 27 July 1694. The first loan by the Bank of England was to finance the Royal Navy by issuing Navy Bills. The start of the Bank of England is often seen as the start of a new era. Fiscal deficits by governments could be financed by means of selling (perpetual) bonds
  • How large has the bond bubble become ?
    • As of 2012, US – $17 T, EU Total – $16T, UK – $10T, Japan – $2.7 T, Australia – $1.7 T, Switzerland – $1.3 T
  • Who supervises central banks ?
    • Over the course of 20th century, many governments took over central banks from private shareholders.
  • Where are the most important decisions about the banking industry made ?
    • Bank of International Settlements ( BIS) in Basel. The BIS can be seen as the mother of central banks and was founded at the International Bankers Conferences at Baden Baden (1929) and The Hague (1930). In April 1945, a decision to liquidate the BIS was made, but it was reversed by the US in 1948. The BIS had survived but was badly wounded. The BIS still operates as a counterparty, asset manager and lender for central banks and international financial institutions. Switzerland agreed to act as the headquarter state for the BIS. The headquarters would be situated in Basel. Today it is 60 central banks. Fed did not join until 1994 as US saw BIS as a rival to their IMF. Soon US joined BIS to get EU support for their war on gold.


  • How did central banking get started in the US ?
    • Robert Morris, a former government official, founded the first central bank in the US in 1781. He is seen as the father of the system of credit in the United States. His Bank of North America was based on the model of the Bank of England and could create as much money as needed through fractional reserve banking.
  • When was the Federal Reserve created ?
    • December 1913. It was the most beautiful Christmas present Wall Street could have wished for. For the third time in US history, the monopoly on the printing of dollars was transferred from government to private banks. Not many politicians realized the far-reaching consequences this decision would have. Immediately after the introduction of the law, all US banks became compulsory
      shareholders of the Fed.
  • Is the Fed really independent ?
    • New York Fed is far more important in the Fed system than all the other 11 regional Reserve banks combined.  Even though Fed presents itself as a normal central bank with 12 districts, NY Fed runs the show
  • When was the dollar system born ?
    • 1944 – Bretton Woods conference, names after the forest surrounding the hotel where the conference took place
  • What was decided at the Bretton Woods conference ?
    • Dollar is the new world currency
  • Why did Europe accept the dollar system ?
    • US proposed Marshall Plan, which was designed to help finance Europe after the devastations of the war.
  • For how long did the Bretton Woods system work ?
    • Following the Bretton Woods conference, all national currencies became pegged to the dollar, which was linked to gold at a rate of $ 35 per ounce. The dollar was the official world’s reserve
      currency and the anchor of the monetary system. The world now operated under a pseudo gold standard which economists call the ‘gold exchange standard’.  However by end of 1960s, the system was falling apart
  • When did the US close its ‘gold window ?
    • Aug 1971
  • How did the world react to Nixon’s decision in 1971 ?
    • Technically speaking, America defaulted in August 1971, since the country could no longer fulfill the obligations agreed upon in Bretton Woods. But surprisingly, the Nixon shock created only
      a relatively short dollar panic in the world’s financial markets.  At first, the inflation caused by the printing of extra dollars was moderate, but later in the 1970s, inflation began to take off, leading to
      a severe recession in 1979 and 1980. It would take years of strong leadership by Fed Chairman Paul Volcker to tame inflation and make the dollar a ‘strong’ currency again.
  • How important is the worldwide oil trade for the survival of the dollar ?
    • Very important. By trading only in dollars, the oil trading countries keep the demand for dollar alive
  • What is the role of the IMF and World Bank in this dollar system ?
    • Support the dollar as a world reserve currency. US insisted that countries could join IMF only after decoupling their currency from gold
  • How transparent is the Fed ?
    • Not very. There is  a strong culture of secrecy within the Fed organization.
  • Have any Wall Street bankers gone to jail ?
    • Very few. Most get away by paying fines.  A study of hundreds of media reports shows that the total amount of fines and settlements paid by Wall Street banks between 2000 and 2013 to avoid prosecution, adds up to $ 100 billion.



  • When did the music stop ?
    • After over thirty years of falling interest rates, the period of unrestrained private build-up of debt came to an end with the start of the credit crisis. Between 2008 and 2013, central banks worldwide created over $ 10 trillion of new money to take over bad loans from the private sector, to monetize debts and to stimulate the economy. The Fed balance sheet grew from $ 800 billion to almost $ 4,000 billion in just five years.
  • What has happened to the US national debt since the start of credit crisis ?
    • The US national debt grew by $ 8 trillion in a five-year period to reach $ 17 trillion at the end of 2013. To put this into perspective, it took 169 years (from 1836 to 2005) for the first $ 8 trillion of national debt to accumulate
    • To resuscitate interbank lending, central banks allowed commercial banks to borrow money at interest rates close to 0
    • Budget deficit in 2009,2010, 2011, 2012, 2013 is –10%, –9%, –8%, –7%, –6% of GDP
  • When does the size of fiscal deficits become dangerous ?
    • Historical analysis of hyperinflations suggests that the the tipping point is when government’s deficit exceeds 40% of the expenditures. Japan is at the risk of hyperinflation where half of govt. revenues goes in to debt servicing. Savings rate in Japan has dropped to 2% because of aging population
  • Didn’t the credit crisis start much earlier in Japan ?
    • After the crash of Japan in 1990s, it decided to turn on the printing press of its central bank. Debt service costs 25% of tax revenues. Public debt is 240% of GDP. Most banks are barely surviving. BoJ has maintained short term interest rate at close to 0 since 1999.
  • Who is most aggressive in their QE policies, Japan or the US ?
    • Japan – Amount set aside for QE is twice that of US QE allocation
  • Is China still financing the US ?
    • After QE1 in 2008, QE2 in 2010 and QE infinity in 2012, there is a widespread concern that dollar might get massively devalued. Hence China has been investing hundreds of billions of dollars per year in hard assets such as gold and other commodities.
    • QE3 is now called QE infinity by many because of the open ended nature of the program where Fed has launched $85 B per month of bond purchasing program
    • Market jumped by 12% after QE1 announcement, 3.1% after QE2 announcement and moved by barely 2% after QE3 – Law of Diminishing effects can be seen in QE announcements too
    • Many suggest that China and other developing countries should invest in Australian and Canadian dollar as the countries have large base of commodity assets
  • How large is China’s credit growth ?
    • China is more addicted to printing press mentality than US or Japan
    • Despite national financial reserves of almost $ 4,000 billion, China has been confronted with its own debt crisis after the banking system’s assets grew by $ 14 trillion between 2008 and 2013.This is the same amount as the entire US banking system.  China’s credit to gross domestic product (GDP) ratio surged to more than 200% last year from just over 110% in 2008
    • Short term trust loans amount to 50% of GDP
    • Shadow banking is rampant in china and is estimated to be 45% of GDP
    • Ironically, GDP itself is speculated to be a cooked number
  • Is the renminbi ready to replace the dollar ?
    • Since renminbi is not fully convertible, it will take many years for this possible scenario to pan out
  • So China is fearful of making too sudden monetary changes ?
    • Yes, History gives enough evidence for the same.
  • How big is Europe’s debt problem ?
    • 1 Trillion Euros
    • The total amount of money created during UK’s QE program from 2010 to 2014 was around $ 598 billion
    • compared with Japan, where the size of QE is double the size of the American program (relative to GDP), money printing has been slowing down in Europe between 2012 and 2014
  • Is Switzerland still a monetary safe haven ?
    • As a result of all the monetary madness after the outbreak of the financial crisis in 2008, more and more money started to flee to Switzerland. This caused the Swiss franc to gain in value, which
      had a substantial negative impact on Swiss exports and tourism. To avoid further harm, the Swiss National Bank (SNB) pegged the Swiss franc to the euro at a value of 1.20 euro.
    • At the end of 2013, the SNB had the most holdings relative to GDP (85%) of any major industrialized country
  • What is happening in the so-called currency wars ?
    • most of the currencies involved have stayed on a par with each other. To the general public, the dollar, the British pound, the euro and the Swiss franc all seem to have kept their value. But this is only with respect to each other. Because of this ‘debasement of currencies’, the smart money has started to flee towards commodities and other hard assets.
  • Can we grow our way out of this debt ?
    • In the eighteen most important countries belonging to the OECD, the total amount of public and private debt (relative to GDP) grew from 160% in 1980 to 321% in 2011. This amassing of debt has not caused any problems, since the interest rate over the same period fell from over 20% in 1980 to almost 0% after the credit crisis. National debts increased by 425% on average and have risen in many countries to almost 100% of their GDP
    • Growing out of debt, works only in the context of strong economic growth. Is there a sign of one in most of the countries today ?
  • How can we get rid of our debts ?
    • Default / Print money and Induce inflation  / Raising taxes
  • How have debt cancellations worked before ?
    • If you go by growth rates, they seem to have worked. But one must always be wary about the lies, damn lies and statistics. Whenever a country is in a crisis and undertakes debt cancellation, it is already at its nadir in terms of the growth, that any improvement in the post debt cancellation stage looks like a stellar performance
  • Possible debt cancellation scenarios
    • Fed cancels its $2 T debt out of a total of $17 T of treasury debts
    • Central banks cancelling government debt
  • When do things go wrong ?
    • when the national debt rises to over 90% of GDP, this tends to slow future economic growth
    • cumulative increase in public debt in the three years following a banking crisis is on average 186%. This explains why public debt in many advanced countries (the US, Japan, the UK) has increased strongly in recent years and reached or even crossed the 90% level.
    • The Minsky moment, named after American economist Hyman Minsky, is the point in time at which, after decades of prosperity, a wave of selling takes place by parties who had made
      investments with too much debt. In order to reduce these debts, they even have to sell good investments at increasingly lower prices. Such a disastrous sell-off of government bonds is one of the
      major risks we are now facing. At some point, central banks could end up buying almost all their domestic government bonds. Investor money would then flee towards equities and hard assets
    • The risks of things going wrong has increased since 2008 as central banks have resorted to unorthodox interventions that would have been considered unthinkable before the credit crisis.


