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題名 Black-Litterman模型結合長時間短期記憶神經網路
Black-Litterman Portfolios with LSTM Derived Views作者 林承緯
Lin, Cheng-Wei貢獻者 廖四郎
Liao, Szu-Lang
林承緯
Lin, Cheng-Wei關鍵詞 深度學習
長時間短期記憶神經網路
Black-Litterman模型
投資組合
時間序列預測
演算法交易
Deep Learning
Long Short-Term Memory
Black-Litterman Model
Portfolio
Prediction of Time Series Data
Algorithm Trading日期 2020 上傳時間 4-Aug-2021 14:49:33 (UTC+8) 摘要 本研究嘗試將長時間短期記憶(LSTM)應用於金融資產價格走勢的預測，並結合Black-Litterman模型建構風險分散的全球性多元化投資組合。本研究以金融資產的價量相關資料及技術指標預測資產價格漲跌及漲跌幅度，將預測結果作為Black-Litterman模型的投資者觀點並進行資產配置，比較投資組合在不同條件限制、不同風險趨避係數及不同共變異數估計方法下的績效表現。本研究實證發現：LSTM在預測資產價格漲跌幅度的方面擁有相對較佳的預測準確率，但是對於漲跌的預測表現並不突出；在投資組合績效回測方面，本研究建構之投資組合的績效表現皆優於市值加權投資組合及iShares Russell 1000 ETF。其中，使用Ledoit-Wolf Shrinkage方法估計共變異數矩陣能夠使投資組合獲得較高的夏普比率。
In this thesis, we try to apply long short-term memory (LSTM) to forecasting price trends of financial assets. We combine the forecasts with the Black-Litterman model and construct various globally diversified portfolios. Historical price and volume related data and technical indicators are used as input data to predict following week’s excess return, and we use the forecasts to arrive at the investor views in the Black-Litterman model. Finally, we test the out-of-sample performance of various Black-Litterman portfolios, where market capitalization weighted portfolio, equally weighted portfolio and iShares Russell 1000 ETF are used as benchmarks. The empirical results show that LSTM has a fine predictive accuracy for the sign of excess return; however, a mediocre performance on forecasting the magnitude of excess return. We also find that the Black-Litterman portfolios we construct outperform the benchmark portfolios, and the portfolios with Ledoit-Wolf Shrinkage covariance estimates generate higher Sharpe ratios.參考文獻 [1]Beach, L., & Orlov, G. (2007). An application of the Black-Litterman model with EGARCH-M derived views for international portfolio management. Financial Market and Portfolio Management, 21(2), 147-166.[2]Black, F., & Litterman, R. (1991). Asset allocation: combining investor views with market equilibrium. The Journal of Fixed Income, 1(2), 7-18.[3]Black, F., & Litterman, R. (1992). Global portfolio optimization. Financial Analysts Journal, 48(5), 28-43.[4]Cao, L. J., & Tay, F. E. H. (2003). Support vector machine with adaptive parameters in financial time series forecasting. IEEE Transactions on Neural Networks, 14(6), 1506-1518.[5]Donthireddy, P. (2018, July 19). Black-Litterman portfolios with machine learning derived views. ResearchGate. Retrieved September 22, 2019, from https://www.researchgate.net/publication/326489143_Black-Litterman_Portfolios_with_Machine_Learning_derived_Views[6]Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: continual prediction with LSTM. Neural Computation, 12(10), 2451-2471.[7]He, G., & Litterman, R. (1999). The intuition behind Black-Litterman model portfolios. New York, NY: Goldman Sachs.[8]Idzorek, T. (2004). A step-by-step guide to the Black-Litterman model, incorporating user specified confidence levels. Chicago, IL: Ibbotson Associates.[9]Kwon, Y. K., & Moon, B. R. (2007). A hybrid neurogenetic approach for stock forecasting. IEEE Transactions on Neural Networks, 18(3), 851-864.[10]Ledoit, O., & Wolf, M. (2003). Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. Journal of Empirical Finance, 10(5), 603-621.