The Prediction of a dynamic, volatile and unpredictable stock market has been a challenging issue for the researchers over the past few years. This paper discusses stock market related technical indicators, computing mathematical models , most preferred algorithms used in data science industries...

The stock market is notorious for its intense uncertainty and instability, and researchers and investors alike often try a detailed and useful way to direct their stock trading. Long short-term memory (LSTM) neural networks are a subtype of Recurrent neural networks (RNNs) having significant...

Stock market prediction has always caught the attention of many analysts and researchers. Popular theories suggest that stock markets are essentially a random walk and it is a fool’s game to try and predict them. Predicting stock prices is a challenging problem in itself because of the number...

In this study the ability of artificial neural network (ANN) in forecasting the daily NASDAQ stock exchange rate was investigated. Several feed forward ANNs that were trained by the back propagation algorithm have been assessed. The methodology used in this study considered the short-term...

We examine return predictability with machine learning in 46 international stock markets. We calculate 148 stock characteristics and use them to feed a repertoire of different models. The algorithms extract predictability mainly from simple, yet popular, factor types—such as momentum,...