  • The essence of the war on gold
    • It is an endeavor to support the dollar. Also, the level of gold price and the general public’s expectations of inflation are highly correlated. The survival of the current financial system depends on people preferring fiat money over gold
  • Do central banks fear a flight to gold ?
    • Yes. The war is being fought not only by central banks but also by commercial financial institutions
    • In 2013, both ABN AMRO and RBS cancelled gold accounts that allowed investors to redeem their value in physical gold. In a letter to clients, ABN AMRO explained that it had changed its precious metals custodian rules and the bank would ‘no longer allow physical delivery’, only paper settlement. And US banks are only allowed to advise investors to buy gold shares when they have a gold analyst on their payroll.
  • Was private ownership of gold ever prohibited ?
    • Post crash of 1929, President Roosevelt presented an economic recovery plan called ‘the New Deal’. The plan included a ‘Gold Reserve Act’, passed by Congress at the end of January 1934, which empowered the federal government to confiscate all of the Fed’s gold and bring it under the US Department of the Treasury. Roosevelt also made use of his special presidential authority to issue Executive Order 6102. This prohibited civilians from possessing gold, gold coins or gold certificates. Anyone caught ‘hoarding’ gold was to be fined $ 10,000. In Europe there has never been a ban on possessing gold.
  • When did the war on gold start ?
    • War on gold took off in the 1960s when trust in dollar started to fray.
  • How was the gold price managed ?
    • London Gold Pool creation in 1961 to keep the gold price low
  • The IMF’s role in the war on gold
    • IMF created international reserve assets called special drawing rights(SDR). Since 1975, the Americans have worked with the IMF time and again to try to control the gold market by unloading tons of
  • How did the IMF amass its gold reserves ?
    • The IMF received most of its gold from member countries, which had to pay 25% of their funding quotas to the IMF in physical bullion. This was because gold played a central role in the international monetary system until the collapse of the Bretton Woods agreements in 1971. Seven years later, the IMF fundamentally changed the role of gold in the international monetary system by eliminating its use as the common denominator of the post-World War II exchange rate system and ended its obligatory use in transactions between the IMF and its member countries
  • Are there more cases of double counting in the US ?
    • Possibly yes
  • How often have US gold reserves in Fort Knox been audited ?
    • None
  • Did the game plan change after 1980 ?
    • The new trick in the town is “expectation management”. Time and again, it has been communicated through press communiqués that the Fed or the IMF was considering selling gold, and time and again we have seen the gold price fall as a result
  • Didn’t the British help by unloading gold in 1999 ?
    • Yes,  Between 1999 and 2002, the UK embarked on an aggressive selling of its gold reserves, when gold prices were at their lowest in 20 years.  The UK sold almost 400 tons of gold over 17 auctions in just three years. So, Britain was hand in glove with US in the war on gold
  • Further evidence of systematic gold price suppression
    • The central bank of Australia confirmed in 2003 that its gold reserves are mainly used to control the price of gold.
  • Recent methods to manipulate the gold price
    • Futures market trading via electronic means has given rise to sharp volatility in gold.  Especially since the start of the credit crisis, market participants have now and again been bombarding precious metal futures markets with a tsunami of sell orders. The price of gold was forced down by $ 200 during a two-day raid in April 2013, and silver was sent 35% lower in three days in September 2011.
    • Speculator driven markets will only drive away long term investors. Inducing volatility is another method that has been used in the fight against gold.
  • More evidence of manipulation of precious metal markets
    • The author provides email transcripts to show that silver markets are rigged big time
  • Investigations into manipulation in precious metals markets
    • CFTC seems to playing a blind eye to the rampant manipulation in the gold and silver market
  • Do regulators now want Wall Street to stop trading commodities ?
    • Yes
  • Why has this gold manipulation not been reported on before ?
    • It has been reported before but the mainstream financial media have so far neglected to pick up this story.



  • Why do you expect a Big Reset of the global financial system ?
    • Two major problems in the world’s financial system have to be addressed: 1) the demise of the US dollar as the world’s reserve currency, and 2) the almost uncontrollable growth in debts and
      in central banks’ balance sheets. For all of these issues, central banks have only been buying time since the start of the credit crisis in 2007. Insiders predict that much more radical action will be needed before 2020.
  • How can the international monetary system be changed ?
    • No easy answers
  • Since when have people started planning a new international monetary system ?
    • Soon after the worldwide crash of financial markets in 2008, the IMF and others began brainstorming about a possible next phase of our international financial system. In 2010, the IMF published a report that looked into the possibility of a financial system without a dollar anchor. As if to underline its intention to reform the international monetary system, in 2012 the IMF added the Australian and
      Canadian dollars
  • Will gold be part of a reset ?
    • While most experts believe there will be no return to a full gold standard, gold will probably play a much greater role in the next phase of the financial systems
  • Will SDRs become the new world currency ?
    • Likely. According to some experts, the IMF needs at least five more years to prepare the international monetary system for the introduction of SDRs.
  • Some other reset scenarios
    • gold-backed dollar could be introduced in the US
    • US still values gold at the historical price of just $ 42 per ounce. This is unusual because the ECB and many other central banks value their gold reserves at market prices. The US government hopes to spread the message that gold is a metal with little value, while the dollar is the value of choice
  • What is China’s master plan ?
    • China has been hoarding gold in order to safeguard the country’s economic stability and to strengthen its defense against ‘external risks’, which could be translated as a collapse of the dollar or the euro or even the global financial system
  • How large are China’s gold holdings compared with the West ?
    • The Chinese want to increase their gold reserves ‘in the shortest time’ possible to at least 6,000 tonnes. That amount would put the Chinese on a par with the US and Europe on a gold-to-GPD ratio.
  • Does China understand the US war on gold ?
    • Yes, from the articles from the Chinese officials. China had accumulated over $ 1 trillion of US Treasuries between 2000 and 2010, a dollar devaluation would be very negative for China
  • Why is a monetary reset desired by China ?
    • They want to de-Americanize the world
  • The Russian point of view
    • Similar to China
  • Could the US confiscate foreign gold reserves stored in New York to introduce a new gold standard?
    • It is a possibility
  • Do we need to fear more financial repression?
    • History has shown that the closer we come to a major reset, the more likely it is that forms of financial repression will be activated. The reset of the Cyprus banking system demonstrated that  very few of those affected were prepared in advance.


The author concludes saying that reset is imminent :

We Westerners concluded our capitalist system, based on free markets, was a superior one because communist countries ‘switched over’ to our side. Well, at the end of 2008 our system also ran aground. However, like the communists leaders in the early 90s we pretend all is still fine. Authorities are now turning to precisely those measures which we so despised in the communist system. Economic figures are increasingly being manipulated and colored to reflect a more rosy picture. Good news  is often the result of propaganda and the work of spin-doctors. The economy and its financial markets are being increasingly centrally controlled. Free markets are disappearing more and more. Interest rates are manipulated, gold wars are fought, the  ‘plunge protection team’ intervenes almost openly in American stock markets, QE as far as the eye can see. We have entered an era of virtual global state capitalism. China is a perfect example. As are Russia and the US, the EU, the Arab World, the UK and Japan. The economies of West and East are now  intertwined in a way never seen before. 

Since the fall of Lehman, central bankers are desperately trying to avoid a collapse of the financial system. Governments and  central bankers know the whole economic system will fall apart once they stop printing money. This leads to the only logical conclusion that we are stuck with infinite QE. As more and more paper assets are being printed, more and more ‘smart money’ will flee towards asset classes that can’t be printed.

For the very first time in history, a financial and monetary crisis has emerged which is so severe that it has the capacity to end in an all-encompassing distrust of paper assets. This could even lead to an unprecedented wave of hyperinflation in which prices explode, debts melt away, the economy collapses and banks will close.

Central bankers are therefore very much aware that it is essential to come up with a reset plan before this occurs. Authorities will do everything possible to modify the financial system in order to avoid another 2008-style collapse. In my opinion it’s not a matter of if, but only when, they will introduce their reset plans


This book is a ~160 page tirade against the book “Flash Boys” that has captured everyone’s mindshare with the marketing slogan – ”U.S. stock market is rigged”. The author, Peter Kovac, has worked with a HFT firm for eight years and claims to be an industry insider. Since “Flash Boys” was basically anti-HFT book, it is natural to expect someone from the HFT to criticize the book. So, there we have Peter Kovac with a book length treatment that has similar title with Lewis book but a different tagline.

For a person who has read “Flash Boys”, it is likely that one might be dazzled by Michael Lewis story telling capability. Who doesn’t love a David vs. Goliath story, i.e. Brad &Co vs. Wall Street story? But there is a possibility that one might be too carried away by the arguments in the book. Peter Kovac’s book delves in to the story, strips off the fluff from “Flash Boys” and critically analyzes numbers, examples, arguments, protagonists from the book.

