A Literature Review on Machine Learning Applications in Financial Forecasting

Home > 2020, Vol. 11 No. 01 > A Literature Review on Machine Learning Applications in Financial Forecasting

Published:

Authors

Muskaan, Pradeepta Kumar Sarangi

DOI Number
https://doi.org/https://doi.org/10.15415/jtmge.2020.111004

Keywords
Time-series analysis, Machine learning, Financial forecasting

Abstract

Analyzing the past trend and predicting the future movement is an important aspect for every business. Knowing the future value makes an organization more efficient in planning specially if it is related to financial factors. This can be achieved by analyzing the historical data of the company. This is called time series analysis. The increased application of computer and information technologies, has made this more effective and accurate which is called machine learning. Methods of Machine Learning (ML) have been proposed as alternative approach to statistical metthods by many researchers in their academic literature. This paper presents a review of the works where the authors have used machine learning techniques in financial forecasting.

References

Aiken, M. (1999). Using a Neural Network to Forecast Inflation. Journal of Industrial Management & Data Systems, 99(7), 296–301.

Bano, S. & Sarangi, P. K, (2014). Future Trend in Indian Automobile Industry: A Statistical Approach”, Apeejay–Journal of Management Sciences and Technology, 1(2), 28-32.

Choudhary, A. and Haider, A. (2003). Neural Network Models for Inflation Forecasting: An Appraisal. Discussion Paper.

Guan, H., Dai Z, Zhao, A & He. J (2018). A Novel Stock Forecasting Model Based on Highorder-fuzzy-fluc-tuation Trends and Back Propagation Neural Net¬work. PLoS ONE, 13(2), e0192366. https://doi.org/10.1371/journal.pone.0192366

Gupta, A.K, & Sarangi, P.K, (2012). Electrical Load Forecasting using Genetic Algorithm Based Back-propagation Method, Journal of Engineering and Applied Sciences, 7(8), 1017-1020.

Guresen, E., Kayakutlu, G. & Tugrul, U. D. (2011). Using Artificial Neural Network Models in Stock Market Index Prediction, Expert Systems with Applications, 38, 10389-10397.

Kara, Y., Boyacioglu, M. A., & Baykan, O. K. (2011). Pre¬dicting Direction of Stock Price Index Movement Using Artificial Neural Networks and Support Vec¬tor Machines: The Sample of the Istanbul Stock Exchange. Expert Systems with Applications, 38, 5311- 5319.

Maditinos, D. (2016). The Use of Neural Networks in Forecasting. Review of Economic Sciences, 6, 161-176.

McAdam, P. and McNelis, P. (2005). Forecasting Inflation with Thick Models and Neural Networks, Economic Modeling, 22(5), 848–867. https://doi.org/10.1016/j.econmod.2005.06.002

Moghaddam, A. H. (2016). Stock market index prediction using artificial neural network. Journal of Economics, Finance and Administrative Science, 21(41), 89-93. http://dx.doi.org/10.1016/j.jefas.2016.07.002

Moshiri, S. & Cameron, M. (2000). Neural network ver¬sus econometric models in forecasting inflation. Journal of Forecasting, 19(3), 201–217.

Muskaan and Sarangi, P.K. (2020). NSE Stock Prediction Using ANN Models. International Journal of Control and Automation, 13(4), 552–559.

Özgür & Taha B. (2017). Stock Market Prediction Per¬formance of Neural Networks: A Literature Review. International Journal of Economics and Finance, 9(11), 100-108. https://dx.doi.org/10.5539/ijef.v9n11p100

Pahwa, N., & Vora, D. (2017). Stock Prediction using Machine Learning a Review Paper. International Journal of Computer Applications, 163(5), 36-43. http://dx.doi.org/10.5120/IJCA2017913453

Qiu, M., Song, Y., & Chaos F. A. (2016). Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market.Proceedings of the World Congress on Engineering and Computer Science, WCECS 2017, 1, 1-7.

Sarangi et al., (2010). Load Forecasting Using Artificial Neural Network: Performance Evaluation with Different Numbers of Hidden Neurons. IUP Journal of Information Technology, 6(1), 34-42.

