This paper investigates the uncertainty about the trading costs associated with a given portfolio strategy. I derive accurate approximations of the ex ante probability distributions of proportional trading costs and portfolio turnover under the conventional assumption of normal asset returns....

This article reviews the literature on sparse high-dimensional models and discusses some applications in economics and finance. Recent developments in theory, methods, and implementations in penalized least-squares and penalized likelihood methods are highlighted. These variable selection...

Portfolio theories are meant to provide a method for managing assets and constructing portfolios. Meanwhile, the mean-variance technique has been heavily criticized by some academics, and its application to real estate portfolio is questionable (Cheng and Liang, 2000). Indeed, the mean-variance...

A well-known pitfall of Markowitz (1952) portfolio optimization is that the sample covariance matrix, which is a critical input, is very erroneous when there are many assets to choose from. If unchecked, this phenomenon skews the optimizer towards extreme weights that tend to perform poorly in...

This is an exploratory study that attempts to identify and provide empirical evidence on the possible determinants of the market capitalisation of the Harare Stock Exchange (HSE) with the view of understanding the development prospects of the HSE and other similar markets. The study used...

This paper proposes to estimate the covariance matrix of stock returns by an optimally weighted average of two existing estimators: the sample covariance matrix and single-index covariance matrix. This method is generally known as shrinkage, and it is standard in decision theory and in empirical...

The estimation of the inverse covariance matrix plays a crucial role in optimal portfolio choice. We propose a new estimation framework that focuses on enhancing portfolio performance. The framework applies the statistical methodology of shrinkage directly to the inverse covariance matrix using...

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...

In this paper, a model is developed to forecast simultaneously a security's price, growth rate, volatility, and high moments (if applicable). The model has many features. It is built based on its own price growth in a certain time horizon. It is not based on many assumptions such as prices being...