In this post, I will briefly summarize the main points from the book:

Introduction: The Dangers of Speed

The central thesis of “Flash boys” is, “stock exchanges + HFT players+ brokers have rigged the markets”, i.e. intermediaries have screwed investors. However the book does not carry a single interview of a person working in a stock exchange or a HFT firm. If you have read previous books by Michael Lewis, you will see a pattern. He analyzes an event after the fact, picks up a few key people responsible for the event, and writes in great detail about the key people in an entertaining and illuminating way. This book is different and is not an after the fact narration. The underdog who is supposed to have led a revolt is still nowhere near declaring a win. IEX is yet to be granted an exchange status. HFT players are still operating and exchanges have not majorly changed any rules. It looks like Michael Lewis himself succumbed to very element that he criticizes, “speed”, and has gone ahead and published the book, before even seeing whether IEX would really make any difference to the U.S markets or not?

Chapter 1: Spread Networks and the Value of Speed

Since the advent of electronic trading a decade ago, the trading costs and spreads have vastly reduced. The increased speed and automation facilitated more precise prices, and investors have benefited tremendously. Every penny of that reduction in “economic friction” is a cash bonus. Why are market participants still looking to go faster if they can’t make their prices any more precise? The answer is: competition. If they don’t get faster and their competitors do, they lose. Speed doesn’t matter for individual investors, since there is no race to be run. Spread networks story is pretty accurate and the reason people lapped up the service is , speed matters. Lewis claims that intermediaries made between $10B to $22B per year in total profits. How does this stack up to the rest of Wall Street? At this point in his story – early 2010 – Goldman Sachs had just paid $ 16.2 billion to its employees in compensation, out of revenues of $ 45.2 billion for the previous year. In other words, Goldman alone paid more in bonuses and salaries than the total profits of all high-frequency trading firms (plus whoever else he lumped in as “financial intermediaries.”) So, perhaps, they weren’t making “more money than people had ever made on Wall Street.”

Chapter 2: The Education of Brad Katsuyama

Brad starts to suspect the market when the shares of a certain script, Solectron go down as soon as he sells shares. Brad thinks his action should not have caused a price collapse since the firm was going to be acquired and the target price was fixed. The problem with this argument is that the price is never stable even in the case of a merger. There is always a band of traders trying to do “merger arbitrage”. Using the number quoted by Lewis,i.e. $0.0029 per share as the cost of front-running by HFT firms, the author says the money Brad lost was predominantly due to his strategy and not because of front running. It is basic economics 101 playing out where he sells hundreds and thousands of shares increasing the supply that causes the price collapse. Katsuyama’s job was to minimize price impact and when he fails to do that, and ends up blaming HFT traders for front-running his order.

There is a lot of criticism on “maker-taker”model but the key idea behind it simple. When an exchange starts off, it often follows a maker-taker pricing model where a maker is given an incentive to quote. As volumes go up, the exchange may decide to invert the “maker-taker” model and start paying the taker to incentivize access to the markets, in the assumption that the previous players who were paid for providing quotes would still remain as the increased volume would make it lucrative. Sometimes this idea works and sometimes it doesn’t.

The real questions that on must address in this area are:

  1. Fragmentation. By providing another dimension for competition, does this model encourage too many new exchanges? Or does fragmentation occur for other reasons? Would restricting price competition among exchanges impact fragmentation?
  2. Winners and losers. Generally , guys like Katsuyama who take liquidity pay the most under the maker-taker model. If, from a policy perspective, we want to help them at the expense of the market-makers, it makes sense to ban maker-taker. On the other hand, if we don’t want policy to favor one class of traders over another, pricing should be determined by market forces.
  3. Needless complexity. Does the maker-taker model create needless economic complexity in our markets? Or can traders easily account for this in their trading models?
  4. Best execution. Does maker-taker pricing create unhealthy incentives for the “best price” fiduciary responsibility of brokers with respect to their client orders? The fees and rebates flow to the broker, while the price of the shares is passed along to the client.

Instead of addressing the above areas, Lewis messes it up in the book.

Another problem with Brad’s argument is that he believes in the perfect view of market, i.e. if he sees a market at a particular bid and ask, he thinks he can buy or sell as many shares he wants at NBBO. But sadly that is not how markets work. Michael Lewis does criticize flash orders, and rightly so. However the largest critic for flash orders was GETCO, one of the largest HFT players in the world.

“Someone out there was using the fact that stock market orders arrived at different times at different exchanges to front-run orders from one market to another.”

Thor, the tool which Brad and his team used to send orders was a clever tool that foiled the market makers attempt at widening their quotes before a large buy/sell order. When this happens again and again, the only way market makers adjust their quotes is widening their spreads as compared to Pre-Thor days. So, is Thor actually causing market volatility and increased spreads? Will IEX also receive the same response from market makers? Also the whole basis of extrapolating that front running was happening all over U.S markets was based on one trade in citigroup shares. It does not look like a fair extrapolation at all.

Chapter 3: Trying to connect the dots


The author dismisses a lot of things that Ronan Ryan describes, such as “moving the server in the colo by three feet” etc., as funny. The guys who moved the server by three feet in the colo center did not have a clue as to why their strategy worked/failed and wanted to come even closer to the main feed from the exchange. Lewis says Ryan is the world authority on colo. But no one has ever heard about him until “Flash boys” got published. There is a reason for the exchanges offering colo, besides the obvious outcome that it generates profits.

Exchange co-location is regulated by the SEC, and, as such, is required to be available to all market participants. Whether or not one thinks it is currently regulated perfectly, it is regulated – thereby providing, if not a guarantee, at least a possibility of fairness. In an exchange data center, the data is broadcast to all traders simultaneously, providing everyone with an equal footing and a fair chance. If exchange co-location were prohibited, traders would still vie to be next to the exchanges. They would just be housed in private data centers, outside the reach of the SEC. Such facilities could discriminate on pricing, or simply establish a monopoly. Any chance at regulation, or fairness, is gone. There would no longer be a common starting line, but instead a system where, unlike today, some firms actually do get a head start. Further, prohibition of co-location would impact the exchanges’ bottom line.

Front running

Any would-be front-runner has to overcome at least five hurdles to rip you off: 1) Determine the price and quantity of shares of your order 2) Buy the same amount of shares you want, before you do 3) Manipulate the market price upward 4) Sell the shares back to you at the higher price 5) Avoid anyone else in the market who could disrupt the scam. With the electronification of the markets last decade and the implementation of Regulation NMS in 2007, the five hurdles above have now become solid barriers to front-running.

Latency Tables

The whole argument of “latency tables” in “Flash boys” falls apart as no order is stamped with a broker name and hence even if a HFT player has a latency table, it would be impossible to tease out whose order was being executed on a specific exchange. If one had a set of perfect latency tables for all algorithms used by all traders at every broker in the market, and all brokers had non-random, precise and unique latencies , one would still never have enough data to figure out who sent an order. Contrary to what Lewis implies, it is utterly impossible to identify anything from a single order on the BATS Exchange. One moment there is nothing. The next moment there is a trade. Nobody knows how long it took the order to hit the BATS Exchange, they only know that a single trade occurred. The next trade on the next exchange isn’t going to be terribly helpful either. One could conceivably measure the difference in time between the trades, but it’s basically useless in identifying anyone since that difference would provide a single data point. Perhaps by the third or fourth exchange one could narrow the field of possible brokers and trading algorithms a bit, but by that time the trade is complete. Remember that all these orders were already in transit anyway, so the whole exercise was pointless to begin with – by the time you saw the first trade report come back, the other orders would be long gone. Lewis completely dodges the question of how any strategy with the help of latency tables, would ever reveal the actual quantity and price of an order. Without this information, there’s no front-running possible.

The example of china ETF order being front-run is a ridiculous example cited in the book. Attributing front-running to such a market behavior is totally unreasonable.

Broker routing orders

Can a HFT firm indulge in front running based on router-driven stock quote of 100 shares on BATS? Impossible, even if a front-runner accurately guessed the quantity of an order, and they fended off all competitors, they are more likely than not to be stuck with a massive losing position as their reward.

Spread Networks – CBSX

The fact that start of Spread networks and SIRI stock volume explosion on CBSX coincided, lead Brad and team to conclude that HFT players were using spread networks to arb. The author attributes the reason to CBSX brilliant decision of using inverse maker-taker pricing for a penny stock because market making in SIRI had an extremely low risk per share. SIRI volumes returned to normalcy when other exchanges followed the same inverse maker-taker model. Which argument to believe is up to you, but the latter sounds far more convincing?

Chapter 4: There’s Another Explanation, but it’s Not As Interesting


Lewis ascribes RegNMS as the main reason for unleashing HFT. However HFT was always present before 2007.The majority of HFT firms trading doesn’t depend upon Regulation NMS, maker-taker pricing, or many of the things that Lewis describes as the foundations of “rigged” markets. Lewis argues that high-frequency trading is bad, and we can fix this if we eliminate co-location, maker -taker pricing, and Regulation NMS’s best execution requirements. But apparently none of these items are necessary for high-frequency trading. If they were eliminated, high-frequency trading would still exist. The only difference would be that big bank equity traders like Katsuyama and friends would have much lower costs (no co-location necessary, much lower trading fees) and much more discretion in obtaining “best execution” for their clients.


There are three things you need to know about the SIP: 1)You can watch the SIP, you can’t trade on the SIP 2) The SIP is faster than you but slower than other data (it ought to be faster) 3) It is sometimes used for “trade-through” protection, sometimes not. One thing to note is that it is guaranteed to be slower than the markets’ direct data feeds since it must consolidate all the market data from every source. The author says that SIP is just a cheap way to show approximately what the current market is. It’s great for CNBC or your favorite Internet finance site, and probably adequate for any retail investor who doesn’t live next to an exchange and doesn’t possess super-human reflex times. Professional investors have the choice of viewing the SIP or using direct feeds from the exchanges. Anyone who trades frequently will likely chose direct feeds. Lewis uses AAPL stock example to show how slower SIP is advantageous to HFT players. The author systematically makes a case against the AAPL example.

