本研究選取所有一般產業的上市櫃普通股（排除金融、保險、證券等業別），自1999年12月到2019年9月之間共20年的公司季資料、半年資料，找出可能影響股價的16項財務比率，例如：每股盈餘(元),淨值報酬率─稅後,淨值報酬率─常續利益,…PSR,CAPM_Beta」等等，再加上各自四期滯後項作為特徵值，透過機器學習分類器進行訓練，預測目標為四期以後股價的250日移動平均值(250MA)，會漲或跌？
本研究使用六種分類器，集成算法就占了其中四種，實作結果，集成算法在測試集資料上，對於四期即一年以後的250MA漲跌均能達到71%以上的預測正確率，除了證明集成算法的表現良好以外，也證明了確實可以使用本研究所選取的特徵，建立股價趨勢的預測模型。

The main purpose of this research is to predict the trend of stock prices using machine learning. We apply machine learning classifiers to analyze 16 financial ratios(such as earnings per share, return on net worth—after-tax, return on net worth—recurring benefits,...PSR, CAPM_Beta, etc.) plus four lags of each feature, to predict if 250-day moving average of stock price after 4 periods will go up or down. The dataset we use includes financial data of common stocks of listed companies and over-the-counter (OTC) companies in all general industries (excluding financial, insurance, securities) in Taiwan, from December 1999 to September 2019.
Four of the six classifiers we use are ensemble methods. As a result of the implementation, the test accuracy of all the four ensemble classifiers is greater than 71%. It not only proves that ensemble classifiers perform well, but also that we can use the features selected in this study to build a predictive model of stock price trends.

【中文文獻】
1.FoSter Provost & Tom Fawcett著，陳亦苓譯，資料科學的商業運用，碁峰資訊，台北，2016。
2.Gregory Zuckerman著，林錦慧譯，洞悉市場的人：量化交易之父吉姆‧西蒙斯與文藝復興公司的故事（The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution），第2章，第4頁，天下文化，台北市，2020.5。
3.Krishna G. Palepu, Paul M. Healy, Erick Peek著 ;郭敏華編譯，財務分析與企業評價，新加坡商聖智學習，2014。
4.吳政勳，股價報酬與財務比率之關聯性--貝氏馬可夫蒙地卡羅之分析研究，國立清華大學經濟學研究所碩士論文，2003。
5.黃國彰，各項選股指標於台灣股票市場的實用性探討--應用對象與應用時機之研究，國立臺灣大學國際企業學研究所碩士論文，2007。
6.蔡世琦，台灣股市績效預測分析的探討:以邏吉斯迴歸為例，國立臺南大學經營與管理學系科技管理所碩士論文，2013。
7.謝劍平，現代投資學：分析與管理，智勝，台北， 2016。
【英文文獻】
1.AI pioneers in investment management, CFA Institute.
2.Andre Ng, Machine Learning Yearning, Draft Version .(2018)
3.Bishop, Christopher M. Pattern Recognition and Machine Learning. New York: Springer,page 658～659.(2006)
4.Hastie, Trevor, Robert, Tibshirani and J. H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer ,New York,(2009)
5.Hiro Takahashi, Hiroyuki Honda, New approach for important factor selection from high dimensional bioinformatics data, Focus on Genetic Screening Research. (2006)
6.Huang, Wei & Nakamori, Yoshiteru & Wang, Shouyang. Forecasting stock market movement direction with support vector machine.. Computers & OR. 32. 2513-2522. (2005).
7.Hyndman, R.J., & Athanasopoulos, G. Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia. OTexts.com/fpp2.(2018)
8.Malkiel, Burton Gordon. A Random Walk down Wall Street : the Time-Tested Strategy for Successful Investing. New York :W.W. Norton, (2015).
9.Milosevic, Nikola.. Equity forecast: Predicting long term stock price movement using machine learning. (2016)
10.MAKRIDAKIS, S. G., WHEELWRIGHT, S. C. and HYNDMAN,R. J. (1998). Forecasting: Methods and Applications, 3rd ed.Wiley, New York. page 288.(1997)
11.Marcos López de Prado, The 10 Reasons Most Machine Learning Funds Fail, The Journal of Portfolio Management 44 (6), 120-133.(2018)
12.Qi Zhang, Haiqing Li, Man Zhang, Zhaofeng He, Zhenan Sun, Tieniu Tan, Fusion of face and Iris Biometrics on modile devices using near-infrared images, Biometric Recognition: 10th Chinese Conference, Ccbr 2015, Tianjin, China, November 13-15.
13.Spruyt V. The Curse of Dimensionality in classification; 2014, Available:http://www.visiondummy.com/2014/04/curse-dimensionality-affect-classification/ [online; accessed 06.06.20].
14.Tianqi Chen, Carlos Guestrin. "XGBoost: A Scalable Tree Boosting System". Technical report, LearningSys, December, (2015)
15.Thomas M. Mitchell. Machine Learning ,1st. ed., McGraw-Hill, Inc., USA. (1997).
16.Vaughan, T. S. and Berry, K. E. Using Monte Carlo techniques to demonstrate the meaning and implications of multicollinearity.J. Statist. Educ. Page 7 (2005)
17.Willem Mertens, Amedeo Pugliese, Jan Recker , Quantitative Data Analysis: A Companion for Accounting and Information Systems Research, 1st ed. (2017)