[11]Lintner, J. (1965). The valuation of risk assets on the selection of risky investments in stock portfolios and capital budgets. Review of Economics and Statistics, 47(1), 13-37.[12]Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.[13]Meucci, A. (2010). The Black-Litterman approach: original model and extensions. In R. Cont (Ed.), The Encyclopedia of Quantitative Finance (pp.196-199). New York, NY: Wiley.[14]Sharpe, W. F. (1964). Capital asset prices: a theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425-442.[15]Thawornwong, S., Enke, D., & Dagli, C. (2003). Neural networks as a decision maker for stock trading: a technical analysis approach. International Journal of Smart Engineering System Design, 5(4), 313-325.[16]Zhai, Y., Hsu, A., & Halgamuge, S. K. (2007). Combining news and technical indicators in daily stock price trends prediction. In D. Liu, S. Fei, Z. Hou, H. Zhang & C. Sun (Eds.), Advances in Neural Networks – ISNN 2007 (pp. 1087-1096). Berlin, Germany: Springer. 描述 碩士
國立政治大學
金融學系
107352024資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107352024 資料類型 thesis dc.contributor.advisor 廖四郎 zh_TW dc.contributor.advisor Liao, Szu-Lang en_US dc.contributor.author (Authors) 林承緯 zh_TW dc.contributor.author (Authors) Lin, Cheng-Wei en_US dc.creator (作者) 林承緯 zh_TW dc.creator (作者) Lin, Cheng-Wei en_US dc.date (日期) 2020 en_US dc.date.accessioned 4-Aug-2021 14:49:33 (UTC+8) - dc.date.available 4-Aug-2021 14:49:33 (UTC+8) - dc.date.issued (上傳時間) 4-Aug-2021 14:49:33 (UTC+8) - dc.identifier (Other Identifiers) G0107352024 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/136353 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融學系 zh_TW dc.description (描述) 107352024 zh_TW dc.description.abstract (摘要) 本研究嘗試將長時間短期記憶(LSTM)應用於金融資產價格走勢的預測，並結合Black-Litterman模型建構風險分散的全球性多元化投資組合。本研究以金融資產的價量相關資料及技術指標預測資產價格漲跌及漲跌幅度，將預測結果作為Black-Litterman模型的投資者觀點並進行資產配置，比較投資組合在不同條件限制、不同風險趨避係數及不同共變異數估計方法下的績效表現。本研究實證發現：LSTM在預測資產價格漲跌幅度的方面擁有相對較佳的預測準確率，但是對於漲跌的預測表現並不突出；在投資組合績效回測方面，本研究建構之投資組合的績效表現皆優於市值加權投資組合及iShares Russell 1000 ETF。其中，使用Ledoit-Wolf Shrinkage方法估計共變異數矩陣能夠使投資組合獲得較高的夏普比率。 zh_TW dc.description.abstract (摘要) In this thesis, we try to apply long short-term memory (LSTM) to forecasting price trends of financial assets. We combine the forecasts with the Black-Litterman model and construct various globally diversified portfolios. Historical price and volume related data and technical indicators are used as input data to predict following week’s excess return, and we use the forecasts to arrive at the investor views in the Black-Litterman model. Finally, we test the out-of-sample performance of various Black-Litterman portfolios, where market capitalization weighted portfolio, equally weighted portfolio and iShares Russell 1000 ETF are used as benchmarks. The empirical results show that LSTM has a fine predictive accuracy for the sign of excess return; however, a mediocre performance on forecasting the magnitude of excess return. We also find that the Black-Litterman portfolios we construct outperform the benchmark portfolios, and the portfolios with Ledoit-Wolf Shrinkage covariance estimates generate higher Sharpe ratios. