政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/136353

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/136353

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 113313/144292 (79%)
Visitors : 50949774 Online Users : 984

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Scope

please add "double quotation mark" for query phrases to get precise results

please goto advance search for comprehansive author search

Adv. Search

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

Goto mobile version

政大典藏 > College of Commerce > Department of Money and Banking > Theses > Item 140.119/136353

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/136353

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. [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.
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:	[Department of Money and Banking] Theses

Files in This Item:

File	Description	Size	Format
202401.pdf		1329Kb	Adobe PDF2	0	View/Open

All items in 政大典藏 are protected by copyright, with all rights reserved.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - Feedback