Addressing stock market time series trends and volatility using optimised DE-LSTM model

Raghavendra Kumar, Pardeep Kumar and Yugal Kumar

International Journal of Operational Research, 2024, vol. 50, issue 4, 426-445

Abstract: Accurate time series prediction is the most challenging trend for research communities in the machine learning era. Stock market data is the most dynamic and volatile time series data that holds the world economy. Recent studies proposed various core and hybrid machine learning models to get accurate stock forecasting. Existing work put forward long short-term memory (LSTM) to implement sequential time series data. In this paper, a new nonlinear hybrid model is proposed using customised LSTM and differential evolution (DE) algorithms. DE brings the optimisation of selection of parameters and provides stability between complexity and learning performance of the hybrid model. The paper explores the forecasting accuracy of the stock market trends and volatility using hybrid model DE-LSTM. The proposed hybrid model obtained significant improvement as MAE, RMSE and MAPE are 0.21167, 2.48198 and 2.68331 respectively, practiced for a diversified portfolio of Bombay Stock Exchange, India (BSE30).

Keywords: stock market trends; volatility; time series data; long short-term memory; LSTM; differential evolution; DE. (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=140482 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijores:v:50:y:2024:i:4:p:426-445

Access Statistics for this article

More articles in International Journal of Operational Research from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().