Application of Intervention Analysis on Stock Market Forecasting
In today’s financial market, different financial events have direct impact on stock market values. Even a slight change in those events may result a huge difference in stock prices. So consideration of those effects is very important in forecasting stock values. Most of the researches as of now only consider about forecasting but not these effects. This paper studies the effects of some of those events in financial market forecasting. In this paper, we focused our study on the effects of financial events such as GDP, Consumer Sentiments and Jobless Claims on stock market forecasting and analyze them. These events are considered as intervention effects. The intervention effect is described in this study as temporary but immediate and abrupt. So we have tried to estimate not only the period of effect of these events but also use intervening values on forecasting. These forecasted values are then compared to forecasted values obtained from fusion model based on Concordance and Genetic Algorithm (GA). The concept is validated using financial time series data (S&P 500 Index and NASDAQ) as the sample data sets. We also have analyzed how often our forecasting values have the same movement as that of actual market values. The developed tool can be used not only for forecasting but also for in depth analysis of the stock market.
KeywordsRoot Mean Square Error Recurrent Neural Network Fusion Model Joint Estimation Time Series Prediction
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