A recommender system for active stock selection
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The goal of this report is to equip equity portfolio managers with a new tool to assist them in the crucial task of narrowing down a broad universe to a list of stocks to be analysed in depth. We explore a number of alternative approaches to building a recommender system, i.e. a predictive model which generates stock recommendations based on observable characteristics and previous investor behaviour. The empirical analysis uses data on a large set of global active mutual funds, observed between 2005 and 2016, to calibrate the models and test their predictive ability out of sample. Our main conclusion is that a simple dimension reduction technique achieves the best compromise between precision and recall. Moreover, our recommender system displays good predictive power, particularly when used to forecast future buy trades.
KeywordsRecommender system Stock selection Collaborative filtering
We would like to thank participants at the Axioma Quant Forum 2016, the AI, Machine Learning and Sentiment Analysis Applied to Finance event 2017, the Risk.net Machine Learning Forum 2018, Andrew Lapthorne, Tony Guida and an anonymous referee for useful comments. All remaining errors are our own. The views expressed in this article reflect the views of the named authors. Nothing in this article should be considered as an investment recommendation or investment advice. This article is based on information obtained from sources believed to be reliable and no representation or warranty is made that it is accurate, complete or up to date. Macquarie accepts no liability whatsoever for any direct, indirect, consequential or other loss arising from any use of this article.
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