As the number of publicly traded companies as well as the amount of their financial data grows rapidly and improvements in hardware infrastructure and information processing technologies enable high-speed processing of large amounts of data, it is highly desired to have tracking, analysis, and eventually stock selections automated. Machine learning has already attained an important place in tra...

In this note, we develop stock option price approximations for a model which takes both the risk o default and the stochastic volatility into account. We also let the intensity of defaults be influenced by the volatility. We show that it might be possible to infer the risk neutral default intensity from the stock option prices. Our option price approximation has a rich implied volatility surfac...

Social media-based forecasting has received significant attention from academia and industries in recent years. With a focus on Twitter, this paper investigates whether sentiments of the tweets regarding the 7 largest US financial service companies (in U.S. dollars) are related to the stock price changes of these companies. The authors’ findings indicate, in the financial services context, nega...

This paper is to explore the possibility to use alternative data and artificial intelligence techniques to trade stocks. The efficacy of the daily Twitter sentiment on predicting the stock return is examined using machine learning methods. Reinforcement learning(Q-learning) is applied to generate the optimal trading policy based on the sentiment signal. The predicting power of the sentiment sig...

this research aimed to explain idea submitting behavior in organization suggestion system in telecommunication company of khorasan-razavi province. social cognitive theory (sct) was used to conceptualize a research model as the basis for our research. for this purpose, a theoretical model designed and developed to investigate and explain the relationships between contextual factors, personal pe...