This paper applies machine learning algorithms to the modeling of realized betas for the purposes of forecasting stock systematic risk. Forecast horizons range from 1 week up to 1 month. The machine learning algorithms employed are ridge regression, decision tree learning, adaptive boosting,...

This paper develops several efficient machine learning models and compare their performance in forecasting the value and direction of stock prices and indices from the ASEAN countries. Although all models adequately forecast the stock indices ranging from 40% to 95% accuracy and outperform...

Initial coin offerings (ICOs) provide a platform of tokens to the public as a way of crowdfunding, primarily to start-ups in cryptocurrencies. This empirical study is one of the first to analyse the determinants of ICO success and post-ICO returns which in recent years allowed start-ups to raise...