Performance Evaluation with Stochastic Discount Factors

We study the use of stochastic discount factor (SDF) models in evaluating the investment performance of portfolio managers. By constructing artificial mutual funds with known levels of investment ability, we evaluate a large set of SDF models. We find that the measures of performance are not highly sensitive to the SDF model, and that most of the models have a mild negative bias when performance is neutral. We use the models to evaluate a sample of U.S. equity mutual funds. Adjusting for the observed bias, we find that the average mutual fund has enough ability to cover its transactions costs. Extreme funds are more likely to have good rather than poor risk adjusted performance. Our analysis also reveals a number of implementation issues relevant to other applications of SDF models.

Portfolio Performance and Agency

The evaluation and compensation of portfolio managers is an important problem for practitioners. Optimal compensation will induce managers to expend effort to generate information and to use it appropriately in an informed portfolio choice. Our general model points the way towards analysis of optimal performance evaluation and contracting in a rich model. Optimal contracting in the model includes an important role for portfolio restrictions that are more complex than the sharing rule. The agent's compensation gives the agent approximately to benchmark return plus an incentive fee equal to a portfolio measure that is approximately the excess of return above the benchmark. This measure is often used by practitioners but is simpler than the Jensen measure and other measures commonly recommended in the academic literature. In addition to the excess return above the fixed benchmark, the manager is given some additional incentive to take a position that deviates from the benchmark to remove an incentive to tend towards being a "closet indexer." Efficient contracting involves restrictions on what portfolio strategies can be pursued, and prior communication of the information gathered

Reputation Effects in Portfolio Management

This paper analyzes a model of moral hazard in portfolio management. Managers wish to earn the higher fees associated with active management but are averse to the effort of identifying superior trading strategies through research. Previous research has focused on contracts which offer explicit incentives. In this paper I address optimal contracting between an investor and a portfolio manager when reputation building is possible. I model reputation in a somewhat different manner than some previous research in which both contracting parties are unsure of the agent’s ability. Here only the investor is unsure about the agent’s skill. No restrictions are made on the form of the contracts. The model predicts that larger funds, or those with higher reputations, will be more likely to give performance bonuses to managers

Performance Evaluation with Stochastic Discount Factors

We study stochastic discount factor (SDF) models for evaluating investment performance. Constructing artificial funds with known levels of ability, we find that the measures of performance are not highly sensitive to the SDF model. Most of the models have a mild negative bias when performance is neutral. We evaluate a sample of U.S. equity mutual funds. Adjusting for the observed bias, the average mutual fund has enough ability to cover transactions costs. Extreme funds are more likely to have good rather than poor risk-adjusted performance. Our analysis reveals a number of implementation issues relevant to other applications.

Evaluating stochastic discount factors from term structure models

This paper examines the feasibility of applying the stochastic discount factor methodology to fixed-income data using modern term structure models. Using this approach the researcher can examine returns on bond portfolios whose exact composition is unknown, as is often the case. This paper proposes an observable proxy for the SDF from continuous-time models and documents via Monte Carlo methods the properties of the GMM estimator based on using this proxy.Stochastic discount factors Term structure models Monte Carlo method GMM estimator

Evaluating stochastic discount factors from term structure models

Thesis (Ph. D.)--University of Washington, 1997This paper introduces a new approach to testing continuous time models which can be applied to almost any pricing model and does not rely on the pricing equation having a closed form solution. It also allows use of more primitive assets then factors without having to specify an error distribution. This is achieved by examining the model in terms of the Stochastic Discount Factor (SDF) implied by the model. In this paper I examine the performance of asset pricing models from the term structure literature to illustrate the approach. I propose an observable proxy for the SDF from continuous time models and demonstrate via monte carlo methods that this proxy is an extremely good approximation to the true, unobservable SDF. I show how to use the SDF to value a series of returns. I find that the most popular one-factor parametric models can be rejected by the data. I also examine the most popular two-factor models which have been proposed in the literature