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Monthly Archives: June 2014
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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Econometric Edge 