Present market instabilities have prompted great interest on the characteristics of specific portfolios such as minimum variance and equally- weighted risk contribution portfolios as these portfolios do not rely on the estimate of expected returns. Indeed, in turmoil periods traditional market...

Volatility is usually considered as a synonym for risk. Mainstream financial theory states that higher portfolio volatility is translated into higher expected returns while diversification helps eliminate idiosyncratic risks. This leaves us with an apparent anomaly as low-risk (low-beta) stocks...

We use minority game model to investigate evolutionary dynamics in complex networks of adaptive agents competing for limited resources. We show that the dynamics and the associated phase structures critically depend on the underlying network organizations, and evolution is a key mechanism for...

We solve the problem of mean-variance hedging for general semimartingale models via stochastic control methods. After proving that the value process of the associated stochastic control problem has a quadratic structure, we characterise its three coefficient processes as solutions of...

We investigate the optimal investment problem when the interest rate is stochastic and the investor must pay proportional transaction costs when buying and selling the risky assets. We first consider a portfolio of bonds and transaction costs.We then add a stock to the portfolio, and analyze the...

This paper empirically examines the impact of dependence structure between the assets on the portfolio optimization, composed of Tehran Stock Exchange Price Index and Borsa Istanbul 100 Index. In this regard, the method of the Copula family functions is proposed as powerful and flexible tool to...

We propose a simple non-equilibrium model of a financial market as an open system with a possible exchange of money with an outside world and market frictions (trade impacts) incorporated into asset price dynamics via a feedback mechanism. Using a linear market impact model, this produces a...

Is there an informational gain by training a Deep Reinforcement Learning agent for automated stock trading using other time series than the one to be traded? In this work, we implement a DRL algorithm in a solid framework within a model-free and actor-critic approach and learn it with 21 global...

We solve the problems of mean-variance hedging (MVH) and mean-variance portfolio selection (MVPS) under restricted information. We work in a setting where the underlying price process S is a semimartingale, but not adapted to the filtration G which models the information available for...

A model captures a community consensus on a coherent field of knowledge, serving as a cumulative benchmark that can guide both research and application design, while also focusing efforts to extend or review it. Here we propose to develop this model for cognitive trading systems, computational...