This paper investigates the impact of news media information on improving short-term GDP growth forecasts by analyzing a large and unique corpus of 12.4 million news articles spanning from 1991 to 2018. We extract business cycle-related sentiment from each article using an annotated dataset from...

We study how the development of Artificial Intelligence (AI) influences the distribution of income between capital and labor and how this, in turn, exacerbates geographic income inequality. To investigate this issue, we first build a theoretical framework and then analyze data from European...

Over the past four decades, automation technologies have replaced routine tasks performed by medium-skilled workers, and contributed to increased labor market polarization. With the advent of artificial intelligence, this dynamic may have shifted, extending task substitution to non-routine tasks...

This paper employs wavelet coherence, Cross-Quantilogram (CQ), and Time-Varying Parameter Vector-Autoregression (TVP-VAR) estimation strategies to investigate the dependence structure and connectedness between investments in artificial intelligence (AI) and eight different energy-focused...

Modeling implied volatility (IV) is important for option pricing, hedging, and risk management. Previous studies of deterministic implied volatility functions (DIVFs) propose two parameters, moneyness and time to maturity, to estimate implied volatility. Recent DIVF models have included factors...

This paper applies deep learning models to predict Bitcoin price directions and the subsequent profitability of trading strategies based on these predictions. The study compares the performance of the convolutional neural network-long short-term memory (CNN-LSTM), long- and short-term...