結合句子為基礎的中文新聞分析與技術指標之股票趨勢預測技術

Stock Trend Prediction Techniques by Combining Sentence-based Chinese News Analysis and Technical Indicators

陳柏曄(Po-Yeh Chen)

指導教授 ： 鄭建富

共同指導教授 ： 陳俊豪

淡江大學/工學院/資訊工程學系碩士班/碩士(2020年)

https://doi.org/10.6846/TKU.2020.00745

摘要

長久以來股票趨勢預測都是個熱門的議題，也吸引了大量的學者跟專業人士熱絡的討論與研究。現有文獻的研究顯示，使用對財經新聞之情緒分析，以及技術性指標所預測股票的準確度，會比只使用情緒分析或技術性指標來的更高。雖然已有許多研究被提出，透過整篇新聞的關鍵字建立的模型進行股票趨勢預測，但是在中文新聞裡的效果並不如預期。故本論文嘗試使用關鍵句子的方式來進行新聞文件分析建立模型，並且搭配技術性指標預測股票的趨勢。所提的方法首先從新聞中產生所有的句子。接著，使用TextRank與word2vec，可計算出每一句子的分數，進而產生Top-k關鍵句子。關鍵句子進一步透過財經情緒字典產生每一句子的情緒分數。具情緒分數的關鍵句子與技術指標形成分類屬性，而股價之開收盤價則用於決定此筆訓練之趨勢。根據句子情緒分數，我們進一步將資料分成正負面新聞資料集，並用來建立正負面新聞趨勢預測模型。最後，在預測階段則可透過這兩個預測模型的結果與布林通道所發出的訊號，進行股票趨勢預測並進行買賣。實驗部分使用了一家台灣股票公司近五年的相關新聞以及股票資訊，透過不同的參數進行實驗評估。結果顯示所提的方法是有效的。

關鍵字

中文新聞探勘 ； 技術分析 ； 股票趨勢預測 ； 情緒分析 ； 支援向量機

並列摘要

The stock trend prediction is always a hot topic, and it attracts a lot of scholars and professionals to discuss and research. From the existing literature, the accuracy of stock trend prediction with both using sentiment analysis of financial news and technical analysis is better than that use only one of them. Although there are many approaches have been proposed to build prediction model based on the key words extracted for news, the literature still shows that the accuracy of the model could be improved when using Chinese news. To handle the problem, this thesis proposes a sentence-based stock trend prediction model along with technical indicators for stock trend prediction. The proposed approach first divides news into sentence. Then, using the TextRank and word2vec, the top-k key sentences are generated. The sentiment scores of those key sentences are calculated through the financial lexicon. The key sentences with sentiment scores and technical indicators are used as classification attributes, and the open and close stock prices are used to determine the stock trend. Based on the sentiment scores of sentences, we divide the dataset into positive and negative datasets, and use them to construct the positive and negative stock trend prediction models. At last, through the two prediction models and the signal indicated by the Bollinger Bands, the stock trend will be determined and trading signals will also be generated. In experiments, the news and stock prices of five years of a company were conducted to show the effectiveness of the proposed approach via different parameter settings.

並列關鍵字

Chinese news data mining ； technique analysis ； stock trend prediction ； sentiment analysis ； support vector mechanism

參考文獻

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