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

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  • Pyo, Sujin
  • Lee, Jaewook

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.

Suggested Citation

  • Pyo, Sujin & Lee, Jaewook, 2018. "Exploiting the low-risk anomaly using machine learning to enhance the Black–Litterman framework: Evidence from South Korea," Pacific-Basin Finance Journal, Elsevier, vol. 51(C), pages 1-12.
    • Handle: RePEc:eee:pacfin:v:51:y:2018:i:c:p:1-12
    DOI: 10.1016/j.pacfin.2018.06.002
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    References listed on IDEAS

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    Cited by:

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    2. Huixian Zeng & Jinguang Zeng, 2022. "Research on Black-Litterman Index Enhancement Strategy——Based on the Ledoit-Wolf Compression Estimation Method to Optimize the CSI 500 Index Enhancement Strategy," International Business Research, Canadian Center of Science and Education, vol. 15(2), pages 1-60, February.
    3. Reza Bradrania & Davood Pirayesh Neghab, 2022. "State-dependent Asset Allocation Using Neural Networks," Papers 2211.00871, arXiv.org.
    4. Barua, Ronil & Sharma, Anil K., 2022. "Dynamic Black Litterman portfolios with views derived via CNN-BiLSTM predictions," Finance Research Letters, Elsevier, vol. 49(C).
    5. Cao, Guangxi & Xie, Wenhao, 2022. "Asymmetric dynamic spillover effect between cryptocurrency and China's financial market: Evidence from TVP-VAR based connectedness approach," Finance Research Letters, Elsevier, vol. 49(C).
    6. Bradrania, Reza & Pirayesh Neghab, Davood, 2021. "State-dependent asset allocation using neural networks," MPRA Paper 115254, University Library of Munich, Germany.
    7. Pengfei Zhao & Haoren Zhu & Wilfred Siu Hung NG & Dik Lun Lee, 2024. "From GARCH to Neural Network for Volatility Forecast," Papers 2402.06642, arXiv.org.

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