Why Thor needed a regulatory approval ?

As market-makers, they take the risk of always being ready to buy or sell stock. Market-makers will adjust their prices up or down based on risk, and based on supply and demand. If some trader using Thor bought up all the shares on the NYSE, NASDAQ, and BATS , a market-maker on the Boston Stock Exchange would think that (a) this new surge in demand will push the price higher , and (b) my offer to sell at the current price is a big risk since the price is about to shoot higher. While this market-maker is pondering this, Thor hits them, too. For the idea of Thor was, of course, to jump all the market-makers on all the exchanges at the same time, before even the last ones could react to the new price. The fact that some might see this tactic as rather predatory may have been why RBC’s upper management thought it might be a good idea to seek the SEC’s blessing before widely publicizing Thor. Was Thor predatory? The premise of using latency to trick market-makers into bearing the price impact isn’t illegal, but it does seem to prey on a particular weakness of a particular (essential) class of market participant.

Spreads have decreased because of automation

Lewis says spreads have decreased because of computerization, not because of HFT. Sadly, exchanges being computerized do not lead to spread attenuation. It is HFT players who play the role of market makers that have made spreads shrink.

Dark pools – play ground for HFT players

Dark pools don’t disseminate market data to any of their clients, high-frequency or otherwise. Some dark pool operators make no guarantees about their own trading in the dark pool. For those that promise that their bank’s proprietary traders have no special advantages, it’s still blind faith: unlike the public markets, there are few police on this beat. For this reason, many high-frequency traders choose not to trade in dark pools – they are afraid that the banks’ traders will rip them off in the dark.

Chapter 5: Sergey Aleynikov

There isn’t much in this chapter, except that the author says Lewis should have done some more research in the story. Aleynikov’s claimed that he only took open source code. Open source code is readily available on the Internet – that’s the idea behind it. Why didn’t he just plan on re-downloading the code at his new job? It’s difficult to buy the idea that it would be easier for Aleynikov to transfer the files to a third-party server, later retrieve them, and then “disentangle” all the Goldman proprietary code.

Chapter 6: How To Take Billions From Wall Street

The author wishes that IEX succeeds so that people might think that HFT problem is solved in the markets, a problem that did not exist in the first place.

Maker-Taker pricing

The pricing used by IEX – $ 0.0009 per share for takers – is far more attractive to a taker than the pricing on any of the major exchanges. Applying Lewis’ logic, this is just another kickback to the big banks (all of whom are on IEX), and IEX is one big “flash trap.” If it seems logically inconsistent, that’s because it is. Realistically, different pricing models are just that: different pricing models. They are set by the exchange to attract business. To claim that high-frequency puppet-masters dictate these pricing structures to the exchanges doesn’t make sense in the case of IEX, NASDAQ, NYSE, or anyone else.

Types of Predatory behavior

The puzzle masters in “Flash boys” categorize the predatory behavior in to three types. 1) front running, 2) rebate arb, 3) slow market arb. Front running is already shown to be impossible in the earlier chapters. The fact that HFT players can get a rebate without doing a trade is impossible. Also slow market arb is again impossible in the examples cited in the book because of trade-through protection provided by RegNMS.


It seems that (1) the tweaks implemented by IEX would not actually prevent the predatory trading that Lewis hypothesizes, but (2) there doesn’t appear to be any predatory trading on IEX. This paradox could be explained two ways. Perhaps the predators are simply scared to show their stripes on IEX. Or perhaps the feared predatory trading is rare to non-existent, and it doesn’t matter that IEX’s defenses are illusory.

Dark Pools

Dark pools and broker internalization facilities aren’t unquestionably bad, but it’s hard to make a compelling case for any significant benefit . For professionals in particular, they make it easier to shoot oneself in the foot. For the public, the lack of transparency doesn’t inspire confidence. And for the markets themselves, there is a legitimate question about whether or not they detract from the price discovery process.

Chapter 7: IEX Launches

One of the often cited reasons for taking a stance against HFT players is that they siphon off trades coming from pension funds and such large institutional clients. It might be worth telling all those critics that the same pension funds are also invested in high-frequency trading firms. For example, more than a third of high-frequency trading behemoth KCG / Getco is owned by institutions and mutual funds. CalPERS and CalSTRS, two of the largest pension funds in the country, own stakes in privately held high-frequency firms as part of their private equity portfolio.

This chapter shows so many chinks in Lewis arguments that I did not feel like summarizing as it would have meant basically replicating the whole chapter from the book. After reading this chapter, I was kind of overwhelmed by how much stuff from “Flash boys” was plain wrong.

Chapter 8 : Dinner with Sergy

About Sergy, the author says that a little more effort should have been taken to understand the truth. He says,

I don’t know what was contained in the 500,000 lines of source code that Aleynikov took. I do know that most trading systems use proprietary code for strategies, but rely on open source operating systems and network processing code. This open source software is often modified for the particular network requirements of trading. And these modifications are quite valuable – they make every single order faster or slower , depending on how clever one is. I wish somebody had asked hard questions.

takeawayTakeaway :

All the books written by Michael Lewis till date other than “Flash Boys” are after the fact narration. In his previous works, one can see a pattern. He identifies few key characters and weaves an entertaining story along with facts so that readers can easily understand the basic theme. It looks like with “Flash Boys”, he has created a catchy narrative but sadly the foundation of the narrative is weak. Peter Kovac has written a 100,000 word book that basically rips apart almost every argument of “Flash boys” and thus defends HFT’s role in today’s markets. Must read for someone who wants to critically analyze the arguments made in “Flash boys”, that says U.S markets are rigged.


Introduction: windows on the world

Michael Lewis starts off by saying that the mental picture of stock market that most people away from Wall Street carry has changed dramatically over the last decade. He claims that his intent of writing this book is to draw a picture that is the new reality of U.S markets.

Hidden in plain sight

Only Michael Lewis can take a “laying a fiber optic cable” story and make it in to a page turner. This chapter talks about Dan Spivey and his efforts to connect Chicago and New York by a line that is as straightlinish as possible. The company formed by Dan Spivey, called “Spread Networks” began work in 2008 and was finally completed in 2010. To get the necessary approvals for constructing the network and selling this network to Wall Street people, Dan partnered with Jim Barksdale, David Barksdale and Larry Tabb. The whole point of the line was to create inside the public markets a private space, accessible only to those willing to pay the tens of millions of dollars in entry fees. Spread Networks first press release was titled,

Round-trip travel time from Chicago to New Jersey has been cut to 13 milliseconds.

Spread Networks set a goal of coming in at under 840 miles and beaten it; the line was 827 miles long. Spread Networks soon found that many Wall Street banks and hedge funds readily signed up for the line. This was an acknowledgement of the new reality of trading, “speed mattered” and it mattered a LOT.

Brad’s problem

clip_image002 Brad Katsuyama

This chapter introduces Brad Katsuyama, an RBC trader and the hero of the book. When Brad gets transferred to Wall Street office, he realizes that the culture on the street is very different from the conservative, team oriented culture he is used to, back in Canada. His real trouble began at the end of 2006, after RBC paid $100 million for a U.S. electronic stock market trading firm called Carlin Financial. There was a clash of cultures between RBC and Carlin. After the subprime crisis, he plans to leave Wall Street for good. However life had different plans for him. RBC severs the relationship with Carlin and Brad becomes the head for electronics trading. Initial thoughts of RBC team members who propose opening a dark pool makes no sense to him and he decides not to get in to dark pools game. As he tried to fix the “electronics trading biz”, he is perplexed with the whole system. He is clueless about certain aspects such as, Why was BATS going for inverse maker-taker model? Why was there a need for a maker-take model at all? The biggest problem that Brad faced when he starts using the “electronic trading systems” is that he never got fills at the prices that the screen showed. Every time he tried hitting the bid or lifting the offer, the market moved as soon as he sent the order. It looked like as though the market had read his mind and moved against him. At first he is not certain where the problem was. He thinks that there is a problem with Carlin’s systems, but soon figures out that traders at many other places were facing the same problem. He assembles a team of technologists and starts doing experiments with some very small size orders. This experimental reveals one surprising fact – When he sends an order to one exchange, the trade happens normally. However when the order is sent to multiple exchanges, only partial fills happen.

He becomes more certain that the stock market was no longer a market. It was a collection of small markets scattered across New Jersey and lower Manhattan. When bids and offers for shares sent to these places arrived at precisely the same moment, the markets acted as markets should. If they arrived even a millisecond apart, the market vanished, and all bets were off. Brad knew that he was being front-run—that some other trader was, in effect, noticing his demand for stock on one exchange and buying it on others in anticipation of selling it to him at a higher price. He’d identified a suspect: high-frequency traders.


Ronan’s problem

clip_image002[5] Ronan Ryan

This chapter is about Ronan Ryan who starts his career in a telecommunications company and soon lands up at Radianz where he is in charge of selling co-lo services. This is where Ronan learns about the importance of latency to every Wall Street firm. Radianz data center at Jersey city could bring down the latency from 43 milliseconds to 3.8 milliseconds, for a firm in Chicago. By early 2008 Ronan was spending a lot of his time abroad, helping high-frequency traders exploit the Americanization of foreign stock markets. In 2009 he is hired by Brad to work RBC as head of HFT trading. Brad educates Ronan about basic market concepts whereas Ronan imparts the tech stuff that he has learnt through his career. Ronan explains the reason for Brad’s successful order execution at BATS and failure at other exchanges. He also explains the reason for BATS strange policy of paying to take liquidity – BATS orders were leading indicators of what was about to happen at other exchanges. HFT players would quickly buy at other exchanges before everyone else and then sell it to the person who had no latency advantage. Brokers were also incentivized to send order flow to certain exchanges as they received payment and kickbacks

The team at RBC slowly realizes that the only way their orders would not get ripped off by HFT players is to send the orders at approximately same time to all the exchanges so that nobody could game it. For this to happen, they had to build their own network. By the end of 2010, Brad and Ronan met with roughly five hundred professional stock market investors who controlled, among them, many trillions of dollars in assets. They never created a PowerPoint; they never did anything more formal than sit down and tell people everything they knew in plain English. Then there was flash crash and Brad’s ideas were in demand.

