PROBLEMS IN SELECTION OF SECURITY PORTFOLIOS
THE PERFORMANCE OF MUTUAL FUNDS IN THE PERIOD 1945-1964
MICHAEL C. JENSEN*
I. INTRODUCTION
A CENTRAL PROBLEM IN FINANCE (and especially portfolio management) has
been that of evaluating the "performance" of portfolios of risky investments.
The concept of portfolio "performance" has at least two distinct dimensions:
1) The ability of the portfolio manager or security analyst to increase re-
turns on the portfolio through successful prediction of future security
prices, and
2) The ability of the portfolio manager to minimize (through "efficient"
diversification) the amount of "insurable risk" born by the holders of
the portfolio.
The major difficulty encountered in attempting to evaluate the performance
of a portfolio in these two dimensions has been the lack of a thorough under-
standing of the nature and measurement of "risk." Evidence seems to indicate
a predominance of risk aversion in the capital markets, and as long as in-
vestors correctly perceive the "riskiness" of various assets this implies that
"risky" assets must on average yield higher returns than less "risky" assets.'
Hence in evaluating the "performance" of portfolios the effects of differential
degrees of risk on the returns of those portfolios must be taken into account.
Recent developments in the theory of the pricing of capital assets by
Sharpe [20], Lintner [15] and Treynor [25] allow us to formulate explicit
measures of a portfolio's performance in each of the dimensions outlined
above. These measures are derived and discussed in detail in Jensen [11].
However, we shall confine our attention here only to the problem of evaluating
a portfolio manager's predictive ability-that is his ability to earn returns
through successful prediction of security prices which are higher than those
which we could expect given the level of riskiness of his portfolio. The founda-
tions of the model and the properties of the performance measure suggested
here (which is somewhat different than that proposed in [11]) are discussed
in Section II. The model is illustrated in Section III by an application of it
to the evaluation of the performance of 115 open end mutual funds in the
period 1945-1964.
A number of people in the past have attempted to evaluate the performance
of portfolios2 (primarily mutual funds), but almost all of these authors have
* University of Rochester College of Business. This paper has benefited from comments and
criticisms by G. Benston, E. Fama, J. Keilson, H. Weingartner, and especially M. Scholes.
1. Assuming, of course, that investors' expectations are on average correct.
2. See for example [2, 3, 7, 8, 9, 10, 21, 24].
389
390 The Journal of Finance
relied heavily on relative measures of performance when what we really need
is an absolute measure of performance. That is, they have relied mainly on
procedures for ranking portfolios. For example, if there are two por ...
The document summarizes the capital asset pricing model (CAPM) and reviews early empirical tests of the model. It begins by outlining the logic and key assumptions of the CAPM, including that the market portfolio must be mean-variance efficient. However, empirical tests found problems with the CAPM's predictions about the relationship between expected returns and market betas. Specifically, cross-sectional regressions did not find intercepts equal to the risk-free rate or slopes equal to the expected market premium. To address measurement error, later tests examined portfolios rather than individual assets. In general, the early empirical evidence revealed shortcomings in the CAPM's ability to explain returns.
This document summarizes the capital asset pricing model (CAPM). It begins by outlining the logic and key assumptions of the CAPM, including that all investors hold the same market portfolio which must lie on the efficient frontier. It then states that the CAPM predicts the expected return of an asset is determined by its beta, or non-diversifiable risk relative to the market. However, the document notes that empirical tests have found the CAPM performs poorly in applications. It concludes the CAPM's failings indicate applications based on the model are invalid, challenging researchers to develop alternative models.
Mutual fund performance an analysis of monthly returns of an emerging marketAlexander Decker
1) The document analyzes the monthly performance of over 15 growth-oriented mutual funds on the Dhaka Stock Exchange of Bangladesh compared to benchmark returns.
2) Risk-adjusted performance measures like the Jensen, Treynor, and Sharpe ratios were used to evaluate performance. Most funds performed better on the Jensen and Treynor measures but not as well on the Sharpe ratio.
3) The analysis found that very few funds were well-diversified and reduced unique risk. Growth funds did not outperform in terms of total risk and did not provide the benefits of diversification and professional management that investors seek. Therefore, mutual funds cannot always outperform the market through their expertise.
Measuring and allocating portfolio risk capital in the real worldAlexander Decker
This document discusses measuring and allocating portfolio risk capital using value-at-risk and expected shortfall. Daily stock price data from the London Stock Exchange over 3 years was used to calculate the risk measures for portfolios in different sectors. The risk capital required for each stock was determined using a fair allocation principle. The results showed that stocks with higher average returns and lower volatility required less risk capital. The portfolios with the lowest quantified risk amounts were mining, media, financial services, banks and the top 10 FTSE companies portfolios.
Questions and Answers At Least 75 Words each.Please answer th.docxmakdul
Questions and Answers: At Least 75 Words each.
Please answer the following questions.
1. What are the differences and similarities between samples and populations?
2. What are the measures of Central Tendency assumptions?
3. What are measures of Dispersion used for and what are the assumptions for each?
4. Define collaboration and how you will apply it in Statistics? (100 Words)
The Capital Asset Pricing Model:
Theory and Evidence
Eugene F. Fama and Kenneth R. French
T he capital asset pricing model (CAPM) of William Sharpe (1964) and JohnLintner (1965) marks the birth of asset pricing theory (resulting in aNobel Prize for Sharpe in 1990). Four decades later, the CAPM is still
widely used in applications, such as estimating the cost of capital for firms and
evaluating the performance of managed portfolios. It is the centerpiece of MBA
investment courses. Indeed, it is often the only asset pricing model taught in these
courses.1
The attraction of the CAPM is that it offers powerful and intuitively pleasing
predictions about how to measure risk and the relation between expected return
and risk. Unfortunately, the empirical record of the model is poor—poor enough
to invalidate the way it is used in applications. The CAPM’s empirical problems may
reflect theoretical failings, the result of many simplifying assumptions. But they may
also be caused by difficulties in implementing valid tests of the model. For example,
the CAPM says that the risk of a stock should be measured relative to a compre-
hensive “market portfolio” that in principle can include not just traded financial
assets, but also consumer durables, real estate and human capital. Even if we take
a narrow view of the model and limit its purview to traded financial assets, is it
1 Although every asset pricing model is a capital asset pricing model, the finance profession reserves the
acronym CAPM for the specific model of Sharpe (1964), Lintner (1965) and Black (1972) discussed
here. Thus, throughout the paper we refer to the Sharpe-Lintner-Black model as the CAPM.
y Eugene F. Fama is Robert R. McCormick Distinguished Service Professor of Finance,
Graduate School of Business, University of Chicago, Chicago, Illinois. Kenneth R. French is
Carl E. and Catherine M. Heidt Professor of Finance, Tuck School of Business, Dartmouth
College, Hanover, New Hampshire. Their e-mail addresses are �[email protected]
edu� and �[email protected]�, respectively.
Journal of Economic Perspectives—Volume 18, Number 3—Summer 2004 —Pages 25– 46
legitimate to limit further the market portfolio to U.S. common stocks (a typical
choice), or should the market be expanded to include bonds, and other financial
assets, perhaps around the world? In the end, we argue that whether the model’s
problems reflect weaknesses in the theory or in its empirical implementation, the
failure of the CAPM in empirical tests implies that most applications of the model
are invalid.
We begin by outlining the logic of t ...
The document discusses the Capital Asset Pricing Model (CAPM) and its relationship between risk and expected return. It defines key terms like expected return, variance, standard deviation, covariance, correlation, diversification, systematic and unsystematic risk. It explains that a security's risk is measured by its beta, which represents its non-diversifiable risk related to market movements. The CAPM holds that the expected return of an individual security or portfolio equals the risk-free rate plus a risk premium that depends on the security's systematic risk relative to the market.
The document discusses the Capital Asset Pricing Model (CAPM) and its relationship between risk and expected return. It defines key terms like expected return, variance, standard deviation, covariance, correlation, diversification, systematic and unsystematic risk. It explains that a security's risk is measured by its beta, which represents its non-diversifiable risk related to market movements. The CAPM holds that the expected return of a security or portfolio equals the risk-free rate plus a risk premium that is proportional to the security's systematic risk relative to the market.
This document summarizes the key points of the article "Risk, Return, and Equilibrium: Empirical Tests" by Eugene F. Fama and James D. MacBeth. The article tests the relationship between average stock returns and risk using the two-parameter portfolio model. It finds that the data is consistent with investors holding efficient portfolios as the model predicts, and that stock prices fully reflect available information as implied by an efficient market. Specifically:
1) Regressions of stock returns on measures of risk find a linear relationship between risk and return as the model implies, with higher risk associated with higher return.
2) The risk measures used capture risk completely and no other risk factors are needed.
The document summarizes the capital asset pricing model (CAPM) and reviews early empirical tests of the model. It begins by outlining the logic and key assumptions of the CAPM, including that the market portfolio must be mean-variance efficient. However, empirical tests found problems with the CAPM's predictions about the relationship between expected returns and market betas. Specifically, cross-sectional regressions did not find intercepts equal to the risk-free rate or slopes equal to the expected market premium. To address measurement error, later tests examined portfolios rather than individual assets. In general, the early empirical evidence revealed shortcomings in the CAPM's ability to explain returns.
This document summarizes the capital asset pricing model (CAPM). It begins by outlining the logic and key assumptions of the CAPM, including that all investors hold the same market portfolio which must lie on the efficient frontier. It then states that the CAPM predicts the expected return of an asset is determined by its beta, or non-diversifiable risk relative to the market. However, the document notes that empirical tests have found the CAPM performs poorly in applications. It concludes the CAPM's failings indicate applications based on the model are invalid, challenging researchers to develop alternative models.
Mutual fund performance an analysis of monthly returns of an emerging marketAlexander Decker
1) The document analyzes the monthly performance of over 15 growth-oriented mutual funds on the Dhaka Stock Exchange of Bangladesh compared to benchmark returns.
2) Risk-adjusted performance measures like the Jensen, Treynor, and Sharpe ratios were used to evaluate performance. Most funds performed better on the Jensen and Treynor measures but not as well on the Sharpe ratio.
3) The analysis found that very few funds were well-diversified and reduced unique risk. Growth funds did not outperform in terms of total risk and did not provide the benefits of diversification and professional management that investors seek. Therefore, mutual funds cannot always outperform the market through their expertise.
Measuring and allocating portfolio risk capital in the real worldAlexander Decker
This document discusses measuring and allocating portfolio risk capital using value-at-risk and expected shortfall. Daily stock price data from the London Stock Exchange over 3 years was used to calculate the risk measures for portfolios in different sectors. The risk capital required for each stock was determined using a fair allocation principle. The results showed that stocks with higher average returns and lower volatility required less risk capital. The portfolios with the lowest quantified risk amounts were mining, media, financial services, banks and the top 10 FTSE companies portfolios.
Questions and Answers At Least 75 Words each.Please answer th.docxmakdul
Questions and Answers: At Least 75 Words each.
Please answer the following questions.
1. What are the differences and similarities between samples and populations?
2. What are the measures of Central Tendency assumptions?
3. What are measures of Dispersion used for and what are the assumptions for each?
4. Define collaboration and how you will apply it in Statistics? (100 Words)
The Capital Asset Pricing Model:
Theory and Evidence
Eugene F. Fama and Kenneth R. French
T he capital asset pricing model (CAPM) of William Sharpe (1964) and JohnLintner (1965) marks the birth of asset pricing theory (resulting in aNobel Prize for Sharpe in 1990). Four decades later, the CAPM is still
widely used in applications, such as estimating the cost of capital for firms and
evaluating the performance of managed portfolios. It is the centerpiece of MBA
investment courses. Indeed, it is often the only asset pricing model taught in these
courses.1
The attraction of the CAPM is that it offers powerful and intuitively pleasing
predictions about how to measure risk and the relation between expected return
and risk. Unfortunately, the empirical record of the model is poor—poor enough
to invalidate the way it is used in applications. The CAPM’s empirical problems may
reflect theoretical failings, the result of many simplifying assumptions. But they may
also be caused by difficulties in implementing valid tests of the model. For example,
the CAPM says that the risk of a stock should be measured relative to a compre-
hensive “market portfolio” that in principle can include not just traded financial
assets, but also consumer durables, real estate and human capital. Even if we take
a narrow view of the model and limit its purview to traded financial assets, is it
1 Although every asset pricing model is a capital asset pricing model, the finance profession reserves the
acronym CAPM for the specific model of Sharpe (1964), Lintner (1965) and Black (1972) discussed
here. Thus, throughout the paper we refer to the Sharpe-Lintner-Black model as the CAPM.
y Eugene F. Fama is Robert R. McCormick Distinguished Service Professor of Finance,
Graduate School of Business, University of Chicago, Chicago, Illinois. Kenneth R. French is
Carl E. and Catherine M. Heidt Professor of Finance, Tuck School of Business, Dartmouth
College, Hanover, New Hampshire. Their e-mail addresses are �[email protected]
edu� and �[email protected]�, respectively.
Journal of Economic Perspectives—Volume 18, Number 3—Summer 2004 —Pages 25– 46
legitimate to limit further the market portfolio to U.S. common stocks (a typical
choice), or should the market be expanded to include bonds, and other financial
assets, perhaps around the world? In the end, we argue that whether the model’s
problems reflect weaknesses in the theory or in its empirical implementation, the
failure of the CAPM in empirical tests implies that most applications of the model
are invalid.
We begin by outlining the logic of t ...
The document discusses the Capital Asset Pricing Model (CAPM) and its relationship between risk and expected return. It defines key terms like expected return, variance, standard deviation, covariance, correlation, diversification, systematic and unsystematic risk. It explains that a security's risk is measured by its beta, which represents its non-diversifiable risk related to market movements. The CAPM holds that the expected return of an individual security or portfolio equals the risk-free rate plus a risk premium that depends on the security's systematic risk relative to the market.
The document discusses the Capital Asset Pricing Model (CAPM) and its relationship between risk and expected return. It defines key terms like expected return, variance, standard deviation, covariance, correlation, diversification, systematic and unsystematic risk. It explains that a security's risk is measured by its beta, which represents its non-diversifiable risk related to market movements. The CAPM holds that the expected return of a security or portfolio equals the risk-free rate plus a risk premium that is proportional to the security's systematic risk relative to the market.
This document summarizes the key points of the article "Risk, Return, and Equilibrium: Empirical Tests" by Eugene F. Fama and James D. MacBeth. The article tests the relationship between average stock returns and risk using the two-parameter portfolio model. It finds that the data is consistent with investors holding efficient portfolios as the model predicts, and that stock prices fully reflect available information as implied by an efficient market. Specifically:
1) Regressions of stock returns on measures of risk find a linear relationship between risk and return as the model implies, with higher risk associated with higher return.
