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| Title: | 圖神經網路於台灣股市長期股票排名預測之應用：基於橫截面數據與優化排序方法
Graph Neural Networks for Long-Term Stock Ranking Prediction in the Taiwan Stock Market: An Approach Based on Cross-Sectional Data and Optimized Learning to Rank |
| Authors: | 鄭玉海
Cheng, Yu-Hai |
| Contributors: | 黃泓智
鄭玉海
Cheng, Yu-Hai |
| Keywords: | 學習排序
圖神經網路
股票排名預測
LambdaRank
集成學習
量化投資
Learning to Rank
Graph Neural Network
Stock Ranking Prediction
LambdaRank
Ensemble Learning
Quantitative Investment |
| Date: | 2025 |
| Issue Date: | 2025-08-04 14:11:01 (UTC+8) |
| Abstract: | 圖神經網路（GNN）已經在多個領域展現出優良的表現，在股票排名預測中展現了捕捉複雜關聯的潛力，但當前主流模型普遍採用的「改良式 Pointwise 方法」存在理論缺陷，即「目標錯配」與「雜訊敏感」，導致模型在優化過程中易受金融市場低信噪比的影響。為解決此問題，本研究提出一個結合多層感知器（MLP）、圖注意力網路（GAT）與 Listwise 排序方法 LambdaRank 的新型股票排名預測框架。該框架首先利用 MLP 學
習個股的橫截面特徵，再透過 GAT 建模同產業股票間的關聯性，並以 LambdaRank 為優化目標，從根本上使模型的學習方向與排序任務的本質保持一致。此外，本研究設計並驗證了一套創新的「頂部排名優化策略」，引導模型聚焦於提升最具投資價值的頂級股票之預測準確性。
透過對台灣股市 2020 年至 2024 年的滾動窗口回測，本研究提出的完整模型展現了卓越的績效，顯著優於市場基準與基線模型。消融實驗進一步證實，「頂部排名優化」策略與「GNN 模塊」均為模型取得成功的關鍵組件，移除任何一者皆會導致績效顯著衰退。研究亦發現，透過與泛化能力更強的模型進行集成，能有效緩解本框架在較大選股數中可能存在的「局部過擬合」風險，從而構建出一個績效更佳且更穩健的投資決策系統。總體而言，本研究不僅提供了一個理論更健全、實證更有效的智能選股框架，也
為深度學習模型在金融市場的設計、評估與應用提供了深刻的洞見。
Graph Neural Networks (GNNs) have demonstrated superior performance across various domains and exhibit significant potential in capturing complex relationships for stock
ranking prediction. However, prevailing GNN stock ranking models commonly employ a "modified pointwise approach," which suffers from theoretical flaws, specifically "objective mismatch" and "noise sensitivity." These issues render models susceptible to the low signal-to-noise ratio inherent in financial markets during optimization. To address these challenges, this study proposes a novel stock ranking prediction framework that integrates a Multi-Layer
Perceptron (MLP), Graph Attention Network (GAT), and the Listwise ranking method,LambdaRank.The proposed framework first utilizes an MLP to learn the cross-sectional features of individual stocks. Subsequently, a GAT models the relationships among stocks within the same industry. By adopting LambdaRank as the optimization objective, the framework fundamentally aligns the model's learning direction with the intrinsic nature of the ranking
task. Furthermore, this research designs and validates an innovative "top-rank optimization strategy" to guide the model in focusing on improving the prediction accuracy of the most investment-worthy, top-tier stocks.
Through rolling-window backtesting on the Taiwan stock market from 2020 to 2024, the proposed model demonstrates exceptional performance, significantly outperforming market
benchmarks and baseline model. Ablation studies confirm that both the "top-rank optimization" strategy and the "GNN module" are critical components for the success, with
the removal of either leading to a substantial decline in performance. The study also reveals that integrating with models possessing stronger generalization capabilities can effectively mitigate the potential risk of "local overfitting" in this framework when dealing with a larger
selection of stocks, thereby constructing a more robust investment decision system with enhanced performance. Overall, this research not only provides a theoretically sound and empirically effective intelligent stock selection framework but also offers profound insights into the design, evaluation, and application of deep learning models in financial markets. |
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| Description: | 碩士
國立政治大學
風險管理與保險學系
112358015 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0112358015 |
| Data Type: | thesis |
| Appears in Collections: | [風險管理與保險學系] 學位論文
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