Exploiting the low-risk anomaly using machine learning to enhance the Black–Litterman framework: Evidence from South Korea

Sujin Pyo and Jaewook Lee

Pacific-Basin Finance Journal, 2018, vol. 51, issue C, 1-12

Abstract: Many studies have revealed that global financial markets are experiencing low-risk anomalies. In the Korean market, for example, even the portfolios of high-risk stocks recorded a loss of about 70% between 2000 and 2016. In this study, we construct a low-risk portfolio that responds to low-risk anomalies in the Korean market using the Black–Litterman framework. We use three machine-learning predictive and traditional time-series models to predict the volatility of assets listed in the Korean Stock Price Index 200 (KOSPI 200) and select the best-performing one. Then, we use the model to classify assets into high- and low-risk groups and create a Black–Litterman portfolio that reflects the investor's view where low-risk stocks outperform high-risk stocks. The experiment shows that reflecting the low-risk view in the market equilibrium portfolio improves profitability and that this view dominates the market portfolio.

Keywords: Low-risk anomaly; Machine learning models; Low beta; The Black–Litterman model; Volatility prediction (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:pacfin:v:51:y:2018:i:c:p:1-12

DOI: 10.1016/j.pacfin.2018.06.002

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