2) The risk measures used capture risk completely and no other risk factors are needed.
Risk Measurement From Theory to Practice: Is Your Risk Metric Coherent and Em...amadei77
I present desirable features for a risk metric, incorporating the coherent risk framework and empirical features of markets. I argue that a desirable risk metric is one that is coherent and focused on measuring tail losses, which significantly affect investment performance. I evaluate 5 risk metrics: volatility, semi-standard deviation, downside deviation, Value at Risk (VaR) and Conditional Value at Risk (CVaR). I demonstrate that CVaR is the only coherent risk metric explicitly focused on measuring tail losses, which are an important, empirical feature of markets. CVaR is the most practically useful risk metric for an investor interested in minimizing declines in the value of a portfolio at stress points while maximizing returns. Through several examples, I demonstrate that the choice of a risk metric may lead to very different portfolios and investment performance due to differences in investment selection, portfolio construction and risk management. I also demonstrate that the focus on tail losses as opposed to volatility results in superior performance - much smaller declines in value at stress points with improvements in average and cumulative returns; similar results can be achieved with other risk metrics, which are not designed to measure tail losses like CVaR Based on empirical data, practical recommendations for investment analysis, portfolio construction and risk management are included throughout the article.
ARBITRAGE PRICING THEORY AND MULTIFACTOR MODELS.pptPankajKhindria
The Arbitrage Pricing Theory (APT) proposes that the expected return of a financial asset can be modeled as a linear function of various macroeconomic factors where sensitivity to changes in each factor is represented by a factor-specific beta coefficient. In contrast to the Capital Asset Pricing Model which relies on a single market factor, the APT allows for multiple common factors that influence asset returns. Empirical tests of the APT have been inconclusive due to difficulty in identifying a set of factors that consistently explains security returns.
Fair valuation of participating life insurance contracts with jump riskAlex Kouam
A C++ based program which prices the fair value of a participating life insurance whereby the underlying follows a Kou process and the insurer's default occurs only at contract's maturity.
Dissertation template bcu_format_belinda -sampleAssignment Help
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The document discusses using multifractal and wavelet analysis to predict financial market crises. It analyzes various financial market indices during crisis periods from 1987 to 2008. Fractals are described as shapes that appear similar at different scales and have non-integer dimensions. Financial markets exhibit fractal properties with long-term memory and volatility clustering. The researchers use techniques like Hurst exponent analysis, time series partitioning, and calculating partition functions and fractal dimension spectra to analyze indices for signs of an impending crisis. The width of the multifractal spectrum is proposed as an indicator, with wider widths preceding crashes.
Presentation final _FINANCE MARKET CRASH PREDICTIONNVictor Romanov
The document discusses using multifractal and wavelet analysis to predict financial market crises. It analyzes various financial market indices during crisis periods between 1997-2008. Fractals are nonlinear patterns that repeat at different scales and can describe financial market prices better than traditional linear models. Multifractal analysis examines the scaling behavior of partition functions to estimate the fractal dimension spectrum, whose width may serve as an indicator for predicting crashes. The methodology involves preprocessing time series data, computing partition functions over varying scales, and using the results to analyze changes before and after crisis periods.
Risk and Return: Portfolio Theory and Assets Pricing ModelsPANKAJ PANDEY
Discuss the concepts of portfolio risk and return.
Determine the relationship between risk and return of portfolios.
Highlight the difference between systematic and unsystematic risks.
Examine the logic of portfolio theory .
Show the use of capital asset pricing model (CAPM) in the valuation of securities.
Explain the features and modus operandi of the arbitrage pricing theory (APT).
This document reviews literature on studies of mutual fund performance in the United States. It discusses seminal works that developed modern portfolio theory and the Capital Asset Pricing Model (CAPM), including works by Markowitz, Sharpe, Lintner, Treynor, Jensen, and others. The document also summarizes various studies that have examined the relationship between mutual fund performance and cash inflows, with mixed and generally insignificant results. It notes limitations of these studies, including small sample sizes and the ambiguity of measuring performance based on different market indexes.
- The chapter discusses portfolio theory and models for determining asset prices like the Capital Asset Pricing Model (CAPM) and Arbitrage Pricing Theory (APT).
- Portfolio risk depends on the correlation and covariance of returns between assets. Diversification reduces unsystematic risk but not systematic market risk.
- CAPM suggests investors should hold a combination of the risk-free asset and the market portfolio. It provides a framework to determine required rates of return based on an asset's systematic risk or beta.
- APT assumes asset returns have predictable and unpredictable components related to macroeconomic factors. It provides an alternative model to CAPM for determining expected returns.
Statistical Arbitrage
Pairs Trading, Long-Short Strategy
Cyrille BEN LEMRID

1 Pairs Trading Model 5
1.1 Generaldiscussion ................................ 5 1.2 Cointegration ................................... 6 1.3 Spreaddynamics ................................. 7
2 State of the art and model overview 9
2.1 StochasticDependenciesinFinancialTimeSeries . . . . . . . . . . . . . . . 9 2.2 Cointegration-basedtradingstrategies ..................... 10 2.3 FormulationasaStochasticControlProblem. . . . . . . . . . . . . . . . . . 13 2.4 Fundamentalanalysis............................... 16
3 Strategies Analysis 19
3.1 Roadmapforstrategydesign .......................... 19 3.2 Identificationofpotentialpairs ......................... 19 3.3 Testingcointegration ............................... 20 3.4 Riskcontrolandfeasibility............................ 20
4 Results
22
2
Contents

Introduction
This report presents my research work carried out at Credit Suisse from May to September 2012. This study has been pursued in collaboration with the Global Arbitrage Strategies team.
Quantitative analysis strategy developers use sophisticated statistical and optimization techniques to discover and construct new algorithms. These algorithms take advantage of the short term deviation from the ”fair” securities’ prices. Pairs trading is one such quantitative strategy - it is a process of identifying securities that generally move together but are currently ”drifting away”.
Pairs trading is a common strategy among many hedge funds and banks. However, there is not a significant amount of academic literature devoted to it due to its proprietary nature. For a review of some of the existing academic models, see [6], [8], [11] .
Our focus for this analysis is the study of two quantitative approaches to the problem of pairs trading, the first one uses the properties of co-integrated financial time series as a basis for trading strategy, in the second one we model the log-relationship between a pair of stock prices as an Ornstein-Uhlenbeck process and use this to formulate a portfolio optimization based stochastic control problem.
This study was performed to show that under certain assumptions the two approaches are equivalent.
Practitioners most often use a fundamentally driven approach, analyzing the performance of stocks around a market event and implement strategies using back-tested trading levels.
We also study an example of a fundamentally driven strategy, using market reaction to a stock being dropped or added to the MSCI World Standard, as a signal for a pair trading strategy on those stocks once their inclusion/exclusion has been made effective.
This report is organized as follows. Section 1 provides some background on pairs trading strategy. The theoretical results are described in Section 2. Section 3
This document provides an overview of portfolio theory and the Capital Asset Pricing Model (CAPM). It defines key concepts like the efficient frontier, market portfolio, capital market line (CML), beta, and the security market line (SML). The CAPM holds that an asset's expected return is determined by its non-diversifiable risk as measured by its beta. Beta measures how an asset's returns co-vary with the market portfolio. The document provides examples of estimating betas and calculating expected returns using the CAPM framework. It concludes by noting the CAPM is a useful but not perfect model of the risk-return relationship.
This document summarizes the Capital Asset Pricing Model (CAPM). It begins by outlining the key assumptions and logic behind the CAPM. The CAPM builds on Harry Markowitz's portfolio choice model by adding assumptions of a risk-free rate and market clearing prices. This implies that the market portfolio must be mean-variance efficient. The CAPM then predicts that an asset's expected return is determined by its beta, or non-diversifiable risk relative to the market. However, the document notes that empirical tests have found the CAPM performs poorly in validating these predictions. It concludes that while theoretical or implementation issues may be to blame, the CAPM's failure in empirical tests means its applications are generally invalid.
These Lecture series are relating the use R language software, its interface and functions required to evaluate financial risk models. Furthermore, R software applications relating financial market data, measuring risk, modern portfolio theory, risk modeling relating returns generalized hyperbolic and lambda distributions, Value at Risk (VaR) modelling, extreme value methods and models, the class of ARCH models, GARCH risk models and portfolio optimization approaches.
Modern portfolio theory (MPT) is a theory of finance that aims to construct portfolios that offer the maximum expected return for a given level of risk or the minimum risk for a given level of expected return. MPT uses diversification and asset allocation to reduce portfolio risk. It assumes investors are rational and markets are efficient. MPT models asset returns as normally distributed and defines risk as standard deviation of returns. It seeks to minimize total portfolio variance by combining assets whose returns are not perfectly correlated. The efficient frontier shows the optimal risk-return tradeoff and the capital allocation line incorporates a risk-free asset into the analysis. MPT is widely used but also faces criticisms around its assumptions.
1) A managed volatility approach seeks to provide competitive returns compared to a benchmark index while maintaining lower volatility over the long term by constructing a portfolio of stocks with low expected volatility.
2) The document summarizes the results of a simulation of a managed volatility strategy for an EMU portfolio between 1999-2010 which showed an improved Sharpe ratio and higher risk-adjusted returns compared to the benchmark index with over 28% lower volatility.
3) Managed volatility strategies that aim to limit downside risk while maintaining potential upside have become increasingly popular with investors seeking to control risk independently from returns.
In this paper, the black-litterman model is introduced to quantify investor’s views, then we expanded
the safety-first portfolio model under the case that the distribution of risk assets return is ambiguous. When
short-selling of risk-free assets is allowed, the model is transformed into a second-order cone optimization
problem with investor views. The ambiguity set parameters are calibrated through programming
This document introduces the concept of "ultimate profitability" to evaluate the effectiveness of market research. Ultimate profitability measures the maximum possible annual return from perfectly timing entry and exit from a market based on its price extremes. The document outlines a methodology to calculate ultimate profitability for different markets and indexes based on varying the scale of price movements considered. It presents an example calculation of ultimate profitability for the Russian equity index RUIX under different scales and finds an inverse power law relationship between profitability and scale.
Bid and Ask Prices Tailored to Traders' Risk Aversion and Gain Propension: a ...Waqas Tariq
Risky asset bid and ask prices “tailored” to the risk-aversion and the gain-propension of the traders are set up. They are calculated through the principle of the Extended Gini premium, a standard method used in non-life insurance. Explicit formulae for the most common stochastic distributions of risky returns, are calculated. Sufficient and necessary conditions for successful trading are also discussed.
Case Study II - The Press Conference as Critical Incident Ho.docxDaliaCulbertson719
Case Study II - The Press Conference as Critical Incident
However skillful we are with framing, at times we are apt to go “off message.” That is, under stress and in times of crisis, we may fail to communicate our best thoughts, self-image or regard for others. Leaders are especially vulnerable to go “off message” when meeting the press. Reporters are seeking a story of dramatic interest for the public. If a leader is not properly prepared for such moments, the leader’s failure may become “the story.” Press encounters require delicate framing and human sensitivity. As such, they provide excellent opportunities to learn about the art of framing in highly pressurized situations.
To begin your case study, select a televised press conference that involves a business, charity or political leader. The kind of conference to select is illustrated by Fairhurst’s (pp. 2-14) discussion of Robert E. Murray’s response to a Utah mine crisis. Consider as well her discussion of Hillary Clinton’s Pakistan encounter (pp. 127-131). View the selected conference and, where possible, obtain a transcript. Write a 5-7 page evaluation on how well the leader communicated his or her message, image and relationship to an audience.
Use these questions to guide your analysis:
How well does the leader enact, or fail to enact, Fairhurst’s “Rules of Reality Construction?”
In your view, does the leader marshal the best “Cultural Discourses” for his or her cause?
Does the leader seem well “primed” for the occasion?
How well does the leader use language forms discussed by Fairhurst (p.93)?
One cannot stay on message, if one lacks a message. Does the leader give evidence of having a vision and mission? Does he or she repeat the “master frame” sufficiently?
Does the leader effectively maintain emotional regulation?
Does the leader create audience rapport?
Requirements:
In a two to three paragraph introduction, provide the context for the press conference including:
Sponsoring organization
Speaker with a brief introduction if possible
Intended audience
Purpose and intent of message
A link to press conference video and possible transcript should be included in the appendix.
In 4 to 6 pages, analysis the communication strategies based on synthesis of the course readings and other resources or references. Use the guiding questions as possible approaches to the analysis.
Discuss how well the leader communicated the message
Discuss how well the leader constructed an image
Discuss how well the leader created and relationship to an audience.
In a 2 to 3 paragraph conclusion, reflect on what you have taken away from this analysis to apply in your communication strategies during a critical incident.
The case study should include the following:
APA Formatting including heading and subheadings.
Graduate level writing free from grammar and mechanical errors.
Citations from readings or other relevant resources to support information presented.
The document should make best.
Case Study Disclosing Individual Genetic Results to Research Partic.docxDaliaCulbertson719
Case Study: Disclosing Individual Genetic Results to Research Participants
Hot Topics Presentation:
Select a case study from the University Library that illustrates your topic.
Topic:
Informational risk and disclosure of genetic information to research participants: Chapter 11
Case Study:
Disclosing Individual Genetic Results to Research Participants
Develop a 4 slide Microsoft® PowerPoint® presentation to brief the class on your topic. Include the following:
A properly formatted title slide
.
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I present desirable features for a risk metric, incorporating the coherent risk framework and empirical features of markets. I argue that a desirable risk metric is one that is coherent and focused on measuring tail losses, which significantly affect investment performance. I evaluate 5 risk metrics: volatility, semi-standard deviation, downside deviation, Value at Risk (VaR) and Conditional Value at Risk (CVaR). I demonstrate that CVaR is the only coherent risk metric explicitly focused on measuring tail losses, which are an important, empirical feature of markets. CVaR is the most practically useful risk metric for an investor interested in minimizing declines in the value of a portfolio at stress points while maximizing returns. Through several examples, I demonstrate that the choice of a risk metric may lead to very different portfolios and investment performance due to differences in investment selection, portfolio construction and risk management. I also demonstrate that the focus on tail losses as opposed to volatility results in superior performance - much smaller declines in value at stress points with improvements in average and cumulative returns; similar results can be achieved with other risk metrics, which are not designed to measure tail losses like CVaR Based on empirical data, practical recommendations for investment analysis, portfolio construction and risk management are included throughout the article.
