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| Title: | Black-Litterman模型結合長時間短期記憶神經網路
Black-Litterman Portfolios with LSTM Derived Views |
| Authors: | 林承緯
Lin, Cheng-Wei |
| Contributors: | 廖四郎
Liao, Szu-Lang
林承緯
Lin, Cheng-Wei |
| Keywords: | 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 |
| Date: | 2020 |
| Issue Date: | 2021-08-04 14:49:33 (UTC+8) |
| Abstract: | 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. |
| Reference: | [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.
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[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.
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[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.
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[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. |
| Description: | 碩士
國立政治大學
金融學系
107352024 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0107352024 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202100714 |
| Appears in Collections: | [金融學系] 學位論文
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