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Exploring the usage of econometric techniques in nonlinear machine learning and data mining

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  • P. Lakshmi
  • S. Visalakshmi

Abstract

The present study, investigates the inter-linkage of the Indian spot market with other global markets and the predictability of S%P CNX NIFTY Index returns with a set of five new international market returns as input variables in artificial neural networks (ANNs). Identifying the right set of exogenous input variables using conventional techniques like OLS, Granger causality and cross correlation substantially increased the predictability of financial time series like stock return in the Indian context. The performance of the ANN model in forecasting NIFTY index returns is evaluated by comparing it for different sample periods in terms of forecasting error functions with statistical measures like mean absolute error, root mean square error, mean absolute percentage error and mean square error. The findings suggest that higher accuracy of the predictive power of neural network is largely influenced by the input variables.

Suggested Citation

  • P. Lakshmi & S. Visalakshmi, 2016. "Exploring the usage of econometric techniques in nonlinear machine learning and data mining," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 9(3), pages 349-362.
    • Handle: RePEc:ids:ijmore:v:9:y:2016:i:3:p:349-362
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    Cited by:

    1. Flavio Barboza & Geraldo Nunes Silva & José Augusto Fiorucci, 2023. "A review of artificial intelligence quality in forecasting asset prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1708-1728, November.
    2. Ding, Xiuying & Liu, Xuemei, 2023. "Renewable energy development and transportation infrastructure matters for green economic growth? Empirical evidence from China," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 634-646.
    3. Li, Ping & Zhou, Ying & Huang, Sijie, 2023. "Role of information technology in the development of e-tourism marketing: A contextual suggestion," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 307-318.

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