ARBITRAGE PRICING THEORY AND MULTIFACTOR MODELS.pptPankajKhindria
The Arbitrage Pricing Theory (APT) proposes that the expected return of a financial asset can be modeled as a linear function of various macroeconomic factors where sensitivity to changes in each factor is represented by a factor-specific beta coefficient. In contrast to the Capital Asset Pricing Model which relies on a single market factor, the APT allows for multiple common factors that influence asset returns. Empirical tests of the APT have been inconclusive due to difficulty in identifying a set of factors that consistently explains security returns.
Fair valuation of participating life insurance contracts with jump riskAlex Kouam
A C++ based program which prices the fair value of a participating life insurance whereby the underlying follows a Kou process and the insurer's default occurs only at contract's maturity.
Dissertation template bcu_format_belinda -sampleAssignment Help
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The document discusses using multifractal and wavelet analysis to predict financial market crises. It analyzes various financial market indices during crisis periods from 1987 to 2008. Fractals are described as shapes that appear similar at different scales and have non-integer dimensions. Financial markets exhibit fractal properties with long-term memory and volatility clustering. The researchers use techniques like Hurst exponent analysis, time series partitioning, and calculating partition functions and fractal dimension spectra to analyze indices for signs of an impending crisis. The width of the multifractal spectrum is proposed as an indicator, with wider widths preceding crashes.
Presentation final _FINANCE MARKET CRASH PREDICTIONNVictor Romanov
The document discusses using multifractal and wavelet analysis to predict financial market crises. It analyzes various financial market indices during crisis periods between 1997-2008. Fractals are nonlinear patterns that repeat at different scales and can describe financial market prices better than traditional linear models. Multifractal analysis examines the scaling behavior of partition functions to estimate the fractal dimension spectrum, whose width may serve as an indicator for predicting crashes. The methodology involves preprocessing time series data, computing partition functions over varying scales, and using the results to analyze changes before and after crisis periods.
Risk and Return: Portfolio Theory and Assets Pricing ModelsPANKAJ PANDEY
Discuss the concepts of portfolio risk and return.
Determine the relationship between risk and return of portfolios.
Highlight the difference between systematic and unsystematic risks.
Examine the logic of portfolio theory .
Show the use of capital asset pricing model (CAPM) in the valuation of securities.
Explain the features and modus operandi of the arbitrage pricing theory (APT).
This document reviews literature on studies of mutual fund performance in the United States. It discusses seminal works that developed modern portfolio theory and the Capital Asset Pricing Model (CAPM), including works by Markowitz, Sharpe, Lintner, Treynor, Jensen, and others. The document also summarizes various studies that have examined the relationship between mutual fund performance and cash inflows, with mixed and generally insignificant results. It notes limitations of these studies, including small sample sizes and the ambiguity of measuring performance based on different market indexes.
- The chapter discusses portfolio theory and models for determining asset prices like the Capital Asset Pricing Model (CAPM) and Arbitrage Pricing Theory (APT).
- Portfolio risk depends on the correlation and covariance of returns between assets. Diversification reduces unsystematic risk but not systematic market risk.
- CAPM suggests investors should hold a combination of the risk-free asset and the market portfolio. It provides a framework to determine required rates of return based on an asset's systematic risk or beta.
- APT assumes asset returns have predictable and unpredictable components related to macroeconomic factors. It provides an alternative model to CAPM for determining expected returns.
Statistical Arbitrage
Pairs Trading, Long-Short Strategy
Cyrille BEN LEMRID

1 Pairs Trading Model 5
1.1 Generaldiscussion ................................ 5 1.2 Cointegration ................................... 6 1.3 Spreaddynamics ................................. 7
2 State of the art and model overview 9
2.1 StochasticDependenciesinFinancialTimeSeries . . . . . . . . . . . . . . . 9 2.2 Cointegration-basedtradingstrategies ..................... 10 2.3 FormulationasaStochasticControlProblem. . . . . . . . . . . . . . . . . . 13 2.4 Fundamentalanalysis............................... 16
3 Strategies Analysis 19
3.1 Roadmapforstrategydesign .......................... 19 3.2 Identificationofpotentialpairs ......................... 19 3.3 Testingcointegration ............................... 20 3.4 Riskcontrolandfeasibility............................ 20
4 Results
22
2
Contents

Introduction
This report presents my research work carried out at Credit Suisse from May to September 2012. This study has been pursued in collaboration with the Global Arbitrage Strategies team.
Quantitative analysis strategy developers use sophisticated statistical and optimization techniques to discover and construct new algorithms. These algorithms take advantage of the short term deviation from the ”fair” securities’ prices. Pairs trading is one such quantitative strategy - it is a process of identifying securities that generally move together but are currently ”drifting away”.
Pairs trading is a common strategy among many hedge funds and banks. However, there is not a significant amount of academic literature devoted to it due to its proprietary nature. For a review of some of the existing academic models, see [6], [8], [11] .
Our focus for this analysis is the study of two quantitative approaches to the problem of pairs trading, the first one uses the properties of co-integrated financial time series as a basis for trading strategy, in the second one we model the log-relationship between a pair of stock prices as an Ornstein-Uhlenbeck process and use this to formulate a portfolio optimization based stochastic control problem.
This study was performed to show that under certain assumptions the two approaches are equivalent.
Practitioners most often use a fundamentally driven approach, analyzing the performance of stocks around a market event and implement strategies using back-tested trading levels.
We also study an example of a fundamentally driven strategy, using market reaction to a stock being dropped or added to the MSCI World Standard, as a signal for a pair trading strategy on those stocks once their inclusion/exclusion has been made effective.
This report is organized as follows. Section 1 provides some background on pairs trading strategy. The theoretical results are described in Section 2. Section 3
This document provides an overview of portfolio theory and the Capital Asset Pricing Model (CAPM). It defines key concepts like the efficient frontier, market portfolio, capital market line (CML), beta, and the security market line (SML). The CAPM holds that an asset's expected return is determined by its non-diversifiable risk as measured by its beta. Beta measures how an asset's returns co-vary with the market portfolio. The document provides examples of estimating betas and calculating expected returns using the CAPM framework. It concludes by noting the CAPM is a useful but not perfect model of the risk-return relationship.
This document summarizes the Capital Asset Pricing Model (CAPM). It begins by outlining the key assumptions and logic behind the CAPM. The CAPM builds on Harry Markowitz's portfolio choice model by adding assumptions of a risk-free rate and market clearing prices. This implies that the market portfolio must be mean-variance efficient. The CAPM then predicts that an asset's expected return is determined by its beta, or non-diversifiable risk relative to the market. However, the document notes that empirical tests have found the CAPM performs poorly in validating these predictions. It concludes that while theoretical or implementation issues may be to blame, the CAPM's failure in empirical tests means its applications are generally invalid.
These Lecture series are relating the use R language software, its interface and functions required to evaluate financial risk models. Furthermore, R software applications relating financial market data, measuring risk, modern portfolio theory, risk modeling relating returns generalized hyperbolic and lambda distributions, Value at Risk (VaR) modelling, extreme value methods and models, the class of ARCH models, GARCH risk models and portfolio optimization approaches.
Modern portfolio theory (MPT) is a theory of finance that aims to construct portfolios that offer the maximum expected return for a given level of risk or the minimum risk for a given level of expected return. MPT uses diversification and asset allocation to reduce portfolio risk. It assumes investors are rational and markets are efficient. MPT models asset returns as normally distributed and defines risk as standard deviation of returns. It seeks to minimize total portfolio variance by combining assets whose returns are not perfectly correlated. The efficient frontier shows the optimal risk-return tradeoff and the capital allocation line incorporates a risk-free asset into the analysis. MPT is widely used but also faces criticisms around its assumptions.
1) A managed volatility approach seeks to provide competitive returns compared to a benchmark index while maintaining lower volatility over the long term by constructing a portfolio of stocks with low expected volatility.
2) The document summarizes the results of a simulation of a managed volatility strategy for an EMU portfolio between 1999-2010 which showed an improved Sharpe ratio and higher risk-adjusted returns compared to the benchmark index with over 28% lower volatility.
3) Managed volatility strategies that aim to limit downside risk while maintaining potential upside have become increasingly popular with investors seeking to control risk independently from returns.
In this paper, the black-litterman model is introduced to quantify investor’s views, then we expanded
the safety-first portfolio model under the case that the distribution of risk assets return is ambiguous. When
short-selling of risk-free assets is allowed, the model is transformed into a second-order cone optimization
problem with investor views. The ambiguity set parameters are calibrated through programming
This document introduces the concept of "ultimate profitability" to evaluate the effectiveness of market research. Ultimate profitability measures the maximum possible annual return from perfectly timing entry and exit from a market based on its price extremes. The document outlines a methodology to calculate ultimate profitability for different markets and indexes based on varying the scale of price movements considered. It presents an example calculation of ultimate profitability for the Russian equity index RUIX under different scales and finds an inverse power law relationship between profitability and scale.
Bid and Ask Prices Tailored to Traders' Risk Aversion and Gain Propension: a ...Waqas Tariq
Risky asset bid and ask prices “tailored” to the risk-aversion and the gain-propension of the traders are set up. They are calculated through the principle of the Extended Gini premium, a standard method used in non-life insurance. Explicit formulae for the most common stochastic distributions of risky returns, are calculated. Sufficient and necessary conditions for successful trading are also discussed.
Similar to PROBLEMS IN SELECTION OF SECURITY PORTFOLIOS THE PERFORMAN (20)
Case Study II - The Press Conference as Critical Incident Ho.docxDaliaCulbertson719
Case Study II - The Press Conference as Critical Incident
However skillful we are with framing, at times we are apt to go “off message.” That is, under stress and in times of crisis, we may fail to communicate our best thoughts, self-image or regard for others. Leaders are especially vulnerable to go “off message” when meeting the press. Reporters are seeking a story of dramatic interest for the public. If a leader is not properly prepared for such moments, the leader’s failure may become “the story.” Press encounters require delicate framing and human sensitivity. As such, they provide excellent opportunities to learn about the art of framing in highly pressurized situations.
To begin your case study, select a televised press conference that involves a business, charity or political leader. The kind of conference to select is illustrated by Fairhurst’s (pp. 2-14) discussion of Robert E. Murray’s response to a Utah mine crisis. Consider as well her discussion of Hillary Clinton’s Pakistan encounter (pp. 127-131). View the selected conference and, where possible, obtain a transcript. Write a 5-7 page evaluation on how well the leader communicated his or her message, image and relationship to an audience.
Use these questions to guide your analysis:
How well does the leader enact, or fail to enact, Fairhurst’s “Rules of Reality Construction?”
In your view, does the leader marshal the best “Cultural Discourses” for his or her cause?
Does the leader seem well “primed” for the occasion?
How well does the leader use language forms discussed by Fairhurst (p.93)?
One cannot stay on message, if one lacks a message. Does the leader give evidence of having a vision and mission? Does he or she repeat the “master frame” sufficiently?
Does the leader effectively maintain emotional regulation?
Does the leader create audience rapport?
Requirements:
In a two to three paragraph introduction, provide the context for the press conference including:
Sponsoring organization
Speaker with a brief introduction if possible
Intended audience
Purpose and intent of message
A link to press conference video and possible transcript should be included in the appendix.
In 4 to 6 pages, analysis the communication strategies based on synthesis of the course readings and other resources or references. Use the guiding questions as possible approaches to the analysis.
Discuss how well the leader communicated the message
Discuss how well the leader constructed an image
Discuss how well the leader created and relationship to an audience.
In a 2 to 3 paragraph conclusion, reflect on what you have taken away from this analysis to apply in your communication strategies during a critical incident.
The case study should include the following:
APA Formatting including heading and subheadings.
Graduate level writing free from grammar and mechanical errors.
Citations from readings or other relevant resources to support information presented.
The document should make best.
Case Study Disclosing Individual Genetic Results to Research Partic.docxDaliaCulbertson719
Case Study: Disclosing Individual Genetic Results to Research Participants
Hot Topics Presentation:
Select a case study from the University Library that illustrates your topic.
Topic:
Informational risk and disclosure of genetic information to research participants: Chapter 11
Case Study:
Disclosing Individual Genetic Results to Research Participants
Develop a 4 slide Microsoft® PowerPoint® presentation to brief the class on your topic. Include the following:
A properly formatted title slide
.
Case Study 2Export Unlimited (EU) – Exporting Apples to Taiwan.docxDaliaCulbertson719
The document discusses a case study involving Export Unlimited (EU), a shipping company that is looking to expand its apple exports from Washington State to Taiwan. The summary is:
1. An account executive is tasked with developing a marketing plan to increase EU's apple shipments to Taiwan for a $10,000 bonus.
2. They conduct research on EU's shipping operations, Washington's apple industry, and consumer preferences in Taiwan.
3. The plan must convince apple farmers, traders, and grocery stores to use EU by addressing their needs - such as reliable delivery times and connections in Taiwan.
4. The account executive learns that Taiwanese prefer Fuji apples, especially around holidays, and that
Case Study 2 Plain View, Open Fields, Abandonment, and Border Searc.docxDaliaCulbertson719
Case Study 2: Plain View, Open Fields, Abandonment, and Border Searches as They Relate to Search and Seizures
Due Week 6 and worth 100 points
Officer Jones asked the neighborhood’s regular trash collector to put the content of the defendant’s garbage that was left on the curb in plastic bags and to turn over the bags to him at the end of the day. The trash collector did as the officer asked in order to not mix the garbage once he collected the defendant’s garbage. The officer searched through the garbage and found items indicative of narcotics use. The officer then recited the information that was obtained from the trash in an affidavit in support of a warrant to search the defendant’s home. The officer encountered the defendant at the house later that day upon execution of the warrant. The officer found quantities of cocaine and marijuana during the search and arrested the defendant on felony narcotics charges.
Write a one to two (1-2) page paper in which you:
Identify the constitutional amendment that would govern Officer Jones’ actions.
Analyze the validity and constitutionality of officer’s Jones’ actions.
Discuss if Officer Jones’ actions were justified under the doctrines of plain view, abandonment, open fields, or border searches.
Use at least two (2) quality references.
Note:
Wikipedia and other Websites do not qualify as academic resources.
Your assignment must follow these formatting requirements:
Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow APA or school-specific format. Check with your professor for any additional instructions.
Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assignment page length.
The specific course learning outcomes associated with this assignment are:
Research and analyze procedures governing the process of arrest through trial.
Critically debate the Constitutional safeguards of key Amendments with specific attention to the 4th, 5th, 6th, and 14th Amendments.
Describe the difference between searchers, warrantless searches, and stops.
Write clearly and concisely about the criminal procedure using proper writing mechanics.
Click here
to view the grading rubric for this assignment.
.