Another incident happened in September, 2010; a sleepy stock exchange called the CBSX switched to inverted maker-taker model and its trading volume skyrocketed. Ronan and Brad put their heads together and figured out that this was another classic case of ripping off money from investors.Spread Networks had flipped its switch and turned itself on just two weeks earlier. CBSX then inverted its pricing. By inverting its pricing—by paying brokers to execute customers’ trades for which they would normally be charged a fee—the exchange enticed the brokers to send their customers’ orders to the CBSX so that they might be front-run back to New Jersey by high-frequency traders using Spread Networks. The information that high-frequency traders gleaned from trading with investors in Chicago they could use back in the markets in New Jersey. It was now very much worth it to them to pay the CBSX to “make” liquidity. It was exactly the game they had played on BATS, of enticing brokers to reveal their customers’ intentions so that they might exploit them elsewhere. But racing a customer order from Weehawken to other points in New Jersey was hard compared to racing it from Chicago on Spread’s new line.

Tracking the predator

clip_image003 John Schwall

Brad’s next hire was John Schwall and this chapter talks briefly about his background. Schwall started out his work at Bank of America. After Bank Am took over Merrill Lynch during financial crisis, Schwall decided to move to RBC. Schwall knew a lot about RegNMS and could clearly understand the way HFT players played the SIP game. NBBO calculation at the centralized server was slower than that of HFT players and this gave to massive arb opportunities for all those who had faster connectivity. Schwall goes through a ton of LinkedIn profiles and figures out that all the dark pools have HFT players as their clientele. By diverting the client’s order flow in to their dark pools, the brokers were ripping of their client. In return HFT players paid a ton of money for doing this.

Putting a face on HFT

clip_image005 Sergey Aleynikov

This chapter narrates the story of Sergey Aleynikov, who is given 8 years of imprisonment for stealing HFT strategy code from Goldman. Much before this book went to the press, there was an article in Vanity Fair, titled, “Did Goldman Sachs Overstep in Criminally Charging Its Ex-Programmer ?”, that has most of the stuff from this chapter. Michael Lewis pieces together Sergey’s childhood, his career at a telecommunications firm , his programming job Goldman and says

Thus the only Goldman Sachs employee arrested by the FBI in the aftermath of a financial crisis Goldman had done so much to fuel was the employee Goldman asked the FBI to arrest.

How to take billions from Wall Street

This chapter talks about how Brad quits his job at RBC, assembles a team to build an exchange, IEX(Investors Exchange), whose philosophy was to save the investor from getting ripped off by financial intermediaries. Ironically, in his fund raising efforts, he had to feign that he was greedy and only then he could manage to hold potential investors attention. By mid-December he’d sewn up $9.4 million from nine different big money managers. Six months later he’d raise $15 million from four new investors. The money Brad needed that he didn’t get he kicked in himself: By January 1, 2013, he’d put his life savings on the line. At the same time, he went looking for people: software developers and hardware engineers. Brad hired Don Bollerman who had spent 7 years at NASDAQ and had seen it all – a sleepy exchange that turned in to HFT player’s favorite ground.

IEX goal was not to exterminate the hyenas and the vultures but, more subtly, to eliminate the opportunity for the kill. To do that, they needed to figure out the ways that the financial ecosystem favored predators over their prey. Brad hired Dan Aisen and Francis Chung who were ace puzzle crackers in the literal sense of it. Brad also hired Constantine Sokoloff(Matching engine specialist from NASDAQ) to mentor the puzzle masters. The team at IEX got to work and started by analyzing various order types. The more analyzed the order types, they found that almost every fancy order type was meant to rip off investors. The team created a taxonomy of predatory behavior in the stock market. The first they called “electronic front-running”—seeing an investor trying to do something in one place and racing him to the next. (What had happened to Brad, when he traded at RBC.) The second they called “rebate arbitrage”—using the new complexity to game the seizing of whatever kickbacks the exchange offered without actually providing the liquidity that the kickback was presumably meant to entice. The third, and probably by far the most widespread, they called “slow market arbitrage.” This occurred when a high-frequency trader was able to see the price of a stock change on one exchange, and pick off orders sitting on other exchanges, before the exchanges were able to react.

The team at IEX wanted to create an exchange where all the predatory behavior could be attacked. They came up with a brilliant idea. Have their matching engine very far away from the place the broker’s point of presence. They zeroed on making a 350 millisecond delay between the point of presence and matching engine and this they achieved by merely coiling the wire innumerable times (another simple yet damn effective way to INCREASE latency). At the same time, IEX laid its infrastructure in such that it was the fastest to reach other exchanges.

Despite this wonderful idea and infrastructure, they faced one big problem – How should they generate order flow for the exchange by playing a fair game?

An army of one

The last section of the book goes through the struggles that IEX faces in building order flow. One big success comes their way when they manage to sign up Goldman Sachs and in fact on one of trading days, their volume exceeds that of AMEX.


imageWhat has happened to IEX since Flash Boys success?

IEX has grown rapidly in 2014. Its daily trading volume has tripled since the first quarter and participant volume has exceeded 100 million shares per day. It is seeking regulatory approval to become a full-fledged stock exchange. If IEX manages to grow its trading volume and its business, it can be a great transformation to the U.S markets. 


The book by Alex Kuznetsov gives a great overview of  the financial markets in U.S. The book is targeted towards a person who is coming from a technical background and intends to work in finance but does not have a clear idea about,”What exactly happens on Wall Street ?” The book is ~ 500 pages and covers quite a lot of ground. The first part of the book gives a basic idea of how a Wall Street sell-side firm is structured, who are key players in the financial industry etc. The second part is the juiciest part of the book where the author covers all the main markets in US, with just enough content allocated to each market, that a curious reader will be enthused to read other books about them. The third part of the book deals with technology areas, and is priceless for a newbie quant at any Wall Street firm. The fourth part is too specific to people who probably see themselves as sys-admins.

The author is very clear about the target audience and hence highlights all the relevant aspects that a technical person who is looking for the “BIG PICTURE”, would immensely appreciate.


LIBOR is the reference rate for 70 percent of the U.S. futures market, most of the swaps market, and nearly half of U.S. adjustable-rate mortgages. LIBOR, undoubtedly can be called the world’s most important number. It was conceptualized to meet a certain need, i.e. “what rate should be used for a floating rate note? “. Primarily it was used in the payoff computation of Eurodollar futures and subsequently it was used in interest rate swaps, derivatives on interest rate swaps, basis swaps, mortgages etc. The fact that LIBOR became a rigged rate was known to a few people in the financial world, i.e. rate traders, derivative traders, hedge fund managers, agencies that published LIBOR every morning. However it was known to a far wider audience after the 2008 WSJ article that screamed, “Emperor is naked ”. The title of this book is apt as it describes the situation before the regulatory agencies took action. Everybody knew what’s going on but nobody said anything. Too much was at stake.

The book reads like some crime thriller based in some fictional land; the only difference is that it is a true story and the victims are state governments, municipalities, rail transport authorities, and many other institutional investors who have lost billions of dollars. The perpetrators in the story are yet to given punishment. Yes, some of them have quit their jobs but NOBODY is yet behind bars! The most saddening aspect of the story is there have been merely a few cosmetic changes made to LIBOR and it is still being used as a reference rate in market place. The book talks about few people who singlehandedly tried to fight against LIBOR. Sadly, their voices were squashed as there were too many people who wanted status-quo. Several traders are going to be tried in 2015 and the author hopes that some justice would be doled out.

By the end of the book, a reader is left with an uneasy feeling about financial markets. If LIBOR can be rigged, what other benchmarks are currently being rigged? Is ISDAFIX being rigged? Is there a currency-fixing scandal waiting to be uncovered?

If you are curious to know the answers to any of the following questions, then the book might be worth a read.

  1. How did two WSJ journalists uncover the fact the LIBOR was rigged? Why did they analyze Credit Default Swap market?
  2. What were the political and economic conditions that led to the flourishing of Eurodollar business?
  3. How was LIBOR computed by British Bankers Association?
  4. LIBOR was set by primarily UK based banks. So, why was it being used as a reference rate for student loans, mortgage loans in US?
  5. What role did interbank brokerage companies play in manipulating LIBOR?
  6. Did Bank of England know about the fact that LIBOR was rigged?
  7. Did U.S Treasury know about the fact that LIBOR was rigged?
  8. What was modus operandi for manipulating LIBOR rate?
  9. How did Tom Alexander William Hayes, Yen-LIBOR trader, manage to rig LIBOR?
  10. Why did banks quote artificially low offer rates when polled every morning?
  11. What was the role of Barclays bank in the whole affair? Why did Bob Diamond resign after the scandal erupted?
  12. Why did British Banker’s association try to cover up the whole affair after WSJ article? What was their motive?
  13. If there were no subprime crisis in US, do you think LIBOR sham would have been exposed? Why or Why not?
  14. Why didn’t the regulators act ?
  15. Who were the major hedge funds involved in the controversy ?
  16. Why did Gensler, the former chairman of CFTC, fail in his mission of abolishing LIBOR?
  17. Interest rate swaps are complicated to value. So, why did municipalities, railways etc. fall in to the trap of investing in them?
  18. Why did Charles Schwab file suit against Wall Street banks?
  19. What happened to traders implicated in LIBOR and EURIBOR scandals?
  20. Post LIBOR scandal, what changes have been made to compute LIBOR?
  21. Is ISDAFIX benchmark rigged too?
  22. Should IR swaps be traded on an exchange? Why did Gensler’s effort in pushing reforms for IR swaps trading, go in vain ?
  23. Was LIBOR waiting to be rigged? Could there have been a better mechanism to capture reference rate?
  24. People have lost faith in benchmark rates and increasingly we are seeing manipulation is not the exception but the rule. Does the author have any suggestions for restoring investors’ faith in benchmark rates?


