The Impact of Asset Cross-sectional Related Information on Portfolio Construction

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林祐陞(Yu-Sheng Lin) 查詢紙本館藏

畢業系所

數學系

論文名稱

(The Impact of Asset Cross-sectional Related Information on Portfolio Construction)

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摘要(中)

本文探討了橫截面數據因素之間的關係，使用了三種數據排列方式：單一、平行和面板。我們採用三種機器學習模型——梯度提升（Gradient Boosting）、XGBoost和隨機森林（Random Forest），以及長短期記憶（LSTM）和隨機效應（RE）模型來進行股市預測。基本面、技術面和籌碼分析數據被選為特徵，對數收益率則作為目標變量。根據模型預測結果，我們檢驗了投資組合的投資績效，包括傳統的Sharpe比率、選定等權重（sEW）和選定Sharpe（sSharp）方法。分析顯示，數據排列方式對投資組合績效有顯著影響。某些模型和期間中，單一排列方式表現較好，而平行和面板排列方式更適合於其他情況。梯度提升在單一數據中表現良好，但在平行數據中效果較差。隨機森林在單一和平行排列方式中均表現出穩健的性能，且平行排列方式通常優於單一排列方式。XGBoost在短期單一排列方式中表現突出，但在其他配置中效果不佳。LSTM適用於平行排列方式的短期投資，而面板排列方式則適用於月度投資期。隨機效應方法在長期投資期比短期更為有效。我們的研究強調了數據排列方式和加權方法的重要性，以提升機器學習模型在金融市場中的預測準確性和績效。

摘要(英)

In this paper, we consider the relationships among cross-sectional data factors with three types of data arrangement: single, parallel, and panel. We use three machine learning models—Gradient Boosting, XGBoost, and Random Forest—along with LSTM and Random Effects (RE) model for stock market predictions. Fundamental, technical, and chip analysis data are selected as features, with log returns as the target variable. Based on the results of the model prediction, we examine the investment performance of portfolios, including traditional Sharp, selected equal weight (sEW) and selected Sharp (sSharp) methods. The analysis shows that the data arrangement significantly impacts the portfolio performance. The single arrangement performs better for some models and periods, whereas the parallel and panel arrangements are more suitable for others. Gradient Boosting performs well with single data but less so with parallel data. Random Forest shows robust performance across both single and parallel arrangements, with parallel generally outperforming single. XGBoost excels in the short-term single arrangement but is less effective in other configurations. LSTM is better suited for the short-term investment period with the parallel arrangement, while the panel arrangement performs better for the monthly period. The Random Effect method proves more effective for long-term investment periods than short-term ones. Our study highlights the importance of data arrangements and weighting methods to improve the predictive accuracy and performance of machine learning models in financial markets.

關鍵字(中)

★ 面板數據
★ 機器學習
★ 神經網絡
★ 投資組合構建

關鍵字(英)

★ Panel Data
★ Machine Learning
★ Neural Networks
★ Portfolio Construction

論文目次

摘要.................................................................................................... i
Abstract.............................................................................................. ii
Acknowledgements .............................................................................. iv
Contents ............................................................................................. v
Figures ................................................................................................ vi
Tables .................................................................................................vii
1 Introduction........................................................................ 1
2 LITERATURE REVIEW................................................... 3
2.1 Portfolio . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2 Machine Learning . . . . . . . . . . . . . . . . . . . 4
3 METHODOLOGY ............................................................. 7
3.1 Machine Learning . . . . . . . . . . . . . . . . . . . 7
3.2 Recurrent Neural Network . . . . . . . . . . . . . . . 10
3.3 Panel Regression . . . . . . . . . . . . . . . . . . . . 12
3.4 Data Arrangement . . . . . . . . . . . . . . . . . . . 13
3.5 Portfolio . . . . . . . . . . . . . . . . . . . . . . . . 15
3.6 Model Evaluation . . . . . . . . . . . . . . . . . . . 17
4 EXPERIMENT .................................................................. 19
4.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.2 Comparison of Portfolio Period . . . . . . . . . . . . 20
4.3 Comparison of Prediction Methods . . . . . . . . . . 23
5 CONCLUSION................................................................... 28
References........................................................................................... 30
v

