STOCK PRICE PREDICTION USING MACHINE LEARNING TECHNIQUES

被引：1

作者：

Sarode, Sumeet ^{[1
]}

Tolani, Harsha G. ^{[1
]}

Kak, Prateek ^{[1
]}

Lifna, C. S. ^{[1
]}

机构：

[1] Vivekanand Educ Soc Inst Technol, Comp Engn Dept, Mumbai, Maharashtra, India

来源：

PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON INTELLIGENT SUSTAINABLE SYSTEMS (ICISS 2019) | 2019年

关键词：

Stock Price Prediction; Stock Market Trends; LSTM (Long Short-Term Memory); Forecast of Stock Prices; Support Vector Machine (SVM); Efficient Market Hypothesis (EMH);

D O I：

10.1109/iss1.2019.8907958

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In today's economy, there is a profound impact of the stock market or equity market. Prediction of stock prices is extremely complex, chaotic, and the presence of a dynamic environment makes it a great challenge. Behavioural finance suggests that decision-making process of investors is to a very great extent influenced by the emotions and sentiments in response to a particular news. Thus, to support the decisions of the investors, we have presented an approach combining two distinct fields for analysis of stock exchange. The system combines price prediction based on historical and real-time data along with news analysis. LSTM (Long Short-Term Memory) is used for predicting. It takes the latest trading information and analysis indicators as its input. For news analysis, only the relevant and live news is collected from a large set of business new. The filtered news is analyzed to predict sentiment around companies. The results of both analyses are integrated together to get a response which gives a recommendation for future increases.

引用

页码：177 / 181

页数：5

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