股票交易參考指標對個股報酬之預測分析–監督式降維模型之應用

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姓名

蔡裕仁(TSAI, YU-JEN) 查詢紙本館藏

畢業系所

財務金融學系在職專班

論文名稱

股票交易參考指標對個股報酬之預測分析–監督式降維模型之應用
(Return Analysis using Dimension Reduction Method)

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摘要(中)

本研究旨在分析多種股票交易參考指標對個股報酬預測的影響，並探討監督式降維模型在預測分析中的應用。隨著股票市場中數據維度的增長和資訊的多樣化，如何有效地利用投資者買賣數據、機構投資者走向、技術面、市場情緒與國際市場指標來提升預測準確性，是此研究的重要課題。本文首先整理並分析多種指標，通過應用偏最小平方迴歸 (Partial Least Squares; PLS) 及偏分量迴歸 (partial quantile regression; PQR) 等監督式降維技術，以濃縮高維數據中的關鍵資訊。此方法同時考量了指標間的共線性問題，並利用降維結果構建預測模型。

本文採用多期數據進行實證分析，以驗證模型的穩定性及可行性。研究結果顯示，與傳統回歸模型相比，PLS 與 PQR 模型在捕捉股價波動及個股報酬的線性與非線性特徵方面表現出色，並能顯著提高預測模型的準確性。特別地，PLS 能夠有效將高維數據壓縮為少數關鍵因子，增強模型的穩健性，並降低過擬合風險；而 PQR 模型則在不同分位數下具備更高的靈活性，使其適用於各種市場狀況和風險偏好的投資決策。本研究的實證結果支持監督式降維模型的應用潛力，尤其在股票交易指標眾多，資訊高度相關的情境中，對於提升個股報酬預測能力及支援投資決策具有重要意涵。

「預測」之所以誘人，在於對未來有效的掌握度。本文使用多種降維技術，其目的是有效利用多維度數據，提升預測準確性和投資決策的可靠性。

摘要(英)

This study examines the impact of various stock trading indicators on individual stock return prediction and explores supervised dimensionality reduction models. With growing data complexity, effectively utilizing trading behaviors, institutional trends, technical factors, market sentiment, and international indicators is crucial. This research applies Partial Least Squares (PLS) and Partial Quantile Regression (PQR) to extract key information from high-dimensional data while addressing collinearity among indicators.

Empirical analysis using multi-period data confirms the models’ stability and effectiveness. Results show that PLS and PQR outperform traditional models in capturing stock price volatility and nonlinear return characteristics, significantly enhancing prediction accuracy. PLS compresses data into key factors, reducing overfitting risks, while PQR offers flexibility under various quantiles, adapting to diverse market conditions and risk preferences.

This study highlights the potential of supervised dimensionality reduction in improving stock return predictions and supporting investment decisions, especially when faced with numerous correlated indicators. By leveraging dimensionality reduction, this approach enhances predictive accuracy and decision-making reliability.

關鍵字(中)

★ 報酬率預測
★ 監督式降維

關鍵字(英)

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 List of Figures vi
表目錄 List of Tables vii
第一章緒論 1
第二章資料建構 3
2-1. 變數類別 3
2-2. 資料處理原則與方法 10
第三章研究方法 16
3-1. 模型架構 16
3-2. 降維演算法 18
3-2-1 PCA 18
3-2-2 KPCA 19
3-2-3 PLS 20
3-2-4 PQR 21
3-3. 預測 22
3-4. 模型衡量 22
第四章實證結果 24
4-1. 全樣本分析 24
4-2. 樣本外分析 25
4-2-1 不區分類別 26
4-2-2 區分類別 27
4-2-3 關鍵類別 27
4-2-4 關鍵變數 28
4-2-5 關鍵變數之績效 31
第五章結論 33
參考文獻 47

參考文獻

周賓凰、張宇志、林美珍 (2007)。投資人情緒與股票報酬互動關係。證券市場發展季刊，第19卷第2期，153-190。

徐婉容 (2020)。認定與預測台灣股市熊市。中央銀行季刊，第42卷第2期，37-72。

莊家彰 & 管中閔 (2005)。台灣與美國股市價量關係的分量迴歸分析。經濟論文 , 33(4), 379-404.

Anowar, F., Sadaoui, S., & Selim, B. (2021). Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE) . Computer Science Review, 40, 100378.

