Asia Pacific Journal of Information Systems (APJIS)

menu05_sub01_ov.gif

Share this page

Current Issue

Date	March 2020
Vol. No.	Vol. 30 No. 1
DOI	https://doi.org/10.14329/apjis.2020.30.1.31
Page	31~52
Title	Stock Price Prediction and Portfolio Selection Using Artificial Intelligence
Author	Sandeep Patalay, Madhusudhan Rao Bandlamudi
Keyword	Stock Prediction, Stock Portfolio, Artificial Intelligence (AI), Machine Learning (ML), Artificial Neural Networks (ANN), K-Means, Multi-Layer Perceptron (MLP)
Abstract	Stock markets are popular investment avenues to people who plan to receive premium returns compared to other financial instruments, but they are highly volatile and risky due to the complex financial dynamics and poor understanding of the market forces involved in the price determination. A system that can forecast, predict the stock prices and automatically a portfolio of top performing stocks is of great value to individual investors who do not have sufficient knowledge to understand the complex dynamics involved in evaluating and predicting stock prices. In this paper the authors propose a Stock prediction, Portfolio Generation and Selection model based on Machine learning algorithms, Artificial neural networks (ANNs) are used for stock price prediction, Mathematical and Statistical techniques are used for Portfolio generation and Un-Supervised Machine learning based on K-Means Clustering algorithms are used for Portfolio Evaluation and Selection which take in to account the Portfolio Return and Risk in to consideration. The model presented here is limited to predicting stock prices on a long term basis as the inputs to the model are based on fundamental attributes and intrinsic value of the stock. The results of this study are quite encouraging as the stock prediction models are able predict stock prices at least a financial quarter in advance with an accuracy of around 90 percent and the portfolio selection classifiers are giving returns in excess of average market returns.

Home l Site Map l Abstracting/Indexing l FAQ l Publisher l Contact Us l Admin Login

© 2013 The Korean Society of Management Information Systems. All rights reserved.