Share Market based Unemployment Prediction using Neural Networks.

Unemployment prediction is an important topic in economics and finance. The stock market is considered as one of the key indicators of economic performance. Therefore, using stock market data to predict unemployment is an interesting and valuable research area. In this paper, a neural network-based approach has been proposed for unemployment prediction using stock market data. Specifically, it uses a Recurrent Neural Network (RNN) architecture to model the time-series relationship between stock market data and unemployment rate. Moreover, S&P 500 index, the NASDAQ index, and the Dow Jones Industrial Average (DJIA) index are used as predictors for unemployment rate. The proposed model was trained and tested on historical data from 1990 to 2020. The experimental results showed that the proposed RNN model achieved an accuracy rate of 96.24% which outperformed the baseline model that used a simple linear regression algorithm. This result suggests that proposed RNN-based models provide accurate predictions of unemployment rates. Predicting unemployment rates accurately can help policymakers make informed decisions on economic policies and programs. Additionally, investors can use this prediction model to make informed decisions about their investment strategies in the stock market. [ABSTRACT FROM AUTHOR]