Books on derivative pricing come in all shades and colors. Some books give a brief introduction of derivatives at a leisurely pace and then suddenly the content becomes very mathematical. There are some books that have theorems and lemmas all through. There are some books that talk about risk-neutral pricing giving very little intuition about the concept. In the gamut of books available, I think this book stands out for a couple of reasons. The fact that we all live in incomplete markets is addressed right from the beginning. This immediacy has the reader’s attention right away. If the markets are incomplete, i.e there are more state variables than the instruments, how does one hedge an exposure ? Can there be a perfect hedge ? If not, how does one compare between two or more hedging options? These are very practical questions for an options trader. An option trader intuitively knows that a perfect option hedge that is taught in a grad school is an idealistic scenario that holds good under a ton of assumptions. Real world is messy. Pick up any book where Black Scholes is derived; in 9 out 10 books, you will see measure theory as a prerequisite to understanding the content. This book though, does not to have a math heavy prerequisites as most of the book can be read with linear algebra, elementary calculus and probability knowledge. So, in a way, this book can be read by a wider audience. Even though this book uses many numerical simulations, the author also believes that

There are computations one can do with pen and paper that even the fastest computers cannot perform.

Even though the book has 13 chapters and organized logically, the author suggests that one can take several trails for first, second or third pass through the book. One can follow discrete finance trail by working through Chapters 1, 2, 5, 6; One can go over the continuous finance trail by working through Chapters 8, 9, 10, 11 and 12; One can go over risk management trail by reading through Chapters 3,4 and 13. If you are like me who likes to read stuff that combines intuitive arguments and math, you might want to go over the entire book cover to cover.

image Takeaway

This book is definitely a class apart from the usual books on math finance. It uses a practitioner’s view to explain many concepts that are tricky to understand at the first go. The use of matrix computations, Fourier transforms, optimization methods, etc. makes this book more appealing to a practitioner rather than a theoretician. Great reference for someone working as a desk quant .


Crisis hits financial markets at regular intervals but the market participants keep assuming that they “understand the behavior” of markets and are in “total control” of the situation until the day things crash. There is an army of portfolio managers, equity research analysts, macro analysts, low frequency quants,derivative modeling quants, high frequency quants etc., all trying to understand the markets and trying to make money out of it. Do their gut /intuitive/quant models come close to how the market behaves ?.

I had bought this book in May 2008 assuming that I will read the book on some weekend. Somehow that weekend never came. Years flew by. For some reason I stumbled on to this book again and decided to read through this book. In the last 6 years I have read many books that have highlighted the inadequacy of quant models to capture market place reality. Before going through the book , I had a question – “How is this book different from all those model bashing books?”.

Here is a list of other questions that I had before reading the book :

  • Gaussian models do not predict the kind of randomness that we see in our markets. I had anticipated that the book is going to be Gaussian bashing book. What’s the alternative to Gaussian models?
  • Some practitioners have included jumps in to the usual Brownian motion based models and have tried to explain various market observed phenomena like option skew, volatility jumps, etc. What does the book have to say about Jump modeling? Are Jump processes good at only explaining the past or Can they serve as a tool for better trading and risk management decisions ?
  • Are fractals a way of looking at price processes?  If so, how can one simulate a price process that is based on fractal geometry ?
  • What are the parameters of a fractal model?
  • How do you fit the parameters of a fractal model?
  • Can one put some confidence bounds on the fractal model parameters? If so, how?
  • Why hasn’t the idea taken off in the financial world, if the fractals are ubiquitous in nature?
  • Why are academics reluctant to take a fractal view of the market?
  • Why are quants reluctant to take a fractal view of the market?
  • What is Mandelbrot’s view of financial modeling and risk management? Can risk in the financial markets be managed at all, given the wild randomness that is inherent in the market ?

This book answers many more questions than those that I have listed above. Let me try to summarize briefly each of the 13 chapters of this book:

Risk, Ruin and Reward:
This chapter gives a preview of the basic idea in the book. Fundamental analysis, technical analysis and the more recent random walk analysis all fail to characterize the market swings. However there has been a massive reluctance to move away from such kind of mental models. Partly the system of incentives is to be blamed. The authors say that the goal of the book is to enable readers appreciate a new way of looking at markets, a fractal view of the market. The background to this view is put forth in terms of five rules of market behavior.

  1. Markets are risky
  2. Trouble runs in streaks
  3. Markets have a personality
  4. Markets mislead
  5. Market time is relative

At the outset the authors say that this is not a “how to get rich” kind of book but more of a pop science book.

By the Toss of a Coin or the Flight of the Arrow:

The authors introduce “types” of randomness in this chapter. There are two types of randomness, “mild” and “wild”, the former is akin to tossing a coin and the second is akin to a drunk archer hitting a target. The former is the Gaussian world where things are normal and significant deviations from the mean are almost 0 probability, whereas the latter is the Cauchy distribution world where averages don’t converge and the deviations appear so big that the term “average” doesn’t make sense. Using these two distributions, the authors give a glimpse in to the spectrum of randomness types possible. Financial research, tools and software in today’s world are predominantly built around with the assumption of mild randomness. However the world seems to be characterized by “wild” randomness.

Bachelier and his Legacy:

The authors dig in to the history and recount Bachelier’s life. He was the first person to apply mathematical principles to stock market. He is credited for viewing the price process as an arithmetic Brownian motion. This view was never appreciated by his contemporaries. Almost 50 years later, Bachelier’s paper was picked up by Samuelson, a professor at MIT. Samuelson was a wizard at mathematics and he was on a course to make economics a quantitative discipline. Samuelson and others like Fama( University of Chicago) worked on Bachelier’s view of the market, improved it and applied to a whole host of areas like portfolio management, risk assessment, etc. Castles were being built on sand and these castles were multiplying too in different locations. Almost no one wanted to verify the basic assumption of continuous Brownian motion process.

The House of Modern Finance:

The authors talk about three pillars of modern finance that is taught all over the universities, i.e. CAPM(Capital Asset Pricing Model), MPT(Modern Portfolio Theory),BSM( Black Scholes and Merton framework). The first was developed by Markowitz, the second by Sharpe and the third by Fischer Black,Myron Scholes and Robert Merton. CAPM dealt with dishing out portfolios to investors based on their risk appetite. MPT simplified CAPM and dished out a market portfolio, that later became the bedrock for index funds, and BSM was a framework for valuing options. The crux of each of the pillar goes back to Bachelier’s work. No one was willing to criticize the component of Brownian motion that was used in all these frameworks. There was a rapid adoption of these tools in the industry, almost blind-faith obedience to all these models. Soon however financial crisis brought semblance amongst investors and traders and some of them began to develop strong dose of skepticism over these models. Warren Buffet, once jested that he would create a fund so that academicians could keep teaching these three pillars of modern finance to people so that he would make money by trading against them.

This book was written in 2004 but did it stop people from using mild randomness models for the market ?. No. The ever wide spread usage of Gaussian copula model before the full fledged onset of subprime crisis is a point in case that the castles of air built on mild randomness are still being used. VIX index is a nice example of how people are shying away from using the volatility based on underlying. VIX uses option prices to get an estimate of ATM volatility. All said and done, it does use GBM. So, it is a “FIX” too.

The Case against Modern Theory of Finance:

The authors use this chapter to set the stage for the next part of the book, i.e. way to understand different types of randomness (mild, wild and everything in between). The following are some assumptions that make all the academic theories that have been developed till date, very fragile. They are

  • People are rational and aim only to get rich
  • All investors are alike
  • Price change is practically continuous
  • Price changes follow a Brownian motion

Look at the daily returns of the Nifty total returns index. They are nowhere near close to that generated by a Geometric Brownian motion process that is assumed in finance literature. The second visual has been generated with the assumption of GBM with observed nifty volatility looks too regular as compared to the reality.





The above picture compares the frequency counts of returns for GBM path and Nifty. One can see that blue bars are all over the place as compared to GBM counts. A few pictures relating to price changes make it abundantly clear that the returns do not follow a GBM for equity, forex, commodity markets. Since various crisis have hit the markets, many researchers have observed CAPM anomalies such as P/E effect, January effect, Market-to-Book effect that do not make sense in a CAPM world. The authors say that the most saddest thing is that when things do not fit the model, the model is tweaked here and there to explain the market behavior. Some of the tweaks that have been created are

  • Instead of one factor, introduce many factors that explain the returns
  • Instead of constant volatility, incorporate a conditional equation for the volatility

The authors vehemently argue that these are all adhoc fixes. All are variations around Gaussian model and hence are fundamentally flawed.

Turbulent Markets: A Preview

The authors make a case for using “turbulence” as a way to study price processes. This chapter gives a few visuals that can be generated via multi fractal geometry.