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究背景與動機 1第二節 研究目的 1第二章 文獻回顧 3第一節 機器學習方法預測時間序列之相關文獻 3第二節 投資組合理論之相關文獻 3第三章 研究方法 5第一節 研究對象 5第二節 Black-Litterman模型 5第三節 長時間短期記憶神經網路 8第四節 模型架構 9第五節 變數篩選 10第六節 共變異數矩陣 12第七節 訓練、驗證及測試資料集 13第八節 投資策略 14第四章 實證分析 16第一節 績效評估 16第二節 實證結果 17第五章 結論與建議 23第一節 結論 23第二節 未來展望 23參考文獻 25 zh_TW dc.format.extent 1361129 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107352024 en_US dc.subject (關鍵詞) 深度學習 zh_TW dc.subject (關鍵詞) 長時間短期記憶神經網路 zh_TW dc.subject (關鍵詞) Black-Litterman模型 zh_TW dc.subject (關鍵詞) 投資組合 zh_TW dc.subject (關鍵詞) 時間序列預測 zh_TW dc.subject (關鍵詞) 演算法交易 zh_TW dc.subject (關鍵詞) Deep Learning en_US dc.subject (關鍵詞) Long Short-Term Memory en_US dc.subject (關鍵詞) Black-Litterman Model en_US dc.subject (關鍵詞) Portfolio en_US dc.subject (關鍵詞) Prediction of Time Series Data en_US dc.subject (關鍵詞) Algorithm Trading en_US dc.title (題名) Black-Litterman模型結合長時間短期記憶神經網路 zh_TW dc.title (題名) Black-Litterman Portfolios with LSTM Derived Views en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1]Beach, L., & Orlov, G. (2007). An application of the Black-Litterman model with EGARCH-M derived views for international portfolio management. Financial Market and Portfolio Management, 21(2), 147-166.[2]Black, F., & Litterman, R. (1991). Asset allocation: combining investor views with market equilibrium. The Journal of Fixed Income, 1(2), 7-18.[3]Black, F., & Litterman, R. (1992). Global portfolio optimization. Financial Analysts Journal, 48(5), 28-43.[4]Cao, L. J., & Tay, F. E. H. (2003). Support vector machine with adaptive parameters in financial time series forecasting. IEEE Transactions on Neural Networks, 14(6), 1506-1518.[5]Donthireddy, P. (2018, July 19). Black-Litterman portfolios with machine learning derived views. ResearchGate. Retrieved September 22, 2019, from https://www.researchgate.net/publication/326489143_Black-Litterman_Portfolios_with_Machine_Learning_derived_Views[6]Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: continual prediction with LSTM. Neural Computation, 12(10), 2451-2471.[7]He, G., & Litterman, R. (1999). The intuition behind Black-Litterman model portfolios. New York, NY: Goldman Sachs.[8]Idzorek, T. (2004). A step-by-step guide to the Black-Litterman model, incorporating user specified confidence levels. Chicago, IL: Ibbotson Associates.[9]Kwon, Y. K., & Moon, B. R. (2007). A hybrid neurogenetic approach for stock forecasting. IEEE Transactions on Neural Networks, 18(3), 851-864.[10]Ledoit, O., & Wolf, M. (2003). Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. Journal of Empirical Finance, 10(5), 603-621.[11]Lintner, J. (1965). The valuation of risk assets on the selection of risky investments in stock portfolios and capital budgets. Review of Economics and Statistics, 47(1), 13-37.[12]Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.[13]Meucci, A. (2010). The Black-Litterman approach: original model and extensions. In R. Cont (Ed.), The Encyclopedia of Quantitative Finance (pp.196-199). New York, NY: Wiley.[14]Sharpe, W. F. (1964). Capital asset prices: a theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425-442.[15]Thawornwong, S., Enke, D., & Dagli, C. (2003). Neural networks as a decision maker for stock trading: a technical analysis approach. International Journal of Smart Engineering System Design, 5(4), 313-325.[16]Zhai, Y., Hsu, A., & Halgamuge, S. K. (2007). Combining news and technical indicators in daily stock price trends prediction. In D. Liu, S. Fei, Z. Hou, H. Zhang & C. Sun (Eds.), Advances in Neural Networks – ISNN 2007 (pp. 1087-1096). Berlin, Germany: Springer. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202100714 en_US
