Abstract
Financial markets and economy forecast are closely related to each other. Forecast of prices of financial assets is therefore of importance for any economy-planning be it global, national or individual. There are various global, local and psychological factors that affect financial markets making its forecasting a non-trivial, complex problem. Numerous machine learning techniques have been applied by various researchers for a last few decades for making forecasts in various fields including the financial one, with varying degree of success. In the present article, time-series data of NIFTY50 of the National Stock Exchange (NSE) of India is considered as a reference data. Forecasting of its prices is done by applying the classical Gradient Descent Method (GDM) and by a few herein proposed modifications of it. The modifications are essentially using variants of the mean square error function of the classical GDM. All the proposed variants, Mean median (MMD) error function, Minkowski (MKW) error function, Logcosh (LCH) error function and Cauchy (CCY) error function, result in significant improvement in all the efficacy parameters of forecasting. Two widely varying time horizons, monthly and daily, have been considered. Significant enhancement in forecasting efficacy is obtained with the application of the Modified GDM methods in all the data sets: training, testing and out-of-sample.
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Verma, S.A., Thampi, G.T., Rao, M. (2020). Efficacy of a Classical and a Few Modified Machine Learning Algorithms in Forecasting Financial Time Series. In: Dey, N., Mahalle, P., Shafi, P., Kimabahune, V., Hassanien, A. (eds) Internet of Things, Smart Computing and Technology: A Roadmap Ahead. Studies in Systems, Decision and Control, vol 266. Springer, Cham. https://doi.org/10.1007/978-3-030-39047-1_1
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