In this literature review, we investigate machine learning techniques that are applied for
stock market prediction. A focus area in this literature review is the stock markets …

We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …

Plucking pivotal years from history can be a deceptive enterprise. Certain dates can accrue
a reputation for being decisive, which is then very difficult to dislodge from the historical …

We propose several econometric measures of connectedness based on principal-
components analysis and Granger-causality networks, and apply them to the monthly …

Elon Musk, one of the richest individuals in the world, is considered a technological
visionary and has a social network of over 110 million followers on social media platform …

We analyze the predictability of the bitcoin market across prediction horizons ranging from 1
to 60 min. In doing so, we test various machine learning models and find that, while all …

Stock market prediction is one of the most challenging problems which has been distressing
both researchers and financial analysts for more than half a century. To tackle this problem …

Half of all Americans have money in the stock market, yet economists can't agree on whether
investors and markets are rational and efficient, as modern financial theory assumes, or …

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …

Multifractality is ubiquitously observed in complex natural and socioeconomic systems.
Multifractal analysis provides powerful tools to understand the complex nonlinear nature of …