Case Study #2 Integrating Disaster Recovery IT Service Continuity.docxDaliaCulbertson719
Case Study #2: Integrating Disaster Recovery / IT Service Continuity with Information Technology Governance Frameworks
Pleases review the attached file. I have included the necessary files for this assignment, including the grading rubic that must be followed to recieve the appropriate grade for this assignment.
.
Case of Anna OOne of the very first cases that caught Freud’s atte.docxDaliaCulbertson719
Case of Anna O
One of the very first cases that caught Freud’s attention when he was starting to develop his psychoanalytic theory was that of Anna O, a patient of fellow psychiatrist Josef Breuer. Although Freud did not directly treat her, he did thoroughly analyze her case as he was fascinated by the fact that her hysteria was “cured” by Breuer. It is her case that he believes was the beginning of the psychoanalytic approach.
Through your analysis of this case, you will not only look deeper into Freud’s psychoanalytic theory but also see how Jung’s neo-psychoanalytic theory compares and contrasts with Freud’s theory.
Review the following:
The Case of Anna O.
One of the first cases that inspired Freud in the development of what would eventually become the Psychoanalytic Theory was the case of Anna O. Anna O. was actually a patient of one of Freud’s colleagues Josef Breuer. Using Breuer’s case notes, Freud was able to analyze the key facts of Anna O’s case.
Anna O. first developed her symptoms while she was taking care of her very ill father with whom she was extremely close. Some of her initial symptoms were loss of appetite to the extent of not eating, weakness, anemia, and development a severe nervous cough. Eventually she developed a severe optic headache and lost the ability to move her head, which then progressed into paralysis of both arms. Her symptoms were not solely physical as she would vacillate between a normal, mental state and a manic-type state in which she would become extremely agitated. There was even a notation of a time for which she hallucinated that the ribbons in her hair were snakes.
Toward the end of her father’s life she stopped speaking her native language of German and instead only spoke in English. A little over a year after she began taking care of her father he passed away. After his passing her symptoms grew to affect her vision, a loss of ability to focus her attention, more extreme hallucinations, and a number of suicidal attempts (Hurst, 1982).
Both Freud and Jung would acknowledge that unconscious processes are at work in this woman's problems. However, they would come to different conclusions about the origin of these problems and the method by which she should be treated.
Research Freud’s and Jung’s theories of personality using your textbook, the Internet, and the Argosy University online library resources. Based on your research, respond to the following:
Compare and contrast Freud's view of the unconscious with Jung's view and apply this case example in your explanations.
On what specific points would they agree and disagree regarding the purpose and manifestation of the unconscious in the case of Anna?
How might they each approach the treatment of Anna? What might be those specific interventions? How might Anna experience these interventions considering her history?
Write a 2–3-page paper in Word format. Apply APA standards to citation of sources. Use the following file naming convention: LastnameFir.
Case managers serve a variety of roles and functions. They may work .docxDaliaCulbertson719
Case managers serve a variety of roles and functions. They may work in a prison, probation and parole, or community environment, among others.
Review the roles and functions outlined in your text, and respond to the following:
Which roles are the most important? Why?
Which roles are the least important?
Does the working environment (prison, probation and parole, community) have an impact on which roles are most and least important?
Are there roles that you feel are inappropriate for a case manager to take on?
Which roles might cause conflict for a case manager in fulfilling his or her core roles?
.
Case Incident 8.2 The Vacation Request Tom Blair has a week’s .docxDaliaCulbertson719
Case Incident 8.2
The Vacation Request
Tom Blair has a week’s vacation coming and really wants to take it the third week in
May, which is the height of the bass fishing season. The only problem is that two of
the other five members of his department have already requested and received
approval from their boss, Luther Jones, to take off that same week. Afraid that Luther
would not approve his request, Tom decided to forward his request directly to Harry
Jensen, who is Luther’s boss and who is rather friendly to Tom (Tom has taken Harry
fishing on several occasions). Not realizing that Luther has not seen the request,
Harry approves it. Several weeks pass before Luther finds out, by accident, that Tom
has been approved to go on vacation the third week of May.
The thing that really bugs Luther is that this is only one of many instances in which
his subordinates have gone directly to Harry and gotten permission to do something.Just last week, in fact, he overheard a conversation in the washroom to the effect that,
“If you want anything approved, don’t waste time with Luther; go directly to Harry.”
Questions
1. What should Harry have done?
2. Who is at fault, Harry or Tom?
3. What if Luther confronts Harry with the problem and he simply brushes it off by
saying he is really only helping?
400 words
.
Case AssignmentBritish citizen Michael Woodford was a superstar ex.docxDaliaCulbertson719
Case Assignment
British citizen Michael Woodford was a superstar executive for Japanese manufacturer Olympus, as he achieved tremendous success heading up the company’s European division. He then became one of the very few Western executives to become a CEO of a Japanese corporation when he was named CEO of Olympus. But his tenure as CEO was to be very brief in one of the most extreme cases of culture class ever seen in recent corporate history. Woodford survived only six months as CEO after being embroiled in an ethics dispute with the chair of the Olympus corporate board.
Before starting this case, carefully review the background materials and pay close attention to cultural differences in leadership across cultures, including the differences between Eastern and Western cultures and the concepts of power distance and individualism/collectivism. Also, review some of the concepts from previous modules such as sources of power and power tactics. Then do some research on Michael Woodford and his stint at Olympus. Here are some articles to get you started:
Rowley, A. (2012, Jan 10). Olympus saga: Lessons in corporate reform.
The Business Times
[Proquest]
Tabuchi, H. (2011, Oct 15). In a culture clash, Olympus ousts its British chief.
New York Times
[Proquest]
Interview: Michael Woodford describes his fall from Olympus. (2011).
Asiamoney
[Proquest]
When you are finished with your research, write a 4- to 5-page paper addressing the following questions:
As a British CEO of a Japanese company, how much power do you think he actually had? What were his sources of power? Refer to concepts from Module 1 regarding power sources as part of your answer in addition to concepts from Module 4.
What role do you think differences in British and Japanese cultural values had in Woodford’s difficulties at Olympus? Refer to specific cultural dimensions such as power distance and individualism/collectivism and make sure to cite at least two of the readings from the background materials page for your answer.
Given the cultural differences, what negotiation tactics and leadership practices should Woodford have taken in order to avoid the conflicts that he faced? Make sure to cite concepts from the background materials in your answer including Sadri (2013) and Chapter 11 from Comfort and Franklin (2014).
Assignment Expectations
Follow the assignment instructions closely and follow all steps listed in the instructions.
Stay focused on the precise assignment questions; don’t go off on tangents or devote a lot of space to summarizing general background materials.
Make sure to cite readings from the background materials page. Rely primarily on the required background readings as your sources of information.
Include both a bibliography and in-text citations. See the
Student Guide to Writing a High-Quality Academic Paper
, including pages 13 and 14 on in-text citations
.
Case AssignmentAll organizations have internal politics. However, .docxDaliaCulbertson719
Case Assignment
All organizations have internal politics. However, most organizations keep their political battles private and it is rare that the public will know the details about political intrigue within the major corporations. However, Hewlett-Packard (HP) is rare in that its political battles were waged publicly. HP will make for an ideal case study both because of the intense political behavior occurring at the top and because many articles have been written about these political battles.
HP has been through five CEOs since 2005, and each change of CEO has been controversial. The drama started in 2005 when then CEO Carly Fiorina was under attack from several members of HP’s Board of Directors. Some board members even took the dispute public by leaking information to the press. Fiorina fought back by investigating the leaks, but ultimately lost the battle and was ousted as CEO. Her replacement, Patricia Dunn, continued to investigate leaks by the board through the use of private investigators. Even more controversy emerged when it was discovered that the investigators used the method of “pretexting” in order to obtain phone records of board members.
For this assignment, make sure to first carefully review the background materials regarding the causes of political behavior, types of political behavior, and the ethics of political behavior. Examples of the causes of political behavior include competition for resources, ambiguous organizational goals, lack of trust, and performance factors. Examples of types of political behavior include blaming others, selectively distributing information, managing impressions, and forming coalitions. Regarding ethics, consider the three main factors:
Does the political action violate individual rights?
Does it improve the welfare of those involved?
Does it increase distributive justice?
Review the background materials and do some research on the political dramas at HP. There is a lot written about HP’s many dramas over the years; here are some articles to get you started:
Veverka, M. (2011). The soap opera at HP continues.
Barron's, 91
(39), 25.
Granelli, J. S. (2006, Sep 20). Lockyer probe of HP spying reaches to '05; sources say the inquiry goes back to the ouster of CEO Carly Fiorina, a possible victim.
Los Angeles Times
[ProQuest]
Kessler, M. (2006, Sep 08). Controversial HP probe started under Fiorina; stock falls as board continues public feud.
USA Today
[ProQuest]
Pearlstein, S. (2011, Sep 25). How HP, silicon valley's darling, became a soap opera.
The Washington Post
Once you have finished reviewing the background materials and have completed your research on HP’s internal politics, write a 4- to 5-page paper addressing the following issues:
What individual and organizational factors of HP and its senior leaders led to the intense political behavior? Refer to the background readings in your answer, and in particular, pages 370–372 of the Nair textbook in your answer.
What types of political.
Case Analysis Read the CASE ANALYSIS Agricultural Subsidies (page .docxDaliaCulbertson719
Case Analysis Read the CASE ANALYSIS: Agricultural Subsidies (page 144).
Write a 5 page paper (1500 or morewords) in
APA format
in response to these questions at least siting four peer reviewed journals articles
a. Provide an overview of this case analysis; summarize the key points
b.Discuss how the Uraguay Round and the Doha Development Agenda impact agricultural subsidies.
c.Discuss the findings in Table 7.3 (page 145). How would you address the findings in a presentation?
Below is a recommended outline.
4. Cover page (See APA Sample paper)
5.Introduction
a.A thesis statement
b.Purpose of paper
c.Overview of paper
6. Body (Cite sources using in – text citations.)
a. Provide an overview of this case analysis; summarize the key points
b. Discuss how the Uraguay Round and the Doha Development Agenda impact agricultural subsidies.
c. Discuss the findings in Table 7.3 (page 145). How would you address the findings in a presentation?
Conclusion
–Summary of main points
a. Lessons Learned and Recommendations
3. References
– List the references you cited in the text of your paper according to APA format.
(Note: Do not include references that are not cited in the text of your paper)
Pg144
The Logic of Collective Action
Given that the costs to consumers are so high for each job saved,why do people tol-
erate tariffs and quotas? Ignorance is certainly the case for some goods,but for some
tariffs and quotas,the costs have been relatively well publicized.For example,many
people are aware that quotas on sugar imports cost each man,woman,and child in
the United States between $5 and $10 per year.The costs are in the form of higher
prices on candy bars,soft drinks,and other products containing sugar.Few of us work
in the sugar industry,so the argument that our jobs depend on it is weak at best.
In a surprising way, however, we probably permit our tariffs and quotas
because of a version of the jobs argument.The economist Mancur Olson studied
this problem and similar ones and noticed two important points about tariffs and
quotas.First,the costs of the policy are spread over a great many people.Second,
the benefits are concentrated.For example,we all pay a little more for candy bars
and soft drinks,but a few sugar producers reap large benefits from our restrictions
on sugar imports.Olson found that in cases such as this,there is an asymmetry in
the incentives to support and to oppose the policy.With trade protection,the ben-
efits are concentrated in a single industry and,consequently,it pays for the indus-
try to commit resources to obtaining or maintaining its protection.The industry
will hire lobbyists and perhaps participate directly in the political process through
running candidates or supporting friendly candidates. If people in the industry
think their entire livelihood depends on their ability to limit foreign competition,
they have a very large incentive to become involved in setting po.
Case Brief ExampleThis is an example of a well-written c.docxDaliaCulbertson719
Case Brief Example
This is an example of a well-written case brief. Note the compliance with the required format and how the student gets right to the important points in plain language. If legal terms are encounter which are not understood, chances are that other students will not understand them, so it is best not to use them unless defined within the brief.
Assignment sub-heading: Sixth Amendment Right to Counsel
TITLE AND CITATION
:
Nix v. Williams
, 467 U.S. 431, 104 S.Ct. 2501 (1984)
TYPE OF ACTION
: Review by the U.S. Supreme Court of a lower court ruling that evidence should be suppressed as a result of a violation of the Sixth Amendment right to counsel. The state (Nix) sought to overturn the motion to suppress that was upheld by the U.S. District Court of Appeals.
FACTS OF THE CASE
:
On December 24, 1968, ten year old Pamela Powers was kidnapped from an Iowa YMCA and her body was later found in a ditch, which was within an extensive area that was being searched by volunteers and law enforcement. The defendant was observed “carrying a large bundle wrapped in a blanket…two legs in it and they were skinny and white.” Williams’ car, which contained clothing items belonging to the victim, was found the next day approximately 160 miles from the incident. Based on this information, an extensive search was started that extended from Des Moines to Davenport, Iowa.
Law enforcement obtained a warrant for Williams’ arrest, and he subsequently turned himself into the authorities in Davenport. Williams was arraigned and had obtained and spoken with an attorney. Des Moines police detectives agreed to transport Williams and not interview him during the drive between Davenport and Des Moines. During the drive, one of the detectives on the case began to speak to Williams regarding the need to find the child’s body before it snowed so that her parents could give her a proper, “Christian” burial. The detective did not ask Williams any specific questions during this conversation. At that point, Williams provided statements to the detectives that led them to the child’s body.
Williams was then tried in state court and was found guilty of first degree murder. Williams filed a motion to suppress the evidence of the body and all related evidence concerning the body’s location based on illegally obtained testimony. When the conviction was affirmed by the Iowa state Supreme Court, Williams sought relief in the U.S. District Court for the Southern District of Iowa. The U.S. District Court, U.S. Court of Appeals, and the U.S. Supreme Court agreed with Williams and determined that he was denied the right to counsel and his statements, which led to the child’s body, could not be introduced into evidence.
Williams was tried in state court a second time, without the use by the prosecution of the statements he had given to detectives. Prosecutors introduced evidence of the child’s body under the premise of “inevitable discovery”, as the chil.
Case 2 Focused Throat Exam Lily is a 20-year-old student at the.docxDaliaCulbertson719
Case 2:
Focused Throat Exam
Lily is a 20-year-old student at the local community college. When some of her friends and classmates told her about an outbreak of flu-like symptoms sweeping her campus over the past two weeks, Lily figured she shouldn't take her three-day sore throat lightly. Your clinic has treated a few cases similar to Lily's. All the patients reported decreased appetite, headaches, and pain with swallowing. As Lily recounts these symptoms to you, you notice that she has a runny nose and a slight hoarseness in her voice but doesn't sound congested.
