Application of artificial intelligence model in financial markets
Keywords:
Artificial Intelligence, Fuzzy Logic, Neural Networks, ANFIS, Financial MarketAbstract
This paper deals with the application of artificial intelligence model in financial markets. Specifically, a hybrid model based on fuzzy logic and artificial neural networks is chosen. The neuro-fuzzy model is then applied to the stock Exchange Traded Funds on the American and European stock markets. Based on four input variables and five attributes, the basis of rules is determined by neural networks. The amount of rules is then reduced by fuzzy clustering. The output is a model serving to support the decision-making for the investor whether or not to invest in the stocks of Exchange Traded Funds.References
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[2] Rajab, S., Sharma, V. (2019). An interpretable neuro-fuzzy approach to stock price forecasting. Soft Computing. 23(3), 921-936.
[3] Chen, Y. S., Cheng, C. H., Chiu, C. L., Huang, S: T. (2016). A study of ANFIS-based multi-factor time series models for forecasting stock index. Applied Intelligence, 45(2), 277-292.
[4] Poddig, T., Rehkugler, H. (1996). A ‘world’ model of integrated financial markets using artificial neural networks. Neurocomputing, 10(3), 251-273.
[5] Mahajan, K. S., Jamsandekar, S. S., Kulkain, R. V. (2015). Portfolio Investment Model Using Neuro Fuzzy Systém. International Journal of Computer Science and Information Technologies, 6(2), 1819-1823.
[6] Chandar, S. (2017). Stock market prediction using subtractive clustering for a neuro fuzzy hybrid approach. Cluster Computing, 1-8.
[7] Brzeszczyński, J, Ibrahim, B. M. (2019). A stock market trading system based on foreign and domestic information. Expert Systems with Applications, 118, 381-399.
[8] Fanita, F., Rustam, Z. (2018). Predicting the Jakarta composite index price using ANFIS and classifying prediction result based on relative error by fuzzy Kernel C-Means, 020206.
[9] Vlasenko, A., Vynokurova, O., Vlasenko, N., Peleshko, M. (2018). A Hybrid Neuro-Fuzzy Model for Stock Market Time-Series Prediction. In: 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP) IEEE, 352-355.
[10] Dostál, P., Rais, K. (2009). Stock Market Decision Machine. International Symposium on Forecasting, 1(1), 171-171.
[11] Tung, K. T., Le, M. H. (2017). An Application of Artificial Neural Networks and Fuzzy Logic on the Stock Price Prediction Problem. JOIV: International Journal on Informatics Visualization, 1(2), 40-49.
[12] Sathe, J. B., Mali, M. P. (2017). A hybrid Sentiment Classification method using Neural Network and Fuzzy Logic. In: 2017 11th International Conference on Intelligent Systems and Control (ISCO). IEEE, 93-96.
[13] Mathur, N., Glesk, I., Buis, A. (2016). Comparison of adaptive neuro-fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for the prediction of skin temperature in lower limb prostheses. Medical Engineering & Physics, 38(10), 1083-1089.
[14] Hammer, M., Janda, O., Ertl, J. (2012), Využití vybraných soft-computingových metod v diagnostice výkonových olejových transformátorů - 1. Část, Elektrorevue, vol. 14(3).
[15] Esfahanipour, A., Aghamiri, W. (2010). Adapted Neuro-Fuzzy Inference System on indirect approach TSK fuzzy rule base for stock market analysis. Expert Systems with Applications, 37(7), 4742-4748.
[16] ICI Factbook. (2018). Investment Company Institute Factbook 2016. Invesment Company Institute.
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2020-12-14
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