Yearly Archives: 2013

Research on news-based indicators, part 2 of 4

In Part 2, Peter Hafez, Director of Quantitative Research for the data provider RavenPack, describes a new indicator he has developed and researched called “news beta.” This is a measurement of the degree to which a security responds to market

Posted in Book Three: Twenty-Five Trading Strategies Based on Scientific Findings About Business and Financial News Tagged with: , , , , , , , , , , , , , , , , ,

Research on news-based indicators, part 1 of 4

In the video here, I interview Peter Hafez, Director of Quantitative Research for the data provider RavenPack. Hafez has published a number of empirical studies dealing with ways of measuring the impact of news stories on securities prices. He has

Posted in Book Three: Twenty-Five Trading Strategies Based on Scientific Findings About Business and Financial News Tagged with: , , , , , , , , , , , , , , , , ,

Using business and financial news, part 5

The number of good news events reported in the business press is very similar to the number of bad news events. However, Green, Hand, and Penn (2012) – from Pennsylvania State University, the University of North Carolina, and Florida State University

Posted in Book Three: Twenty-Five Trading Strategies Based on Scientific Findings About Business and Financial News Tagged with: , , , , , , , , , , , , , , , , ,

Using business and financial news, part 4

Groß-Klußmann and Hautsch (2011) confirmed the usefulness of the machine-indicated relevance of news items. Significant market responses to news were only observable for items which were identified as being relevant. Their results showed that the classification was crucial to filter out

Posted in Book Three: Twenty-Five Trading Strategies Based on Scientific Findings About Business and Financial News Tagged with: , , , , , , , , , , , , , , , , , , , , , , ,

Using business and financial news, part 3

Prototype experiments for predicting market reaction to financial news. The research literature has described a number of prototypes for predicting market’s reaction to news. The first recorded approach was by the trader Victor Niederhoffer in the early 1970’s. Niederhoffer organized

Posted in Book Three: Twenty-Five Trading Strategies Based on Scientific Findings About Business and Financial News Tagged with: , , , , , , , , , , , , , , , , , , , , , , , , ,

Using business and financial news, part 2

Periodicities of Earnings Announcements Earnings announcements have distinct characteristics. The following charts show that there are periodicities to news announcements, particularly earnings announcements: The day-of-week chart above, for example, shows that companies have a clear preference for making earning announcements

Posted in Book Three: Twenty-Five Trading Strategies Based on Scientific Findings About Business and Financial News Tagged with: , , , , , , , , , , , , , , , , , , , , , , , , ,

Using business and financial news, part 1

Efficiency. It’s a simple word. In the world of modern finance, it means that stock market prices ­– rapidly and without prejudice – reflect what’s going on in the business world. This notion was settled upon because it was believed

Posted in Book Three: Twenty-Five Trading Strategies Based on Scientific Findings About Business and Financial News Tagged with: , , , , , , , , , , , , , , , , , , , , , ,

The correlation between volume and price direction, part 3

The opposite was true for low-volume stocks. When past week returns were negative, a drop in volume led to a continuation of negative returns. When the past week’s returns were positive, a drop in volume led to a reversal in

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

The correlation between volume and price direction, part 2

However, the primary result (as presented in the previous blog post, Part 1) did not hold in the price-winner, high-volume and price-winner, low-volume portfolios. Overall, high volume led to continuation in weekly returns while low volume led to reversal in

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

The correlation between volume and price direction, part 1

Alsubaie and Najand (2009), from the Department of Public Administration, Saudi Arabia, and Old Dominion University, Norfolk, Virginia, examined the relationship between abnormal changes in trading volume and short-term price behavior in the Saudi stock market. Considering prior research and

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