In the traditional portfolio model, investors calculate the expected return of assets and the covariance matrix for optimal asset allocation. This paper divides market sentiment period into three states and selectes the securities in the Chinese stock market to construct portfolios. We implement both the Fama-French five-factor model and the robust median covariance matrix approach for predicti...

Research on stock price prediction and trading model using evolutionary computation has been done in recent years. As we know, prediction in the stock market is quite difficult for a number of reasons. First, the ultimate goal of our research is not to minimize the prediction error, but to maximize the profits. Second, the weak relationships among variables tend to be nonlinear, and they may ho...

Portfolio selection represents a challenge where investors look for the best firms of the market to be selected. This research presents a real world application at the Mexican Stock Exchange (La Bolsa) using a set of heuristic algorithms for portfolio selection. The heuristic algorithms (random, genetic, greedy, hill-climbing and simulated annealing) were implemented based on the Markowitz Mode...

This paper presents a model of portfolio choice and stock trading volume with lossaverse investors. The demand function for risky assets is discontinuous and non-monotonic: as wealth rises beyond a threshold investors follow a generalized portfolio insurance strategy. This behavior is consistent with the evidence in favor of the disposition effect. In addition, loss-averse investors will not ho...

The portfolio selection is an important technique to decrease the risk in stock investment. In portfolio selection, the investor’s property is distributed among a set of stocks in order to minimize the financial risk in market downturns. With this in mind, and aiming to develop a tool to assist the investor in finding balanced portoflios, we achieved a generic method for feature clustering with...

In this paper a portfolio optimization algorithm based on Evolution Strategies is presented. This method makes use of artificial trading experts discovered earlier by a genetic algorithm. These experts, consisting of technical analysis rules, are trained to process financial time series and to generate trading advice. Evolution Strategies lead to the optimization of portfolio structures where i...

where u1 is the proportion of the portfolio held in stocks, and u2 is the consumption rate. Evidently it is natural to take 0 ≤ u1 ≤ 1 and u2 ≥ 0, but in fact we shall allow −∞ < u1 <∞. If u1 > 1 then the cash part of the portfolio is negative, so we are borrowing money to buy stocks. If u1 < 0 then the stock part of the portfolio is negative, so we are so called shorting the stock. The goal is...