Machine learning algorithms for time series in financial markets

Document Type: Research Paper

Authors

1 Computer Engineering Department, Yazd University,Yazd, Iran

2 Management and Economics Department, Faculty of Economics, Yazd University,Yazd, Iran

10.22034/amfa.2020.674946

Abstract

This research is related to the usefulness of different machine learning methods in forecasting time series on financial markets. The main issue in this field is that economic managers and scientific society are still longing for more accurate forecasting algorithms. Fulfilling this request leads to an increase in forecasting quality and, therefore, more profitability and efficiency. In this paper, while we introduce the most efficient features, we will show how valuable results could be achieved by the use of a financial time series technical variables that exist on the Tehran stock market. The suggested method benefits from regression-based machine learning algorithms with a focus on selecting the leading features to find the best technical variables of the inputs. The mentioned procedures were implemented using machine learning tools using the Python language. The dataset used in this paper was the stock information of two companies from the Tehran Stock Exchange, regarding 2008 to 2018 financial activities. Experimental results show that the selected technical features by the leading methods could find the best and most efficient values for the parameters of the algorithms. The use of those values results in forecasting with a minimum error rate for stock data. 

Keywords


[1] Abbasi, E., Samavi, M.E., Koosha, E., Performance Evaluation of the Technical Analysis Indicators in Comparison with the Buy and Hold Strategy in Tehran Stock Exchange Indices, Advances in Mathematical Finance and Applications, 2020, 5(3), P. 285-301. Doi: 10.22034/AMFA.2020.1893194.1376

 

[2] Khaleghi Kasbi, P., Aghaei, M. A., Rezaei, F., Salience Theory and Pricing Stock of Corporates in Tehran Stock Exchange, Advances in Mathematical Finance and Applications, 2018, 3(4), P.1-16.

Doi: 10.22034/AMFA.2018.577140.1120

 

[3] Gupta, R., Pierdzioch, C., Selmi, R., Wohar, M. E., Does partisan conflict predict a reduction in US stock market (realized) volatility? Evidence from a quantile-on-quantile regression model, North American Journal of Economics and Finance, 2018, 43(2), P. 87–96.  Doi: 10.1016/j.najef.2017.10.006

 

[4] Nadkarni, J., Ferreira Neves, R., Combining NeuroEvolution and Principal Component Analysis to trade in the financial markets, Expert Systems with Applications, 2018, 103(1), P.184–195.

Doi: 10.14419/ijet.v7i4.21723

 

[5] Zhong, X., Enke, D., Forecasting daily stock market return using dimensionality reduction, Expert Systems with Applications, 2017, 67(1), P. 126–139. Doi: 10.1016/j.eswa.2016.09.027

 

[6] Ballings, M., Van Den Poel, D., Hespeels, N., Gryp, R., Evaluating multiple classifiers for stock price direction prediction, Expert Systems with Applications, 2015, 42(20), P. 7046–7056.

Doi: 10.1016/j.eswa.2015.05.013

 

[7] Patel, J., Shah, S., Thakkar, P., Kotecha, K., Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques, Expert Systems with Applications, 2015, 42(1), P. 259–268. Doi: 10.1016/j.eswa.2014.07.040

 

[8] Ghiassi, M., Burnley, C., Measuring effectiveness of a dynamic artificial neural network algorithm for classification problems, Expert Systems with Applications, 2010, 37(4), P. 3118–3128.

Doi: 10.1016/j.eswa.2009.09.017

 

[9] Pang, X., Zhou, Y., Wang, P., Lin, W., Chang, V., An innovative neural network approach for stock market prediction, The Journal of Supercomputing, 2018. Doi: 10.1007/s11227-017-2228-y

 

[10] Hsu, M. W., Lessmann, S., Ma, T., Johnson, J. E. V., Bridging the divide in financial market forecasting: machine learners vs. financial economists, Expert Systems with Applications, 2016, 61(1), P. 215–234.         Doi : 10.1016/j.eswa.2016.05.033

 

[11] Cavalcante, R. C., Brasileiro, R. C., Souza, V. L. F., Nobrega, J. P., Oliveira, A. L. I., Computational Intelligence and Financial Markets: A Survey and Future Directions, Expert Systems with Applications, 2016, 55(1), P. 194–211. Doi: 10.1016/j.eswa.2016.02.006

 

[12] Izadikhah, M., Improving the Banks Shareholder Long Term Values by Using Data Envelopment Analysis Model, Advances in Mathematical Finance and Applications, 2018, 3(2), P. 27-41.

Doi: 10.22034/AMFA.2018.540829

 

[13] Gurav, U., Sidnal, N., Predict Stock Market Behavior: Role of Machine Learning Algorithms, in Intelligent Computing and Information and Communication, Springer, 2018, P. 383–394. 

Doi: 10.1007/978-981-10-7245-1_38

 

[14] Chen, Y., Hao, Y., A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction, Expert Systems with Applications, 2017,  80(1), P. 340–355.

Doi: 10.1016/j.eswa.2017.02.044

 

[15] Barak, S., Modarres, M., Developing an approach to evaluate stocks by forecasting effective features with data mining methods, Expert Systems with Applications, 2015, 42(3), P. 1325–1339.

Doi: 10.1016/j.eswa.2014.09.026