.
case analysis 1. Jonas is 18 and recently finished high sch.docxDaliaCulbertson719
case analysis
1. Jonas is 18 and recently finished high school. He lives at home with his mom and dad. While collecting dirty laundry in his room one day, Jonas’ mother discovered some of Jonas’ clothing with dried blood on them. She also found a bloody survival knife and muddy boots under his bed, as well as a bracelet that said “Lynn.” A few days earlier, police had discovered the missing body of Jonas’ high school sweetheart, Lynn, in the woods. Lynn had recently broken up with him. The medical examiner had determined that Lynn had died from repeated stabbing. When Jonas had been questioned by the police at the station, he claimed he knew nothing of the incident, and the police have no evidence tying Jonas to the disappearance or murder. Analyze these facts using ethical concepts or concerns from Module 8. (You are not evaluating elements of murder, or due process issues for example.)
2.
District Attorney Schultz has brought charges against three players of the University football team. They have been charged with raping a stripper at a party attended by team members. The case has received much publicity and the media have discovered that the three players have a history of violence towards women. (Last year, two other women claimed they had been raped, but the cell phone video showing the forced sex had been excluded based on an illegal police search, and the players were found "not guilty.”) Shultz believes these players are guilty, and has given approximately 60 media interviews on the case. Schultz has also been campaigning for reelection, and a conviction here would go a long way. Unfortunately for Schultz, the DNA tests he ran do not match any of the three players to the victim’s assault. When he questioned her about this, the victim made contradictory statements, and she had no other evidence to corroborate the events. In fact, while her statements confirm that they raped her, she admitted to having consensual sex with two other men at the party, which weakens the case. Schultz decides to not tell anyone about the DNA results unless asked, and instructs the victim/witness to deny the other sexual encounters at trial. Analyze these facts using ethical concepts or concerns from Module 8. (You are not evaluating elements of rape or due process issues for example.) Assuming that Schultz had a strong belief that the defendants were guilty, include in your analysis whether this affects the moral and legal permissibility of his conduct.
3.
Michelle worked two jobs as a security guard in Phoenix, Arizona. She was walking outside the building where she works at 6:30 AM, Monday, when two bundles of money fell out of an armored truck en route to a bank. Inside the bundles was approximately $500,000. Michelle had an inheritance that would post to her bank account on Wednesday. She decides to take the day off and head to Las Vegas to play poker. Unfortunately, Michelle lost all of the money she gambled, but luckily, as expected, on W.
Case Analysis
Cisco Systems Architecture
Material
Cisco Systems Architecture: ERP and Web-enabled IT. Richard L. Nolan; Kelley Porter; Christina,
Akers. Product #: 301099-PDF-ENG
https://hbr.org/product/cisco-systems-architecture-erp-and-web-enabled-it/301099-PDF-ENG
I will post more details later
.
Case Activity 3 Basic Case ProblemsAnalyze the following Business.docxDaliaCulbertson719
Case Activity 3: Basic Case Problems
Analyze the following Business Case Problems and answer questions pertaining to each Case Problem.
Use the basic steps in legal reasoning form “IRAC method” Issue, Rule, Application and Conclusion along with the Facts for each case.
Paper should be in APA Format along with cite/reference page. No more than 3 pages Non Plagiarism paper.
Please see below the cases and use the “IRAC” method along with Facts for each case.
Case Problem 10-4: Cyber Crime
Case:
[United States v. Klimecek
, _F.3d_ (7
th
Cir. 2009)]
Question: Did Klimecek commit a crime? If so, was he a “minor participant” entitled to a reduced sentence? Explain
Case Problem 10-9: A Question of Ethics: Identity Theft
Case:
[United States v. Omole
, 523 F.3d 691 (7
th
Cir. 2008)]
Question: Omole displayed contempt for the court and ridiculed his victims, calling them stupid for having been cheated. What does this behavior suggest about Omole’s ethics?
Question: Under federal sentencing guidelines, Omole could have been imprisoned for more than eight years. He received only three years, however, two of which comprised the mandatory sentence for identity theft. Was this sentence too lenient? Explain
Case Problem 11-4: Spotlight on Taco Bell – Implied Contract
Case: [Wrench, L.L.C. v. Taco Bell Corp., 256 F.3d 446 (6
th
Cir. 2001), cert. denied, 534 U.S. 114, 122 S.Ct. 921, 151 L.Ed.2d 805
(2002)
]
Question: Do these facts satisfy the requirements for an implied contract? Why or why not?
.
Carefully read through all components (listed below) required for co.docxDaliaCulbertson719
Carefully read through all components (listed below) required for completion of the Research Project. In selecting your project topic, ensure that you will be able to ascertain the appropriate data/information needed to complete the project in terms of the deliverables.
Select a health care organization (local or national, large or small, public or private) and perform a needs assessment/gap analysis. You may utilize your own organization if you are employed in a health care related company. You may approach the Research Project from a (1) Human Resources, (2) Operations, or (3) Facilities perspective. You may select an organization in your own community.
Human Resources
: staffing, training, recruitment, retention, job function redesign, etc.
Operations
: delivery of service/care, access, wait times, equipment usage, process improvements, resource optimization, regulatory compliance, etc.
Facilities
: space planning, construction, redesign, relocation.
The components for the Research Project include the following:
Title Page
Executive Summary (Needs Content Criteria)
Description of the organization (history, length in service/operation, how many beds? clients served? location; rural vs. urban, satellite locations, total number of staff, client usage information/demographics, etc.)
Needs Assessment/Gap Analysis: What is not currently being offered? Room for improvements? Service delivery deficits? Personnel issues/shortages? Justify with supporting data and statistics.
Propose an intervention (service or facility) based on the needs/gap analysis.
Justify your proposed intervention by providing an analysis from:
Cultural
Social
Legal
Economics
Regulatory
Reimbursement
Managed care
Health legislation
Contracts perspectives
Pick a minimum of three of the elements listed above depending on the organization selected and which apply to the specific organization/situation selected.
Create a plan to implement your intervention. Identify the stakeholders involved, and their role (s) in implementing the intervention. Include finance and staffing elements required to implement the intervention.
Develop a marketing communication plan on how the stakeholders will be informed, kept up-to-date, etc. prior to the intervention, during the intervention, and post intervention.
Develop a plan for measurement effectiveness of the intervention. What indicators will determine if the intervention is successful?
Reference page.
Writing the Research Project
The Research Project:
Must be 10 to 12 double-spaced pages in length, and formatted according to APA style as outlined in the Ashford Writing Center.
Must include a title page with the following:
Title of paper
Student’s name
Course name and number
Instructor’s name
Date submitted
Must begin with an introductory paragraph that has a succinct thesis statement.
Must address the topic of the paper with critical though.
Career Interview Instructions1.Select a professional who is em.docxDaliaCulbertson719
Career Interview Instructions
1.
Select a professional who is employed in your chosen/preferred profession to interview. During the interview, you will discuss and take notes on the following:
·
Professional’s academic/experiential background
·
Preparation for his/her position
·
Major duties (note if it is a secular/religious organization/business)
·
Best/worst points about the position
·
Ask about suggestions for you as the student to consider for employment in such a position
·
Ask if you could possibly have a written copy of a job description
NOTE: Be very professional and courteous when arranging for the interview. Be early for the interview and dress professionally. Be sure to explain your assignment and ask if it would be permissible to take notes. Make this interview brief.
2.
Prepare a 1-page Microsoft Word document with at least 3 paragraphs (5–7 sentences each) that detail your interview. Format would include the following heading:
Career Interview by ___________________________, Interviewer
Your name
Date/Time of Interview: _________________________________
Interviewee: __________________________________________
Professional’s name
__________________________________________
Position/Title
__________________________________________
Company
__________________________________________
Phone number and E-mail address of Interviewee
Career Report: Insert your 3 paragraphs (which include at least an introductory sentence and summary statement)
3.
Name the file “INDS400_section#_name_CareerInterview” and upload in the submission area for Module/Week 5.
Submit your Career Interview by 11:59 p.m. (ET) on Monday of Module/Week 5.
.
Cardiovascular and Peripheral Vascular DisordersComplete your assi.docxDaliaCulbertson719
Cardiovascular and Peripheral Vascular Disorders
Complete your assigned disease presentation below, include three differential diagnoses, pathology and epidemiology data.
Remember to include an evidence-based clinical practice guideline source/link relevant to the disorder
Submit your response as a reply to this post.
Presentation A
: Discuss
systolic murmurs
to include characteristics, location and radiation, diagnostic tests, special considerations, management, and education for patients. You may present in table format.
At least 375 words with 3 intext citations no older than years APA format
.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
PROBLEMS IN SELECTION OF SECURITY PORTFOLIOS THE PERFORMAN
1. PROBLEMS IN SELECTION OF SECURITY PORTFOLIOS
THE PERFORMANCE OF MUTUAL FUNDS IN THE PERIOD
1945-1964
MICHAEL C. JENSEN*
I. INTRODUCTION
A CENTRAL PROBLEM IN FINANCE (and especially portfolio
management) has
been that of evaluating the "performance" of portfolios of risky
investments.
The concept of portfolio "performance" has at least two distinct
dimensions:
1) The ability of the portfolio manager or security analyst to
increase re-
turns on the portfolio through successful prediction of future
security
prices, and
2) The ability of the portfolio manager to minimize (through
"efficient"
diversification) the amount of "insurable risk" born by the
holders of
the portfolio.
The major difficulty encountered in attempting to evaluate the
performance
of a portfolio in these two dimensions has been the lack of a
thorough under-
2. standing of the nature and measurement of "risk." Evidence
seems to indicate
a predominance of risk aversion in the capital markets, and as
long as in-
vestors correctly perceive the "riskiness" of various assets this
implies that
"risky" assets must on average yield higher returns than less
"risky" assets.'
Hence in evaluating the "performance" of portfolios the effects
of differential
degrees of risk on the returns of those portfolios must be taken
into account.
Recent developments in the theory of the pricing of capital
assets by
Sharpe [20], Lintner [15] and Treynor [25] allow us to
formulate explicit
measures of a portfolio's performance in each of the dimensions
outlined
above. These measures are derived and discussed in detail in
Jensen [11].
However, we shall confine our attention here only to the
problem of evaluating
a portfolio manager's predictive ability-that is his ability to earn
returns
through successful prediction of security prices which are
higher than those
which we could expect given the level of riskiness of his
portfolio. The founda-
tions of the model and the properties of the performance
measure suggested
here (which is somewhat different than that proposed in [11])
are discussed
in Section II. The model is illustrated in Section III by an
application of it
to the evaluation of the performance of 115 open end mutual
3. funds in the
period 1945-1964.
A number of people in the past have attempted to evaluate the
performance
of portfolios2 (primarily mutual funds), but almost all of these
authors have
* University of Rochester College of Business. This paper has
benefited from comments and
criticisms by G. Benston, E. Fama, J. Keilson, H. Weingartner,
and especially M. Scholes.
1. Assuming, of course, that investors' expectations are on
average correct.
2. See for example [2, 3, 7, 8, 9, 10, 21, 24].
389
390 The Journal of Finance
relied heavily on relative measures of performance when what
we really need
is an absolute measure of performance. That is, they have relied
mainly on
procedures for ranking portfolios. For example, if there are two
portfolios A
and B, we not only would like to know whether A is better (in
some sense)
than B, but also whether A and B are good or bad relative to
some absolute
standard. The measure of performance suggested below is such
an absolute
measure.3 It is important to emphasize here again that the word
4. "perfor-
mance" is used here only to refer to a fund manager's
forecasting ability. It
does not refer to a portfolio's "efficiency" in the Markowitz-
Tobin sense. A
measure of "efficiency" and its relationship to certain measures
of diversifica-
tion and forecasting ability is derived and discussed in detail in
Jensen [11].
For purposes of brevity we confine ourselves here to an
examination of a fund
manager's forecasting ability which is of interest in and of itself
(witness the
widespread interest in the theory of random walks and its
implications regard-
ing forecasting success).
In addition to the lack of an absolute measure of performance,
these past
studies of portfolio performance have been plagued with
problems associated
with the definition of "risk" and the need to adequately control
for the vary-
ing degrees of riskiness among portfolios. The measure
suggested below takes
explicit account of the effects of "risk" on the returns of the
portfolio.
Finally, once we have a measure of portfolio "performance" we
also need
to estimate the measure's sampling error. That is we want to be
able to
measure its "significance" in the usual statistical sense. Such a
measure of
significance also is suggested below.
5. II. THE MODEL
The Foundations of the Model.-As mentioned above, the
measure of port-
folio performance summarized below is derived from a direct
application of
the theoretical results of the capital asset pricing models
derived independently
by Sharpe [20], Lintner [15] and Treynor [25]. All three models
are based
on the assumption that (1) all investors are averse to risk, and
are single
period expected utility of terminal wealth maximizers, (2) all
investors have
identical decision horizons and homogeneous expectations
regarding invest-
ment opportunities, (3) all investors are able to choose among
portfolios
solely on the basis of expected returns and variance of returns,
(4) all trans-
actions costs and taxes are zero, and (5) all assets are infinitely
divisible.
Given the additional assumption that the capital market is in
equilibrium, all
three models yield the following expression for the expected
one period return,4
E(Rj), on any security (or portfolio) j:
E(Rj) = RF + (j3[E(Rk1) - RF] (1)
where the tildes denote random variables, and
3. It is also interesting to note that the measure of performance
suggested below is in many
respects quite closely related to the measure suggested by
Treynor [24].
6. 4. Defined as the ratio of capital gains plus dividends to the
initial price of the security. (Note,
henceforth we shall use the terms asset and security
interchangeably.)
Performance of Mutual Funds 391
RF ~ the one-period risk free interest rate.
Pij
c(Rj R
= the measure of risk (hereafter called systematic risk)
o2(R3r) which the asset pricing model implies is crucial in
determining the prices of risky assets.
E(Rm) = the expected one-period return on the "market
portfolio" which consists
of an investment in each asset in the market in proportion to its
fraction
of the total value of all assets in the market.
Thus eq. (1) implies that the expected return on any asset is
equal to the
risk free rate plus a risk premium given by the product of the
systematic risk
of the asset and the risk premium on the market portfolio.5 The
risk premium
on the market portfolio is the difference between the expected
returns on
the market portfolio and the risk free rate.
