運用機器學習方法預測股價報酬

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郭家瑞(Jia-Ruei Guo) 查詢紙本館藏

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運用機器學習方法預測股價報酬

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★ 運用總體經濟變數預測日經指數報酬率

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摘要(中)

股票報酬預測在經濟與財金領域是一個相當重要的議題，本文研究目的為運用25個美國與臺灣財金經濟預測因子，採用七種不同機器學習模型預測已除權除息之臺灣加權股價指數報酬，並運用樣本外R²值與Clark-West統計量檢定模型之顯著性，繪出模型預測臺灣股市報酬線圖，並觀察模型所預測之股市報酬與實際股市報酬間的差異性，找尋適合預測臺灣股價報酬之機器學習模型。
本文首先採用Fama-French三因子模型為基礎，運用t統計量方式，初步選出適合之預測因子個數，接著，使用彈性網絡法選擇重要預測因子。接下來，運用Dong et al. (2022) 所定義之平均預測報酬指標，計算並繪出不同期間之平均預測報酬指標，初步觀察顯示，當期間愈長，其平均預測報酬指標呈現愈平滑的趨勢；接著，使用七種不同機器學習模型計算預測臺灣加權指數股價報酬，實證結果得出在Clark-West 檢定顯著之模型中，平均預測法為本文之最佳機器學習模型，而除了Clark-West 檢定顯著模型之外，在R²值大於零之模型方面，簡單組合法為本文之較佳機器學習模型。

摘要(英)

Predicting stock returns is a significant issue in the field of economics and finance. The purpose of this study is to utilize 25 financial and economic forecasting factors from the United States and Taiwan and employ seven different machine learning models to predict the returns of the Taiwan Weighted Stock Index after adjusting for dividends and stock splits. The out-of-sample R² value and Clark-West statistic are used to assess the significance of the models. The study then presents a graphical representation of the predicted stock market returns based on the models and observes the differences between the predicted returns and actual stock market returns, aiming to identify a suitable machine learning model for predicting Taiwan stock price returns.
In this paper, the Fama-French three-factor model is initially used, employing t-statistics to select the appropriate number of predictive factors. Subsequently, the elastic net method is applied to select the important predictive factors. Next, the average forecast return indicator defined by Dong et al. (2022) is calculated and plotted for different periods. Preliminary observations show that the average forecast return indicator becomes smoother as the period lengthens. Seven different machine learning models are then used to compute the predicted returns of the Taiwan Weighted Stock Index. The empirical results indicate that the average forecast method is the best machine learning model in terms of significant results from the Clark-West test. However, among the models with R² values greater than zero but not significant according to the Clark-West test, the simple combination method is a better machine learning model in this study.

關鍵字(中)

★ 臺灣股價報酬預測
★ Fama-French三因子模型
★ Clark-West 統計檢定
★ 機器學習模型

關鍵字(英)

★ Taiwan stock return prediction
★ Fama-French three-factor model
★ Clark-West statistic test
★ machine learning models

論文目次

中文摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章緒論 1
1-1 研究背景與研究動機 1
1-2 研究方法概述 2
1-3 研究架構 3
第二章文獻回顧 4
2-1 總體經濟預測因子與股票報酬 4
2-2 財金預測因子與股票報酬 5
2-3 機器學習預測相關模型 5
第三章研究方法與模型 7
3-1 變數解釋 7
3-1-1美國總體經濟與財金預測因子 7
3-1-2臺灣總體經濟與財金預測因子 9
3-1-3其他總體經濟預測因子 10
3-2 Fama-French三因子模型 10
3-3 平均預測報酬指標 11
3-4 機器學習模型解釋 11
3-4-1 普通最小平方法 12
3-4-2 彈性網絡法 12
3-4-3 簡單組合法 13
3-4-4 組合ENet法 14
3-4-5 平均預測法 15
3-4-6 主成分法 16
3-4-7 偏最小平方法 17
3-5 樣本外R^2值與假設檢定 17
第四章實證結果 19
4-1 預測變量異常報酬因子敘述統計結果 19
4-2 平均預測報酬指標 22
4-3 機器學習方法之樣本外R²值與 Clark-West 檢定 26
4-4 機器模型變異程度分析 27
4-5 機器模型之對數累積預測超額股價報酬分析 31
4-5-1 Clark-West 檢定顯著模型 32
4-5-2 樣本外R²值大於零之模型 35
第五章結論與建議 38
參考文獻 40

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指導教授

徐之強廖志興(Chih-Chiang Hsu Chih-Hsing Liao)

審核日期

2024-2-20

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