کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7374033 | 1479840 | 2018 | 29 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Exploiting the low-risk anomaly using machine learning to enhance the Black-Litterman framework: Evidence from South Korea
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موضوعات مرتبط
علوم انسانی و اجتماعی
اقتصاد، اقتصادسنجی و امور مالی
اقتصاد و اقتصادسنجی
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pacific-Basin Finance Journal - Volume 51, October 2018, Pages 1-12
Journal: Pacific-Basin Finance Journal - Volume 51, October 2018, Pages 1-12
نویسندگان
Sujin Pyo, Jaewook Lee,