This article presents some new results on an unexplored dataset on hedge fund performance. The results indicate that hedge funds follow strategies that are dramatically different from mutual funds, and support the claim that these strategies are highly dynamic. The article finds five dominant investment styles in hedge funds, whichwhenadded to Sharpe's (1992) asset class factor model can provide an integrated framework for style analysis of both buy-and-hold and dynamic trading strategies

The paper studies the impact of the portfolio constraint imposed by the consumption demand for housing (the "housing constraint") on the household's optimal holdings of financial assets. Since the ratio of housing to net worth declines as the household accumulates wealth, the housing constraint induces a life-cycle pattern in the portfolio shares of stocks and bonds. For reasonable degrees of risk aversion, the changes in portfolio composition over the life-cycle can be dramatic. For example, for a coefficient of relative risk aversion of 3, the ratio of stocks to net worth in the optimal portfolio is .09 for the youngest households (ages 18-30) and .60 for the oldest (age 70 and over). Using data from the PSID on home values to construct household level panel data on the real after-tax return to owner-occupied housing, as well as data on the returns to financial assets, the paper estimates the vector of expected returns and the covariance matrix for the set of assets consis...

Abstract. Mean-variance portfolio analysis provided the first quantitative treatment of the tradeoff between profit and risk. We describe in detail the interplay between objective and constraints in a number of single-period variants, including semivariance models. Particular emphasis is laid on avoiding the penalization of overperformance. The results are then used as building blocks in the development and theoretical analysis of multiperiod models based on scenario trees. A key property is the possibility of removing surplus money in future decisions, yielding approximate downside risk minimization.

Introduction There has been a controversy in this journal about using downside risk measures in portfolio analysis. The downside risk measures supposedly are a major improvement over traditional portfolio theory. That is where the battle lines clashed when Rom and Ferguson (1993, 1994b) and Kaplan and Siegel (1994a, 1994b) engaged in a "tempest in a teapot". I should confess that I am strong supporter of downside risk measures and have used them in my teaching, research and software for the past two decades. Therefore, you should keep that bias in mind as you read this article. One of the best means to understand a concept is to study the history of its development. Understanding the issues facing researchers during the development of a concept results in better knowledge of the concept. The purpose of this paper is to provide an understanding of the measurement of downside risk. First, it helps to define terms. Portfolio theory is the application of decision-making tools unde

Portfolio management in the finance literature has typically used optimization algorithms to determine security allocations within a portfolio in order to obtain the best tradeoff between risk and return. These algorithms, despite some improvements, are restrictive in terms of an investor's risk aversion (utility function). Since individual investors have different levels of risk aversion, this paper proposes two portfolio optimization algorithms that can be tailored to the specific level of risk aversion of the individual investor and performs ex-post evaluation tests of the algorithm performance. I.

Adaptation to context is likely to be a key element in ensuring that pervasive devices make the most efficient use of the limited resources available to them. This adaptation can occur on different timescales: from very rapid adaptation to network congestion, through software component discovery and loading to allow for the addition of new functionality, to longer-term component update and patching. In the latter two cases, dynamic code loading introduces security problems, particularly because it may be from untrusted sources with whom pervasive devices happen to be networked at the time of need. As a consequence, we propose a local decision-making process that aims at producing better-informed decisions for pervasive devices when they contemplate whether or not to load software from other devices. This process has three key elements: (i) explicit identification of potential risks, given the device’s context and the type of application; (ii) computation of likelihoods with which the risks will occur, based on trust mechanisms; (iii) integration of the risk attitude of the user of the device, through customisable elementary utility functions. 1

We extend and test empirically the multifractal model of asset returns based on a multiplicative cascade of volatilities from large time scale to small time scales. Inspired by an analogy between price dynamics and hydrodynamic turbulence [Ghashghaie et al., 1996; Arneodo et al., 1998a], it models the time scale dependence of the probability distribution of returns in terms of a superposition of Gaussian laws, with a log-normal distribution of the Gaussian variances. This multifractal description of assets fluctuations is generalized into a multivariate framework to account simultaneously for correlations across times scales and between a basket of assets. The reported empirical evidences show that this extension is pertinent for financial modelling. Two sources of non-normality are discussed: at large time scales, the distinction between discretely and continuously discounted returns lead to the usual log-normal deviation from normality; at small time scales, the multiplicative cascade process leads to multifractality and strong deviations from normality. By perturbation expansions, we are able to quantify precisely on the cumulants of the distribution of returns the interplay and crossover between these two mechanisms. The second part of the paper applies this theory to portfolio optimisation.

In many applications it is necessary to use a simple and therefore highly misspecified econometric model as the basis for decision-making. We propose an approach to developing a possibly misspecified econometric model that will be used as the beliefs of an objective expected utility maximiser. A discrepancy between model and ‘truth ’ is introduced that is interpretable as a measure of the model’s value for this decision-maker. Our decision-based approach utilises this discrepancy in estimation, selection, inference and evaluation of parametric or semiparametric models. The methods proposed nest quasilikelihood methods as a special case that arises when model value is measured by the Kullback-Leibler information discrepancy and also provide an econometric approach for developing parametric decision rules (e.g. technical trading rules) with desirable properties. The approach is illustrated and applied in the context of a CARA investor’s decision problem for which analytical, simulation and empirical results suggest it is very effective.

Abstract: Risk control and optimal diversification constitute a major focus in the finance and insurance industries as well as, more or less consciously, in our everyday life. We present a discussion of the characterization of risks and of the optimization of portfolios that starts from a simple illustrative model and ends by a general functional integral formulation. A major item is that risk, usually thought one-dimensional in the conventional mean-variance approach, has to be addressed by the full distribution of losses. Furthermore, the time-horizon of the investment is shown to play a major role. We show the importance of accounting for large fluctuations and use the theory of Cramér for large deviations in this context. We first treat a simple model with a single risky asset that examplifies the distinction between the average return and the typical return and the role of large deviations in multiplicative processes, and the different optimal strategies for the investors depending on their size. We then analyze the case of assets whose price variations are distributed according to exponential laws, a situation that is found to describe reasonably well daily price variations. Several portfolio optimization strategies are presented that aim at controlling large risks. We end by extending the standard mean-variance portfolio optimization theory, first within the quasi-Gaussian approximation and then using a general formulation for non-Gaussian correlated assets in terms of the formalism of functional integrals developed in the field theory of critical phenomena. 1 1

Empirical research on Lower Partial Moment (LPM) has ignored its portfolio algorithms and the major benefit of such analysis: that its utility function is as general as the utility function assumed by stochastic dominance analysis. Since efficient algorithms for stochastic dominance do not exist, an LPM algorithm may be a viable substitute. This paper is concerned with the composition of portfolios selected by an LPM algorithm, specifically the effect on the characteristics of LPM-selected portfolios whenever the LPM utility function is changed throughout its range. I.