本研究參照美商MSCI的 BARRA股票市場多因子線性模型建構流程，以個股基本面、價格面要素資訊，分組後以特定權重合成九大風格因子，並加入依照台灣經濟新報資料庫產業別資料形成二十八個產業因子，建構代表個股資訊的因子資料，並使用上述因子建模，達到預測個股報酬的目的。
　　多因子線性模型建構後，本研究進而嘗試將模型以多層感知器(Multi-Layer Perception)框架改寫，建構深度學習多因子模型，達到要素資料的標準化、因子合成權重決定、因子值標準化、資產報酬估計等建模步驟自動化的目的。同時以多模型集成學習的機制，得到最終的個股報酬預測結果。
　　實證結果顯示，使用2009至2018年上市股票資料進行回測，依模型預測報酬高低排序，使用每月篩選出預測報酬前5、10、20、30名股票依等權重、市值加權的方式建構投資組合，並持有一個月的方式，在考慮交易成本後，深度學習版本模型相較線性模型有更高的年化報酬、更低的年化風險，以及更小的最大回撤。

　　This research refers to Barra stock market multi-factor model construction process which published in MSCI Barra research note (2009). We constructed nine style factors and 28 industry factors which are based on stock’s fundamental information, price information, and industry code of the company, which grouped by Taiwan Stock Exchange. Each factor forms the daily frequency data of market to representing individual stock information. Those factors are used to build model to achieve the purpose of predicting individual stock returns.
After construct a linear version factor model, this study further attempts to rewrite the original model with a deep learning framework to construct a deep learning multi-factor model to automate steps such as standardization of factor data, determination of weights to construct factor exposure, and standardization of factor exposure, and the estimated return of each stock. Furthermore, we also test the prediction power of the multi-model voting mechanism.
By using the information of listed stock information from 2009 to 2018 is used for modeling. The empirical results show that after sorting stocks according to the model's predicted return level, and pick the top 5, 10, 20, and 30 stocks that are screened out for the predicted return each month are used to construct investment based on equal weight and market value weighting. After considering transaction costs, the MLP version model has higher annual returns, lower annual risks, and smaller maximum drawdowns than the BARRA (2009) linear version model.

學位論文審定書 I
摘要 II
ABSTRACT III
圖次 VI
表次 VIII
第一章 緒論 1
第一節 研究動機 1
第二節 研究目的 2
第三節 研究架構 2
第二章 文獻回顧 4
第一節 資產定價及多因子模型 4
第二節 深度學習 5
第三節 深度學習財務預測應用 6
第四節 小結 7
第三章 研究方法 8
第一節 實驗架構 8
第二節 研究資料 10
第三節 模型建構 17
第四章 實證結果 25
第一節 模型預測力測試 25
第二節 線性模型績效 28
第三節 深度學習模型績效 33
表13等權重投組報酬差異T檢定 40
表14市值加權投組報酬差異T檢定 40
第五章 研究結論 41
第一節 結論 41
第二節 後續研究建議 42
第六章 參考文獻 43
第一節 英文文獻 43
第二節 中文文獻 46
附錄：風格因子列表 47

第一節　英文文獻
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12. Menchero, Jose. “Characteristics of Factor Portfolios, March 2010.” SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, March 30, 2010.
13. Menchero, Jose, and B. Davis. “The Characteristics of Factor Portfolios.” Journal of Performance Measurement 15, no. 1 (2010): 52–62.
14. Menchero, Jose, and Jyh-Huei Lee. “Efficiently Combining Multiple Sources of Alpha.” Journal Of Investment Management 13, no. 4 (2015): 71–86.
15. Menchero, Jose, and Andrei Morozov. “Capturing Equity Risk Premia (August 2010).” SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, August 3, 2010.
16. Menchero, Jose and Morozov, Alexander. “Decomposing Global Equity Cross-Sectional Volatility.” Financial Analysts Journal 67, no. 5 (2011): 58–68.
17. Menchero, Jose, Andrei Morozov, and Peter Shepard. “Global Equity Risk Modeling.” In Handbook of Portfolio Construction, 439–480. Springer, 2010.
18. Menchero, Jose, Andrei Morozov, and Peter Shepard. “The Barra Global Equity Model (Gem2).” MSCI Barra Research Notes 53 (2008).
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20. Sezer, Omer Berat, A. Murat Ozbayoglu, and Erdogan Dogdu. “An Artificial Neural Network-Based Stock Trading System Using Technical Analysis and Big Data Framework.” In Proceedings of the Southeast Conference, 223–226, 2017.
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23. Usmani, Mehak, Syed Hasan Adil, Kamran Raza, and Syed Saad Azhar Ali. “Stock Market Prediction Using Machine Learning Techniques.” In 2016 3rd International Conference on Computer and Information Sciences (ICCOINS), 322–327. IEEE, 2016..
 
第二節　中文文獻
24. 蔡彥鈞（2011）。多因子風險模型建構與其在增值指數基金上之應用－以新加坡市場為例。國立中山大學財務管理學系研究所碩士論文。
25. 李政儒（2010）。多因子風險模型建構與應用於增值指數基金之績效分析－以中國大陸市場為例。國立中山大學財務管理學系研究所碩士論文。
26. 蔡宗勳（2009）。多因子風險模型系統平台。國立中山大學財務管理學系研究所碩士論文。