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What Differences Does Machine learning Make in Finance?

Part of the AFL Virtual Research Seminar Series.

  • 1 Dec 2020, 4.00pm to 1 Dec 2020, 5.15pm GMT
  • This is an online event.
  • This event is free

Prof Guofu Zhou, Olin Business School, Washington University, St. Louis, will discuss this papers 'Anomalies and the Expected Market Return' and 'Exchange Rate Prediction with Machine Learning and a Smart Carry Trade Portfolio'. He is also an Associated Editor of Journal of Financial and Quantitative Analysis, Journal of Financial Markets, and Journal of Empirical Finance.

Exchange Rate Prediction with Machine Learning and a Smart Carry Trade Portfolio

We establish the out-of-sample predictability of monthly exchange rate changes via machine learning techniques based on 70 predictors capturing country characteristics, global variables, and their interactions. To guard against overfitting, we use the elastic net to estimate a high-dimensional panel predictive regression and find that the resulting forecast consistently outperforms the na¨ıve no-change benchmark, which has proven difficult to beat in the literature. The forecast also markedly improves the performance of a carry trade portfolio, especially during and after the global financial crisis. When we allow for more complex deep learning models, nonlinearities do not appear substantial in the data.

Anomalies and the Expected Market Return

We provide the first systematic evidence on the link between long-short anomaly portfolio returns—a cornerstone of the cross-sectional literature—and the time-series predictability of the aggregate market excess return. Using 100 representative anomalies from the literature, we employ a variety of shrinkage techniques (including machine learning, forecast combination, and dimension reduction) to efficiently extract predictive signals in a high dimensional setting. We find that long-short anomaly portfolio returns evince statistically and economically significant out-of-sample predictive ability for the market excess return. Economically, the predictive ability of anomaly portfolio returns appears to stem from asymmetric limits of arbitrage and overpricing dominance.

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