
On the Directional Predictability of Equity Premium Using Machine Learning [pdf] - Bostonian
https://editorialexpress.com/cgi-bin/conference/download.cgi?db_name=IAAE2019&paper_id=624
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Bostonian
Now published in the Journal of Forecasting:
[https://onlinelibrary.wiley.com/doi/abs/10.1002/for.2632](https://onlinelibrary.wiley.com/doi/abs/10.1002/for.2632)
. The data is at
[http://www.hec.unil.ch/agoyal/docs/PredictorData2018.xlsx](http://www.hec.unil.ch/agoyal/docs/PredictorData2018.xlsx)
.

The classification models aim to predict whether the monthly stock market
return is positive or negative. I wonder how you modify a classification
problem when some errors are more costly than others (other than just treating
it as a regression problem). Predicting the stock market goes up is more
costly when it falls 10% than when it falls 1%.

The working paper "Predicting Stock Market Returns with Machine Learning" at
[https://sites.google.com/site/albertorossifrb/research](https://sites.google.com/site/albertorossifrb/research)
attacks the same problem using machine learning regression models.

