Predicting stock market price using support vector regression

Phayung Meesad · Risul Islam Rasel

74 citations0/0 files storedInternational Conference on Informatics, Electronics & Vision (ICIEV)

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Stock Market Forecasting Methods Forecasting Techniques and Applications Time Series Analysis and Forecasting

Abstract

In this study, support vector regression (SVR) analysis is used as a machine learning technique in order to predict the stock market price as well as to predict stock market trend. Moreover, different types of windowing operators are used as data preprocess or input selection technique for SVR models. This is a new approach which uses different types of windowing functions as data preprocess for predicting time series data. Support vector regression is a useful and powerful machine learning technique to recognize pattern of time series dataset. It can produce good prediction result if the value of important parameters can be determined properly. Different kinds of Windowing operators are used in this experiment in order to feed more reliable inputs into regression models. This study is done on a well known company of Dhaka stock exchange (DSE), named ACI group of company Limited. Four year's historical time series dataset are collected from the DSE from 2009 to 2012, as daily basis for experimentations. Finally, predicted results from WinSVR models are compared with actual price values of DSE to evaluate the model prediction performance.

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References

2003Financial time series forecasting using support vector machines 2012Support vector regression with chaos-based firefly algorithm for stock market price forecasting 2010Financial Stock Market Forecast using Data Mining Techniques 2007Kernel principal component analysis and support vector machines for stock price prediction

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