股市是高度複雜化的非線性動態變化的系統，預測股市變化是具有極大挑戰性和不准確性的，傳統的數理統計方式可能使預測效果不是很顯著。深度學習(deep learning)是深層非線性網絡結構，可以捕捉那些非線性的特徵與訊息，使預測精度提高。
本研究目的為探討台灣股票報酬率的影響因素與預測能力，採用基本面資料包括Fama-French五因子相關因素、動量因素、財務比率，總體經濟因素等四個方面選取股價報酬率預測的33個因素作為主要變量進行實證研究。
在基本面的檢驗效果上，總體上來說，除了總體經濟因素的變量呈現顯著負相關，其他三個方面因素變量大多呈現顯著正相關，表示公司的經營能力，財務狀況等基本面的因素，會對預測股票報酬率有著顯著的正向預測能力。
在預測方面，我們分別以傳統panel data做線性迴歸與深度學習的DBN模型進行分析與預測。通過預測實驗我們可以發現， DBN模型對於股價報酬率未來的漲跌預測表現比傳統的線性迴歸表現更優異。傳統線性迴歸的趨勢預測準確率平均為47%，而DBN模型的趨勢預測準確率平均為67%。但是DBN模型在預測精確度不高，且在短中長期表現不夠穩定，同時DBN模型訓練耗時較長。
我們基於傳統線性迴歸與非線性迴歸的DBN模型的對比分析，兩者在具體預測股票報酬率中的表現，進一步分析其產生的原因，並對未來作出展望。

In this paper, we study the fundamental factors that may affect Taiwan stocks returns, using OLS and deep learning to predict future stock returns. From 2000 to 2017, we selected 802 listed companies in Taiwan, and select factors from 4 aspects Fama-French, momentum, financial ratio and Macroeconomics for empirical research.
As a result of the fundamentals, we found out that there are 8 factors that have a significant impact on long-term and short-term forecasted rates of return. In general, except for the Macroeconomics factors, other factors from rest of three aspects have a significant positive correlation, indicating that the company’s operating capabilities, financial status, and other fundamental factors will have a significant positive affect and predict stock returns.
For forecasting, we use traditional OLS model and DBN model of deep learning to forecast and analysis. We can find out that the DBN model performs better than the traditional linear regression on predicting the stock price return in the future. The average accuracy of traditional linear regression is 47 %, while the average accuracy of DBN model is 66 %. However, the error in the DBN model is not accurate, and it is not stable during the period. At the same time, the DBN model training takes a long time. Based on a comparative analysis of two, which performs in the specific prediction on stock returns, then further analyze the reasons for its occurrence and make an outlook for the future.

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