In this systematic literature review, we examine the existing studies predicting realized
volatility and implied volatility indices using artificial intelligence and machine learning. We …

The Investor Sentiment Index (ISI) is widely regarded as a useful measure to gauge the
overall mood of the market. Investor panic may result in contagion, causing failure in …

We analyze the predictive value of climate risks for state-level realized stock market volatility,
computed, along with other realized moments, based on high-frequency intra-day US data …

In this paper we examine the role of machine learning (ML) in predicting stock market trends
within BRICS economies. Complex, interdependent global and regional economic factors …

The core objective of this work is to predict stock market indices' using autoregressive
integrated moving average (ARIMA), artificial neural network (ANN) and their combination in …

This article focuses on examining the effects of the COVID-19 pandemic and gold prices on
the stock market. It primarily analyzes the relationship between COVID-19 cases and stock …

This study comprehensively investigates stock market volatility based on over one hundred
monthly macroeconomic variables, applying machine learning models. Methodological …

Studying the question of whether macroeconomic predictors play a role in forecasting stock-
market volatility has a long and significant tradition in the empirical finance literature. We …

In this study, we proposed two types of hybrid models based on the heterogeneous
autoregressive (HAR) model and support vector regression (SVR) model to forecast realized …

This research examines the predictive effect of real-time investor sentiment on high-
frequency stock returns. Utilizing text sentiment analysis, we extract investor sentiment with a …