7. Equation (1) then simply tells us what any security (or
portfolio) can be
expected to earn given its level of systematic risk, Pi. If a
portfolio manager
or security analyst is able to predict future security prices he
will be able to
earn higher returns that those implied by eq. (1) and the
riskiness of his
portfolio. We now wish to show how (1) can be adapted and
extended to
provide an estimate of the forecasting ability of any portfolio
manager. Note
that (1) is stated in terms of the expected returns on any
security or port-
folio j and the expected returns on the market portfolio. Since
these expecta-
tions are strictly unobservable we wish to show how (1) can be
recast in
terms of the objectively measurable realizations of returns on
any portfolio j
and the market portfolio M.
In [11] it was shown that the single period models of Sharpe,
Lintner,
and Treynor can be extended to a multiperiod world in which
investors are
allowed to have heterogeneous horizon periods and in which the
trading of
securities takes place continuously through time. These results
indicate that
we can generalize eq. (1) and rewrite it as
E(Rjt) = RFt + Pj3[E(Rkt) - RFt] (la)
where the subscript t denotes an interval of time arbitrary with
respect to
8. length and starting (and ending) dates.
It is also shown in [5] and [11] that the measure of risk, P3i, is
approxi-
mately equal to the coefficient bi in the "market model" given
by:
Rjt = E (Rjt) + bjTtt + eJt j - 1,)2, ... ., N (2)
where bj is a parameter which may vary from security to
security and ?Ct is
an unobservable "market factor" which to some extent affects
the returns on all
5. Note that since c2(RM) is constant for all securities the risk
of any security is just
cov(R1, RM). But since cov(RM, RM,) = U2(RM) the risk of
the market portfolio is just Y2(RJ1),
and thus we are really measuring the riskiness of any security
relative to the risk of the market
portfolio. Hence the systematic risk of the market portfolio,
coV(RkM,R,)/o2(RM), is unity, and
thus the dimension of the measure of systematic risk has a
convenient intuitive interpretation.
392 The Journal of Finance
securities, and N is the total number of securities in the
market.6 The vari-
ables ;t and the &it are assumed to be independent normally
distributed
random variables with
E(tt) = 0 (3a)
9. E(ejt) =? j_ 1,2, ... , N (3b)
COV(it,'ijt) =? j_ 1,2, ... , N (3c)
(0 j#i
cov(jt, jeit) =1 j 1,2, ..., N (3d)
a 2(jj), j =;i
It is also shown in [11] that the linear relationships of eqs. (la)
and (2)
hold for any length time interval as long as the returns are
measured as
continuously compounded rates of return. Furthermore to a
close approxima-
tion the return on the market portfolio can be expressed as7
Rzmt -E (Rmt) + nt. (4)
Since evidence given in [1, 11] indicates that the market model,
given by
eqs. (2) and (3a) = (3d), holds for portfolios as well as
individual securities,
6. The "market model" given in eqs. (2) and (3a)-(3d) is in
spirit identical to the "diagonal
model" analyzed in considerable detail by Sharpe [19, 22] and
empirically tested by Blume [1].
The somewhat more descriptive term "market model" was
suggested by Fama [5]. The "diagonal
model" is usually stated as
Rjt = aj + bjIt + ujt (2a)
where I is some index of market returns, iij is a random variable
uncorrelated with I, and a;
10. and bi are constants. The differences in specification between
(2) and (2a) are necessary in
order to avoid the overspecification (pointed out by Fama [5])
which arises if one chooses to
interpret the market index I as an average of security returns or
as the returns on the market
portfolio, M (cf., [15, 201). That is, if I is some average of
security returns then the assumption
that uj is uncorrelated with I (equivalent to (3c)) cannot hold
since I contains iuj.
N
7. The return on the market portfolio is given by RM = Z XJRJ
where Xi is the ratio of
J=1
the total value of the j'th asset to the total value of all assets.
Thus by substitution from (2) we
have
RMt = XjE(Rjt) + Xjbjt + E X,t
Note that the first term on the right hand side of (3) is just
E(Rkmt), and since the market factor
n is unique only up to a transformation of scale (cf. [5]) we can
scale x such that z Xjbj = 1 and
the second term becomes just 3x. Furthermore by assumption,
the ejt in the third term are
independently distributed random variables with E (jt) = 0, and
empirical evidence indicates that
the G2(e) are roughly of the same order of magnitude as 02(x)
(cf. [1, 13]). Hence the variance
11. of the last term on the right hand side of (3), given by
2
will be extremely small since on average Xi will be equal to
1/N, and N is very large. But since
the expected value of this term ( Xjejt) is zero, and since we
have shown its variance is
extremely small, it is unlikely that it will be very different from
zero at any given time. Thus to a
very close approximation the returns on the market portfolio
will be given by eq. (4).
Performance of Mutual Funds 393
we can use (2) to recast (la) in terms of ex post returns.8
Substituting for
E(RMt) in (la) from (4) and adding P5jct + ejt to both sides of
(la) we have
E(Rjt) + Pj1tt + eCit - RFt+ ?j [RMt - Xt - RFt] + Pjt + ejt- (5)
But from (2) we note that the left hand side of (5) is just Rjt.
Hence (5)
reduces to:9
-jt = RFt +? %[Rt - RFt] + ejt. (6)
Thus assuming that the asset pricing model is empirically
valid,10 eq. (6)
says that the realized returns on any security or portfolio can be
expressed as
a linear function of its systematic risk, the realized returns on
the market
12. portfolio, the risk free rate and a random error, ejt, which has
an expected
value of zero. The term RFt can be subtracted from both sides
of eq. (6),
and since its coefficient is unity the result is
Rkjt -RFt =j [ Rmt -RFt] + jt. (7)
The left hand side of (7) is the risk premium earned on the j'th
portfolio.
As long as the asset pricing model is valid this premium is
equal to
Pi [Rmt - RFt] plus the random error term 6t.
The Measure of Performance.-Furthermore eq. (7) may be used
directly
for empirical estimation. If we wish to estimate the systematic
risk of any
individual security or of an unmanaged portfolio the constrained
regression
estimate of P in eq. (7) will be an efficient estimate" of this
systematic
risk. However, we must be very careful when applying the
equation to man-
aged portfolios. If the manager is a superior forecaster (perhaps
because of
special knowledge not available to others) he will tend to
systematically select
securities which realize ijt > 0. Hence his portfolio will earn
more than the
"normal" risk premium for its level of risk. We must allow for
this possibility
in estimating the systematic risk of a managed portfolio.
Allowance for such forecasting ability can be made by simply
not constrain-
13. ing the estimating regression to pass through the origin. That is,
we allow for
the possible existence of a non-zero constant in eq. (7) by using
(8) as the
estimating equation.
-jt RFt - aj + Pj3[RIt - RFt_ + ijt. (8)
8. Note that the parameters fj (in (la)) and bj (in (2)) are not
subscripted by t and are thus
assumed to be stationary through time. Jensen [11] has shown
(2) to be an empirically valid
description of the behavior of the returns on the portfolios of
115 mutual funds, and Blume [1]
has found similar results for the behavior of the returns on
individual securities.
In addition it will be shown below that any non-stationarity
which might arise from attempts to
increase returns by changing the riskiness of the portfolio
according to forecasts about the market
factor it lead to relatively few problems.
9. Since the error of approximation in (6) is very slight (cf.
[11], and note 7), we henceforth
use the equality.
10. Evidence given in [113 suggests this is true.
11. In the statistical sense of the term.
394 The Journal of Finance
The new error term uit will now have E(5it) - 0, and should be
serially
14. independent.'2
Thus if the portfolio manager has an ability to forecast security
prices, the
intercept, aj, in eq. (8) will be positive. Indeed, it repr esents the
average in-
cremental rate of return on the portfolio per unit time which is
due solely to
the manager's ability to forecast future security prices. It is
interesting to
note that a naive random selection buy and hold policy can be
expected to
yield a zero intercept. In addition if the manager is not doing as
well as a
random selection buy and hold policy, aj will be negative. At
first glance it
might seem difficult to do worse than a random selection policy,
but such
results may very well be due to the generation of too many
expenses in un-
successful forecasting attempts.
However, given that we observe a positive intercept in any
sample of re-
turns on a portfolio we have the difficulty of judging whether or
not this
observation was due to mere random chance or to the superior
forecasting
ability of the portfolio manager. Thus in order to make
inferences regarding
the fund manager's forecasting ability we need a measure of the
standard
error of estimate of the performance measure. Least squares
regression theory
provides an estimate of the dispersion of the sampling
distribution of the
15. intercept aj. Furthermore, the sampling distribution of the
estimate, a&, is a
student t distribution with nj-2 degrees of freedom. These facts
give us the
information needed to make inferences regarding the statistical
significance
of the estimated performance measure.
It should be emphasized that in estimating aj, the measure of
performance,
we are explicitly allowing for the effects of risk on return as
implied by the
asset pricing model. Moreover, it should also be noted that if
the model is
valid, the particular nature of general economic conditions or
the particular
market conditions (the behavior of r) over the sample or
evaluation period
has no effect whatsoever on the measure of performance. Thus
our measure
of performance can be legitimately compared across funds of
different risk
levels and across differing time periods irrespective of general
economic and
market conditions.
The Effects of Non-Stationarity of the Risk Parameter.-It was
pointed
out earlier'3 that by omitting the time subscript from Pi (the risk
parameter
in eq. (8)) we were implicitly assuming the risk level of the
portfolio under
consideration is stationary through time. However, we know this
need not be
strictly true since the portfolio manager can certainly change
the risk level
16. of his portfolio very easily. He can simply switch from more
risky to less risky
equities (or vice versa), or he can simply change the distribution
of the assets
of the portfolio between equities, bonds and cash. Indeed the
portfolio man-
ager may consciously switch his portfolio holdings between
equities, bonds
and cash in trying to outguess the movements of the market.
This consideration brings us to an important issue regarding the
meaning
12. If 7it were not serially independent the manager could
increase his return even more by
taking account of the information contained in the serial
dependence and would therefore eliminate
it.
13. See note 8 above.
Performance of Mutual Funds 395
of "forecasting ability." A manager's forecasting ability may
consist of an
ability to forecast the price movements of individual securities
and/or an
ability to forecast the general behavior of security prices in the
future (the
"market factor" a in our model). Therefore we want an
evaluation model
which will incorporate and reflect the ability of the manager to
forecast the
market's behavior as well as his ability to choose individual
17. issues.
Fortunately the model outlined above will also measure the
success of these
market forecasting or "timing" activities as long as we can
assume that the
portfolio manager attempts on average to maintain a given level
of risk in his
portfolio. More formally as long as we can express the risk of
the j'th port-
folio at any time t as
Pit = Pi + ijt (9)
where P3 is the "target" risk level which the portfolio manager
wishes to main-
tain on average through time, and ijt is a normally distributed
random
variable (at least partially under the manager's control) with
E(ijt) = 0.
The variable ijt is the vehicle through which the manager may
attempt to
capitalize on any expectations he may have regarding the
behavior of the
market factor 'c in the next period. For example if the manager
(correctly)
perceives that there is a higher probability that a will be
positive (rather
than negative) next period, he will be able to increase the
returns on his
portfolio by increasing its risk,'4 i.e., by making Fjt positive
this period. On
the other hand he can reduce the losses (and therefore increase
the average
returns) on the portfolio by reducing the risk level of the
portfolio (i.e., making
ejt negative) when the market factor a is expected to be
18. negative. Thus if the
manager is able to forecast market movements to some extent,
we should find
a positive relationship between cjt and aft. We can state this
relationship for-
mally as:
jt-afiit + ?jt (10)
where the error term vjt is assumed to be normally distributed
with E (Wjt) - 0.
The coefficient aj will be positive if the manager has any
forecasting ability
and zero if he has no forecasting ability. We can rule out aj < 0,
since as a
conscious policy this would be irrational. Moreover, we can rule
out aj < 0
caused by perverse forecasting ability since this also implies
knowledge of
nt and would therefore be reflected in a positive aj as long as
the manager
learned from past experience. Note also that eq. (10) includes
no constant term
since by construction this would be included in fj in eq. (9). In
addition we note
that while aj will be positive only if the manager can forecast x,
its size will
depend on the manager's willingness to bet on his forecasts. His
willingness
to bet on his forecasts will of course depend on his attitudes
towards taking
these kinds of risks and the certainty with which he views his
estimates.
Substituting from (9) into (8) the more general model appears as
19. kjt - Rpt = a;j + (pj + ijt) [Rmt -RFt] + Ujt- .l
14. Perhaps by shifting resources out of bonds and into equities,
or if no bonds are currently
held, by shifting into higher risk equities or by borrowing funds
and investing them in equities.
396 The Journal of Finance
Now as long as the estimated risk parameter ( is an unbiased
estimate of the
average risk level 13j, the estimated performance measure (aj)
will also be
unbiased. Under the assumption that the forecast error ijt is
uncorrelated
with tt (which is certainly reasonable), it can be shown'5 that
the expected
value of the least squares estimator PJ is:
E(> =cov[(Rjt - RFt), (Rmt - RFt)]
E (pj) = , 8 2 ()12)
Thus the estimate of the risk parameter is biased downward by
an amount
given by aj E(Rkm), where aj is the parameter given in eq. (10)
(which de-
scribes the relationship between Fjt and :it). By the arguments
given earlier
aj can never be negative and will be equal to zero when the
manager possesses
no market forecasting ability. This is important since it means
that if the
manager is unable to forecast general market movements we
obtain an un-
20. biased estimate of his ability to increase returns on the portfolio
by choosing
individual securities which are "undervalued."
However, if the manager does have an ability to forecast market
move-
ments we have seen that aj will be positive and therefore as
shown in eq. (12)
the estimated risk parameter will be biased downward. This
means, of course,
that the estimated performance measure (a) will be biased
upward (since
the regression line must pass through the point of sample
means).
Hence it seems clear that if the manager can forecast market
movements at
all we most certainly should see evidence of it since our
techniques will tend
to overstate the magnitude of the effects of this ability. That is,
the perfor-
mance measure, aj, will be positive for two reasons: (1) the
extra returns
actually earned on the portfolio due to the manager's ability,
and (2) the
positive bias in the estimate of caj resulting from the negative
bias in our
estimate of P3i.
III. THE DATA AND EMPIRICAL RESULTS
The Data.-The sample consists of the returns on the portfolios
of 115
open end mutual funds for which net asset and dividend
information was
available in Wiesenberger's Investment Companies for the ten-
21. year period
1955-64.1" The funds are listed in Table 1 along with an
identification number
and code denoting the fund objectives (growth, income, etc.).
Annual data
were gathered for the period 1955-64 for all 115 funds and as
many additional
observations as possible were collected for these funds in the
period 1945-54.
15. By substitution from (11) into the definition of the
covariance and by the use of eq. (10),
the assumptions of the market model given in (3a)-(3d), and the
fact that 02(RM) a2(Q)
(see note 7).