Sarangi et al., (2010). Short-Term Load Forecasting Using Neural Network Technology. IUP Journal of Computer Sciences, 4(2), 109-116.

Shaikh, M. & Gyankamal, J. C. (2012). Global Journal of Computer Science and Technology Neural & Artificial Intelligence. Review on Financial Forecasting using Neural Network and Data Mining Technique, 12(11), 0975-4350.

Singh, N., & Singh, R. (2009). Short term load forecasting using artificial neural network: a comparison with genetic algorithm implementation. Journal of Engineering and Applied Sciences, 4(9), 88-93.

http://dx.doi.org/10.1109/ICNGIS.2016.7854003

Singh, N., & Chauhan, R.K. (2009). Short term load forecasting using neuro genetic hybrid approach: Results analysis with different network architectures. Journal of Theoretical and Applied Information, 109-116

Singh, S. & Sarangi. P.K. (2014). Growth Rate of Indian Spices Exports: Past Trend and Future Prospects. Apeejay Journal of Management Sciences and Technology, 2(1), 29-34.

Singla, C., & Sahoo, A.K, (2019). Modelling Consumer Price Index: An Empirical Analysis Using Expert Modeler, Journal of Technology Management for Growing Economies, 10(1), 43-50.

http://dx.doi.org/10.15415/jtmge.2019.101004.

Sinha, D, & Sinha, S, (2019). Forecasting Consumer Price Index using Neural Networks models. Innovative Practices in Operations Management and Information Technology, 84-93.

Sinha, D., and Sinha, S. (2019). Financial Modeling Using ANN Technologies: Result Analysis with Different Network Architectures and Parameters. Indian Journal of Research in Capital Markets, 6(1), 21-33.

Tripathi, S., and Madan, M. (2014). Use of Artificial Neural Network in Stock Exchange Market. Interna¬tional Journal of Innovations & Advancement in Computer Science, 2(4), 23-29.

How to Cite

Muskaan, Pradeepta Kumar Sarangi. A Literature Review on Machine Learning Applications in Financial Forecasting. J.Technol. Manag. Grow. Econ.. 2020, 11, 23-27

A Literature Review on Machine Learning Applications in Financial Forecasting

Current Issue

Periodicity	Biannually
Issue-1	May
Issue-2	November
ISSN Print	2321-2217
ISSN Online	2321-2225
RNI No.	CHAENG/2013/50088

OA Policy

Publisher's policy of the journal at Sherpa UK for the submitted, accepted, and published articles. Click OAPolicy

Plan-S Compliance

To check compliance, one has to use the Journal Check Tool (JCT). This tool provided by cOAlition S (European funders) for the researchers (fundee) to check the compliance with the journal.

Recommend journal to your library

You can recommend the journal being a researcher or faculty member to your library. We will post a copy of the Journal to your library on your behalf at free of cost.
Click here: Recommend Journal

Preprint Arxiv Submission

The authors are encouraged to submit the author’s copy (preprint) to appropriate preprint archives e.g. https://arxiv.org and/or on https://indiarxiv.org or institutional repositories (e.g., D Space) before paper acceptance by the editor of Journal. After publications of the paper author(s) should mention the citation information, title and abstract along with DOI number of the publication carefully on the required page of the depository(ies).

Contact: Phone: +91-172-2741000, +91-172-4691800

Email : editor.tmg@chitkara.edu.in;

Abstract and Indexing

Information

This work is licensed under a Creative Commons Attribution 4.0 International License.

Articles in Journal of Technology Management for Growing Economies(J.Technol. Manag. Grow. Econ.) by Chitkara University Publications are Open Access articles that are published with licensed under a Creative Commons Attribution- CC-BY 4.0 International License. Based on a work at https://tmg.chitkara.edu.in/. This license permits one to use, remix, tweak and reproduction in any medium, even commercially provided one give credit for the original creation.

View Legal Code of the above-mentioned license, https://creativecommons.org/licenses/by/4.0/legalcode

View Licence Deed here https://creativecommons.org/licenses/by/4.0/