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Modified Grey Wolf Optimizer with Sparse Autoencoder for Financial Crisis Prediction in Small Marginal Firms by Rajib Bhattacharya; Kafila; Somanchi Hari Krishna; Bhadrappa Haralayya; Pooja Nagpal; Chitsimran is a scholarly article available to read on EtoBox.

What is Modified Grey Wolf Optimizer with Sparse Autoencoder for Financial Crisis Prediction in Small Marginal Firms about?

S mall marginal firms play an important role in the economy, and their failure has widespread consequences. As a result, it is critical that small marginal firms be capable of predicting financial crises in order to mitigate their negative impact. Financial Crisis Prediction (FCP) is the process of recognizing the possibility of a future financial crisis. FCP is an important task for financial institutions, policymakers, and investors because it helps them to prepare for and mitigate the negative impact of financial crisis. Machine learning (ML) approaches are used to forecast financial crisis in small and marginal firms. It is completed by training a model on historical data and using it to predict the likelihood of a future financial crisis. As a result, this article proposes a Modified Grey Wolf Optimizer with S parse Autoencoder (MGWO -S AE) for predicting financial crisis situations in small marginal firms. The goal of the MGWO-S AE technique is to forecast financial crisis effectively in small marginal firms. To accomplish this, the proposed MGWO-S AE technique employs data preprocessing to convert the input financial data into the correct format. The MGWO-S AE technique empl

Author

Rajib Bhattacharya; Kafila; Somanchi Hari Krishna; Bhadrappa Haralayya; Pooja Nagpal; Chitsimran

Publisher

IEEE

Published

2023

Language

EN