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| Title: | 以機器學習探討動能現象 -- 以台灣股市為例
Exploring Momentum Phenomena in Taiwan Stock Market through Machine Learning |
| Authors: | 章翔軒
Chang, Hsiang-Hsuan |
| Contributors: | 羅秉政
Kendro Vincent
章翔軒
Chang,Hsiang-Hsuan |
| Keywords: | 橫斷面動能
時間序列動能
機器學習
投資組合
Cross-Sectional Momentum
Time Series Momentum
Machine Learning
Portfolio |
| Date: | 2025 |
| Issue Date: | 2025-08-04 14:31:21 (UTC+8) |
| Abstract: | 在股市動能現象的研究中，主要分為橫斷面動能（Cross-Sectional Momentum）與時間序列動能（Time Series Momentum）。前者著重於資產在同期相較於其他資產的報酬率如果具有相對優勢，此趨勢預期將會持續；後者則關注某個資產的報酬率是否為正，若資產的報酬率大於0，則預期該趨勢將會延續。本研究以台灣股票為樣本，透過機器學習預測的方式探討兩種動能現象，透過羅吉斯迴歸、決策樹集成式學習(XGBoost、隨機森林)與類神經網路進行預測；接者將預測結果以 Goyal and Jegadeesh (2018) 的加權方式形成不同投資組合，比較各模型和加權方式之間的獲利表現差異。實證結果顯示，橫斷面動能做為被解釋變數在同模型預測的準確率高於時間序列動能，但整體預測上具有提升空間；橫斷面動能投資組合報酬多數高於時間序列動能投資組合報酬，但只有部分模型預測組建的橫斷面動能組合在報酬率上勝過純粹以報酬率建構的基準動能組合。
In the study of stock market momentum phenomena, two primary types are commonly examined: Cross-Sectional Momentum (CSM) and Time Series Momentum (TSM). CSM focuses on the relative performance of assets compared to one another during the same period—if an asset outperforms its peers, the trend is expected to continue. In contrast, TSM emphasizes the past return of a single asset—if an asset’s return is positive, it is expected that the trend will persist.
This study uses Taiwan’s stock market as the empirical setting and applies machine learning techniques to investigate both types of momentum. Models used include logistic regression, tree-based ensemble methods (XGBoost and Random Forest), and neural networks. Based on the prediction results, investment portfolios are constructed following the weighting methodology proposed by Goyal and Jegadeesh (2018), and the profitability across models and weighting schemes is compared. The empirical findings show that using CSM as the target variable leads to higher prediction accuracy than TSM under the same model framework. However, overall predictive performance still has room for improvement. In terms of portfolio returns, CSM-based strategies generally outperform those based on TSM. Nonetheless, only a subset of CSM portfolios constructed from machine learning predictions achieved higher returns than the benchmark momentum portfolios built solely on raw return rankings. |
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| Description: | 碩士
國立政治大學
金融學系
110352031 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0110352031 |
| Data Type: | thesis |
| Appears in Collections: | [Department of Money and Banking] Theses
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