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Equity indexing: optimize your passive investments
Carol Alexander and Anca Dimitriu discuss two strategies for enhanced index tracking designed to best suit a passive investment framework.
Sources of Over-performance in Equity Markets: Mean Reversion, Common Trends and Herding
In the field of optimisation models for passive investments, we propose a general portfolio construction model based on principal component analysis. The portfolio is designed to replicate the first principal component of a group of stocks, instead of a traditional benchmark, thus capturing only the common trend in the stock returns. The main advantage of this approach is that the reduction of the noise present in stock returns facilitates the replication task considerably and the optimal portfolio structure is very stable. We analyse the portfolio performance over different time horizons and in different international equity markets. The strategy over-performs both equally weighted and price weighted benchmarks, even after transaction costs. A market premium, a value premium associated with mean reversion in stock returns, and a volatility premium which give the strategy characteristics of a benchmark enhancer, all explain the over-performance, but have time-varying contributions to it. A behavioural explanation for the mean reversion mechanism leads to the conclusion that the portfolio performance is influenced by the extent of investors herding towards the common trend in stock returns.common trends, mean revrsion, herding, principal component analysis, abnormal returns, value strategies, behavioural finance
The Art of Investing in Hedge Funds: Fund Selection and Optimal Allocations
With institutional investors increasingly involved in alternative investments, portfolio optimisation within a large universe of hedge funds has become a key area for research. This paper develops a portfolio construction model that is specifically designed for funds of hedge funds, incorporating specific controls for operational limitations, data biases and incompleteness. Absolute performance is targeted by selecting funds according to their relative abnormal return, alpha. Whilst different factor models provide quite different estimates of a hedge fund’s alpha, we find that ranking funds according to their alpha is an efficient selection process. In an extensive out-of-sample historical analysis, funds of funds that are selected in this way and then allocated using constrained minimum variance optimisation are shown to perform much better than the equally weighted portfolio of all funds, or minimum variance portfolios of randomly selected funds. This is true even when hedge funds are selected according to their alphas produced by the simplest factor model. Of the four factor models considered in this analysis the best out-of-sample performance is obtained using the statistical factor model.Hedge fund, risk adjusted performance, mean-variance, constrained optimisation
A Comparison of Cointegration & Tracking Error Models for Mutual Funds & Hedge Funds
We present a detailed study of portfolio optimisation based on cointegration, a statistical tool that here exploits a long-run equilibrium relationship between stock prices and an index price. We compare the theoretical and empirical properties of cointegration optimal equity portfolios with those of portfolios optimised on the tracking error variance. From an eleven year out of sample performance analysis we find that for simple index tracking the additional feature of cointegration between the tracking portfolio and the index has no clear advantages or disadvantages relative to the tracking error variance (TEV) minimization model. However ensuring a cointegration relationship does pay off when the tracking task becomes more difficult. Cointegration optimal portfolios clearly dominate the TEV equivalents for all of the statistical arbitrage strategies based on enhanced indexation, in all market circumstancescointegration, tracking error, index tracking, statistical arbitrage
Going Beyond Counting First Authors in Author Co-citation Analysis
The present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
We provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
We conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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