This paper presents a computational approach for predicting the S&P CNX Nifty 50 Index. A neural network based model has been used in predicting the direction of the movement of the closing value for the next day of trading. The model presented in the paper also confirms that it can be used to...

This paper shows that CEO tweets contain informational content on the U.S. stock markets and provide investors with value-relevant information on predicting the stock price movement. We create a large, unique sample of CEO users on Twitter, extract hashtags and sentiments that can be used as...

Echo State Neural Networks (ESN) were applied to forecast the realized variance time series of 19 major stock market indices. Symmetric ESN and asymmetric AESN models were constructed and compared with the benchmark realized variance models HAR and AHAR that approximate the long memory of the...

This paper develops textual sentiment measures for China's stock market by extracting the textual tone of 60 million messages posted on a major online investor forum in China from 2008 to 2018. We conduct sentiment extraction by using both conventional dictionary methods based on customized word...

We study the non-linear causal relation between uncertainty-due-to-infectious-diseases and stock-bond correlation. To this end, we use high-frequency 1-min data to compute daily realized measures of correlation and jumps, and then, we employ a nonlinear Granger causality test with the use of...

For stock market predictions, the essence of the problem is usually predicting the magnitude and direction of the stock price movement as accurately as possible. There are different approaches (e.g., econometrics and machine learning) for predicting stock returns. However, it is non-trivial to...

Global financial markets frequently experience extreme volatility, which poses significant challenges in forecasting stock returns, particularly following market crashes. Traditional models often falter under these conditions due to heightened investor sentiment and strong regulatory...

We propose an ensemble of Long-Short Term Memory (LSTM) Neural Networks for intraday stock predictions, using a large variety of Technical Analysis indicators as network inputs. The proposed ensemble operates in an online way, weighting the individual models proportionally to their recent...