This paper investigates the predictability of stock market movements using text data extracted from the social media platform, Twitter. We analyse text data to determine the sentiment and the emotion embedded in the Tweets and use them as explanatory variables to predict stock market movements....

This research examines the structural properties of the macroscopic model introduced in [AlShelahi and Saigal, 2018]. We present a theoretical analysis of the behavior of the macroscopic variables. In particular, we show that the model exhibits shock-like solutions, providing a new narrative for...

In this paper, we examine the performance of three DeMark indicators (Sequential, Combo and Setup trend), which constitute specific implementations of technical analysis often used by practitioners, over twenty-one commodity futures markets and ten years of daily data. Our work addresses price...

This paper applies Markov-switching multifractal (MSM) processes to model and forecast carbon dioxide (CO2) emission price volatility, and compares their forecasting performance to the standard GARCH, fractionally integrated GARCH (FIGARCH) and the two-state Markov-switching GARCH (MS-GARCH)...

In this work we use Recurrent Neural Networks and Multilayer Perceptrons, to predict NYSE, NASDAQ and AMEX stock prices from historical data. We experiment with different architectures and compare data normalization techniques. Then, we leverage those findings to question the efficient-market...