Dynamic Black–Litterman Portfolios Incorporating Asymmetric Fractal Uncertainty
This study investigates the profitability of portfolios that integrate asymmetric fractality within the Black–Litterman (BL) framework. It predicts 10-day-ahead exchange-traded fund (ETF) prices using recurrent neural networks (RNNs) based on historical price information and technical indicators; th...
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MDPI AG
2024-10-01
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| Series: | Fractal and Fractional |
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| Online Access: | https://www.mdpi.com/2504-3110/8/11/642 |
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| author | Poongjin Cho Minhyuk Lee |
| author_facet | Poongjin Cho Minhyuk Lee |
| author_sort | Poongjin Cho |
| collection | DOAJ |
| description | This study investigates the profitability of portfolios that integrate asymmetric fractality within the Black–Litterman (BL) framework. It predicts 10-day-ahead exchange-traded fund (ETF) prices using recurrent neural networks (RNNs) based on historical price information and technical indicators; these predictions are utilized as BL views. While constructing the BL portfolio, the Hurst exponent obtained from the asymmetric multifractal detrended fluctuation analysis is employed to determine the uncertainty associated with the views. The Hurst exponent describes the long-range persistence in time-series data, which can also be interpreted as the uncertainty in time-series predictions. Additionally, uncertainty is measured using asymmetric fractality to account for the financial time series’ asymmetric characteristics. Then, backtesting is conducted on portfolios comprising 10 countries’ ETFs, rebalanced on a 10-day basis. While benchmarking to a Markowitz portfolio and the MSCI world index, profitability is assessed using the Sharpe ratio, maximum drawdown, and sub-period analysis. The results reveal that the proposed model enhances the overall portfolio return and demonstrates particularly strong performance during negative trends. Moreover, it identifies ongoing investment opportunities, even in recent periods. These findings underscore the potential of fractality in adjusting uncertainty for diverse portfolio optimization applications. |
| format | Article |
| id | doaj-art-bf66b4f86d544bfaa78ec999d2727ccb |
| institution | OA Journals |
| issn | 2504-3110 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj-art-bf66b4f86d544bfaa78ec999d2727ccb2025-08-20T01:53:41ZengMDPI AGFractal and Fractional2504-31102024-10-0181164210.3390/fractalfract8110642Dynamic Black–Litterman Portfolios Incorporating Asymmetric Fractal UncertaintyPoongjin Cho0Minhyuk Lee1School of Computing, Gachon University, Seongnam 13120, Republic of KoreaDepartment of Business Administration, Pusan National University, Busan 46241, Republic of KoreaThis study investigates the profitability of portfolios that integrate asymmetric fractality within the Black–Litterman (BL) framework. It predicts 10-day-ahead exchange-traded fund (ETF) prices using recurrent neural networks (RNNs) based on historical price information and technical indicators; these predictions are utilized as BL views. While constructing the BL portfolio, the Hurst exponent obtained from the asymmetric multifractal detrended fluctuation analysis is employed to determine the uncertainty associated with the views. The Hurst exponent describes the long-range persistence in time-series data, which can also be interpreted as the uncertainty in time-series predictions. Additionally, uncertainty is measured using asymmetric fractality to account for the financial time series’ asymmetric characteristics. Then, backtesting is conducted on portfolios comprising 10 countries’ ETFs, rebalanced on a 10-day basis. While benchmarking to a Markowitz portfolio and the MSCI world index, profitability is assessed using the Sharpe ratio, maximum drawdown, and sub-period analysis. The results reveal that the proposed model enhances the overall portfolio return and demonstrates particularly strong performance during negative trends. Moreover, it identifies ongoing investment opportunities, even in recent periods. These findings underscore the potential of fractality in adjusting uncertainty for diverse portfolio optimization applications.https://www.mdpi.com/2504-3110/8/11/642Black–Littermanmultifractalasymmetrydeep learningforecasting |
| spellingShingle | Poongjin Cho Minhyuk Lee Dynamic Black–Litterman Portfolios Incorporating Asymmetric Fractal Uncertainty Fractal and Fractional Black–Litterman multifractal asymmetry deep learning forecasting |
| title | Dynamic Black–Litterman Portfolios Incorporating Asymmetric Fractal Uncertainty |
| title_full | Dynamic Black–Litterman Portfolios Incorporating Asymmetric Fractal Uncertainty |
| title_fullStr | Dynamic Black–Litterman Portfolios Incorporating Asymmetric Fractal Uncertainty |
| title_full_unstemmed | Dynamic Black–Litterman Portfolios Incorporating Asymmetric Fractal Uncertainty |
| title_short | Dynamic Black–Litterman Portfolios Incorporating Asymmetric Fractal Uncertainty |
| title_sort | dynamic black litterman portfolios incorporating asymmetric fractal uncertainty |
| topic | Black–Litterman multifractal asymmetry deep learning forecasting |
| url | https://www.mdpi.com/2504-3110/8/11/642 |
| work_keys_str_mv | AT poongjincho dynamicblacklittermanportfoliosincorporatingasymmetricfractaluncertainty AT minhyuklee dynamicblacklittermanportfoliosincorporatingasymmetricfractaluncertainty |