AI and data driven solutions have been applied to different fields with outperforming and promising results. In this research work we apply k-Nearest Neighbours, eXtreme Gradient Boosting and Random Forest classifiers to direction detection problem of three cryptocurrency markets. Our input data includes price data and technical indicators. We use these classifiers to design a strategy to trade in those markets. Our test results on unseen data shows a great potential for this approach in helping investors with an expert system to exploit the market and gain profit. Our highest gain for an unseen 66 day span is 860 dollars per 1800 dollars investment. We also discuss limitations of these approaches and their potential impact to Efficient Market Hypothesis.

We propose a fast and flexible method to scale multivariate return volatility predictions up to high-dimensions using a dynamic risk factor model. Our approach increases parsimony via time-varying sparsity on factor loadings and is able to sequentially learn the use of constant or time-varying parameters and volatilities. We show in a dynamic portfolio allocation problem with 455 stocks from the S&P 500 index that our dynamic risk factor model is able to produce more stable and sparse predictions, achieving not just considerable portfolio performance improvements but also higher utility gains for the mean-variance investor compared to the traditional Wishart benchmark and the passive investment on the market index.

We study generalized backward stochastic differential equations (BSDEs) up to a random time horizon $\vartheta$, which is not a stopping time, under minimal assumptions regarding the properties of $\vartheta$. In contrast to existing works in this area, we do not impose specific assumptions on the random time $\vartheta$ and we study the existence of solutions to BSDEs and reflected BSDEs with a random time horizon through the method of reduction. In addition, we also examine BSDEs and reflected BSDEs with a l\`adl\`ag driver where the driver is allowed to have a finite number of common jumps with the martingale part.

The paper presents the results of a behavioral experiment conducted between February 2020 and March 2021 at Universit\`a Cattolica del Sacro Cuore, Milan Campus in which students were matched to either a human or a humanoid robotic partner to play an iterated Prisoner's Dilemma. The results of a Logit estimation procedure show that subjects are more likely to cooperate with human rather robotic partners; that are more likely to cooperate after receiving a dialogic verbal reaction following the realization of a sub-obtimal social outcome; that the effect of the verbal reaction is independent on the nature of the partner. Our findings provide new evidence on the effect of verbal communication in strategic frameworks. Results are robust to the exclusion of students of Economics related subjects, to the inclusion of a set of psychological and behavioral controls, to the way subjects perceive robots' behavior and to potential gender biases in human-human interactions.

The study introduces an automated trading system for S\&P500 E-mini futures (ES) based on state-of-the-art machine learning. Concretely: we extract a set of scenarios from the tick market data to train the models and further use the predictions to statistically assess the soundness of the approach. We define the scenarios from the local extrema of the price action. Price extrema is a commonly traded pattern, however, to the best of our knowledge, there is no study presenting a pipeline for automated classification and profitability evaluation. Additionally, we evaluate the approach in the simulated trading environment on the historical data. Our study is filling this gap by presenting a broad evaluation of the approach supported by statistical tools which make it generalisable to unseen data and comparable to other approaches.

An evolutionary game model is developed to study the interplay between consumers and producers when trade takes place on an e-commerce marketplace. The type of delivery service available and consumers' taste are particularly important regarding both game payoffs and players' strategies. The game payoff matrix is then adapted to analyse the different trading patterns that were developed during the COVID-19 pandemic in both the traditional retail and e-commerce sectors. In contrast to the former, investment in logistics and warehouses in the e-commerce sector allowed for the emergence of a trend in which fast delivery and eager consumers are becoming the norm.

This paper studies time-inconsistent stopping control problems under general multi-dimensional controlled diffusion model. We first formulate the time-inconsistent stopping control problems and propose a formal definition of their equilibriums. This definition for equilibriums is consistent with the ones in pure control/stopping problems and thus can be seen as nontrivial extension to the existing theory. We show that an admissible pair $(\hat{u},C)$ of control-stopping policy is equilibrium if and only if the axillary function associated to it solves the extended Hamiltonian system. We provide almost equivalent conditions to the boundary term of this Hamiltonian system, which is basically the celebrated smooth fitting principles. In this paper we further reformulate them and propose strong and weak smooth fitting principles. We also give one concrete example that illustrates our theoretical results.