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Author Archives: lstake
False discoveries everywhere
Since John Ioannidis published a paper in 2005 provocatively titled Why Most Published Research Findings are False, the general public and also researchers have gained a greater awareness of the unreliability of scientific discoveries based on “a single study assessed by formal … Continue reading
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Is two still the magic number?
When doing data analysis, we have come to regard two as the threshold that a t-statistic must clear in order to declare a variable statistically significant. As most readers will know, this critical value ensures a 5% level of significance given a … Continue reading
Posted in statistics, stock market forecasting
Tagged backtesting, data snooping, false discovery, stock market
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Data snooping in a nutshell
Data snooping is pervasive in financial research, both in academia and in industry. In my experience, the level of awareness about data snooping varies widely among practitioners. All too often, however, huge amounts of time and effort are wasted by following a … Continue reading
Posted in machine learning, stock market forecasting
Tagged backtesting, data snooping, false discovery
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Noise in asset returns
One of the goals of this blog is to discuss various approaches to forecasting asset returns taken from both the economics and machine learning fields. Before diving into specific models and techniques, however, I begin by discussing the issue of noise in … Continue reading
The Hedgehog and the Fox Redux
Many fund managers will be aware of Philip Tetlock’s book “Expert Political Judgment” published in 2005. In the book, Tetlock analyzes forecasts collected from 284 experts over twenty years. While he focuses primarily on the ability of political experts to … Continue reading
Is out-of-sample testing of forecasting models a myth?
When working with forecasting models, a well-known observation is that in-sample performance is usually better, often much better, than out-of-sample performance. That is, a model generally produces better forecasts over the data that it was constructed on than over new data. … Continue reading
Posted in machine learning, stock market
Tagged cross validation, out-of-sample, overfitting
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R/Finance 2014 Conference
Last week I attended the R/Finance conference held in Chicago. About 300 developers, academics, and practitioners gathered at the two-day conference to discuss the latest applications of the R open-source programming language to finance. I’ve mostly coded in Matlab, but … Continue reading
Big Data and Economics
Lorie and Fisher. Big Data circa 1960. For much of its history as a discipline, economics has been trapped in a Small Data paradigm. Macroeconomists analyzed output and inflation using annual or quarterly data spanning several decades at best. Microeconomic … Continue reading
Econometric Edge 