Enhanced Stock Market Prediction Using Hybrid LSTM Ensemble

Title

Enhanced Stock Market Prediction Using Hybrid LSTM Ensemble

Creator

Roy R.P.; Thiruthuvanathan M.M.

Description

Stock market value prediction is the activity of predicting future market values so as to increase gain and profit. It aids in forming important financial decisions which help make smart and informed investments. The challenges in stock market predictions come due to the high volatility of the market due to current and past performances. The slightest variation in current news, trend or performance will impact the market drastically. Existing models fall short in computation cost and time, thereby making them less reliable for large datasets on a real-time basis. Studies have shown that a hybrid model performs better than a stand-alone model. Ensemble models tend to give improved results in terms of accuracy and computational efficiency. This study is focused on creating a better yielding model in terms of stock market value prediction using technical analysis, and it is done by creating an ensemble of long short-term memory (LSTM) model. It analyzes the results of individual LSTM models in predicting stock prices and creates an ensemble model in an effort to improve the overall performance of the prediction. The proposed model is evaluated on real-world data of 4 companies from Yahoo Finance. The study has shown that the ensemble has performed better than the stacked LSTM model by the following percentages: 21.86% for the Tesla dataset, 22.87% for the Amazon dataset, 4.09% for Nifty Bank and 20.94% for the Tata dataset. The models implementation has been justified by the above results. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Lecture Notes on Data Engineering and Communications Technologies, Vol-114, pp. 49-61.

Date

2022-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Deep learning; LSTM; Stock prediction

Coverage

Roy R.P., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bengaluru, India; Thiruthuvanathan M.M., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bengaluru, India

Rights

Restricted Access

Relation

ISSN: 23674512

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1007/978-981-16-9416-5_4

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