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Forecasting Stock Indices: Stochastic and Artificial Neural Network Models

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Abstract

In recent years, there has been a bloom in the stock investors due to availability of various platforms that have provided an opportunity even for small scale investors to earn profits from the market. However, due to very high uncertainty, bad investments can lead to large financial losses and hence need for tools that can predict stock behaviour, arises. The main objective of this article is to provide a comparative empirical analysis of stochastic models with artificial neural networks in the prediction of stock indices across different markets. We consider three types of models, namely the time series models: autoregressive integrated moving average and autoregressive fractionally integrated moving average; jump diffusion models: Merton jump diffusion and Kou jump diffusion; the artificial neural network models: feed-forward network and the long short term memory. These models are used to forecast 10, 20 and 30 days ahead prices of major stock indices across different markets which include both developed and emerging economies. It is shown that the long short-term memory performs better than other considered models on most of the considered indices over all the time horizons. The results also indicate the forecasts provided by the LSTM model are significant from both statistical point of view and can possibly be used for profitable investments.

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Data availability

All the data used in the study is freely available on yahoo finance, see the link: https://finance.yahoo.com/. Data can also be accessed using the url: https://drive.google.com/drive/folders/1D-RSp9MmCvynUZNPhuGJ0amW0gN9WCFX?usp=sharing

Notes

  1. https://finance.yahoo.com/. The data can also be accessed using the link: https://drive.google.com/drive/folders/1D-RSp9MmCvynUZNPhuGJ0amW0gN9WCFX?usp=sharing.

Abbreviations

AR:

Autoregression

MA:

Moving average

ARMA:

Autoregressive moving average

ARIMA:

Autoregressive integrated moving average

ARFIMA:

Autoregressive fractionally integrated moving average

LRD:

Long range dependent

MJD:

Merton jump diffusion

KJD:

Kou jump diffusion

GBM:

Geometric Brownian motion

ANN:

Artificial Neural Networks

FNN:

Feedforward Neural Network

LSTM:

Long short term memory

DJIA:

Dow Jones Industrial Average

Nikkei 225:

Nikkei 225 Stock Average

NIFTY 50:

National Stock Exchange 50

SENSEX:

Stock Exchange Sensitive Index

ASX 300:

Australian Securities Exchange 300

SP 500:

Standard’s and Poors 500

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Acknowledgements

The first author would like to thank University Grants Commission, India for supporting his PhD research. This work was partially supported by the FIST program of the Department of Science and Technology, Government of India, Reference No. SR/FST/MS-I/2018/22(C). The authors would like to thank the reviewers for helpful comments and suggestions which have lead to the improvements in the paper.

Funding

The first author would like to thank University Grants Commission (UGC), India for the research funding.

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  1. All authors have contributed equally to this work.

Authors and Affiliations

  1. Department of Mathematics, Indian Institute of Technology Ropar, Nangal Road, Ropar, Punjab, 140001, India

    Naman Krishna Pande, Arun Kumar & Arvind Kumar Gupta

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  1. Naman Krishna Pande
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  2. Arun Kumar
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Pande, N.K., Kumar, A. & Gupta, A.K. Forecasting Stock Indices: Stochastic and Artificial Neural Network Models. Comput Econ (2024). https://doi.org/10.1007/s10614-024-10615-3

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