A Literature Review on Machine Learning Applications in Financial Forecasting
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.
Aiken, M. (1999). Using a Neural Network to Forecast Inflation. Journal of Industrial Management & Data Systems, 99(7), 296–301. DOI: https://doi.org/10.1108/02635579910291984
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 DOI: 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. DOI: https://doi.org/10.1016/j.eswa.2011.02.068
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. DOI: https://doi.org/10.1016/j.eswa.2010.10.027
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 DOI: 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 DOI: https://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. DOI: https://doi.org/10.1002/(SICI)1099-131X(200004)19:3<201::AID-FOR753>3.0.CO;2-4
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 DOI: https://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 DOI: https://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. DOI: https://doi.org/10.1016/j.chaos.2016.01.004
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.
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.
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. DOI: https://doi.org/10.17010/ijrcm/2019/v6/i1/144039
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.
Copyright (c) 2020 Muskaan and Pradeepta Kumar Sarangi
This work is licensed under a Creative Commons Attribution 4.0 International License.