Stock Market Prediction Using Machine Learning
Deepali N. Bhaturkar
Assistant Professor, Department of Information Technology,
Hope Foundation's International Institute of Information Technology (I2IT)
Phase I, Hinjewadi, Pune - 411057, India
Email: deepalib@isquareit.edu.in
Samarth Aher
Department of Information Technology,
Hope Foundation's International Institute of Information Technology (I2IT)
Phase I, Hinjewadi, Pune - 411057, India
Email: samarth1555@gmail.com
Aishwarya Ghatge
Department of Information Technology,
Hope Foundation's International Institute of Information Technology (I2IT)
Phase I, Hinjewadi, Pune - 411057, India
Email: aishughatge@gmail.com
Shruti Jadhav
Department of Information Technology,
Hope Foundation's International Institute of Information Technology (I2IT)
Phase I, Hinjewadi, Pune - 411057, India
Email: shrutivijayjadhav@gmail.com
Ria Nair
Department of Information Technology,
Hope Foundation's International Institute of Information Technology (I2IT)
Phase I, Hinjewadi, Pune - 411057, India
Email: riarajnair2102@gmail.com
Abstract – This research paper aims to analyze 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. Based on our findings, algorithmic trading programs are developed and simulated using Quintilian Technical. Overall, the findings suggest that machine learning techniques hold promise for stock market prediction. However, it is important to consider the limitations and challenges associated with these methods, such as the non-stationarity of financial data, the presence of noise and outliers, and the potential impact of external factors.
Index Terms – Machine Learning, Stock Market Prediction, Predictive Modeling, Time Series Analysis, Pattern Recognition
