Portfolio selection is a resource allocation problem in a finance market. The investor’s asset optimization requires the distribution of a set of capital (resources) among a set of entities (assets) with the trade-off between risk and return. The ANN with nonlinear capability is proven to solve a large-scale complex problem effectively. It is suitable to solve NP-hard resource allocation proble...

Classical statistical models can solve the problem of portfolio optimization and can determine the efficient frontier of investment when there are few investable assets and constraints. But these models cannot easily solve optimization problems when we consider real-world constraints. Therefore, data mining techniques such as evolutionary algorithms are important in portfolio optimization. The ...

In this analysis of the risk and return of stocks in global markets, we apply several applications of robust regression techniques in producing stock selection models and several optimization techniques in portfolio construction in global stock universes.We find that (1) that robust regression applications are appropriate for modeling stock returns in global markets; and (2) mean-variance techn...

This paper investigates the price adjustment and lead-lag relations between returns on five sizebased portfolios in the Taiwan stock market. It finds evidence that the price adjustment of smallstock portfolios is not slower than that of large-stock portfolios. Additionally, limited evidence supports a positive leading role of large-stock portfolio returns over small-stock portfolio returns. The...

A portfolio optimal model with cardinality constraints is researched, in which the minimum of Value-at-Risk is taken as the objective function. We give a hybrid differential evolution algorithm to solve the model and make the case study with sixteen alternative stocks from Shanghai and Shenzhen stock market. The numerical results show that the given model is reasonable and the given algorithm i...

We consider the optimal portfolio problem of a power investor who wishes to allocate her wealth between several credit default swaps (CDSs), a stock index, and a money market account. We model contagion risk among the reference entities in the portfolio using a reduced form Markovian model with interacting default intensities. Using the dynamic programming principle, we establish a lattice depe...

The central message of this paper is that nobody should be using the sample covariance matrix for the purpose of portfolio optimization. It contains estimation error of the kind most likely to perturb a mean-variance optimizer. In its place, we suggest using the matrix obtained from the sample covariance matrix through a transformation called shrinkage. This tends to pull the most extreme coeff...

Evolutionary algorithms consist of several heuristics able to solve optimization tasks by imitating some aspects of natural evolution. In the field of computational finance, this type of procedures, combined with neural networks, swarm intelligence, fuzzy systems and machine learning has been successfully applied to a variety of problems, such as the prediction of stock price movements and the ...

Let X ∈ R denote a random stock market return vector, where Xj is the value of a one unit investment in stock j at the end of the trading day. We require that Xj ≥ 0 for j = 1, 2, . . . , m, that is, an investor cannot lose more than the invested capital. Let b, bj ≥ 0, ∑m j=1 bj = 1, denote a portfolio, that is, an allocation of investor’s capital across the investment alternatives. Let B deno...

A requirement analysis for a portfolio management in stock trading is presented. This provides a theoretical foundation for a stock trading system. The overall portfolio management tasks include eliciting user profiles, collecting information on the user’s initial portfolio position, monitoring the environment on behalf of the user, and making decision suggestions to meet the user’s investment ...