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A Neuro‐wavelet Model for the Short‐Term Forecasting of High‐Frequency Time Series of Stock Returns

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  • Luis Ortega
  • Khaldoun Khashanah

Abstract

ABSTRACT We propose a wavelet neural network (neuro‐wavelet) model for the short‐term forecast of stock returns from high‐frequency financial data. The proposed hybrid model combines the capability of wavelets and neural networks to capture non‐stationary nonlinear attributes embedded in financial time series. A comparison study was performed on the predictive power of two econometric models and four recurrent neural network topologies. Several statistical measures were applied to the predictions and standard errors to evaluate the performance of all models. A Jordan net that used as input the coefficients resulting from a non‐decimated wavelet‐based multi‐resolution decomposition of an exogenous signal showed a consistent superior forecasting performance. Reasonable forecasting accuracy for the one‐, three‐ and five step‐ahead horizons was achieved by the proposed model. The procedure used to build the neuro‐wavelet model is reusable and can be applied to any high‐frequency financial series to specify the model characteristics associated with that particular series. Copyright © 2013 John Wiley & Sons, Ltd.

Suggested Citation

  • Luis Ortega & Khaldoun Khashanah, 2014. "A Neuro‐wavelet Model for the Short‐Term Forecasting of High‐Frequency Time Series of Stock Returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(2), pages 134-146, March.
    • Handle: RePEc:wly:jforec:v:33:y:2014:i:2:p:134-146
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    Cited by:

    1. Dhanya Jothimani & Ravi Shankar & Surendra S. Yadav, 2016. "Discrete Wavelet Transform-Based Prediction of Stock Index: A Study on National Stock Exchange Fifty Index," Papers 1605.07278, arXiv.org.
    2. Xiaojie Xu, 2018. "Causal structure among US corn futures and regional cash prices in the time and frequency domain," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(13), pages 2455-2480, October.
    3. Fathi Abid & Bilel Kaffel, 2018. "The extent of virgin olive-oil prices’ distribution revealing the behavior of market speculators," Review of Quantitative Finance and Accounting, Springer, vol. 50(2), pages 561-590, February.
    4. Kong Ao & Zhu Hongliang, 2018. "Predicting Trend of High Frequency CSI 300 Index Using Adaptive Input Selection and Machine Learning Techniques," Journal of Systems Science and Information, De Gruyter, vol. 6(2), pages 120-133, April.
    5. Geetu Aggarwal & Navdeep Aggarwal, 2021. "Risk-adjusted Returns from Statistical Arbitrage Opportunities in Indian Stock Futures Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(1), pages 79-99, March.
    6. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "“Regional tourism demand forecasting with machine learning models: Gaussian process regression vs. neural network models in a multiple-input multiple-output setting"," IREA Working Papers 201701, University of Barcelona, Research Institute of Applied Economics, revised Jan 2017.
    7. Xing, Jieli & Zhang, Yongjie & Chu, Gang & Pan, Qi & Zhang, Xiaotao, 2021. "Detection and reconstruction of catastrophic breaks of high-frequency financial data with local linear scaling approximation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).

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