Abstract. The objective of this research is to improve traffic safety through collecting and distributing up-to-date road surface condition information using mobile phones. Road surface condition information is seen useful for both travellers and for the road network maintenance. The problem we consider is to detect road surface anomalies that, when left unreported, can cause wear of vehicles, lesser driving comfort and vehicle controllability, or an accident. In this work we developed a pattern recognition system for detecting road condition from accelerometer and GPS readings. We present experimental results from real urban driving data that demonstrate the usefulness of the system. Our contributions are: 1) Performing a throughout spectral analysis of tri-axis acceleration signals in order to get reliable road surface anomaly labels. 2) Comprehensive preprocessing of GPS and acceleration signals. 3) Proposing a speed dependence removal approach for feature extraction and demonstrating its positive effect in multiple feature sets for the road surface anomaly detection task. 4) A framework for visually analyzing the classifier predictions over the validation data and labels.

This paper proposes a novel database merging approach and re-examines the fundamental questions regarding hedge fund performance. Before drawing conclusions about fund performance, we form an aggregate database by exploiting all available information across and within seven commercial databases so that the widest possible data coverage is obtained and the effect of data biases is mitigated. Average performance is significantly lower but more persistent when these conclusions are inferred from the aggregate database than from some of the individual commercial databases. Although hedge funds deliver performance persistence, the average fund does not deliver significant risk-adjusted net-of-fee returns while the gross-of-fee returns remain significantly positive. Consistent with previous literature, we find a significant association between fund characteristics related to share restrictions as well as compensation structure and risk-adjusted returns.

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This paper documents a decline in aggregate hedge fund performance over the past decade. We test whether a set of prediction models can select subsets of individual funds that buck the trend and subsequently outperform. Two of the predictors reliably pick funds that lower the volatility and raise the Sharpe ratio of a multi-asset class portfolio relative to a stock/bond portfolio over the full 1997-2016 sample. Hedge fund allocations reduce volatility across two sub-periods but fail to improve the Sharpe ratio from 2008 onwards. Potential explanations for the erosion of hedge fund performance are explored.