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

NCUIR > School of Management at National Central University > Executive Master of Finance Management > Electronic Thesis & Dissertation > Item 987654321/96240

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Title:	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
Authors:	蔡裕仁;TSAI, YU-JEN
Contributors:	財務金融學系在職專班
Keywords:	報酬率預測;監督式降維
Date:	2025-01-17
Issue Date:	2025-04-09 17:23:16 (UTC+8)
Publisher:	國立中央大學
Abstract:	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。 ;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.
Appears in Collections:	[Executive Master of Finance Management] Electronic Thesis & Dissertation

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