Monthly Archives: November 2013

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

Posted in Book Two: Twenty-Four Trading Strategies Based on Scientific Findings About Technical Analysis Tagged with: , , , , , , , , , , , , , , , , , , , ,

Identifying expert microblog forecasters

Bar-Haim and colleagues (2011) from the Hebrew University of Jerusalem downloaded tweets from the 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

Posted in scientific understanding of financial markets, Uncategorized Tagged with: , , , , , , , , , , , , , , , , , , , ,

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

Posted in scientific understanding of financial markets Tagged with: , , , , , , , , , , , , , , , , , , , ,

The rise of the supercomputer

In this era of cloud computing, big data, server farms, and the smartphone in your pocket that’s vastly more powerful than a roomful of computers of previous generations, it can be easy to lose sight of the very definition of

Posted in scientific understanding of financial markets, Uncategorized Tagged with: , , , , , , , , , , , , , , , , , , , ,

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