Abstract
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.
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Notes
Standard financial data platforms like Bloomberg and FactSet offer sophisticated screening tools for professional investors.
A useful survey of the literature can be found in Ekstrand et al. (2011).
Hau and Rey (2008) contains empirical evidence and a useful survey of the literature.
There are exceptions, e.g. Hsu et al. (2004) use as the dependent variable the number of items purchased.
To be precise, we focused on the largest 25 US mutual funds classified as GARP and identified potential buys as stocks for which buyers outnumber sellers by at least 3 in the period May–August 2016. We then selected ten names with equal probabilities. A similar approach is used to pick sells.
We repeated the exercise with 20 buys and 20 sells and found that 9 out of 10 recommended companies had reported a profit. However, the median fund in our sample reports 17 buys and 4 sells and therefore our illustration with 20 simulated trades seems more realistic.
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Acknowledgements
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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De Rossi, G., Kolodziej, J. & Brar, G. A recommender system for active stock selection. Comput Manag Sci 17, 517–547 (2020). https://doi.org/10.1007/s10287-018-0342-9
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DOI: https://doi.org/10.1007/s10287-018-0342-9