參考文獻

[1] Harry M Markowitz and G Peter Todd. Mean-variance analysis
in portfolio choice and capital markets, volume 66. John Wiley &
Sons, 2000.
[2] Tien-Yu Hsu. Machine learning applied to stock index performance
enhancement. Journal of Banking and Financial Technology,
5(1):2133, 2021.
[3] Yilin Ma, Ruizhu Han, andWeizhongWang. Prediction-based portfolio
optimization models using deep neural networks. Ieee Access,
8:115393115405, 2020.
[4] Andrés García-Medina and Ester Aguayo-Moreno. Lstmgarch hybrid
model for the prediction of volatility in cryptocurrency portfolios.
Computational Economics, 63(4):15111542, 2024.
[5] Priya Singh and Manoj Jha. Portfolio optimization using novel ewmv
method in conjunction with asset preselection. Computational
Economics, pages 130, 2024.
[6] Benjamin Bruder, Nicolas Gaussel, Jean-Charles Richard, and
Thierry Roncalli. Regularization of portfolio allocation. Available
at SSRN 2767358, 2013.
[7] Chuting Sun, Qi Wu, and Xing Yan. Dynamic cvar portfolio construction
with attention-powered generative factor learning. Journal
of Economic Dynamics and Control, 160:104821, 2024.
[8] Zihao Zhang, Stefan Zohren, and Stephen Roberts. Deep learning
for portfolio optimization. arXiv preprint arXiv:2005.13665, 2020.
[9] Wuyu Wang, Weizi Li, Ning Zhang, and Kecheng Liu. Portfolio
formation with preselection using deep learning from long-term -
nancial data. Expert Systems with Applications, 143:113042, 2020.
[10] Fábio Daros de Freitas, Alberto Ferreira De Souza, and Ailson
Rosetti de Almeida. A prediction-based portfolio optimization
model. In Proc. 5st Int. Symp. Robot. Automat., pages 520525,
2006.
[11] Prakash K Aithal, M Geetha, U Dinesh, Basri Savitha, and Parthiv
Menon. Real-time portfolio management system utilizing machine
learning techniques. IEEE Access, 11:3259532608, 2023.
[12] Indu Kumar, Kiran Dogra, Chetna Utreja, and Premlata Yadav.
A comparative study of supervised machine learning algorithms for
stock market trend prediction. In 2018 Second International Con-
ference on Inventive Communication and Computational Technolo-
gies (ICICCT), pages 10031007. IEEE, 2018.
[13] Xianghui Yuan, Jin Yuan, Tianzhao Jiang, and Qurat Ul Ain. Integrated
long-term stock selection models based on feature selection
and machine learning algorithms for china stock market. IEEE Ac-
cess, 8:2267222685, 2020.
[14] Shubharthi Dey, Yash Kumar, Snehanshu Saha, and Suryoday
Basak. Forecasting to classication: Predicting the direction of
stock market price using xtreme gradient boosting. PESIT South
Campus, pages 110, 2016.
[15] Sanjiban Sekhar Roy, Rohan Chopra, Kun Chang Lee, Concetto
Spampinato, and Behnam Mohammadi-ivatlood. Random forest,
gradient boosted machines and deep neural network for stock price
forecasting: a comparative analysis on south korean companies. In-
ternational Journal of Ad Hoc and Ubiquitous Computing, 33(1):62
71, 2020.
[16] Luckyson Khaidem, Snehanshu Saha, and Sudeepa Roy Dey. Predicting
the direction of stock market prices using random forest.
arXiv preprint arXiv:1605.00003, 2016.
[17] Eunsuk Chong, Chulwoo Han, and Frank C Park. Deep learning
networks for stock market analysis and prediction: Methodology,
data representations, and case studies. Expert Systems with Appli-
cations, 83:187205, 2017.
[18] Christopher Krauss, Xuan Anh Do, and Nicolas Huck. Deep neural
networks, gradient-boosted trees, random forests: Statistical arbitrage
on the s&p 500. European Journal of Operational Research,
259(2):689702, 2017.
[19] Pham Hoang Vuong, Trinh Tan Dat, Tieu Khoi Mai, Pham Hoang
Uyen, et al. Stock-price forecasting based on xgboost and lstm.
Computer Systems Science & Engineering, 40(1), 2022.
[20] David MQ Nelson, Adriano CM Pereira, and Renato A De Oliveira.
Stock market′s price movement prediction with lstm neural networks.
In 2017 International joint conference on neural networks
(IJCNN), pages 14191426. Ieee, 2017.
[21] Zhichao Zou and Zihao Qu. Using lstm in stock prediction and
quantitative trading. CS230: Deep learning, winter, pages 16,
2020.
[22] Xavier Martínez-Barbero, Roberto Cervelló-Royo, and Javier Ribal.
Portfolio optimization with prediction-based return using long
short-term memory neural networks: Testing on upward and downward
european markets. Computational Economics, pages 126,
2024.
[23] Jerome H Friedman. Stochastic gradient boosting. Computational
statistics & data analysis, 38(4):367378, 2002.
[24] Tianqi Chen and Carlos Guestrin. Xgboost: A scalable tree boosting
system. In Proceedings of the 22nd acm sigkdd international
conference on knowledge discovery and data mining, pages 785794,
2016.
[25] Yan Wang and Xuelei Sherry Ni. A xgboost risk model via feature
selection and bayesian hyper-parameter optimization. arXiv
preprint arXiv:1901.08433, 2019.
[26] Leo Breiman. Random forests. Machine learning, 45:532, 2001.
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指導教授

陳亭甫(Ting-Fu Chen)

審核日期

2024-7-22

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