Pohlmeier, Winfried
0000-0003-0707-7417 Berufsbeschreibung
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Bagged Pretested Portfolio Selection
This paper exploits the idea of combining pretesting and bagging to choose between competing portfolio strategies. We propose an estimator for the portfolio weight vector, which optimally trades off Type I against Type II errors when choosing the best investment strategy. Furthermore, we accommodate the idea of bagging in the portfolio testing problem, which helps to avoid sharp thresholding and reduces turnover costs substantially. Our Bagged Pretested Portfolio Selection (BPPS) approach borrows from both the shrinkage and the forecast combination literature. The portfolio weights of our strategy are weighted averages of the portfolio weights from a set of stand-alone strategies. More specifically, the weights are generated from pseudo-out-of-sample portfolio pretesting, such that they reflect the probability that a given strategy will be overall best performing. The resulting strategy allows for a flexible and smooth switch between the underlying strategies and outperforms the corresponding stand-alone strategies. Besides yielding high point estimates of the portfolio performance measures, the BPPS approach performs exceptionally well in terms of precision and is robust against outliers resulting from the choice of the asset space.
Widened Learning of Index Tracking Portfolios
Index investing has an advantage over active investment strategies, because less frequent trading results in lower expenses, yielding higher long-term returns. Index tracking is a popular investment strategy that attempts to find a portfolio replicating the performance of a collection of investment vehicles. This paper considers index tracking from the perspective of solution space exploration. Three search space heuristics in combination with three portfolio tracking error methods are compared in order to select a tracking portfolio with returns that mimic a benchmark index. Experimental results conducted on real-world datasets show that Widening, a metaheuristic using diverse parallel search paths, finds superior solutions than those found by the reference heuristics. Presented here are the first results using Widening on time-series data.
A simple and successful shrinkage method for weighting estimators of treatment effects
A simple shrinkage method is proposed to improve the performance of weighting estimators of the average treatment effect. As the weights in these estimators can become arbitrarily large for the propensity scores close to the boundaries, three different variants of a shrinkage method for the propensity scores are analyzed. The results of a comprehensive Monte Carlo study demonstrate that this simple method substantially reduces the mean squared error of the estimators in finite samples, and is superior to several popular trimming approaches over a wide range of settings.
Potential short‐term earthquake forecasting by farm animal monitoring
Whether changes in animal behavior allow for short‐term earthquake predictions has been debated for a long time. Before, during and after the 2016/2017 earthquake sequence in Italy, we deployed bio‐logging tags to continuously observe the activity of farm animals (cows, dogs, and sheep) close to the epicenter of the devastating magnitude M6.6 Norcia earthquake (Oct–Nov 2016) and over a subsequent longer observation period (Jan–Apr 2017). Relating 5,304 (in 2016) and 12,948 (in 2017) earthquakes with a wide magnitude range (0.4 ≤ M ≤ 6.6) to continuously measured animal activity, we detected how the animals collectively reacted to earthquakes. We also found consistent anticipatory activity prior to earthquakes during times when the animals were in a building (stable), but not during their time on a pasture. We detected these anticipatory patterns not only in periods with high, but also in periods of low seismic activity. Earthquake anticipation times (1–20 hr) are negatively correlated with the distance between the farm and earthquake hypocenters. Our study suggests that continuous bio‐logging of animal collectives has the potential to provide statistically reliable patterns of pre‐seismic activity that could yield valuable insights for short‐term earthquake forecasting. Based on a priori model parameters, we provide empirical threshold values for pre‐seismic animal activities to be used in real‐time observation stations.
Testing out-of-sample portfolio performance
This paper studies the quality of portfolio performance tests based on out-of-sample returns. By disentangling the components of the out-of-sample performance, we show that the observed differences are driven largely by the differences in estimation risk. Our Monte Carlo study reveals that the puzzling empirical findings of inferior performances of theoretically superior strategies result mainly from the low power of these tests. Thus, our results provide an explanation as to why the null hypothesis of equal performance of the simple equally-weighted portfolio compared to many theoretically-superior alternative strategies cannot be rejected in many out-of-sample horse races. Our findings turn out to be robust with respect to different designs and the implementation strategies of the tests.
For the applied researcher, we provide some guidance as to how to cope with the problem of low power. In particular, we make use of a novel pretest-based portfolio strategy to show how the information regarding performance tests can be used optimally.
A Note on the Regularized Approach to Biased 2SLS Estimation with Weak Instruments
The presence of weak instruments is translated into a nearly singular problem in a control function representation. Therefore, the L2-norm type of regularization is proposed to implement the 2SLS estimation for addressing the weak instrument problem. The L2-norm regularization with a regularized parameter O(n) allows us to obtain the Rothenberg (1984) type of higher-order approximation of the 2SLS estimator in the weak instrument asymptotic framework. The proposed regularized parameter yields the regularized concentration parameter O(n), which is used as a standardized factor in the higher-order approximation. We also show that the proposed L2-norm regularization consequently reduces the finite sample bias. A number of existing estimators that address finite sample bias in the presence of weak instruments, especially Fuller's limited information maximum likelihood estimator, are compared with our proposed estimator in a simple Monte Carlo exercise.
The CAPM with Measurement Error : 'There's life in the old dog yet!'
This paper takes a closer look at the consequences of using a market index as a proxy for the latent market return in the capital asset pricing model. In particular, the consequences of two major sources of misspecification are analyzed: (i) the use of inaccurate weights and (ii) the use of only a subset of the asset universe to construct the index. The consequences resulting from the use of a badly chosen market proxy reach from inconsistent parameter estimates to misinterpretation of test outcomes indicating the existence of abnormal returns.
A minimum distance approach of estimating the CAPM under measurement error is presented, which identifies the CAPM parameters by exploiting the crossequation cross-sectional restrictions resulting from a common measurement error. The new approach allows for quantifying the impact of measurement error and for testing the presence of spurious abnormal returns. Practical guidelines are presented to mitigate potential biases in the estimated CAPM parameters.
What determines forecasters' forecasting errors?
This paper contributes to the growing body of literature in macroeconomics and finance on expectation formation and information processing by analyzing the relationship between expectation formation at the individual level and the prediction of macroeconomic aggregates. Using information from business tendency surveys, we present a new approach of analyzing forecasters’ qualitative forecasting errors. Based on a quantal response approach with misclassification, we define forecasters’ qualitative mispredictions in terms of deviations from the qualitative rational expectation forecast, and relate them to the individual and macro factors that are driving these mispredictions. Our approach permits a detailed analysis of individual forecasting decisions, allowing for the introduction of individual and economy-wide determinants that affect the individual forecasting error process.
A Simple and Successul Method to Shrink the Weight
We propose a simple way to improve the efficiency of the average treatment effect on propensity score based estimators. As the weights become arbitrarily large for the propensity scores being close to one or zero, we propose to shrink the propensity scores away from these boundaries. Using a comprehensive Monte Carlo study we show that this simple method substantially reduces the mean squared error of the estimators in finite samples.