整合分位數回歸分析與單因子選股的股票選股策略回測與績效評估之研究

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姓名

劉浩平(Hao-Ping Liu) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

整合分位數回歸分析與單因子選股的股票選股策略回測與績效評估之研究
(On Integrating Quantile Regression with Factor Analysis for Efficient Backtesting and Performance Evaluation of Stock Selection Strategies)

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摘要(中)

隨著資料科學與人工智慧的迅速發展，量化交易逐漸成為主流的投資方式。然而，現有的量化交易系統在應對多變的金融市場時存在一定的局限性。本研究旨在結合Python與Cython開發一個自動化股票回測平台，結合分位數迴歸模型，比較不同因子在不同回報分位數的影響效果，從而優化因子選擇的過程。透過分位數迴歸，可以對每個因子在不同回報分位數下的表現進行評估，並確定最適合的分位數，從而提高選擇因子的準確性和穩健性。
系統架構由資料存取層、邏輯運算層和應用層組成，藉由資料存取層與資料庫串接存取資料，並交付給邏輯運算層中的回測模組進行運算，其中核心的backtest_core子模組負責核心回測運算，包括資金分配、手續費計算以及最終績效等等，此子模組由Python與Cython所開發，有效降低運算時間成本，最終將投資組合績效傳遞給應用層產出視覺化工具展示回測結果。
本研究實驗結果顯示，該系統能夠有效評估不同因子在台灣股票市場中的表現，並自動生成投資組合的收益與風險分布圖。實驗還驗證藉由分位數迴歸建構單因子選股模型的有效性，藉由選取影響係數絕對值越大之分位數能夠增加投資組合之CAGR並有效降低MDD。本實驗於分位數迴歸模型中引入時間與群數的交互項，以此觀察因子在不同時間窗格中的穩健性，最終得出若是交互項不顯著（p值大於0.05）則較不受時間之影響。

摘要(英)

With the rapid advancement of data science and artificial intelligence, quantitative trading has gradually become a mainstream investment method. However, existing quantitative trading systems have certain limitations when coping with the volatile financial markets. This study aims to develop an automated stock backtesting platform using Python and Cython, integrating quantile regression models to compare the effects of different factors across various return quantiles, thereby optimizing the factor selection process. Through quantile regression, the performance of each factor can be evaluated at different return quantiles to determine the most appropriate quantile,
thereby enhancing the accuracy and robustness of factor selection.
The system architecture consists of three layers: data access layer, logic processing layer, and application layer. The data access layer interfaces with the database to retrieve data, which is then processed by the backtesting module in the logic processing layer. The core backtest_core submodule is responsible for the primary backtesting computations, including fund allocation, fee calculation, and final performance evaluation. This submodule is developed using Python and Cython, effectively reducing computational time costs. The performance results of the investment portfolio are then passed to the application layer to produce visual tools that display the
backtesting results.
The experimental results of this study indicate that the system can effectively evaluate the performance of different factors in the Taiwan stock market and automatically generate the return and risk distribution charts of the investment portfolio. The experiments also validate the effectiveness of constructing single-factor stock selection models using quantile regression. Selecting the quantile with the highest absolute value of the impact coefficient can increase the CAGR and effectively reduce the MDD of the investment portfolio. The experiments introduce an interaction term between time and group number into the quantile regression model to observe the robustness of factors across different time windows. The results show that if the interaction term is not significant (p-value greater than 0.05), the factor is less affected
by time.

關鍵字(中)

★ 量化交易
★ 單因子模型
★ 分位數迴歸
★ Python
★ Cython

關鍵字(英)

★ Quantitative Trading
★ Single-Factor Model
★ Quantile Regression
★ Python
★ Cython

論文目次

摘要 i
Abstract ii
致謝辭 iv
目錄 v
圖目錄 vii
表目錄 viii
程式碼目錄 ix
一、緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 4
二、文獻探討 6
2.1 因子模型 6
2.2 分位數回歸（Quantile Regression） 7
2.3 量化交易 8
2.4 Cython 10
三、系統設計與實作 12
3.1 單因子選股及分位數迴歸模型 12
3.1.1 單因子選股 13
3.1.2 分位數迴歸模型 13
3.1.3 單因子選股與分位數迴歸 16
3.1.4 基於分位數迴歸的單因子選股模型穩健性分析 16
3.2 回測語言的設計與延伸 17
3.2.1 回測語言概述 18
3.2.2 Python與Cython的應用 18
3.2.3 Extended DataFrame的設計與應用 19
3.3 系統架構 21
3.3.1 邏輯運算層 21
3.3.2 資料存取層 23
3.4 程式碼實現 25
3.4.1 因子切割 25
3.5 績效計算指標 26
四、系統驗證與分析 27
4.1 實驗變數 27
4.1.1 資料來源 27
4.1.2 因子選擇 27
4.2 實驗設計 30
4.3 實驗結果 31
4.3.1 單因子分位數回歸分析 31
4.3.2 不同時間窗格因子穩健性檢驗 37
4.3.3 不同產業因子績效表現 41
4.3.4 比較系統回測時間表現 49
五、結論 51
5.1 結論 51
5.2 研究限制 52
5.3 未來建議 52
參考文獻 53

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指導教授

許智誠

審核日期

2024-7-3

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