Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km2 to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere.

We apply a new bootstrap statistical technique to examine the performance of the U.S. open-end, domestic equity mutual fund industry over the 1975 to 2002 period. A bootstrap approach is necessary because the cross section of mutual fund alphas has a complex nonnormal distribution due to heterogeneous risk-taking by funds as well as nonnormalities in individual fund alpha distributions. Our bootstrap approach uncovers findings that differ from many past studies. Specifically, we find that a sizable minority of managers pick stocks well enough to more than cover their costs. Moreover, the superior alphas of these managers persist.WAS PETER LYNCH, FORMER MANAGER of the Fidelity Magellan fund, a "star" stockpicker, or was he simply endowed with stellar luck? The popular press seems to assume that Mr. Lynch's fund performed well due to his unusual acumen in identifying underpriced stocks. In addition, Marcus (1990) concludes that the prolonged superior performance of the Magellan fund is difficult to explain as a purely random outcome, that is, where Mr. Lynch and the other Magellan managers have no true stockpicking skills and are merely the luckiest of a large group of fund managers. More recently, the Schroder Ultra Fund topped the field of 6,000 funds (across all investment objective categories) with a return of 107% per year over the 3 years ending in 2001. This fund closed to new investors in 1998 due to overwhelming demand, presumably because investors credited the fund manager with having extraordinary skills.Recent research documents that subgroups of fund managers have superior stock-picking skills, even though most prior studies conclude that the average The Journal of Finance mutual fund underperforms its benchmarks, net of costs. 1 For example, Chen, Jegadeesh, and Wermers (2000) examine the stockholdings and active trades of mutual funds and find that growth-oriented funds have unique skills in identifying underpriced large-capitalization growth stocks. Furthermore, Wermers (2000) finds that high-turnover mutual funds hold stocks that substantially beat the Standard and Poor's 500 index over the 1975 to 1994 period.The apparent superior performance of a small group of funds such as Magellan or the Schroder Ultra Fund raises the question of whether such performance is credible evidence of genuine stock-picking skills, or whether it simply ref lects the extraordinary luck of a few individual fund managers. Given that hundreds of new funds are launched every year, and by January 2005, over 4,500 equity mutual funds existed in the United States (holding assets valued at almost $4.4 trillion), it is natural to expect that some funds will outperform market indexes by a large amount simply by chance. However, past studies of mutual fund performance do not explicitly recognize and model the role of luck in performance outcomes. Indeed, to a large extent, the literature on performance persistence focuses on measuring out-of-sample performance to control for luck. These models discount the possibility that luck...

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In this paper we utilize White's Reality Check bootstrap methodology~White~1999!! to evaluate simple technical trading rules while quantifying the data-snooping bias and fully adjusting for its effect in the context of the full universe from which the trading rules were drawn. Hence, for the first time, the paper presents a comprehensive test of performance across all technical trading rules examined. We consider the study of Brock, Lakonishok, and LeBaron~1992!, expand their universe of 26 trading rules, apply the rules to 100 years of daily data on the Dow Jones Industrial Average, and determine the effects of data-snooping. TECHNICAL TRADING RULES HAVE BEEN USED in financial markets for more than a century. Numerous studies have been performed to determine whether such rules can be employed to provide superior investing performance. 1 By and large, recent academic literature suggests that technical trading rules are capable of producing valuable economic signals. In perhaps the most comprehensive recent study of technical trading rules using 90 years of daily stock prices, Brock, Lakonishok, and LeBaron~1992!~BLL, hereafter! find that 26 technical trading rules applied to the Dow Jones Industrial Averagẽ DJIA! significantly outperform a benchmark of holding cash. Their findings are especially strong because every one of the trading rules they consider is capable of beating the benchmark. When taken at face value, these results indicate either that the stock market is not efficient even in the weak form-a conclusion which, if found to be robust, will go against most researchers' prior beliefs-or that risk premia display considerable variation even over very short periods of time~i.e., at the daily interval!. 1647 occurs when a given set of data is used more than once for purposes of inference or model selection. When such data reuse occurs, there is always the possibility that any satisfactory results obtained may simply be due to chance rather than to any merit inherent in the method yielding the results. With respect to their choice of technical trading rules, BLL state that "numerous moving average rules can be designed, and some, without a doubt, will work. However, the dangers of data snooping are immense"~p. 1736!. Thus, BLL rightfully acknowledge the effects of data-snooping. They go on to evaluate their results by fitting several models to the raw data and resampling the residuals to create numerous bootstrap samples. The goal of this effort is to determine the statistical significance of their findings. However, as acknowledged by BLL, they are not able "to compute a comprehensive test across all rules. Such a test would have to take into account the dependencies between results for different rules"~p. 1743!. 2 This task has thus far eluded researchers.A main purpose of our paper is to extend and enrich the earlier research on technical trading rules by applying a novel procedure that permits computation of precisely such a test. Although the bootstrap approach~intro-duced by Efron~1979!! is not new to...

In situations where a sequence of forecasts is observed, a common strategy is to examine "rationality" conditional on a given loss function. We examine this from a different perspective-supposing that we have a family of loss functions indexed by unknown shape parameters, then given the forecasts can we back out the loss function parameters consistent with the forecasts being rational even when we do not observe the underlying forecasting model? We establish identification of the parameters of a general class of loss functions that nest popular loss functions as special cases and provide estimation methods and asymptotic distributional results for these parameters. This allows us to construct new tests of forecast rationality that allow for asymmetric loss. The methods are applied in an empirical analysis of IMF and OECD forecasts of budget deficits for the G7 countries. We find that allowing for asymmetric loss can significantly change the outcome of empirical tests of forecast rationality. Copyright The Review of Economic Studies Limited, 2005.