Role of proliferation COVID-19 media chatter in predicting Indian stock market: Integrated framework of nonlinear feature transformation and advanced AI.

The outbreak of the COVID-19 pandemic has transpired the global media to gallop with reports and news on the novel Coronavirus. The intensity of the news chatter on various aspects of the pandemic, in conjunction with the sentiment of the same, accounts for the uncertainty of investors linked to financial markets. In this research, Artificial Intelligence (AI) driven frameworks have been propounded to gauge the proliferation of COVID-19 news towards Indian stock markets through the lens of predictive modelling.

Can financial stress be anticipated and explained? Uncovering the hidden pattern using EEMD-LSTM, EEMD-prophet, and XAI methodologies.

Global financial stress is a critical variable that reflects the ongoing state of several key macroeconomic indicators and financial markets. Predictive analytics of financial stress, nevertheless, has seen very little focus in literature as of now. Futuristic movements of stress in markets can be anticipated if the same can be predicted with a satisfactory level of precision.

COVID-19 news and the US equity market interactions: An inspection through econometric and machine learning lens.

This study investigates the impact of COVID-19 on the US equity market during the first wave of Coronavirus using a wide range of econometric and machine learning approaches. To this end, we use both daily data related to the US equity market sectors and data about the COVID-19 news over January 1, 2020-March 20, 2020. Accordingly, we show that at an early stage of the outbreak, global COVID-19s fears have impacted the US equity market even differently across sectors.

A residual driven ensemble machine learning approach for forecasting natural gas prices: analyses for pre-and during-COVID-19 phases.

The natural gas price is an essential financial variable that needs periodic modeling and predictive analysis for many practical implications. Macroeconomic euphoria and external uncertainty make its evolutionary patterns highly complex. We propose a two-stage granular framework to perform predictive analysis of the natural gas futures for the USA (NGF-USA) and the UK natural gas futures for the EU (NGF-UK) for pre-and during COVID-19 phases.