An Efficient Multi-Objective Optimization-Based Framework for Stock Market Prediction

An Efficient Multi-Objective Optimization-Based Framework for Stock Market Prediction
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© 2022 by IJETT Journal
Volume-70 Issue-2
Year of Publication : 2022
Authors : Jyothi R, Dr Krishnamurthy G. N.
DOI :  10.14445/22315381/IJETT-V70I2P235

How to Cite?

Jyothi R, Dr Krishnamurthy G. N., "An Efficient Multi-Objective Optimization-Based Framework for Stock Market Prediction," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 310-318, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I2P235

Abstract
Stock prediction is an important parameter for all business applications to improve business deals. To analyze the reason for the available stocks, the stocks should be estimated in the primary section. Several statistical and neural models were implemented to meet these issues, but the data`s complexity can still reduce the prediction outcomes. So, Long Short term memory (LSTM) has been introduced. However, the LSTM model has observed the worst results in some cases by consuming more time and less prediction accuracy. The current work has focused on designing a novel Artificial Bee and Buffalo-based Recurrent LSTM (AB-BRL) for the stock prediction framework to enhance the stock forecasting exactness score up to the desired level. In addition, to analyze the stability of the designed model, different error statistics were measured and compared with other models. Besides, the planned design is executed in the python environment. Finally, the novel AB-BRL has gained a good outcome by reducing the error percentage and maximizing the forecasting accuracy.

Keywords
Stock Prediction, Mean Square Error, Big Data, Hybrid Optimization, Stock Prediction Accuracy.

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