利用機率模型與機器學習，向量表示詞語並預測股價方向

Using Probability Models and Machine Learning to Represent Words in the Prediction of Stock Price Directions

洪御恆(Yu-Heng Houng)

指導教授 ： 呂育道

國立臺灣大學/電機資訊學院/資訊工程學研究所/碩士(2019年)

https://doi.org/10.6342/NTU201901597

摘要

本論文主要討論以新聞標題作為依據預測股價的漲跌方向，對於以消息面來做為依據的投資策略，新聞是很重要的一個消息來源，於是我們嘗試利用自然語言處理領域中有名的Doc2vec將新聞標題以向量的方式解讀，一個以神經網路與機率作為基本架構的模型來表示新聞標題，再使用經典機器學習模型預測特定股價的漲跌，預測準確率最高可達70%，以期望用於輔助投資策略的決策。

關鍵字

漲跌預測 ； 股價 ； 自然語言 ； 機器學習 ； 新聞

並列摘要

This thesis mainly discusses how to use news headlines as features to predict stock price directions. It is well-known that some investors believe in news analysis strategy, which is highly depending on the news. Thus we will use the popular method Doc2vec, which uses vectors to represent words based on neural networks and probability, to represent each headline as a vector. Then we use the classical machine learning model to predict individual stock price directions. Our method best perform 70% accuracy for stock price directions prediction and expect to help investors making
the right strategy.

並列關鍵字

Prediction in the Price Directions ； Stock Price ； Nature Language ； Machine Learning ； News

參考文獻

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