Dynamic General Equilibrium and T-Period Fund Separation

In a dynamic general equilibrium model, we derive conditions for a mutual fund separation property by which the savings decision is separated from the asset allocation decision. With logarithmic utility functions, this separation holds for any heterogeneity in discount factors, while the generalization to constant relative risk aversion holds only for homogeneous discount factors but allows for any heterogeneity in endowments. The logarithmic case provides a general equilibrium foundation for the growth-optimal portfolio literature. Both cases yield equilibrium asset pricing formulas that allow for investor heterogeneity, in which the return process is endogenous and asset prices are determined by expected discounted relative dividends. Our results have simple asset pricing implications for the time series as well as the cross section of relative asset prices. It is found that on data from the Dow Jones Industrial Average, a risk aversion smaller than in the logarithmic case fits bes

Look-Ahead Benchmark Bias in Portfolio Performance Evaluation

Performance of investment managers are evaluated in comparison with benchmarks, such as financial indices. Due to the operational constraint that most professional databases do not track the change of constitution of benchmark portfolios, standard tests of performance suffer from the "look-ahead benchmark bias," when they use the assets constituting the benchmarks of reference at the end of the testing period, rather than at the beginning of the period. Here, we report that the "look-ahead benchmark bias" can exhibit a surprisingly large amplitude for portfolios of common stocks (up to 8% annum for the S&P500 taken as the benchmark) -- while most studies have emphasized related survival biases in performance of mutual and hedge funds for which the biases can be expected to be even larger. We use the CRSP database from 1926 to 2006 and analyze the running top 500 US capitalizations to demonstrate that this bias can account for a gross overestimation of performance metrics such as the Sharpe ratio as well as an underestimation of risk, as measured for instance by peak-to-valley drawdowns. We demonstrate the presence of a significant bias in the estimation of the survival and look-ahead biases studied in the literature. A general methodology to test the properties of investment strategies is advanced in terms of random strategies with similar investment constraints.Comment: 16 pages, 1 table, 4 figure

Data-analysis strategies for image-based cell profiling

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.Peer reviewe

A dynamic model of the financial-real interaction as a model selection criterion for nonparametric stock market prediction

Inspired by findings of low–dimensional nonlinearities and the Theorem of Takens (1983) forecasting models of financial time series are often built upon nonparametric, i.e. universal nonlinear, univariate relationships. Empirical investigations, however, are seriously contaminated by the problem of overfitting. Since statistical model selection theory in the nonlinear case is still in its infancy we would like to suggest the application of economic model selection criteria. It is a method of combining the flexibility of nonparametric regressions and important structural information in dynamic economic models. Therefore, conditions of economic models are imposed on the embedded nonlinear dynamical system to be estimated nonparametrically. In our empirical investigations we apply an univariate nonparametric forecasting model of stock returns, implemented via the Local Linear Maps of Ritter (1991), by an economic model selection criterion based on a discretized form of a continuous–time dynamic model on the interaction of real activity and asset markets. The dynamic economic model is estimated based on the Maximum Entropy inference since unobservable variables are involved. Results for monthly U.S. data show that nonparametric model selection is improved by this economic model selection criterion. On the other hand this result may be interpreted as support for the economic model

Working Paper Series

A dynamic model of the financial–real interaction as a model selection criterion for nonparametric stock market predictio

Dynamic asset pricing models with nonparametric expectations

Woehrmann P. Dynamic asset pricing models with nonparametric expectations. Marburg: Tectum-Verl.; 2002

Nonparametric Estimation of the Time-varying Sharpe Ratio in Dynamic Asset Pricing Models

Economic research of the last decade linking macroeconomic fundamentals to asset prices has revealed evidence that standard intertemporal asset pricing theory is not successful in explaining (unconditional) first moments of asset market characteristics such as the risk-free interest rate, equity premium and the Sharpe-ratio. Subsequent empirical research has pursued the question whether those characteristics of asset markets are time varying and, in particular, varying over the business cycle. Recently intertemporal asset pricing models have been employed to replicate those time varying characteristics. The aim of our contribution is (1) to relax some of the assumptions that previous work has imposed on underlying economic and financial variables, (2) to extend the solution technique of Marcet and Den Haan (1990) for those models by nonparametric expectations and (3) to propose a new estimation procedure based on the above solution technique. To allow fornnonparametric expectations in the expectations approach for numerically solving the intertemporal economic model we employ the Local Linear Mapsn(LLMs) of Ritter, Martinetz and Schulten (1992) to approximate conditional expectations in the Euler equation. In our estimation approach based on non-parametric expectations we are able to use full structural information and,nconsequently, Monte Carlo simulations show that our estimations are less biased than the widely applied GMM procedure. Based on quarterly U.S. data we also empirically estimate structural parameters of the model and explore its time varying asset price characteristics for two types of preferences, power utility and habit persistence. We in particular focus on the Sharpe-ratio and find indication that the model is able to capture the time variation of thenSharpe-ratio

Working Paper Series Strategic Asset Allocation and Market Timing: A Reinforcement Learning Approach Strategic Asset Allocation and Market Timing: A Reinforcement Learning Approach

Abstract We apply the recurrent reinforcement learning method o

A dynamic model of the financial–real interaction as a model selection criterion for nonparametric stock market prediction

Inspired by findings of low–dimensional nonlinearities and the Theorem of Takens (1983) forecasting models of financial time series are often built upon nonparametric, i.e. universal nonlinear, univariate relationships. Empirical investigations, however, are seriously contaminated by the problem of overfitting. Since statistical model selection theory in the nonlinear case is still in its infancy we would like to suggest the application of economic model selection criteria. It is a method of combining the flexibility of nonparametric regressions and important structural information in dynamic economic models. Therefore, conditions of economic models are imposed on the embedded nonlinear dynamical system to be estimated nonparametrically. In our empirical investigations we apply an univariate nonparametric forecasting model of stock returns, implemented via the Local Linear Maps of Ritter (1991), by an economic model selection criterion based on a discretized form of a continuous–time dynamic model on the interaction of real activity and asset markets. The dynamic economic model is estimated based on the Maximum Entropy inference since unobservable variables are involved. Results for monthly U.S. data show that nonparametric model selection is improved by this economic model selection criterion. On the other hand this result may be interpreted as support for the economic model.model selection, dynamic model, interaction, nonparametric, stock market, prediction