Studies in Roughness:

The authors use extensive set of images to convey the basic properties of a fractal. A fractal can be identified based on three characteristics

  1. Initiator – Classical geometric object , i.e. a straight line, triangle, solid ball etc.
  2. Generator – A template from which fractal will be made.
  3. Rule of Recursion – This decides the way fractal is repeated

The authors generalize the Euclidean dimension and introduce fractional dimension. This concept is made clear via examples and pictures. The concept of dimension is based on the shrinking ruler length and the increasing measurement length. In most of the phenomena that authors have researched, they have found a surprising convergence for the logarithmic ratio of the two quantities, ruler length and measurement length. This ratio is summarized as a parameter for the scaling law that is put forth in the book.

The Mystery of Cotton:

This is one of the most fascinating chapters of the book. It deals with connection between fractal geometry and cotton prices. There are three pieces to this connection

  1. Power laws
  2. Power laws in economics
  3. alpha stable distribution

Combining these three aspects, Mandelbrot plots the change in cotton prices vs. frequency count on a log-log scale and finds a remarkable pattern across time scales. He finds that the cotton prices obey an alpha stable distribution with alpha being -1.7. This piece of evidence was stubbornly resisted by many academicians. During the same time period, there was a massive wave of CAPM, MPT, BSM that was sweeping through academia and the industry. Amidst this wave, Mandelbrot’s cotton price analysis did not find the right traction.

Here is the log log plot for up move vs. its frequency, down moves vs. its frequency for Nifty total returns index. image

The slope for the above graphs is approximately -1.1. What do the above graphs say? The frequency of up moves and down moves follows power law. A great many small jumps and small gigantic jumps is what characterizes NIFTY. The above is based on daily returns. One can do a similar analysis at a 5 min interval scale or 15 min interval scale and see that this power law relationship scales across time intervals.

Some people might not believe that such a scaling law exists among financial markets. One simple test that the author recommends is : Take an intraday transaction price data, remove the x axis labels and y axis labels. If you show this graph to someone, they might easily mistake it to be a chart based on closing prices. In physics there is a natural boundary between the laws. The laws that work on macroscopic level are different from that of the laws that work on microscopic level. The scale matters in such field. In finance, there is no reason for such a time scale to exist. Any model you build should take in to consideration this scaling aspect of financial time series.

How does one interpret the value of the slope –1.1 ? It turns out that the slope value for wild randomness is close to -1 ( Cauchy distribution) and slope value for mild randomness is -2 ( coin tossing context). For Nifty, the value is close to –1, i.e wild randomness. Well, one can only wish a dose of good luck to all who are trying to make money in such wild markets.


Long Memory, from the Nile to the Marketplace:

image H.E.Hurst

The authors narrate the story of H.E.Hurst , an Englishman, a chief scientist at Cairo who immersed himself in a particular problem, i.e. forecasting the flooding intensity of river Nile. Because the amount of flood varied from year to year, there was a need to create “century storage” to stockpile water against the worst possible draughts. This task was assigned to Hurst. As he began investigating the data, he found that the yearly rainfall was Gaussian. The problem however was with the “runs”. He found the range from highest Nile flood to lowest widened faster than what a coin-tossing rule predicted. The highs were higher, the lows, lower. This meant that not only the size of the floods, but also their precise sequence matter. Hurst went on a data collection exercise and collected all sorts of data without any preconceptions. He looked through any reliable, long-running records he could find that were in any way related to climate, for a total of fifty-one different phenomena, 5915 yearly measurements. In almost all cases, when he plotted the number of years measured against the high-to-low range of each records, he found that the range widened too quickly – just like the Nile. In fact, he found as he looked around the world, it all fit the same neat formula : the range widened not by a square-root law as coin-tossing, but as a three-fourth power. A strange number, but it was , Hurst asserted, a fundamental fact of nature.

This “Nile flood” example is one that is very often quoted to illustrate “Long memory processes”. So, in simple words, these are time series where the dependency dies down very slowly. If you plot the autocorrelation plot, the series dies down slowly as compared to any stationary process. One can easily get the Nile data from the web. This plot shows that yearly flood data is nearly normal but the second plot shows that ACF decays very slowly. The third plot shows a linear relationship between log range and log duration and the slope of the line is estimated as the Hurst coefficient.




In a chance conversation with a professor at Harvard, Mandelbrot starts working on long memory processes. To paraphrase the author here,

The whole edifice of modern financial theory is, as described earlier, founded on a few simplifying assumptions. It presumes that man is rational and self-interested. Wrong, suggests the experience of the irrational, mob-psychology bubble and burst of the 1990’s. A further assumption: that price variations follow the bell curve. Wrong, suggest the by-now widely accepted research of me and many others since the 1960s. And now the next wobbles: that price variations are what statisticians call i.i.d independently and identically distributed – like the coin game with each toss unaffected by the last. Evidence for short-term dependence has already been mounting. And now comes the increasingly accepted by still confusing evidence of long-term dependence. Some economists, when thinking about long memory, are concerned that it undercuts the efficient market hypothesis that prices fully reflect all relevant information; that the random walk is the best metaphor to describe such markets; and that you cannot beat such an unpredictable market. Well, the efficient market hypothesis is no more than that, a hypothesis. Many a grand theory has died under the onslaught of real data

Mandelbrot incorporates this dependency in his fractal theory and denotes it as H, the Hurst coefficient. For a Brownian motion, H equals ½ and for any other persistent process, H tends to fall between ½ and 1. The more the persistence, the more H will be close to 1. If H is smaller than ½, then the process is in a mean reverting mode. With this context, the authors introduce “Fractional Brownian motion”.

Noah, Joseph and Market Bubbles

The title of the chapter is meant to aid readers to quickly recall the two effects that are present in the financial markets; first characterized by the sudden jumps in the prices and second characterized by the long memory nature of prices. The authors use two stories from Bible and they probably resonate with someone who is familiar with Christian mythology. The takeaway for me is that Mandelbrot’s theory is a combination of two parameters, “alpha” that takes in to consideration the scaling law and Hurst coefficient that tries to characterize the long term memory of the price process. The wildness that characterizes markets can be described in two words – “abrupt change” and “almost-trends”. These are the two basic facts of financial market, the fact than any model must accommodate. They are two aspects of one reality. Mandelbrot also mentions about a test to tease out two effects, a non parametric test called “rescaled range analysis”. But in some cases the two effects are closely related and teasing out the two effects becomes challenging. The chapter also talks about the interplay of Noah and Joseph effect in creating financial crisis at regular intervals.

The Multifractal Nature of Trading time

This chapter combines the two aspects, dependency and discontinuity and mixes it with trading time, a concept explained in this chapter. This combination results in a Multifractal model. Honestly I need to work with this model to get more understanding about the way it works. Pictures do convey a good idea about the model but my understanding as of now is very hazy.

Ten Heresies of Finance

This chapter lists a collection of facts that authors have found useful in their work

  1. Markets are Turbulent – Two characteristics of turbulence are scaling and long-term dependence. These characteristics are found in a number of examples from natural to economic phenomenon
  2. Markets are very, very risky – More risky than the standard theories imagine: Average stock market returns mean nothing to the investor. It is the extremes that matter. That’s where most of the money is made or lost. This is what explains the equity risk premium puzzle.
  3. Market “Timing” Matters Greatly – Big Gains and Losses Concentrate in to small Packets of Time : Ignore your broker’s advise that their clients should buy and hold. Most of the returns(positive or negative) are made in small concentrated time units.
  4. Prices often leap, not glide – That adds to the risk: Continuity is a common assumption and when you backtest a strategy, it is likely that your entry and exit points might seem perfect. The only problem is, in reality, the price jumps and by the time you are in the market based on your entry point, the market has squeezed out a significant proportion of the profit from the proposed strategy. The mathematics of Bachelier, Markowitz, Sharpe and Black-Scholes all assume continuous change from one price point to the next. All the assumptions behind them are false. The capacity for jumps, or discontinuity, is the principal conceptual difference between economics and classical physics. Discontinuity is massively profitable for market makers. You got to model it and incorporate it in your strategy.
  5. In Markets, Time is Flexible – The crux of fractal analysis is that same formulae apply to any scale , be it at a day scale, or an hour scale or a monthly scale. Only the magnitudes differ, not the proportions. In Physics, there is a barrier between subatomic laws of quantum physics and the macroscopic law of mechanics. In finance, there is no such barrier. In finance, the more dramatic the price change, the more the trading time-scale expands. The duller the price chart, the slower runs the market clock
  6. Markets in All Places and Ages Work Alike – This is like throwing away localized market microstructure studies away!
  7. Markets are inherently uncertain and Bubbles are inevitable – This section has a very good analogy, comparing “land of ten thousand lakes with a neighborhood called Foggy bottoms” to “trading world”. Very interesting analogy.
  8. Markets are deceptive – Be aware of the problems with just going with technical analysis.
  9. Forecasting Prices may be perilous, but you can estimate the odds of future volatility – A 10% fall yesterday may well increase the odds of another 10% move today – but provide no advance way of telling whether it will be up or down. This implies correlation vanishes despite of the strong dependence. Large price changes tend to be followed by more large changes, positive or negative. Small changes tend to be followed by more small changes. Volatility clusters. This means that at least you have some handle on it. However you cannot predict anything with precision. Forecasting volatility is like forecasting the weather. You can measure things but you can never be sure enough. This section also mentions about “Index of Market Shocks”, a scale that is used to measure financial crisis.
  10. In Financial Markets, the idea of “Value” has limited value – All value investors will have a shock treatment after reading this section. Value is a slippery concept and one whose usefulness is vastly over-rated

The authors end this chapter with the following quote:

The prime mover in a financial market is not value or price, but price differences, not averaging but arbitraging

In the Lab

The last chapter begins with mentioning academicians, quants and traders who are working on fractals in finance.

imageRichard Olsen

For more than two decades Olsen and his team ( of mathematicians have been analyzing high frequency data. The authors narrate the team’s history and their work. Olsen started his firm after having a sense of frustration with the way financial models have been modeled and used. The sheer lack of understanding in to financial markets makes him get up every day and head off to do his research. Olsen firmly believes that world is a fractal and hence uses all the techniques from Mandelbrot’s theory to understand and trade in the markets. More specifically, he uses multifractal analysis that rests on the assumption that “trading time” is different from “clock time”.

imageJean-Philippe Bouchad

Capital Fund Management, a hedge fund based out of Europe uses a mix of statistical arbitrage and multi fractal analysis for their trading strategies. The fund firmly believes in scaling by power law and long-term dependence and hence uses a different kind of portfolio management techniques. They call it “generalized efficient frontier” where they focus only on the odds for a crash and their scaling formula minimizes the assets in a portfolio crashing at the same time.

imageEdgar Peters

Edger Peters, another asset manager believes in fractals and has written two books on them. However he does not use them in his funds as he says that his conservative clientele were not interested. In the hindsight of so many crisis that have happened, conservative clientele should have been more than interested in fractals as it comes close to actually explaining the market behavior.

