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.
