Blog Archives

Knowledge engineering and expert systems in the financial markets

Between 1986 and 2002, I hosted the weekly television series, Thinking Allowed, interviewing leading figures about psychology, philosophy, science, health, and spirituality. In the interview that is excerpted below, I interviewed Edward Feigenbaum and his colleague Penny Nii. Feigenbaum was widely

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The January effect: A predictable, seasonal market pattern

Over the past hundred years, a large body of economics and finance literature has documented monthly seasonality of returns on various assets, such as stocks, bonds, futures, currencies and commodities. More than 150 scientific journal articles, using a variety of

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Predicting financial markets using associative remote viewing

Earlier this year, the Journal of Parapsychology, published  the results from a 13-year experiment conducted by Greg Kolodziejzyk, using a unique approach to the associative remote viewing (ARV) protocol which allows a single operator to conduct the full ARV process beginning to end.

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Reversion to the mean: An evergreen trading strategy

Although my posts on this website are almost exclusively related to published, academic research on the financial markets, I have also been a trader myself since 1999. Currently, I am studying daytrading methods with Rob Hoffman, of Become A Better

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Patterns in the 24 hour trading cycle

Berkman, Koch, Tuttle and Zhang (2012) – from the University of Auckland, New Zealand; the University of Kansas; the American University of Sharjah, United Arab Emirates; and Missouri State University – examined the intraday stock prices of the 3,000 largest

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Profiting from changes to the Nasdaq 100 index

Yuanbin Xu (2012), writing his masters degree thesis at Brock University, in St. Catherines, Ontario, Canada, examined stock market reactions around the Nasdaq-100 index reconstitutions from 1997 to 2010. The Nasdaq-100 index changes have been performed in two ways: regular

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Information dissipation as an early warning signal

The theoretical underpinnings of bifurcations and phase transitions in finance have been around for many years. In the 1970s, the mathematical framework of catastrophe theory became a popular field of research, as it provided one of the first formalizations that

Posted in Bubbles and Crashes Tagged with: , , , , , , , , , , , , , , , , , , , ,

Trend recall: A novel approach to trend following

The following example of a breakthrough in pattern-recognition technology is reprinted from my book The Alpha Interface: Empirical Research on the Financial Markets, Book Two. It exemplifies the level of creativity and power available to the new generation of personal

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Identifying expert microblog forecasters

Bar-Haim and colleagues (2011) from the Hebrew University of Jerusalem downloaded tweets from the StockTwits.com website during two periods: from April 25, 2010, to November 1, 2011, and from December 14, 2010, to February 3, 2011. A total of 340,000

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Supercomputer research uncovers factors leading to CEO recklessness

Many respondents to my previous post on the rise of supercomputers in the world of finance focused on high frequency trading (HFT). However, I believe that the use of supercomputers for financial research is at least as important. Here is

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Book Three: Trading With The News

Learn about a news-based trading system that yielded a back-tested, average annualized, compounded return from 2000 to 2011 of 58.6%.

“Only once you’ve done your homework will you be able to understand how the stock market works and learn to distinguish between news and noise.” Maria Bartiromo, Use The News

Book Two: Technical Analysis

Learn about the "trend recalling" algorithm that yielded researchers a simulated annual return of greater than 400% in multiple tests.

“The scientific method is the only rational way to extract useful knowledge from market data and the only rational approach for determining which technical analysis methods have predictive power.”
David Aronson, Evidence Based Technical Analysis

Book One: Analysts’ Forecasts

Learn the strategy, based on analysts' revised forecasts, that yielded researchers an average of 1.13% - 2.19% profit per trade, for trades lasting one to two days?

Learn how certain analysts' recommendations, following brokerage hosted investment conferences, yielded profits of over 3% during a two-day holding period?

Learn how researchers found an average profitability of 1.78% for two-hour trades following an earnings announcement?

"This set of tools can help both ordinary and professional investors alike to re-think and re-vitalize their stock picking, timing and methods. A young, aspiring Warren Buffet could put this book to good use."
James P. Driscoll, PhD, investor

Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments by David Aronson (software included)

Evidence-Based Technical Analysis by David Aronson

Archive of Earlier Posts