This work aims the development of an enhanced portfolio selection method, which is based on the classical portfolio theory proposed by Markowitz (1952) and incorporates the local Gaussian correlation model for optimization. This novel method of portfolio selection incorporates two assumptions: the non-linearity of returns and the empirical observation that the relation between assets is dynamic...

We consider the optimality of portfolios not subject to short-selling constraints and derive conditions that a universe of securities must satisfy for an optimal active portfolio to be dollar neutral or beta neutral. We find that following the common practice of constraining long–short portfolios to have zero net holdings or zero betas is generally suboptimal. Only under specific unlikely condi...

this study examines the empirical validity of claims that value stocks (stocks with high ratios of book value to price) have higher average returns than growth stocks (stocks with low book-to-market ratios). the analyses are performed using data pertaining to 70 firms for the period 1381-1389 and used the panel data methodology. this paper contains significant and consistent results. the result...

return and volatility spillovers are important for portfolio selection, asset valuation and market efficiency investigation. using a var-bekk framework model, this paper investigates return and volatility spillover effects between three size-sorted equity indices in tehran stock exchange (tse). although daily return of large stocks leads small stocks (lead-lag effect), there wasn’t any spillove...

Genetic network programming (GNP) as an evolutionary computation method has been used for stock trading recently. Former researches confirm the efficiency of trading rules which are created by GNP. In this paper, GNP has been applied for stock portfolio optimization by generating risk-adjusted trading rules. There are two main novelties in this paper: 1) we use conditional Sharp ratio as a risk...

Relying on a comprehensive data set of news releases, we construct monthly firm-level news sentiment scores during the 2000–2014 period and document a news momentum phenomenon that stocks with more positive news in the past generate more positive news in the future. We propose two hypotheses to explain this phenomenon and find that news momentum is driven by the persistence of firms’ fundamenta...

Our objective is to develop a methodology to detect regions in risk-return space where the out-of-sample performance of portfolios is consistent with their in-sample performance. We use the Berkowitz statistic to evaluate the accuracy of the density forecast, derived from in-sample portfolio returns, of out-of-sample portfolio returns. Defined by its coordinates in risk-return space, a portfoli...

A Bayesian network is a tool for modeling large multivariate probability models and for making inferences from such models. A Bayesian network combines traditional quantitative analysis with expert judgement in an intuitive, graphical representation. In this paper, we show how to use Bayesian networks to model portfolio risk and return. Traditional financial models emphasize the historical rela...