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Big Ideas

These blog entries offer some big ideas of lasting value relevant for investing and trading.

Stopping Tests after Lucky Streaks?

Might purveyors of trading strategies be presenting performance results biased by stopping them when falsely successful? In other words, might they be choosing lucky closing conditions for reported positions? In the December 2018 revision of their paper entitled “p-Hacking and False Discovery in A/B Testing”, Ron Berman, Leonid Pekelis, Aisling Scott and Christophe Van den Bulte investigate whether online A/B experimenters bias results by stopping monitored commercial (marketing) experiments based on latest p-value. They hypothesize that such a practice may exist due to: (1) poor training in statistics; (2) self-deception motivated by desire for success; or, (3) deliberate deception for selling purposes. They employ regression discontinuity analysis to estimate whether reaching a particular p-value causes experimenters to end their tests. Using data from 2,101 online A/B experiments with daily tracking of results during 2014, they find that:

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It Can’t All Be Data Snooping?

Is it possible that all the 300+ published factors that predict stock returns (such as size, value, profitability, investment, momentum…) derive from data snooping? In his October 2018 paper entitled “The Limits of Data Mining: A Thought Experiment”, Andrew Chen estimates how much data snooping would be required to “discover” all these factors by pure luck. Specifically, he calibrates a pure luck model built on the assumption that the probability of publishing a factor discovery increases with the degree to which the discovery is convincing (t-statistic). Using this model, he estimates the number of unpublished factor studies required for the published set to be attributable to pure luck. He considers two sets of factor t-statistics: 156 from factor replications via equal-weighted long-short extreme fifths (quintiles) of factor stock sorts; and, a hand-collected set of 316 from published factor studies. Using the specified approach and these two sets of t-statistics, he finds that: Keep Reading

Curbing Data Snooping

How should researchers applying machine learning to quantitative finance address the field’s data limitations, which exacerbate data snooping bias? In their October 2018 paper entitled “A Backtesting Protocol in the Era of Machine Learning”, Robert Arnott, Campbell Harvey and Harry Markowitz take a step back and re-examine financial markets research methods, with focus on suppressing backtest overfitting of investment strategies. They introduce a research protocol recognizing that self-deception is easy. Their goal is that the protocol offers the best way to match or beat expectations in live trading. Based on logic and their collective experience, they conclude that: Keep Reading

Free Data and the Collapse of Trading Costs

How have costs of U.S. stock trading data evolved in recent years? In his October 2018 paper entitled “Retail Investors Get a Sweet Deal: The Cost of a SIP of Stock Market Data”, James Angel examines costs of U.S. stock market data. He also describes the production of these data and their consolidation/distribution via Securities Information Processors (SIP). Using data for U.S. trading costs as far back as 1987, he finds that:

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Investment Strategy Development Coursework

In a series of nine presentation slide sets (Lectures 1-9 of 10) on “Advances in Financial Machine Learning”, Marcos Lopez de Prado provides part of Cornell University’s ORIE 5256 graduate course at the School of Engineering (“Special Topics in Financial Engineering V”). The course description includes: “Machine learning (ML) is changing virtually every aspect of our lives. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations [see the chart below]. Students will learn scientifically sound ML tools used in the financial industry.” Key points in these slide sets include: Keep Reading

Most Stock Anomalies Fake News?

How does a large sample of stock return anomalies fare in recent replication testing? In their October 2018 paper entitled “Replicating Anomalies”, Kewei Hou, Chen Xue and Lu Zhang attempt to replicate 452 published U.S. stock return anomalies, including 57, 69, 38, 79, 103, and 106 anomalies 57 momentum, 69 value-growth, 38 investment, 79 profitability, 103 intangibles and 106 trading frictions (trading volume, liquidity, market microstructure) anomalies. Compared to the original papers, they use the same sample populations, original (as early as January 1967) and extended (through 2016) sample periods and similar methods/variable definitions. They test limiting influence of microcaps (stocks in the lowest 20% of market capitalizations) by using NYSE (not NYSE-Amex-NASDAQ) size breakpoints and value-weighted returns. They consider an anomaly replication successful if average high-minus-low tenth (decile) return is significant at the 5% level, translating to t-statistic at least 1.96 for pure standalone tests and at least 2.78 assuming multiple testing (accounting for aggregate data snooping bias). Using required anomaly data and monthly returns for U.S. non-financial stocks during January 1967 through December 2016, they find that:

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Basic U.S. Stock Market Return Statistics

What do basic U.S. stock market return statistics say about consistency of equity risks and predictability of returns? We define basic statistics as first through fourth moments of the return distribution: mean (average), standard deviation, skewness and kurtosis. For tractability, we calculate these four statistics month-by-month based on daily returns. Using daily closes of the Dow Jones Industrial Average (DJIA) since January 1930 and the S&P 500 Index since January 1950, both through September 2018, we find that: Keep Reading

A Few Notes on Heads I Win, Tails You Lose

Patrick Donohoe introduces his 2018 book, Heads I Win, Tails You Lose: A Financial Strategy to Reignite the American Dream, by stating that the book: “…will teach you many of the principles and strategies to help discover your own path to financial freedom. Most importantly, it will show you the mindset required to carry out a successful plan. …almost everything you will gain from this book conflicts with what the typical financial planner, financial celebrity, and most financial publications tell you to do. …You will…discover how to pivot the foundation of your wealth to…the private mutual insurance company.” Based on his experience, market research and many examples, he concludes that: Keep Reading

A Few Notes on The Geometry of Wealth

Brian Portnoy introduces his 2018 book, The Geometry of Wealth: How To Shape A Life Of Money And Meaning, by stating that the book is: “…a story told in three parts,…from purpose to priorities to tactics. Each step has a primary action associated with it. The first is adaptation. The second is prioritization. The third is simplification. …The principle that motors us along the entire way is what I call ‘adaptive simplicity,’ a means of both rolling with the punches and and cutting through the noise.” Based on his two decades of experience in the mutual fund and hedge fund industries, including interactions with many investors, along with considerable cited research (much of it behavioral), he concludes that: Keep Reading

When Machine Learning Works for Investing

In what areas does machine learning have advantages over conventional financial/investment analysis? In his June 2018 presentation entitled “Nine Financial Applications of Machine Learning”, Marcos Lopez de Prado summarizes investing-related areas in which well-supervised machine learning outperforms conventional methods. Based on relevant research and his experience, he asserts that: Keep Reading

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