無論學界或是業界，預測股市的漲、跌走勢一直以來都是人們所感興趣的主題。然而經濟學有一個隨機漫步理論 (Random Walk Theory) 認為，股票價格變化具有相同分佈及彼此獨立的特性，所以股票價格或市場的過去走勢或趨勢不能用來預測其未來走勢，因此一直以來無法有效運用過去的歷史資訊對未來做出預判。但近年來盛行的深度學習可有效處理大量的雜訊與非線性資料，讓預測未來這件事有了全新的解決方法，因此我們希望透過深度學習尋找出有效的方法來解決此問題。本研究收集Reddit社交新聞網站的頭條新聞以及道瓊工業平均指數 (DJIA) 市場交易資料，做為深度學習所需的訓練及驗證資料集，由於考量股市的時序性及新聞影響的持續性，我們提出一套結合新聞資訊與技術分析指標為基礎的長短期記憶神經網路 (long short-term memory，LSTM)模型並運用特徵選取方式大幅減低訓練模型時間，藉此即時掌握股票市場漲、跌趨勢，讓使用者可借助人工智慧的技術掌握未來股票市場走向趨勢，輔助投資人提高決策投資準確度

Regardless of academia or industry, predicting the stock market's up and down trend has always been the subject of interest. Nevertheless, there is a Random Walk Theory in economic theory that stock price changes have the same distribution and are independent of each other, so the past movement or trend of a stock price or market information cannot be used to predict its future movement. Therefore, it has not been effectively use past historical information to predict the future. However, the deep learning prevailing in recent years can efficiently deal with a bunch of noise and non-linear data. It is a whole new way to predict the future, so we hope to find an effective way to solve this problem through deep learning. In this study, we will try to find an effective way to solve this problem through deep learning technology. We collect headlines from the Reddit social news website and DJIA market trading data as a training and validation data set for deep learning. In consideration of sequential stock prices and continuous news effects, we propose a LSTM (long short-term memory) model based on news and technical analysis indexes and use the feature selection method to significantly reduce the training model time. This forecast can allow users immediately grasp the trend of the stock market in the future with artificial intelligence technology and help investors improve the accuracy of investment decisions.

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