Baker, M., & Wurgler, J. (2006). Investor Sentiment and the Cross-Section of Stock Returns. The Journal of Finance, 61(4), 1645–1680.

Barber, Brad M.; Lee, Yi-Tsung; Liu, Yu-Jane; Odean, Terrance . (2009). Just How Much Do Individual Investors Lose by Trading?. Review of Financial Studies, 22(2), 609–632.

Bushee, B. J., & Noe, C. F. (2000). Corporate Disclosure Practices, Institutional Investors, and Stock Return Volatility. Journal of Accounting Research, 38(), 171–202.

Campbell, John Y.; Shiller, Robert J. . (1988). The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors. Review of Financial Studies, 1(3), 195–228.

Chordia, T., Subrahmanyam, A., & Anshuman, V. R. (2001). Trading activity and expected stock returns. Journal of financial Economics, 59(1), 3-32.

Diebold, F. X., & Mariano, R. S. (2002). Comparing predictive accuracy. Journal of Business & Economic Statistics, 20(1), 134-144.

Giglio, S., Kelly, B., & Pruitt, S. (2016). Systemic Risk and the Macroeconomy: An Empirical Evaluation. Journal of Financial Economics, 119(3), 457–471.

Gu, Shihao; Kelly, Bryan; Xiu, Dacheng . (2020). Empirical Asset Pricing via Machine Learning. The Review of Financial Studies, (), hhaa009–.

Jegadeesh, N. (1990). Evidence of predictable behavior of security returns. The Journal of finance, 45(3), 881-898.

Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65–91.

Kelly, B., & Pruitt, S. (2013). Market Expectations in the Cross-Section of Present Values. The Journal of Finance, 68(5), 1721–1756.

Kelly, B., & Pruitt, S. (2015). The three-pass regression filter: A new approach to forecasting using many predictors. Journal of Econometrics, 186(2), 294–316.

Kim, K., & Han, I. (2000). Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Systems with Applications, 19(2), 125–132.

Kim, TaeHyuk; Ha, Aejin . (2010). Investor Sentiment and Market Anomalies. SSRN Electronic Journal, (), –.

Kumar, A., & Lee, C. M. C. (2006). Retail Investor Sentiment and Return Comovements. The Journal of Finance, 61(5), 2451–2486.

Mele, A. (2007). Asymmetric stock market volatility and the cyclical behavior of expected returns. Journal of Financial Economics, 86(2), 446–478.

Menard, S. (2000). Coefficients of Determination for Multiple Logistic Regression Analysis. The American Statistician, 54(1), 17–24.

Neely, C. J., Rapach, D. E., Tu, J., & Zhou, G. (2014). Forecasting the Equity Risk Premium: The Role of Technical Indicators. Management Science, 60(7), 1772–1791.

Papailias, F., & Thomakos, D. D. (2015). An improved moving average technical trading rule. Physica A: Statistical Mechanics and Its Applications, 428, 458–469.

Park, T.-J., Lee, Y., & Song, K. “Roy.” (2014). Informed trading before positive vs. negative earnings surprises. Journal of Banking & Finance, 49, 228–241.

Stock, J. H., & Watson, M. W. (2002). Forecasting Using Principal Components From a Large Number of Predictors. Journal of the American Statistical Association, 97(460), 1167–1179.

Stock, J. H., & Watson, M. W. (2002). Macroeconomic Forecasting Using Diffusion Indexes. Journal of Business & Economic Statistics, 20(2), 147–162.

Tsai, C.-F., & Hsiao, Y.-C. (2010). Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches. Decision Support Systems, 50(1), 258–269.

Wang, L., Wu, Y., & Li, R. (2012). Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension. Journal of the American Statistical Association, 107(497), 214–222.

Yang, C., & Zhou, L. (2015). Investor trading behavior, investor sentiment and asset prices. The North American Journal of Economics and Finance, 34, 42–62.

Zakamulin, V. (2014). The real-life performance of market timing with moving average and time-series momentum rules. Journal of Asset Management, 15(4), 261–278.

Zhong, X., & Enke, D. (2017). Forecasting daily stock market return using dimensionality reduction. Expert Systems with Applications, 67, 126–139.

指導教授

葉錦徽

審核日期

2025-1-17

推文