Mandelbrot says that very little work has actually been done in using fractals in finance. He lists the following areas where fractals can truly play a great role

  • Analyzing investments
    • There are so many types of numbers that are seen in the trading/investment world, p/e , volume, EBIDTA,PAT, etc. However when it comes to measuring risk,the industry’s toolkit is surprisingly bare. The two most common tools are volatility and beta of the stock. These two numbers are used again and again everywhere. The assumption behind these numbers is bell curve. How could it possibly be that one and the same probability distribution can describe all and every type of financial asset ? Mandelbrot says that finance today is in a primitive state. Its concepts and tools are limited. The work on scaling, long term memory has just begun. There are many methods to compute Hurst coefficient but no major work has been done show the robustness of methods
  • Building Portfolios
    • CAPM or its variants are all driven by mild randomness assumptions. In 1965, Fama showed that one needs a far greater set of stocks in a portfolio if one makes wild randomness assumption. There is a need for creating a new, correct portfolio theory. Today, building a portfolio by the book is a game of statistics rather than intelligence. In any case, Mandelbrot suggests that every portfolio manager should simulate price series using Monte Carlo methods and test their ideas. Using the methods from the book, one can easily simulate a series based on multifractal geometry and then test out whatever strategy one wants to test out.
  • Valuing options
    • This is another area that cries for guidance. Black schools theory is just that, a theory that makes the very existence of an option redundant. If one can perfectly replicate an option, aren’t options redundant? The observed volatility smile is corrected using a range of fixes such as GARCH, Variance gamma process etc. In the same bank, different groups might be using different ways to value options. Isn’t it strange that we do not have any sensible valuation method for one of the oldest trading instruments?
  • Managing Risk
    • This book was published in 2004 and the author criticizes VAR at length. Somehow no one paid heed to Mandelbrot’s warning. The author is somewhat pleased that Extreme value theory is being used by some people for risk management. However he says that Long memory behavior is not being incorporated in to such theories.

By the end of this book, a reader is thoroughly exposed to the key themes of the book : scaling, power laws, fractality and multifractality.


Most of the financial models taught in universities world over, are “mild-randomness” types. Most of the academic research is concentrated on developing “fixes” to mild-randomness models. However the markets are driven by “wild-randomness”. As long as portfolio managers, traders and quants use “mild-randomness” models, catastrophic losses are inevitable. The book says there are just about 100 odd Fractalists throughout the world who have realized the futility of the current models, have abandoned them for good and are using fractals in their work. For anyone wanting to look at the financial world with fresh eyes, this book is a must read.



The book broadly deals with two strategies, “mean reversion” and “momentum”.These strategies cover six chapters of the book, out of which four of them are on mean reversion and two of them are on momentum strategies. Besides these six chapter, there is one chapter on backtesting strategies and there is another on risk management.

Given the importance of backtesting in any strategy, the first chapter starts off with some of the pitfalls of backtesting. It also gives three general methods for backtesting any strategy. First method is the usual frequentist method of testing whether the null : “returns from the strategy is 0”. Second method is to simulate various return paths and check for the number of times the strategy beat the returns based on historical data. The third method involves randomizing the longs and shorts and seeing whether the strategy makes sense. One can also think about a fourth method where you resample the returns series and general price path and then check out your strategy. The author also cautions about the relevance of backtesting and says that any regime shifts will make all the backtesting irrelevant and hence the strategy, despite looking good on paper, is going to fall flat. The author ends with a brief account of the various software available for a algo trader. Even though there are software that are mentioned that could help a non-programming trader, I fail to see what value such software can add. A basic requirement of anybody thinking of algo trading is working knowledge of at least one or two programming languages. UI driven interfaces can only supplement the code and not replace it.

The second chapter goes in to some basic stat/math skills to test stationarity of a series, check for a cointegration etc. Four methods are used to check for the stationarity of a time series, i.e. Dickey Fuller test, Hurst exponent, Variance ratio test, Half life of AR(1) process. Well, one can say, what’s the point in all these tests such as unit tests, variance ratio tests etc. Why not just backtest and decide whether to go ahead with the strategy or not. One point to consider is that these tests are far more statistically significant that what your strategy results show. The strategy by its very nature will have limited data points whereas the above tests use almost all the data points. Besides that if a series shows stationarity or a set of series show stationarity, then one can atleast put an effort in coming up with a strategy. What’s the point in testing strategy after strategy when the series itself is close to random ?.

For finding cointegrated assets, the chapter discusses 2 step Engle-Granger method and Johansen procedure. The former is easy to understand and implement but is plagued by some problems that are explained in this chapter. To understand Johansen procedure, one needs to sweat it out by reading the math from some other book. This chapter is more of "here is how Johansen function can be used to find cointegrating relations". If your concern is merely to know the method, then you don’t need any further reference. But if you are like me who wants to understand what the test does, i guess there is no option but to spend time understanding the math behind it.

The third chapter begins with a very interesting take on the input to a cointegration exercise. What can be the input ? raw prices or log prices or ratio of two stocks ? The first variant of pair trading that is typically seen is the ratio trade, compute the ratio between two stocks and trade the spread. The stocks might not be cointegrating but the spread might be mean reverting in a shorter time frame. In fact this was a recurrent theme in almost all the pair reports that I used to see from Brokerage houses. These were pushed under the title cointegrated pairs. I always use to wonder why should a ratio of two stocks be stationary ? Equivalently, why should the hedge ratio between two stocks be 1 ? This chapter kind of nails that question down by saying that ratio based trading is convenient and works if the spread is stationary on a short time frame. So, given a choice between a cointegrating vector based trading and ratio based trading, which one should be selected? The author says he has no clear answer except his backtesting shows that it is better to take cointegration based hedge than to rely on ratio based trades. The chapter introduces Bollinger bands and Kalman filter based strategies for getting a higher return and Sharpe ratio compared to naive strategies.

The fourth  starts off with the author discussing about the difficulties with pursuing mean reverting strategies that involve stocks.Subsequently, mean reverting strategies for ETFs are explored. One of the first strategy that is explored in the book is Buy-on-Gap strategy. The author says he had used this strategy for his fund as well as his personal account and it made money till 2009. The chapter explores arbitrage strategies between ETFs and its constituent stocks. MATLAB results for the strategy are given and the author provides his interesting commentary on the results. The chapter ends with an exploration of cross sectional mean reversion where stocks are longed or shorted based on the relative movement with respect to sector index or a market index.

The  fifth chapter deals with some specific aspects of pairs trading in currencies. After reading this section I had a feeling that it is more important to identify the correct instruments for setting up a cointegrating equation. It is easy to make a mistake while backtesting the strategy.The strategies explored are trading currency cross rates, trading futures calendar spreads. The basic message that the author tries to convey is the decomposition of returns in to spot returns and roll returns. It is often that a strategy performs well over long backtesting period because of roll returns. One must be careful in attributing the performance of a strategy to the respective return types. The author tests out various Futures intermarket spreads and shows that none of the spreads form a stationary series. The chapter has a section where VIX futures and E-mini S&P 500 futures contracts are used to create a cointegrating pair. At least on paper, the strategy looks promising.

The sixth and seventh chapters deal with interday and intraday momentum strategies.The author says that interday momentum strategies have been performing badly since crisis and the entire action nowadays is in the intraday game. The last chapter deals with Kelly criterion and other risk management techniques. In fact the author covers quite extensively the topic of Kelly criterion in his previous book. If you are new to Kelly criterion, it is worthwhile to read the paper by Thorpe and understand various aspects of Kelly criterion. The paper is so well organized that you will have many aha moments along the way. After going through the paper, I could understand most of the material in this chapter. The book ends with discussing stop losses and CPPI techniques for dealing with the problems of applying Kelly criterion in a practical scenario.

All the code is written in MATLAB. Thanks to the author’s site, I could find most of the datasets that are used in the book. If you are a non MATLAB user like me, you can easily through the MATLAB code and translate the code in to whatever language you are comfortable with, to verify the strategy results mentioned in the book.


image Takeaway

The book discusses “mean reverting strategies” and “momentum strategies” at length. This book helped me tie a couple of loose ends in my thought process relating to mean reversion strategies. The practical insights in to Kelly criterion and Risk management makes this book a great resource for risk managers and prop traders.

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