Stock Market Prediction and Portfolio Management with AI

Abstract

Stock market forecast is an important task and is essential for predicting stock prices which can lead to profits and also making proper decisions. But at the same time the task is very complex in nature. Stock market forecast is a major challenge owing to non-stationary, blaring, and chaotic data, and thus, the prediction becomes challenging among the investors to invest the money for making profits. There are several techniques devised to predict the stock market trends such as Bayesian model, Artificial Neural Networks (ANN), Support Vector Machine (SVM) classifier, Neural Network (NN), Machine Learning Methods, and time series models like Auto Regression Integrated Moving Average (ARIMA), Seasonal Auto Regression Integrated Moving Average (SARIMA), Auto Regression Fractional Integrated Moving Average (ARFIMA). The stock market prediction is a very complex task, anddifferent factors should be considered for predicting the future of the market more accurately and efficiently. Forecast models help traders to reduce investment risk andselect the most profitable stocks. The goal of this paper is to analyse different set of models which uses different prediction and clustering techniques and present the results after comparing various approaches. This can help the researchers to upgrade the future works.