Gold is often used by investors as a hedge against inflation or adverse economic times. Consequently, it is important for investors to have accurate forecasts of gold prices. This paper uses several machine learning tree-based classifiers (bagging, stochastic gradient boosting, random forests)...

Historically, exchange rate forecasting models have exhibited poor out-of-sample performances and were inferior to the random walk model. Monthly panel data from 1973 to 2014 for ten currency pairs of OECD countries are used to make out-of sample forecasts with artificial neural networks and...

This paper proposes a new combined semiparametric estimator of the conditional variance that takes the product of a parametric estimator and a nonparametric estimator based on machine learning. A popular kernel-based machine learning algorithm, known as the kernel-regularized least squares...

Climate change, green consumers, energy security, fossil fuel divestment, and technological innovation are powerful forces shaping an increased interest towards investing in companies that specialize in clean energy. Well informed investors need reliable methods for predicting the stock prices...

The paper addresses the forecasting of realised volatility for financial time series using the heterogeneous autoregressive model (HAR) and machine learning techniques. We consider an extended version of the existing HAR model with included purified implied volatility. For this extended model,...

Analyzing the success of movies has always been a popular research topic in the film industry. Artificial intelligence and machine learning methods in the movie industry have been applied to modeling the financial success of the movie industry. The new contribution of this research combined...

This paper develops ensemble machine learning models (XGBoost, Gradient Boosting, and AdaBoost in addition to Random Forest) for predicting stock returns of Indian banks using technical indicators. These indicators are based on three broad categories of technical analysis: Price, Volume, and...

This paper examines how individual religiosity at the top level of organizations affects the quality of their disclosure practices, as measured by the readability of annual reports. Our paper extends the recent accounting and finance literature that moves away from a location-based measure to an...

The research purpose of this paper is to obtain an algorithm model with high prediction accuracy for the price of Bitcoin on the next day through random forest regression and LSTM, and to explain which variables have influence on the price of Bitcoin. There is much prior literature on Bitcoin...