Work

Forecasting Equity using LSTM Value-at-Risk Estimation

Year: 2023

Type: article

Abstract: A deep learning hybrid approach (LSTM-VaR) is proposed for risk-based stock value prediction by comparing the relationship and temporal sequence of stock value data. By utilizing time in its predictions, the model can improve accuracy and reduce volatility in stock price projections. It can anticipate changes in stock market indices and develop a reliable strategy for projecting future costs while calculating normal fluctuations of indices.

Source: International Journal on Recent and Innovation Trends in Computing and Communication

DOI: 10.17762/ijritcc.v11i9s.7745

Authors: Sayem Patni, Amit R. Gadekar

Institution: Sandip University Sijoul Madhubani Bihar

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: diamond

OpenAlex ID: W4386963407

FWCI:

Cites: 22

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Related to: 10