Understanding Mutual Fund and Hedge Fund Styles Using Return Based Style Analysis

We provide an introduction to the use of return based style analysis of Sharpe (1992) in practice. We demonstrate the importance of selecting the right style benchmarks and how the use of inappropriate style benchmarks may lead to wrong conclusions. When style analysis is applied to sector oriented funds such as healthcare, precious metals, energy, technology, etc., the set of benchmarks should include sector or industry indexes. Following Glosten and Jagannathan (1994), Fung and Hsieh (2001), and Agarwal and Naik (2001), we show how to analyze the investment style of hedge fund managers by including the returns on selected option based strategies as style benchmarks. In the examples we consider, return based style analysis provides insights not available through commonly used 'peer' evaluation alone.

Beyond standard benchmarks: Parameterizing performance evaluation in visual object tracking

Object-to-camera motion produces a variety of apparent motion patterns that significantly affect performance of short-term visual trackers. Despite being crucial for designing robust trackers, their influence is poorly explored in standard benchmarks due to weakly defined, biased and overlapping attribute annotations. In this paper we propose to go beyond pre-recorded benchmarks with post-hoc annotations by presenting an approach that utilizes omnidirectional videos to generate realistic, consistently annotated, short-term tracking scenarios with exactly parameterized motion patterns. We have created an evaluation system, constructed a fully annotated dataset of omnidirectional videos and the generators for typical motion patterns. We provide an in-depth analysis of major tracking paradigms which is complementary to the standard benchmarks and confirms the expressiveness of our evaluation approach

TRACKING THE PERFORMANCE OF MARKETING PROFESSIONALS: 1995-2000 RESULTS FOR CORN AND SOYBEANS

The purpose of this research bulletin is to summarize the pricing performance of professional market advisory services for the 1995-2000 corn and soybean crops. The pricing performance results over 1995-2000 suggest several key findings. First, advisory programs in corn do not consistently beat market benchmarks, but they do consistently beat the farmer benchmark. Second, advisory programs in soybeans tend to beat both market and farmer benchmarks. Third, in terms of 50/50 revenue, advisory programs only marginally beat market benchmarks, but consistently beat the farmer benchmark. Overall, there is mixed evidence that advisory programs as a group outperform market benchmarks, while substantial evidence exists that advisory programs as a group outperform the farmer benchmarks. Caution should be used when considering the results, due to the relatively small sample of crop years available for analysis. In particular, the presence of sharp downward price trends in most crop years makes it difficult to determine whether the 1995-2000 sample provides a statistically reliable picture of future differences in pricing performance.Marketing,

Parallelism-Aware Memory Interference Delay Analysis for COTS Multicore Systems

In modern Commercial Off-The-Shelf (COTS) multicore systems, each core can generate many parallel memory requests at a time. The processing of these parallel requests in the DRAM controller greatly affects the memory interference delay experienced by running tasks on the platform. In this paper, we model a modern COTS multicore system which has a nonblocking last-level cache (LLC) and a DRAM controller that prioritizes reads over writes. To minimize interference, we focus on LLC and DRAM bank partitioned systems. Based on the model, we propose an analysis that computes a safe upper bound for the worst-case memory interference delay. We validated our analysis on a real COTS multicore platform with a set of carefully designed synthetic benchmarks as well as SPEC2006 benchmarks. Evaluation results show that our analysis is more accurately capture the worst-case memory interference delay and provides safer upper bounds compared to a recently proposed analysis which significantly under-estimate the delay.Comment: Technical Repor

Global-local methodologies and their application to nonlinear analysis

An assessment is made of the potential of different global-local analysis strategies for predicting the nonlinear and postbuckling responses of structures. Two postbuckling problems of composite panels are used as benchmarks and the application of different global-local methodologies to these benchmarks is outlined. The key elements of each of the global-local strategies are discussed and future research areas needed to realize the full potential of global-local methodologies are identified

Analysing Symbolic Regression Benchmarks under a Meta-Learning Approach

The definition of a concise and effective testbed for Genetic Programming (GP) is a recurrent matter in the research community. This paper takes a new step in this direction, proposing a different approach to measure the quality of the symbolic regression benchmarks quantitatively. The proposed approach is based on meta-learning and uses a set of dataset meta-features---such as the number of examples or output skewness---to describe the datasets. Our idea is to correlate these meta-features with the errors obtained by a GP method. These meta-features define a space of benchmarks that should, ideally, have datasets (points) covering different regions of the space. An initial analysis of 63 datasets showed that current benchmarks are concentrated in a small region of this benchmark space. We also found out that number of instances and output skewness are the most relevant meta-features to GP output error. Both conclusions can help define which datasets should compose an effective testbed for symbolic regression methods.Comment: 8 pages, 3 Figures, Proceedings of Genetic and Evolutionary Computation Conference Companion, Kyoto, Japa