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34
The performance of mutual funds in the period 19451964
 Journal of Finance
, 1968
"... In this paper I derive a riskadjusted measure of portfolio performance (now known as "Jensen's Alpha") that estimates how much a manager's forecasting ability contributes to the fund's returns. The measure is based on the theory of the pricing of capital assets by Sharpe (1964), Lintner (1965a) and ..."
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Cited by 275 (0 self)
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In this paper I derive a riskadjusted measure of portfolio performance (now known as "Jensen's Alpha") that estimates how much a manager's forecasting ability contributes to the fund's returns. The measure is based on the theory of the pricing of capital assets by Sharpe (1964), Lintner (1965a) and Treynor (Undated). I apply the measure to estimate the predictive ability of 115 mutual fund managers in the period 19451964—that is their ability to earn returns which are higher than those we would expect given the level of risk of each of the portfolios. The foundations of the model and the properties of the performance measure suggested here are discussed in Section II. The evidence on mutual fund performance indicates not only that these 115 mutual funds were on average not able to predict security prices well enough to outperform a buythemarketandhold policy, but also that there is very little evidence that any individual fund was able to do significantly better than that which we expected from mere random chance. It is also important to note that these conclusions hold even when we measure the fund returns gross of management expenses (that is assume their bookkeeping, research, and other expenses except brokerage commissions were obtained free). Thus on average the funds apparently were not quite successful enough in their trading activities to recoup even their brokerage expenses. Keywords: Jensen's Alpha, mutual fund performance, riskadjusted returns, forecasting ability, predictive ability.
Nonlinear Pricing Kernels, Kurtosis Preference, and the CrossSection of Assets Returns
 Journal of Finance
, 2002
"... This paper investigates nonlinear pricing kernels in which the risk factor is endogenously determined and preferences restrict the definition of the pricing kernel. These kernels potentially generate the empirical performance of nonlinear and multifactor models, while maintaining empirical power and ..."
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Cited by 82 (2 self)
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This paper investigates nonlinear pricing kernels in which the risk factor is endogenously determined and preferences restrict the definition of the pricing kernel. These kernels potentially generate the empirical performance of nonlinear and multifactor models, while maintaining empirical power and avoiding ad hoc specifications of factors or functional form. Our test results indicate that preferencerestricted nonlinear pricing kernels are both admissible for the cross section of returns and are able to significantly improve upon linear single and multifactor kernels. Further, the nonlinearities in the pricing kernel drive out the importance of the factors in the linear multifactor model. A PRINCIPAL IMPLICATION OF THE Capital Asset Pricing Model ~CAPM! is that the pricing kernel is linear in a single factor, the portfolio of aggregate wealth. Numerous studies over the past two decades have documented violations of this restriction. 1 In response, researchers have examined the performance of alternative models of asset prices. These models have generally fallen into two classes: ~1! multifactor models such as Ross ’ APT or Merton’s ICAPM, in which factors in addition to the market return determine asset prices; or ~2! nonparametric models, such as Bansal et al. ~1993!, Bansal and Viswanathan ~1993!, and Chapman ~1997!, in which the pricing kernel is not
Applying independent component analysis to factor model,” in Intelligent Data Engineering and Automated Learning
 IDEAL 2000, Data Mining, Financial Engineering and Intelligent
, 2000
"... Abstract. Factor model is a very useful and popular model in finance. In this paper, we show the relation between factor model and blind source separation, and we propose to use Independent Component Analysis (ICA) as a data mining tool to construct the underlying factors and hence obtain the corres ..."
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Cited by 10 (5 self)
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Abstract. Factor model is a very useful and popular model in finance. In this paper, we show the relation between factor model and blind source separation, and we propose to use Independent Component Analysis (ICA) as a data mining tool to construct the underlying factors and hence obtain the corresponding sensitivities for the factor model. 1
The influence of analysts, institutional investors, and insiders on the incorporation of market, industry and firmspecific information into stock prices. Accounting Review 79
, 2004
"... Business School and Washington University’s 2002 Financial Accounting Miniconference for helpful comments and suggestions on a prior draft of this manuscript. All errors are our own. Analyst forecasts have been generously ..."
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Cited by 8 (0 self)
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Business School and Washington University’s 2002 Financial Accounting Miniconference for helpful comments and suggestions on a prior draft of this manuscript. All errors are our own. Analyst forecasts have been generously
Maximizing Predictability in the
, 1991
"... We construct portfolios of stocks and of bonds that are maximally predictable with respect to a set of ex ante observable economic variables, and show that these levels of predictability are statistically significant, even after controlling for datasnooping biases. We disaggregate the sources for p ..."
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Cited by 5 (2 self)
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We construct portfolios of stocks and of bonds that are maximally predictable with respect to a set of ex ante observable economic variables, and show that these levels of predictability are statistically significant, even after controlling for datasnooping biases. We disaggregate the sources for predictability by using several asset groups, including sizesorted and industrysorted portfolios, and find that the sources of maximal predictability shift considerably across sectors and size classes as the returnhorizon changes. Using three outofsample measures of predictability, we show that the predictability of the maximally predictable portfolio is genuine and economically significant.
