Stock prediction using machine learning

Abstract

We analyse existing and new methods of stock market prediction. We take three different approaches at the problem: Fundamental analysis, Technical Analysis, and the application of Machine Learning. We find evidence in support of the weak form of the Efficient Market Hypothesis, that the historic price does not contain useful information but out of sample data may be predictive. We show that Fundamental Analysis and Machine Learning could be used to guide an investor’s decisions. We demonstrate a common flaw in Technical Analysis methodology and show that it produces limited useful information. In the finance world stock trading is one of the most important activities. Stock market predic-tion is an act of trying to determine the future value of a stock other financial instrument tradedon a financial exchange.The technical and fundamental or the time series analysis is used bythe most of the stockbrokers while making the stock predictions. The programming languageis used to predict the stock market using machine learning is Python. In our project we proposea Machine Learning (ML) approach that will be trained from the available stocks data and gainintelligence and then uses the acquired knowledge for an accurate prediction. In this contextstudy uses a machine learning technique called Support Vector Machine (SVM) or Long ShortTerm-Memory (LSTM) to predict stock prices. Keywords: Mechine Learning, Data Mining, Training set, Training Data, Au-tomated System, pattern Recognition, Deep learning, Knowledgeextraction, Data preprocessing, knowledge extraction, Web mod-ule,Artificial Intelligence.