This is a book which I have been planning to read for quite some time, may be since an an year . Finally found sometime to spend on this book.
However the book turned out be just about OK, not engaging, considering the amount of hype around it .Having said that, this book has to be read by anyone who is in to back testing trading rules / strategies and has only a vague idea of stats!
Anyways, Here are my brief takeaways :
Chapter 1 – Objective Rules and Their Evaluation
- One needs to formulate a basic idea of a rule. Rule is an objective statement that can be implemented by a computer program and that generates unambiguous long/short/neutral signals
- Position bias needs to be tested in a trading rule. Randomize + Equivalent position would serve as a good control group
- Detrending is very imp in back testing rules
- Avoiding look-ahead bias and accounting for trading costs are very important!
Chapter 2 – The Illusory Validity of Subjective Technical Analysis
- Important to understand the various bias that can creep in validating a claim
- Overconfidence,Optimism bias
- Hindsight bias
- Confirmation bias
- Illusory correlation
- Sample size neglect
- Representative bias
- Biased second hand knowledge
- Attribution bias
- Knowledge Illusion
- It is important to recognize it is easy to fit a pattern to a time series. However it could be only wishful thinking unless it is tested vigorously with scientific rigor.
- Good stories well told can make people misweight or ignore facts.
- People are not naturally rigorous logicians and statisticians. A need to simplify complexity and cope with uncertainty makes us prone to seeing and accepting unsound correlations. We tend to overweight vivid examples, recent data and inferences from small samples.
- Scientific method is a reliable path to validity, mitigating the misleading effects of our cognitive biases
Chapter 3 – The Scientific Method and Technical Analysis
This chapter summarizes the development of scientific method. Aristotle’s syllogisms, Bacon’s contradictory thinking, Popper’s falsification method, Null Hypothesis-Alternate hypothesis development. This chapter leaves the reader with a powerful impression that advancement in any field needs the 5 stages of work
Unless these become a part of TA, practitioners of TA are nothing but astrologers, alchemists and folk healers
Chapter 4 – Statistical Analysis
Nothing much …For a person who doesn’t know abt stats, this chapter might be useful…Definitely skimmable for someone who uses stats for rozee roti
Chapter 5 – Confidence Intervals
Nothing much…Except that one can use resampling method Or Montecarlo method for testing hypothesis and establishing confidence intervals.
Most of the content in this chapter is Stats101. One takeaway for me,is that one has to zero-center the sample for conducting resampling method. This is done to align with the universal null hypothesis of a trading rule –Ho– Returns from the trading rule are 0.
Chapter 6 – Data mining Bias – Fool’s gold of Objective TA
For me, this section is the meat of the book. I took quite some time to read slowly what this section talks about and here are my takeaways.
After the fact probability tends to much higher before the fact. A monkey coming up with a master piece is very less, but given a history, the probability of a masterpiece being produced is very high
Be aware of data mining bias – Random component + Genuine predictive power of rule , the random component dominates the data mining route. You are just lucky with the rule in the history and out of sample is bound to under perform.
Data mining – Always the best performing rule is chosen…Well, the rule was just lucky in that period
Five Factors one needs to think about Number of rules being tested, Data size, Correlation between rules, variation in the expected returns amongst the rules, Presence of outliers
Three ways to cut DM Bias – Out of sample testing(walk-forward testing), Markowitz scaling down method, Resampling methods and Montecarlo methods
Chapter 7 – Theories of Non Random Price motion
This section looks at the shaky foundations of Efficient Market Hypothesis. All forms of EHM are analyzed. Arguments based on logic as well as empirical evidence are put forth in order to show that EMH is crap and there is enough scope for Arbitrage opps in a market
S&P Case study:
The last part of the book deals with testing about 60,000 binary signals to S&P data. If you can read through relatively dry chapters 1-7, you are bound to enjoy this part of the book. Once you start thinking about the ways in which trading rules can be built , this section is priceless as allows to zoom in to various aspects of back testing..
Personally, I think that the S&P case study at the end is far more valuable than the first 400 pages of dry read. However if you haven’t used stats for a while, you might like the first 400 pages too.
My takeaway from the book is : There are a ton of insights from the S&P case study towards the end of the book!, the rest is jazz.