Erroneous knowledge often arises from systematic errors in how we process information, particularly in complex and uncertain situations like financial markets. These biases, unlike random errors, occur repeatedly in similar circumstances, making them predictable and potentially avoidable. This phenomenon can be measured by analyzing the variability of the results in the database. According to Aronson, the greater the variability of strategy performance metrics in the databank, the greater the risk of bias from data mining.
The hindsight bias distorts our perception of past events, making them seem more predictable than they actually were. This creates a false sense of confidence in our ability to make predictions. The knowledge illusion is a false confidence in what we know—both in terms of quantity and quality.
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What was not explicitly explained was the concept of “degrees of freedom” as explained in Robert Pardo’s book, “Design, Testing, and Opimization of Trading Systems,” (1992) and his second edition, (2008). From the 1st edition, “Placing to many restrictions on the price data is the primary cause of overfitting” pg. This provides for the migration of strategies between islands. Evolutionary management can also play an important role. Especially if we restart genetic evolution with too many generations. You may end up with more correlated strategies in the databank.
This point is considered by Aronson to be the most important of all factors. He argues that the larger the sample of data obtained, the smaller the negative impact of the other factors. When optimizing an existing strategy, pay attention to the parameter ranges and the number of steps. In general, the more options, the greater the chance of chance.
Erroneous Knowledge Stems from Cognitive Biases
Aronson’s background includes a five-year stint as a proprietary trader before transitioning to academia. His work focuses on applying scientific methods and statistical analysis to trading strategies, challenging traditional subjective approaches. Aronson is known for his skepticism towards conventional technical analysis techniques and his advocacy for evidence-based methods.
This bias inhibits learning and reinforces erroneous knowledge. The enduring appeal of the Elliott Wave Principle may be attributed to its comprehensive cause-effect story, which promises to decipher the market’s past and divine its future. However, its flexibility and loosely defined rules make it difficult to test objectively. The self-attribution bias further distorts our perception of reality by attributing successes to our skills and failures to external factors. This self-serving interpretation of events reinforces overconfidence and hinders learning from mistakes. A “file MD5” is a hash that gets computed from the file contents, and is reasonably unique based on that content.
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- While working as a broker for Merrill Lynch between 1973 and 1977, Aronson wrote several internal technical analysis memos including one in December of 1973 to Robert Farrell, Merrill’s head technician.
- Evidence-Based Technical Analysisexamines how you can apply the scientific method, and recently developed statistical tests, to determine the true effectiveness of technical trading signals.
- In this case, the larger the values and ranges you specify, the greater the risk of data mining bias.
To combat the hindsight bias, subjective practitioners should make falsifiable forecasts, clearly specifying the conditions under which their predictions would be considered wrong. This allows for objective evaluation and feedback, reducing the illusion of validity. These What If Cross Checks allow you to test the performance of the strategy without the most profitable or the most profitable trades. If the results of the strategy are unreasonably different, you need to be careful. Aronson criticizes the subjective TA methods but also emphasizes that mistakes can be made even when using objective TA.
It is based on the false premise that more information should translate into more knowledge. Approaching TA, or any discipline for that matter, in a scientific manner is not easy. Scientific conclusions frequently conflict with what seems intuitively obvious. To early humans it seemed obvious that the sun circled the earth.
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- For too long TA practitioners have used overly vague terminology and methods for predicting the market.
- Evidence-Based Technical Analysis examines how you can apply the scientific method, and recently developed statistical tests, to determine the true effectiveness of technical trading signals.
- You will never hear any of these words in any of the trading books you normally come across.
- It is based on the false premise that more information should translate into more knowledge.
- In general, the more options, the greater the chance of chance.
Just like I would not recommend any other trading or investing book that claims to predict future prices. You don’t need any of that when you know that the US equity market goes up in the long run and that all individual stocks are highly correlated to the market. In particular, he wanted to put under scrutiny many of those technical analysis rules that have for so long been deemed as “predictive”. He does not do exactly what I described above because he uses something called MonteCarlo Permutation Method. This compares favorably to the ARR for the buy and hold strategy (11.05%) and to the best results obtained using the system with no technical analysis knowledge embedded (13.35% with 126 trades).
The magnitude of the data-mining bias is influenced by several factors, including the number of rules tested, the number of observations used to compute performance statistics, and the correlation among rule returns. Human intelligence, while powerful, is maladapted to making accurate judgments in uncertain environments. Our brains evolved to find patterns, but not necessarily to distinguish valid from invalid ones. This predisposes us to adopt false beliefs, especially when dealing with complex phenomena like financial markets.
In general, the larger the data sample (number of trades in out of sample), the higher the statistical power of the results. In the following chapters, Aronson explains the importance of rigorous statistical analysis in evaluating strategies. A scientific hypothesis must be falsifiable, meaning that it can be tested and potentially disproven by empirical evidence. This distinguishes science from pseudoscience, which is often characterized by untestable claims and resistance to empirical challenge. The goal of science is to discover rules that predict new observations and theories that explain previous observations. Predictive accuracy and explanatory power are key criteria for evaluating scientific knowledge.
Ivan Hudec, known as “Clonex” on the forum, is an experienced algorithmic trader, consultant, and researcher who has been trading for 15 years and using StrategyQuant X (SQX) since 2014. He contributes to the SQX blog and enhances the software by adding new indicators, snippets, and incorporating Python programming for advanced data analysis, machine learning algorithms, and quantitative modeling. Ivan offers his expertise to help others accelerate their trading projects and approach them in innovative ways. The use of scientific methods in technical/quantitative analysis is the basic theme of the entire book.
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Readers appreciate its rigorous methodology, statistical focus, and debunking of subjective TA myths. The book is praised for its unique perspective and valuable insights, particularly on data mining bias and statistical testing. However, some find it overly long and academic, with excessive focus on basic concepts.
Because they cannot be objectively tested or refuted, claims of their effectiveness are essentially meaningless. Examples include classical chart pattern analysis, hand-drawn trend lines, and Elliott Wave Principle. 13This refers to the presence of very large returns in a rule’s performance history, for example, evidence based technical analysis a very large positive return on a particular day. In other words, more observations dilute the biasing effect of positive outliers.
Part II: Case Study: Signal Rules for the S&P 500 Index
It is the ethical and legal responsibility of all analysts to make recommendations that have a reasonable basis and not to make unwarranted claims. Objective evidence, obtained through rigorous scientific methods, is the only reasonable basis for asserting that an analysis method has value. The future of TA lies in a partnership between human experts and computers, leveraging their complementary strengths. Humans excel at proposing new ideas and formulating hypotheses, while computers excel at processing large datasets and identifying complex patterns. Much of popular or traditional TA stands where medicine stood before it evolved from a faith-based folk art into a practice based on science. Its claims are supported by colorful narratives and carefully chosen (cherry picked) anecdotes rather than objective statistical evidence.