16. The data were obtained primarily from the 1955 and 1965
editions of Wiesenberger [26],
but some data not available in these editions were taken from
the 1949-54 editions. Data on the
College Retirement Equities Fund (not listed in Wiesenberger)
were obtained directly from
annual reports.
All per share data were adjusted for stock splits and stock
dividends to represent an equivalent
share as of the end of December 1964.
Performance of Mutual Funds 397
TABLE 1
LISTING OF 115 OPEN END MUTUAL FUNDS IN THE
SAMPLE
22. ID
Number Codel Fund
140 0 Aberdeen Fund
141 0 Affiliated Fund, Inc.
142 2 American Business Shares, Inc.
144 3 American Mutual Fund, Inc.
145 4 Associated Fund Trust
146 0 Atomics, Physics + Science Fund, Inc.
147 2 Axe-Houghton Fund B, Inc.
1148 2 Axe-Houghton Fund A, Inc.
2148 0 Axe-Houghton Stock Fund, Inc.
150 3 Blue Ridge Mutual Fund, Inc.
151 2 Boston Fund, Inc.
152 4 Broad Street Investing Corp.
153 3 Bullock Fund, Ltd.
155 0 Canadian Fund, Inc.
157 0 Century Shares Trust
158 0 The Channing Growth Fund
1159 0 Channing Income Fund, Inc.
2159 3 Channing Balanced Fund
160 3 Channing Common Stock Fund
162 0 Chemical Fund, Inc.
163 4 The Colonial Fund, Inc.
164 0 Colonial Growth + Energy Shares, Inc.
165 2 Commonwealth Fund-Plan C
166 2 Commonwealth Investment Co.
167 3 Commonwealth Stock Fund
168 2 Composite Fund, Inc.
169 4 Corporate Leaders Trust Fund Certificates, Series "B"
171 3 Delaware Fund, Inc.
172 0 De Vegh Mutual Fund, Inc. (No Load)
173 0 Diversified Growth Stock Fund, Inc.
174 2 Diversified Investment Fund, Inc.
23. 175 4 Dividend Shares, Inc.
176 0 Dreyfus Fund Inc.
177 2 Eaton + Howard Balanced Fund
178 3 Eaton + Howard Stock Fund
180 3 Equity Fund, Inc.
182 3 Fidelity Fund, Inc.
184 3 Financial Industrial Fund, Inc.
185 3 Founders Mutual Fund
1186 0 Franklin Custodian Funds, Inc.-Utilities Series
2186 0 Franklin Custodial Funds, Inc.-Common Stock Series
187 3 Fundamental Investors, Inc.
188 2 General Investors Trust
189 0 Growth Industry Shares, Inc.
190 4 Group Securities-Common Stock Fund
1191 0 Group Securities-Aerospace-Science Fund
2191 2 Group Securities-Fully Administered Fund
192 3 Guardian Mutual Fund, Inc. (No Load)
193 3 Hamilton Funds, Inc.
194 0 Imperial Capital Fund, Inc.
195 2 Income Foundation Fund, Inc.
398 The Journal of Finance
TABLE 1 (Continued)
ID
Number Codel Fund
197 1 Incorporated Income Fund
198 3 Incorporated Investors
200 3 The Investment Company of America
201 2 The Investors Mutual, Inc.
24. 202 3 Investors Stock Fund, Inc.
203 1 Investors Selective Fund, Inc.
205 3 Investment Trust of Boston
206 2 Istel Fund, Inc.
207 3 The Johnston Mutual Fund Inc. (No-Load)
208 3 Keystone High-Grade Common Stock Fund (S-1)
1209 4 Keystone Income Common Stock Fund (S-2)
2209 0 Keystone Growth Common Stock Fund (S-3)
210 0 Keystone Lower-Priced Common Stock Fund (S-4)
1211 1 Keystone Income Fund-(K-1)
2211 0 Keystone Growth Fund (K-2)
1212 1 The Keystone Bond Fund (B-3)
2212 1 The Keystone Bond Fund (B-4)
215 2 Loomis-Sayles Mutual Fund, Inc. (No Load)
216 0 Massachusetts Investors Growth Stock Fund, Inc.
217 3 Massachusetts Investors Trust
218 2 Massachusetts Life Fund
219 4 Mutual Investing Foundation, MIF Fund
220 2 Mutual Investment Fund, Inc.
221 0 National Investors Corporation
222 4 National Securities Stock Series
1223 0 National Securities-Growth Stock Series
2223 1 National Securities-Income Series
224 1 National Securities-Dividend Series
225 2 Nation-Wide Securities Company, Inc.
226 2 New England Fund
227 4 Northeast Investors Trust (No Load)
231 3 Philadelphia Fund, Inc.
232 4 Pine Street Fund, Inc. (No Load)
233 3 Pioneer Fund, Inc.
234 0 T. Rowe Price Growth Stock Fund, Inc. (No Load)
235 1 Puritan Fund, Inc.
236 2 The George Putnam Fund of Boston
25. 239 2 Research Investing Corp.
240 2 Scudder, Stevens + Clark Balanced Fund, Inc. (No Load)
241 3 Scudder, Stevens + Clark Common Stock Fund, Inc. (No
Load)
243 3 Selected American Shares, Inc.
244 2 Shareholders' Trust of Boston
245 3 State Street Investment Corporation (No Load)
246 2 Stein Roe + Farnham Balanced Fund, Inc. (No Load)
247 0 Stein Roe + Farnham International Fund, Inc. (No Load)
249 0 Television-Electronics Fund, Inc.
250 0 Texas Fund, Inc.
251 3 United Accumulative Fund
252 4 United Income Fund
253 0 United Science Fund
254 1 The Value Line Income Fund, Inc.
255 0 The Value Line Fund, Inc.
Performance of Mutual Funds 399
TABLE 1 (Continued)
ID
Number Code1 Fund
256 4 Washington Mutual Investors Fund, Inc.
257 2 Wellington Fund, Inc.
259 3 Wisconsin Fund, Inc.
260 2 Composite Bond and Stock Fund, Inc.
1261 3 Crown Western-Diversified Fund (D-2)
2261 2 Dodge + Cox Balanced Fund (No Load)
2262 2 Fiduciary Mutual Investing Company, Inc.
263 4 The Knickerbocker Fund
267 4 Southwestern Investors, Inc.
26. 1268 2 Wall Street Investing Corporation
2268 2 Whitehall Fund, Inc.
1000 0 College Retirement Equities Fund
1 Wiesenberger classification as to fund investment objectives:
0 - Growth, 1 Income, 2 =
Balanced, 3 = Growth-Income, 4 = Income-Growth.
For this earlier period, 10 years of complete data were obtained
for 56 of the
original 115 funds.
Definitions of the Variables.-The following are the exact
definitions of the
variables used in the estimation procedures:
St = Level of the Standard and Poor Composite 500 price
index17 at the end
of year t.
Dt = Estimate of dividends received on the market portfol io in
year t as
measured by annual observations on the four quarter moving
average18
of the dividends paid by the companies in the composite 500
Index
(stated on the same scale as the level of the S & P 500 Index).
St + Dt
RMt = loge t + = The estimated annual continuously
compounded
St_1 rate of return on the market portfolio M for year t.
NAjt- Per share net asset value of the j'th fund at the end of
year t.
27. IDt_ Per share "income" dividends paid by the j'th fund during
year t.
CG t Per share "Capital gains" distributions paid by the j'th
fund during
year t.
NAjt + IDjt + CGjt
Rjt = loge + ? The annual continuously compounded
NAj, t -1 rate of return on the j'th fund during
year t. (Adjusted for splits and stock
dividends.) 19
17. Obtained from [23]. Prior to March 1, 1957, the S & P index
was based on only 90
securities (50 industrials, 20 rails and 20 utilities) and hence for
the earlier period the index is
a poorer estimate of the returns on the market portfolio.
18. Obtained from [23]. Since the use of this moving average
introduces measurement errors
in the index returns it would be preferable to use an index of the
actual dividends, but such an
index is not available.
19. Note that while most funds pay dividends on a quarterly
basis we treat all dividends as
though they were paid as of December 31 only. This assumption
of course will cause the measured
returns on the fund portfolios on average to be below what they
would be if dividends were
400 The Journal of Finance
28. rt -Yield to maturity of a one-year government bond at the
beginning of
year t (obtained from Treasury Bulletin yield curves).
Rt= loge((l + rt) = Annual continuously compounded risk free
rate of return
for year t.
nj = The number of yearly observations of the j'th fund. 10 < nj
< 20.
The Empirical Results.-Table 2 presents some summary
statistics of the
frequency distributions of the regression estimates of the
parameters of eq.
(8) for all 115 mutual funds using all sample data available for
each fund in
the period 1945-64. The table presents the mean, median,
extreme values,
and mean absolute deviation of the 115 estimates of a, (,, r2,
and p(ut, ut-i)
(the first order autocorrelation of residuals). As can be seen in
the table the
average intercept was -.011 with a minimum value of -.078 and
a maximum
value of .058. We defer a detailed discussion of the implications
of these esti-
mated intercepts for a moment.
TABLE 2
SUMMARY OF ESTIMATED REGRESSION STATISTICS
FOR EQUATION (8) FOR
115 MUTUAL FUNDS USING ALL SAMPLE DATA
AVAILABLE IN THE
29. PERIOD 1945-64. RETURNS CALCULATED NET OF ALL
EXPENSES
Rit-Rpt= a+jRmt-RFt] +ujt j=1,2,...,115 (8)
Extreme Values Mean
Mean Median Absolute
Item Value Value Minimum Maximum Deviation*
-.011 -.009 -0.080 0.058 .016
.840 .848 0.219 1.405 .162
2r2 .865 .901 0.445 0.977 .074
P(Et,tit-1)** -.077 -.064 -0.688 0.575 .211
n 17.0 19.0 10.0 20.0 3.12
115 _
vIX - Xii
z . * Defined as 115
** First order autocorrelation of residuals. The average p2 is
.075.
Since the average value of ,B was only .840, on average these
funds tended
to hold portfolios which were less risky than the market
portfolio. Thus any
attempt to compare the average returns on these funds to the
returns on a
market index without explicit adjustment for differential
riskiness would be
biased against the funds. The average squared correlation
coefficient, ^2, was
.865 and indicates in general that eq. (8) fits the data for most
30. of the funds
quite closely. The average first order autocorrelation of
residuals, -.077, is
quite small as expected.
Our primary concern in this paper is the interpretation of the
estimated
considered to be reinvested when received, but the data needed
to accomplish this are not easily
available. However, the resulting bias should be quite small. In
addition, the same bias is incor-
porated into the measured returns on the market portfolio.
Performance of Mutual Funds 401
TABLE 3
ESTIMATED INTERCEPTS, a, AND "t" VALUES FOR
INDIVIDUAL MUTUAL
FUNDS CALCULATED FROM EQUATION 8 AND ALL
SAMPLE DATA
AVAILABLE IN THE PERIOD 1945-64 USING NET
RETURNS
a
Fund ID t(a) - Number of
Number a Y(a) Observations
1191 -.0805 -1.61 13
2211 -.0783 -1.91 14
198 -.0615 -4.82 20
222 -.0520 -4.43 20
160 -.0493 -2.41 17
35. 227 .0170 1.40 14
169 .0191 1.89 20
267 .0198 0.99 10
234 .0219 1.21 14
162 .0219 0.86 20
233 .0232 1.34 20
1186 .0582 2.03 14
intercepts. They are presented in Table 3 along with the fund
identification
number and the "t" values and sample sizes. The observations
are ordered
from lowest to highest on the basis of a. The estimates range
from -.0805
TABLE 4
FREQUENCY DISTRIBUTION OF ESTIMATED INTERCEPTS
FOR EQUATION (8)
FOR 115 MUTUAL FUNDS FOR SEVERAL TIME
INTERVALS. FUND
RETURNS CALCULATED BOTH NET AND GROSS OF
EXPENSES
56 Funds All Funds
All Funds Entire 20 Years 10 Years
Sample Period* 1945-64 1955-64
Net Gross Gross Gross
Class Interval Returns Returns Returns Returns
(1) (2) (3) (4)
.06 a < .07 0 1 0 0
.05 < a .06 1 0 0 1
.04 d < .05 0 0 0 0
36. .033 a < .04 0 1 1 1
.02 < K .03 3 9 2 12
.01 a^ < .02 12 16 8 1 5
.0 < a < .01 23 21 13 31
-.01 < < < .0 22 29 17 12
-.02 < a <-.01 21 14 6 13
-.03 < a <-.02 12 11 5 12
-.04 < a ?-.03 9 9 2 3
-.05 < a <-.04 8 1 1 1
-.06 < a <-.Q)5 1 1 1 1
-.07 < <- -.06 1 0 0 0
-.08< a <-.07 1 2 0 0
-.09 < a <-.08 1 0 0 1
Average & -.011 -.004 -.032 -.001
* Sample sizes range from 10 to 20 annual observations among
the funds.
404 The Journal of Finance
35-
301
2 5-
23
22
21
k 20 -
37. ZS~~
5-
NX ~~~~~~~3
-.o 9 -.o 8 -o 7 -.o 6 -.o s -.04 -.o 3 -.o 2 .01 0 .01 .02 .03 .04
.05 .06 .07 .08 .09
ESTIMATED INTERCEPT (&)
FIGURE 1
Frequency distribution (from col. (1), Table 4) of estimated
intercepts (a) for eq. (8) for 115
mutual funds for all years available for each fund. Fund returns
calculated net of all expenses.
to +.0582. Table 4 and Figures 1-4 present summary frequency
distributions
of these estimates (along with the distributions of the
coefficients estimated
for several other time intervals which will be discussed below).
In order to obtain additional information about the forecasting
success of
fund managers eq. (8) was also estimated using fund returns
calculated be-
fore deduction of fund expenses as well as after. Fund loading
charges were
ignored in all cases.20 Columns 1 and 2 of Table 4 and Figures
1 and 2 present
the frequency distributions of the estimated a's obtained by
using all sample
data available for each fund. The number of observations in the
estimating
equation varies from 10 to 20 and the time periods are
obviously not all
38. identical. Column 1 and Figure 1 present the frequency
distribution of the
20. The loading charges have been ignored since our main
interest here is not to evaluate the
funds from the standpoint of the individual investor but only to
evaluate the fund managers'
forecasting ability.
Performance of Mutual Funds 405
35
30 29
25-
21
20
ZS~~~~~~~~~~~~~~l
16
14
'? 9
5-
2
0-
-.09-.08-.07-.06-.05-.04-.03-.02-.oI 0 .01 .02 .03 .04 .05 .06 .07
.08 .09
39. ESTIMATED INTERCEPT (a)
FIGURE 2
Frequency distribution (from col. (2), Table 4) of estimated
intercepts (a) for eq. (8) for 115
mutual funds for all years available for each fund. Fund returns
calculated gross of all management
expenses.
