COVID-19 has had significant impact on US stock market volatility. This study focuses on
understanding the regime change from lower to higher volatility identified with a Markov …

Stock price prediction is a rich research topic that has attracted interest from various areas of
science. The recent success of machine learning in speech and image recognition has …

In this paper, we compare various approaches to stock price prediction using neural
networks. We analyze the performance fully connected, convolutional, and recurrent …

Forecasting directional movement of stock price using machine learning tools has attracted
a considerable amount of research. Two of the most common input features in a directional …

Stock price prediction has been the focus of a large amount of research but an acceptable
solution has so far escaped academics. Recent advances in deep learning have motivated …

We provide detailed descriptions, including over 550 mathematical formulas, for over 150
trading strategies across a host of asset classes (and trading styles). This includes stocks …

We examine an application of machine learning to exchange traded fund investments in the
US market. To find how the changes in exchange traded fund prices are associated with …

We show how one can use deep neural networks with macro-economic data in conjunction
with price-volume data in a walk-forward setting to do tactical asset allocation. Low cost …

We propose a hybrid system for supporting complex decisions through integrating neural
networks (NNs) and genetic algorithms (GAs). We investigate the feasibility of leveraging the …

We examine options trading in leveraged Exchange‐Traded Funds (ETFs) and their impact
on the performance of the underlying funds. Using implied volatility innovations in call and …