STOCK PRICE PREDICTION USING MACHINE LEARNING
Swarup G. Dhote1, Shubham S. Deshmukh2, Amruta H. Mohod3,
Suyoga A. Nimbhorkar4, Prof. A. R. Kale5.
1Student, P.R. Pote Patil College of Engineering and Management, Amravati
2Student, P.R. Pote Patil College of Engineering and Management, Amravati
3Student, P.R. Pote Patil College of Engineering and Management, Amravati
4Student, P.R. Pote Patil College of Engineering and Management, Amravati
5Professor, P.R. Pote Patil College of Engineering and Management, Amravati
ABSTRACT— 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. Behavioral 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. SVM and Random Forest is used for predicting. It takes the latest trading information and analysis indicators as its input.
Stock market investment strategies are complex and rely on an evaluation of vast amounts of data. In recent years, machine learning techniques have increasingly been examined to assess whether they can improve market forecasting when compared with traditional approaches. The objective for this study is to identify directions for future machine learning stock market prediction research based upon a review of current literature. A systematic literature review methodology is used to identify relevant peer-reviewed journal articles from the past twenty years and categorize studies that have similar methods and contexts. Four categories emerge: artificial neural network studies, support vector machine studies, studies using genetic algorithms combined with other techniques, and studies using hybrid or other artificial intelligence approaches. Studies in each category are reviewed to identify common findings, unique findings, limitations, and areas that need further investigation. The final section provides overall conclusions and directions for future research.
