Combining of random forest estimates using LSboost for stock market index prediction

Nonita Sharma · Akanksha Juneja

78 citations0/0 files storedIEEE International Conference for Convergence in Technology (I2CT)

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Forecasting Techniques and Applications

Abstract

This research work emphases on the prediction of future stock market index values based on historical data. The experimental evaluation is based on historical data of 10 years of two indices, namely, CNX Nifty and S&P Bombay Stock Exchange (BSE) Sensex from Indian stock markets. The predictions are made for 1-10, 15, 30, and 40 days in advance. This work proposes to combine the predictions/estimates of the ensemble of trees in a Random Forest using LSboost (i.e. LS-RF). The prediction performance of the proposed model is compared with that of well-known Support Vector Regression. Technical indicators are selected as inputs to each of the prediction models. The closing value of the stock price is the predicted variable. Results show that the proposed scheme outperforms Support Vector Regression and can be applied successfully for building predictive models for stock prices prediction.

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References

1986Generalized autoregressive conditional heteroskedasticity 1995Random Walks in Stock Market Prices 1991Chaos and Nonlinear Dynamics: Application to Financial Markets 2014Predicting stock market index using fusion of machine learning techniques 2010Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting 2010Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming

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