Identifying Regularities in Stock Portfolio Tilting
 Interim Report IR9766, International Institute for Applied Systems Analysis
, 1997
"... The paper deals with the issues associated with identification of stocks generating abnormal returns. Following the findings of a finance theory regarding portfolio tilting, a set of pricerelated stocks' attributes was analyzed. The analysis was conducted with the help of rough sets methodology wh ..."
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Cited by 3 (0 self)
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The paper deals with the issues associated with identification of stocks generating abnormal returns. Following the findings of a finance theory regarding portfolio tilting, a set of pricerelated stocks' attributes was analyzed. The analysis was conducted with the help of rough sets methodology which allows to distinguish "important" attributes for problem description, and to generate decision rules which can be later used to predict stocks' performance. Validity of the approach was tested on the Toronto Stock Exchange data. Keywords: rough sets, decision rules, reducts, portfolio tilting, anomalies theory About the Authors Roman Slowinski is Professor of Decision and Computer Sciences and Head of the Laboratory of Intelligent Decision Support Systems, Institute of Computer Science, Poznan University of Technology, Poznan, Poland. Robert Susmaga is on the research and teaching staff of the Institute of Computer Science at Poznan University of Technology, Poznan, Poland. Wojtek Mic...
Evaluating the Performance of Nearest Neighbour Algorithms when Forecasting US Industry Returns 1
"... Using both industryspecific data on 55 US industry sectors and an extensive range of macroeconomic variables, the authors compare the performance of nearest neighbour algorithms, OLS, and a number of twostage models based on these two methods, when forecasting industry returns. As industry returns ..."
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Using both industryspecific data on 55 US industry sectors and an extensive range of macroeconomic variables, the authors compare the performance of nearest neighbour algorithms, OLS, and a number of twostage models based on these two methods, when forecasting industry returns. As industry returns are a relatively underresearched area in the Finance literature, we also give a brief review of the existing theories as part motivation for our specific choice of variables, which are commonly employed by asset managers in practice. Performance is measured by the Information Coefficient (IC), which is defined as the average correlation between the 55 forecasted returns and the realised returns across industries over time. Due to transaction costs, investors and asset managers typically want a steady outperformance over time. Hence, the volatility of IC is taken into account through the application of “Sharpe Ratios”. We find that twostage procedures mixing industryspecific information with macroeconomic indicators generally outperform both the standalone nearest neighbour algorithms and timeseries based OLS macroeconomic models.
InterIndustry Wage Structures: New Evidence from the OECD
, 1999
"... and is copyright (c) 1999 by the authors. This paper presents an analysis of the evolution of industrial wages in a selection of OECD countries, using data drawn from the Structural Analysis Database and a sequence of techniques that apply cluster and discriminant analysis to timeseries of wage cha ..."
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Cited by 1 (1 self)
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and is copyright (c) 1999 by the authors. This paper presents an analysis of the evolution of industrial wages in a selection of OECD countries, using data drawn from the Structural Analysis Database and a sequence of techniques that apply cluster and discriminant analysis to timeseries of wage change by industry. The principal finding is that a small number of well defined groups of industries usually exist, whose crossgroup differences account for almost all interindustry wage variation. While the specific structure of groups varies according to patterns of natural resources, comparative advantage and trade union organization within each country, the betweengroup variation across time usually reflects the movement of macroeconomic variables, some of them internal and other external, such as inflation and exchange rates. In other words, individual countries appear to be able to control their internal institutional structures, perhaps best understood as pattern bargains, wage contours, or industrial sectors distinguished by type and degree of exposure to international trade. But they do not exercise internal control over the evolution of wage differentials across these groups, except insofar as they can manipulate the
Criteria, Models and Strategies in Portfolio Selection
, 2000
"... In this paper, we survey ideas and principles of modeling the investment decision process of economic agents. We start with the criteria of Markowitz of formulating return and risk as mean and variance, and also its extensions. We then look into other related criteria which are based on probability ..."
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In this paper, we survey ideas and principles of modeling the investment decision process of economic agents. We start with the criteria of Markowitz of formulating return and risk as mean and variance, and also its extensions. We then look into other related criteria which are based on probability assumptions on future prices of securities. We also present methodologies which, instead of assuming probability distributions, rely on the best solution for the worst case scenario or in the average. A few multiple stage optimization models are discussed. Finally we give a few remarks on some interesting topics for further investigations.
STOCK MARKET RISKRETURN INFERENCE. AN UNCONDITIONAL NONPARAMETRIC APPROACH
"... By means of a detailed analysis of the returns of the Standard & Poors 500 (S&P 500) composite stock index over the last fifty years we show how theoretical results and methodological recommendations from the statistical theory of nonparametric curve inference allow one to consistently estimate ex ..."
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By means of a detailed analysis of the returns of the Standard & Poors 500 (S&P 500) composite stock index over the last fifty years we show how theoretical results and methodological recommendations from the statistical theory of nonparametric curve inference allow one to consistently estimate expected return and volatility. In this approach we do not postulate an apriori relationship riskreturn nor do we specify the evolution of the first two moments through covariates. Our analysis gives statistical evidence that the expected return of the S&P 500 index as well as the market price of risk (the ratio expected return minus risk free interest rate over volatility) vary significantly through time both in size and sign. In particular, the periods of negative (positive) estimated expected return and market price of risk coincide with the bear (bull) markets of the index as defined in the literature. A complex relationship between risk and expected return emerges which is far from the common assumption of a positive linear timeinvariant relation.