Abstract
The introduction of a brand-new product line predicting future stock prices is an important part of financial decision-making and investment since stock values fluctuate often. Although the value of the share may reduce due to market movements, there is still a danger of losing money. The stock price and trade volume are affected by these swings, making the forecast even more difficult. There are a wide range of methods and techniques that may be used to anticipate the stock market's behavior, and these methods and techniques can help investors respond more quickly and accurately to know when to purchase or sell the stock therefore a tremendous diversity of strategies have been created. Even though a number of strategies have been developed, none of them reliably anticipate stock prices. Stock price prediction concerns are being solved via data mining and evolutionary strategies. In data mining, the extraction of a large amount of information from a big database is called “mining”. Data mining techniques are aimed to assist investors in uncovering hidden patterns from the historical data that include plausible forecasting capability in the stock market because of the enormous volume of data. Stock market robots have been created by combining predictive analytics with data mining. Prediction models are built using historical data, which helps investors find patterns in the data and anticipate future returns. The evolutionary algorithm, on the other hand, is critical in properly projecting stock values. Evolutionary strategies have been found to outperform other parametric approaches in a number of studies. Evolutionary approaches may be used to improve more formal procedures since they are simple to apply and comprehend. They also do not suffer from the negative consequences of dimensionality.
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References
Shen J, Shafiq MO (2020) Short-term stock market price trend prediction using a comprehensive deep learning system. J Big Data 7:66. https://doi.org/10.1186/s40537-020-00333-6
Wang Y (2017) Stock price direction prediction by directly using prices data: An empirical study on the Kospi and hsi. In: arXiv.org. http://arxiv.org/abs/1309.7119. Accessed 7 May 2022
Guresen E, Kayakutlu G, Daim TU (2011) Using artificial neural network models in stock market index prediction. Expert Syst Appl 38:10389–10397. https://doi.org/10.1016/j.eswa.2011.02.068
Dutta G, Jha P, Laha AK, Mohan N (2006) Artificial neural network models for forecasting stock price index in the Bombay stock exchange. J Emerg Market Finance 5(3):283–295. https://doi.org/10.1177/097265270600500305
Dase RK, Pawar DD (2010) Application of artificial neural network for stock market predictions: a review of literature. Int J Mach Intell 2. https://doi.org/10.9735/0975-2927.2.2.14-17
Fischmann J, Bruzda W, Khoruzhenko BA et al (2012) Induced ginibre ensemble of random matrices and quantum operations. J Phys A: Math Theor 45:075203. https://doi.org/10.1088/1751-8113/45/7/075203
Imandoust SB, Bolandraftar M (2013) Application of K-nearest neighbor (KNN) approach for predicting economic events theoretical background. Int J Eng Res Appl 3:605–610
Sharma K, Sharma S, Vinod (2012) Comparative analysis of machine learning techniques in sale forecasting. Int J Comput Appl 53:51–54. https://doi.org/10.5120/8429-2198
Liao Z, Wang J (2010) Forecasting model of global stock index by stochastic time effective neural network. Expert Syst Appl 37:834–841. https://doi.org/10.1016/j.eswa.2009.05.086
Meesad P, Rasel RI (2013) Predicting stock market price using support vector regression. In: 2013 International conference on informatics, electronics and vision (ICIEV), pp 1–6. https://doi.org/10.1109/ICIEV.2013.6572570
Zhang S, Feng X (2021) Distributed identification of heterogeneous treatment effects. Comput Statistics 37:57–89. https://doi.org/10.1007/s00180-021-01114-2
Awad M, Khanna R (2015) Support vector regression, In Efficient learning machines. Apress, Berkeley, CA, pp 67–80
Nathan L, Adrian B (2010) Supervised aggregation of classifiers using artificial prediction markets. ICML 2010—Proceedings, 27th international conference on machine learning 591–598
Patel J, Shah S, Thakkar P, Kotecha K (2015) Predicting stock market index using fusion of machine learning techniques. Expert Syst Appl 42:2162–2172
Eapen J, Bein D, Verma A (2019) Novel deep learning model with CNN and bi-directional LSTM for improved stock market index prediction. In: 2019 IEEE 9th annual computing and communication workshop and conference (CCWC). IEEE, pp 0264–0270
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Kharkwal, T., Meena, S. (2023). Prediction Markets Using Machine Learning. In: Chakraborty, B., Biswas, A., Chakrabarti, A. (eds) Advances in Data Science and Computing Technologies. ADSC 2022. Lecture Notes in Electrical Engineering, vol 1056. Springer, Singapore. https://doi.org/10.1007/978-981-99-3656-4_22
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DOI: https://doi.org/10.1007/978-981-99-3656-4_22
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