隨著網際網路的快速發展，人們可以很方便地進行股市交易，股票市場也被視為最重要的金融市場之一。現今資訊技術也開始應用在傳統金融產業中，並結合深度學習探討市場與股價的關係。然而目前研究方法大多選擇單一股價或是新聞做為參考資料，在股市交易中，選擇權(Options)與存託憑證也是很重要的投資參考選項，其中包含買權(call)與賣權(put)等多項數據。本研究將選擇權具有的多變量與時間序列特徵，與多變量長短期記憶(Multivariate LSTM)模型結合，使模型能有效的學習時間序列中的資訊，並得到最終預測結果。根據實驗結果發現具有多變量的LSTM模型能提升預測股價變化研究的準確度。

With the rapid development of the Internet, people can easily trade in the stock market, and the stock market is also regarded as one of the most important financial markets. In these days, information technology has also begun to be applied in the traditional financial industry, and combined with deep learning to explore the relationship between the market and stock prices. However, most of the current research methods choose a single stock price or news as reference materials. In stock market trading, options and depositary receipts are also very important investment reference options, which include multiple data such as call and put. In this study, the options with time series features are combined with the Multivariate LSTM model to enable the model more effectively learning the information in the time series and obtain the final prediction result. According to the experimental findings, it is found that the multivariate LSTM model can improve the accuracy of the research on predicting stock price changes.

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