In this paper the static portfolio and the dynamic log-optimal portfolio for memoryless and for stationary market processes are studied. We illustrate their performance (average annual yield) for NYSE data. 1 Notations Consider a market consisting of d assets. The evolution of the market in time is represented by a sequence of price vectors s1, s2, . . . ∈ R+, where sn = (s (1) n , . . . , s (d...

Portfolio selection models based on second-order stochastic dominance (SSD) have the advantage of providing portfolios that reflect behavior risk-averse investors without need to specify utility function. Several scholars apply SSD conditions with respect a reference distribution, typically market index, find its dominant portfolio. However, since distribution could strongly influence asset all...

This paper finds that mean-variance portfolio optimization of stocks, bonds, hedge funds, real estate investment trusts and commodities is sufficiently exact to optimize the investor’s utility. We approximate the expected utility using a Taylor series expansion including terms involving third and fourth order moments. The empirial findings for monthly data from August 1994 August 2009 suggest t...

There has been a recent debate in the marketing literature concerning the possible mispricing of customer satisfaction. While earlier studies claim that portfolios with attractive out-of-sample properties can be formed by loading on stocks whose firms enjoy high customer satisfaction, later studies challenge this finding. A large part of the disagreement stems from the difficulty of how to actu...

The literature on portfolio analysis assumes that the securities returns are random variables with fixed expected returns and variances values (see Bachelier [1], Briec et al. [4] and Markowitz [10]). However, since investors receive efficient or inefficient information from the real world, ambiguous factors usually exist in it. Consequently, we need to consider not only random conditions but a...

This paper considers efficient set mathematics for the case where the covariance matrix of asset returns is assumed known but ex ante the vector of expected returns is replaced by an estimated or forecast value. It is shown that the ex post mean and variance differ from the standard results. Consequently the maximum Sharpe ratio portfolio also differs from the standard result. However, even wit...

Many portfolio managers measure performance with reference to a benchmark. The difference in return between a portfolio and its benchmark is the active return of the portfolio. Portfolio managers and their clients want to know what caused this active return. Performance attribution decomposes the active return. The two most common approaches are the Brinson-Hood-Beebower (hereafter referred to ...

Many portfolio managers measure performance with reference to a benchmark. The difference in return between a portfolio and its benchmark is the active return of the portfolio. Portfolio managers and their clients want to know what caused this active return. Performance attribution decomposes the active return. The two most common approaches are the Brinson-Hood-Beebower (hereafter referred to ...

In this paper, we consider a stochastic programming approach to multi-stage posttax portfolio optimization. Asset performance information is speci ed as a scenario tree generated by two alternative methods based on simulation and optimization. We assume three tax wrappers involving the same instruments for an eÆcient investment strategy and determine optimal allocations to di erent instruments ...