115 intercepts estimated on the basis of fund returns cal culated
net of all
expenses. Column 2 of Table 4 and Figure 2 present the
frequency distribu-
tions of the estimates obtained from the fund returns calculated
before
deductions of management expenses (as given by Wiesenberger
[26] 21).
The average value of a calculated net of expenses was -.011
which indi-
cates that on average the funds earned about 1.1%o less per year
(compounded
continuously) than they should have earned given their level of
systematic
risk. It is also clear from Figure 1 that the distribution is
skewed to the low
side with 76 funds having aj < 0 and only 39 with j > 0.
21. Actual expense data were available only for the 10 years
1955-64. Therefore in estimating
gross returns for the years 1945-54 the expense ratio for 1955
was added (before adjustment to
a continuous base) to the returns for these earlier years.
40. 406 The Journal of Finance
35
30
25-
20
144 17
15
13
to
5~~~~~~~~
0
-.09 -.08 -.07 -.06 -.0 5 -.04 -.03 -.0 2 -.01 0 .01 .02 .03 .04 .05
.06 .07 .08 .09
ESTIMATED INTERCEPT (A)
FIGURE 3
Frequency distribution (from col. (3), Table 4) of estimated
intercepts (a) for eq. (8) for 56
mutual funds for which complete data were available in the
period 1945-64. Fund returns calculated
gross of all management expenses.
The model implies that with a random selection buy and hold
policy one
should expect on average to do no worse than a 0. Thus it
appears from
41. the preponderance of negative a's that the funds are not able to
forecast
future security prices well enough to recover their research
expenses, man-
agement fees and commission expenses.
In order to examine this point somewhat more closely the a's
were also
estimated on the basis of returns calculated gross of all
management ex-
penses.22 That is Rjt was taken to be
NAjt + CGjt + IDjt + Ejt
Rjt= loge ( NAj, 1tJ
22. It would be desirable to use the fund returns gross of all
expenses including brokerage com-
mions as well as the management expenses. However, overall
commission data are not yet
available.
Performance of Mutuail Funds 407
35
31
30-
25-
20-
L&J
42. 1 3~ ~ 1
00lffi~~~~1 12 12
150
5-
-.09-.08-.07-.06-.05-.04-.03-.02-.01 0 .01 .02 .03 .04 .05 .06 .07
.08 .09
ESTIMATED INTERCEPT (o4)
FIGURE 4
Frequency distribution (from col. (4), Table 4) of estimated
intercepts (a) for eq. (8) for 115
mutual funds for the 10 years 1955-64. Fund returns calculated
gross of all management expenses.
where Ejt is the estimated per share dollar value of all expens es
except broker-
age commissions, interest and taxes (the latter two of which are
small) for
the j'th fund in year t obtained from [26]. Now when the
estimates are based
on gross returns any forecasting success of the funds (even if
not sufficient to
cover their expenses) should be revealed by positive &'s.
The results shown in Column 2 of Table 4 indicate the average a
estimated
from gross return data was -.004 or -.4% per year, with 67 funds
for which
a < 0 and 48 for which a > 0. The frequency distribution,
plotted in Figure
2, is much more symmetric than the distribution obtained from
43. the net returns.
Thus it appears that on average during this 20-year period the
funds were not
able to increase returns enough by their trading activities to
recoup even
their brokerage commissions (the only expenses which were not
added back
to the fund returns).
408 The Journal of Finance
In order to avoid the difficulties associated with non-identical
time periods
and unequal sample sizes, the measures for the 56 funds for
which data
were available for the entire 20-year period are summarized in
Column 3 of
Table 4 and Figure 3. The results indicate an average a of -.032
with 32
funds for which &i < 0 and 24 funds for which & > 0. It is very
likely that
part of this apparently poorer gross performance is due to the
method used
in approximating the expenses for the years prior to 1955. It
was noted earlier
that the expenses for these earlier years were assumed to be
equal to the
expenses for 1955. But since these expense ratios were
declining in the earlier
period these estimates are undoubtedly too low.
Finally in order to avoid any difficulty associated with the
estimates of the
expenses before 1955, the measures were estimated for each of
44. the 115 funds
using only the gross return data in the 10-year period 1955-64.
The frequency
distribution of the a's is given in Column 4 of Table 4 and
Figure 4. The
average a for this period was -.00 1 or -.1 % per year with 55
funds for which
a < 0 and 60 funds for which a > 0. The reader must be careful
about plac-
ing too much significance on the seemingly larger number of
funds with a > 0.
It is well known that measurement errors (even though
unbiased) in any in-
dependent variable will cause the estimated regression
coefficient of that vari-
able to be attenuated towards zero (cf. [12, chap. 6]). Since we
know that
there are undoubtedly some errors in the measurement of both
the riskless
rate and the estimated returns on the market portfolio, the
coefficients Pj are
undoubtedly slightly downward biased. This of course results in
an upward
bias in the estimates of the aj since the least squares regression
line must pass
through the point of means.
There is one additional item which tends to bias the results
slightly against
the funds. That is, the model implicitly assumes the portfolio is
fully invested.
But since the mutual funds face stochastic inflows and outflows
they must
maintain a cash balance to meet them. Data presented in [8, pp.
120-127]
indicate that on average the funds appear to hold about 2% of
45. their total
net assets in cash. If we assume the funds had earned the
riskless rate on
these assets (about 3% per year) this would increase their
returns (and the
average a) by about (.02) (.03) = .0006 per year. Thus the
adjusted average
a is about -.0004, and it is now getting very difficult to say that
this is really
different from zero. Thus, let us now give explicit consideration
to these
questions of "significance."
The "Significance" of the Estimates.-We now address ourselves
to the
question regarding the statistical significance of the estimated
performance
measures. Table 3 presents a listing of the "t" values for the
individual funds,
the intercepts, and the number of observations used in obtaining
each esti-
mate. We noted earlier that it is possible for a fund manager to
do worse than
a random selection policy since it is easy to lower a fund's
returns by un-
wisely spending resources in unsuccessful attempts to forecast
security prices.
The fact that the a's shown in Table 3 and Figure 1 are skewed
to the left
indicate this may well be true. Likewise an examination of the
"t" values
given in Table 3 and plotted in Figure 5 indicates that the t
values for 14 of
46. Performance of Mutual Funds 409
40
35
32
30 30
28
25-
20-
15-
10- 1o 10
5-
0
-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7
lit VALUE"1
FIGURE S
Frequency distribution (from col. (1), Table 5) of "t" values for
estimated intercepts in eq. (8)
for 115 mutual funds for all years available for each fund. Fund
returns calculated net of all
expenses.
47. 410 The Journal of Finance
the funds were less than -2 and hence are all significantly
negative at the
5%o level.23 However, since we had little doubt that it was
easy to do worse
than a random policy we are really interested mainly in testing
the significance
of the large positive performance measures.
An examination of Column 3 of Table 3 reveals only 3 funds
which have
performance measures which are significantly positive at the 5%
level. But
before concluding that these funds are superior we should
remember that
even if all 115 of these funds had a true a equal to zero, we
would expect
(merely because of random chance) to find 5%o of them or
about 5 or 6 funds
yielding t values "significant" at the 5% level. Thus, henceforth
we shall
concentrate on an examination of the entire frequency
distribution of the
estimated t values to see whether we observe more than the
expected number
of significant values. Unfortunately because of the differing
degrees of freedom
among the observations plotted in Figure 5 and Figure 6 (which
contains
the gross estimates), the frequency distributions are somewhat
difficult to
interpret.
However Figure 7 presents the frequency distribution of the t
values
48. calculated on the basis of gross returns for the 56 funds for
which 20 complete
years of data were available. The t value for the one-tail 2.5%
level of sig-
nificance is 2.1, and thus we expect (.025)(56) = 1.4
observations with t
values greater than 2.1. We observe just one. Again we also
observe a definite
skewness towards the negative values and no evidence of an
ability to forecast
security prices. It is interesting to note that if the model is valid
and if we
have indeed returned all expenses to the funds, these
distributions should be
symmetric about zero. However, we have not added back any of
the brokerage
commissions and have used estimates of the expenses for the
years 1945-54
which we strongly suspect are biased low. Thus the results
shown in Figure 7
are not too surprising.
As mentioned above, in order to avoid some of these difficulties
and to test
more precisely whether or not the funds were on average able to
forecast well
enough to cover their brokerage expenses (even if not their
other expenses)
the performance measures were estimated just for the period
1955-64. The
frequency distribution for the t values of the intercepts of the
115 funds
estimated from gross returns is given in Figure 8 and column 4
of Table 5.
All the observations have 8 degrees of freedom, and the
maximum and mini-
49. mum values are respectively +2.17 and -2.84. It seems clear
from the sym-
metry of this distribution about zero and especially from the
lack of any
values greater than +2.2 that there is very little evidence that
any of these
115 mutual funds in this 10-year period possessed substantial
forecasting
ability. We refrain from making a strict formal interpretation of
the sta-
tistical significance of these numbers and warn the reader to do
likewise since
there is a substantial amount of evidence (cf. [4, 18]) which
indicates the
normality assumptions on the residuals, iijt, of (8) may not be
valid. We also
point out that one could also perform chi-square goodness of fit
tests on the t
distributions presented, but for the same reasons mentioned
above we refrain
23. The t value for 5% level of significance (one-tail) with 8
degrees of freedom (the minimum
in the sample) is 1.86 and for 18 degrees of freedom (the
maximum in the sample) is 1.73.
Performance of Mutual Funds 411
41
40
3 5
50. 3 0
28
25
20-
1 5 15
10
5
~~~~~~~~~~~5
0-
-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7
If t VA LU E"l
FIGURE 6
Frequency distribution (from col. (2), Table 5) of "t" values for
estimated intercepts in eq. (8)
for 115 mutual funds for all years available for each fund. Fund
returns calculated gross of all
expenses.
412 The Journal of Finance
40
35
30
51. 25
20
La4 2 0
::Zs
I 0 15
10
-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7
" t VALUE'"
FIGURE 7
Frequency distribution (from col. (3), Table 5) of "t" values for
estimated intercepts in eq.
(8) for 56 mutual funds for which complete data were available
in the period 1945-64. Fund returns
calculated gross of all management expenses.
Performance of Mutual Funds 413
40
37
36
35
30-
25
52. 21
k20
Is- 15
10-
5 4
2
-7 -6 -5 -4 -3 -2 -I o 1 2 3 4 5 6 7
"t tVALUE'l
FIGURE 8
Frequency distribution (from col. (4), Table 5) of "t" values for
estimated intercepts in eq. (8)
for 115 mutual funds for the 10 year period 1955-64. Fund
returs calculated gross of all manage-
ment expenses.
414 Tke Journal of Finance
TABLE 5
FREQUENCY DISTRIBUTION OF "t" VALUES* FOR
ESTIMATED INTERCEPTS IN
EQUATION (8) FOR 115 MUTUAL FUNDS FOR SEVERAL
TIME INTERVALS.
FUND RETURNS CALCULATED BOTH NET AND GROSS OF
EXPENSES
53. 56 Funds All Funds
All Funds Entire 20 Years 10 Years
Sample Period** 1945-64 1955-64
Net Gross Gross Gross
Class Interval Returns Returns Returns Returns
(1) (2) (3) (4)
4? t((a) < 5 0 0 0 0
3 < t(a) < 4 0 0 0 0
2 < t(a) < 3 1 5 1 2
1 t(a) < 2 10 15 7 21
0 < t(a) < 1 28 28 15 37
-1 < t(a) < 0 32 41 20 36
-2< t(a) <-1 30 21 8 15
-3< t(a) ?-2 10 2 2 4
-4 < t(a) ?-3 1 2 2 0
-5 < t(a) <-4 3 1 1 0
* Defined as t(a) = ('
** Sample sizes from 10 to 20 annual observations among the
funds.
from doing so. That is, if the residuals are not normally
distributed the esti-
mates of the parameters will not be distributed according to the
student t
distribution, and therefore it doesn't really make sense to make
formal good-
ness of fit tests against the "t" distribution.
However, while the possible non-normality of these
disturbances causes
54. problems in attempting to perform the usual types of
significance tests, it
should be emphasized that the model itself is in no way
crucially dependent
on this assumption. Wise [27] has shown that the least squares
estimates of
bj in (2) are unbiased and consistent if the disturbance terms uj
conform to
the symmetric and finite mean members of the stable class of
distributions.
Furthermore, Fama [6] has demonstrated that the capital asset
pricing model
results (eq. (1)) can still be obtained in the context of these
distributions. A
complete discussion of the issues associated with this
distributional problem
and their relationship to the portfolio evaluation problem is
available in [11]
and will not be repeated here. It is sufficient to reiterate the fact
that the
normality assumption is necessary only in order to perform the
strict tests of
significance, and we warn the reader to interpret these tests as
merely sug-
gestive until the state of stable distribution theory is developed
to the point
where strict tests of significance can be legitimately performed.
It is important to note in examining the empirical results
presented above
that the mutual fund industry (as represented by these 115
funds) shows
very little evidence of an ability to forecast security prices.
Furthermore
there is surprisingly little evidence that indicates any individual
funds in
55. Performance of Mutual Funds 415
the sample might be able to forecast prices. These results are
even stronger
when one realizes that the biases in the estimates24 all tend to
either exaggerate
the magnitude of any forecasting ability which might exist25 or
tend to show
evidence of forecasting ability where none exists.
IV. CONCLUSION
The evidence on mutual fund performance discussed above
indicates not
only that these 115 mutual funds were on average not able to
predict security
prices well enough to outperform a buy-the-market-and-hold
policy, but also
that there is very little evidence that any individual fund was
able to do sig-
nificantly 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 book-
keeping, research, and other expenses except brokerage
commissions were
obtained free). Thus on average the funds apparently were not
quite success-
ful enough in their trading activities to recoup even their
brokerage expenses.
56. It is also important to remember that we have not considered in
this paper
the question of diversification. Evidence reported elsewhere (cf.
Jensen [11])
indicates the funds on average have done an excellent job of
minimizing the
"insurable" risk born by their shareholders. Thus the results
reported here
should not be construed as indicating the mutual funds are not
providing a
socially desirable service to investors; that question has not
been addressed
here. The evidence does indicate, however, a pressing need on
the part of the
funds themselves to evaluate much more closely both the costs
and the benefits
of their research and trading activities in order to provide
investors with
maximum possible returns for the level of risk undertaken.
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