We apply four machine learning methods to cross-sectional return prediction for hedge fund
selection. We equip the forecast model with a set of idiosyncratic features, which are derived …

The results of recent replication studies suggest that false positive findings are a big problem
in empirical finance. We contribute to this debate by reviewing a sample of articles dealing …

The contribution of this paper is to provide an overview and new empirical evidence on
hedge fund performance persistence, which has been a controversial issue in the academic …

In a recent study of the fractal dynamics of gold-silver spreads, Batten et al.(2013) suggest
that the Hurst coefficient (a simple measure of long-range dependence) may be a promising …

The two recent studies of Cajueiro and Tabak (2004b) and Hull and McGroarty (2014)
investigate the predictability of emerging stock market returns based on the Hurst coefficient …

The tremendous growth in assets managed in hedge funds is well recognized. However,
monitoring, valuation and performance assessment is confounded by the paucity and …

The major contribution of this paper is to make use of generalized runs tests (Cho and White,
2011) to analyze the randomness, ie the lack of persistence, in both absolute and relative …

Motivated by the deteriorating performance of traditional cross-sectional momentum
strategies in commodity futures markets, we propose to resurrect momentum by …

This study analyses the performance of US Mutual Funds, from the perspective of Long
Memory (LM), exploring if the returns of MFs are systematic due to their active management …

The few studies on COVID-19's impact on stock market efficiency have reached mixed
conclusions. However, the findings lean towards a negative impact, no studies have …