A comparative time series analysis to improve US Stock Market forecast performance by using univariate and multivariate deep learning algorithms - Mendes, D. A.; Ferreira, N. B.; Mendes, V. (CARMA20, 2020)
The prediction of complex stock dynamics is a challenging task. Prices in developed stock markets are among the most complex financial data available to model since their fluctuations are irregular, nonlinear and change dynamically with high uncertainty. The neural network models, and in particular, the LSTM algorithm, has been increasingly used by researchers for prediction and analysis of stock market data, with an important role in today’s economy. Stock market index prediction is an example, and by modifying the definitions of input variables and output of each module, the same framework as the proposed method can be applied to various deep learning-based financial problems, including financial market movement classification, volatility prediction, portfolio optimisation, and option pricing. The majority of empirical work deal with daily data since they could collect a bigger dataset to analyse. However, this issue of daily data can be largely extended to a VAR analysis after controlling for the effect of macroeconomic factors.