market efficiency and market anomalies three essays investigating the opinions and behavior of finance professors both as researchers and as investors

Their investing behavior, though, suggests that finance professors accept markets as semi-strong form efficient; twice as many finance professors passively invest than actively invest..

THE FLORIDA STATE UNIVERSITY

COLLEGE OF BUSINESS

MARKET EFFICIENCY AND MARKET ANOMALIES:

THREE ESSAYS INVESTIGATING THE OPINIONS AND BEHAVIOR OF FINANCE PROFESSORS BOTH AS RESEARCHERS AND AS INVESTORS

By COLBRIN (COLBY) A WRIGHT

A Dissertation submitted to the Department of Finance

in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Degree Awarded:

Summer Semester, 2007

UMI Number: 3282678

3282678 2007

Copyright 2007 by Wright, Colbrin A.

UMI Microform Copyright

All rights reserved This microform edition is protected against unauthorized copying under Title 17, United States Code.

ProQuest Information and Learning Company

300 North Zeeb Road P.O Box 1346 Ann Arbor, MI 48106-1346 All rights reserved.

by ProQuest Information and Learning Company

The members of the Committee approve the dissertation of Colbrin A Wright defended

Committee Member

_ James Doran

Committee Member Approved:

_

William Christiansen, Chair, Department of Finance

_

Caryn L Beck-Dudley, Dean, College of Business

The Office of Graduate Studies has verified and approved the above named committee

members

To my wonderful wife, Misty, whose unremunerated and oft-times unrecognized work as a mother is both unequivocally more challenging and infinitely more important than any of my professional accomplishments Thanks for being my rock when the winds and rains have beat upon me

ACKNOWLEDGEMENTS

No great work in life is ever accomplished alone While readily acknowledging that my dissertation is no “great work,” I would like to gratefully acknowledge the many individuals who have improved the quality of this dissertation and influenced me along the way First, thanks to my dissertation chair, Dr David Peterson, who was mercilessly forced to read literally hundreds of pages of my writing His questions, candor, direction, and suggestions have been invaluable He, more than anyone else, has taught me how to

be a researcher Also, I sincerely thank each of the other members of my dissertation committee Dr Gary Benesh provided the voice of reason and experience His candid feedback on the survey instrument and on the volume of work I initially proposed proved invaluable and prophetic Dr Mike Brady has been my brightly burning torch in an otherwise dark room as I constructed, distributed, and analyzed the responses to the survey Dr James Doran became my reason to keep working On the days of frustration and disappointment, which accompany all dissertations, he was the optimistic cheerleader and demanding coach pushing me to keep going His suggestions tremendously

improved the quality of the work I also want to thank Dr Bill Christiansen whose instruction, mentoring, advice, and friendship have meant more to me than he will ever know

In addition to those mentioned above, I wish to thank the following individuals for their insightful suggestions, questions, and interest in my work: Prithviraj Banerjee, Jim Brau, Ronnie Clayton, Dean Diavatopolous, Michael Ehrhardt, Campbell Harvey, Matthew Spiegel, Tom Noe, Jeff Rockwell, Brian Tarrant, seminar participants at Central Michigan University, all respondents to my survey (especially those who provided

feedback), and the entire finance faculty at Florida State University Dave Horowitz, Tim Munyon, and Andrew Wilson provided much appreciated guidance in executing the structural equation modeling testing in the dissertation Also, surveyZ and Qualtrics.com saved me countless hours of work by generously allowing me to use their survey software free of charge Jean Heck graciously gave me access to his database that also greatly expedited the completion of the dissertation Lastly, I thank Misty Wright for her expert assistance in collecting the data for this study All errors in this dissertation are mine

TABLE OF CONTENTS

LIST OF TABLES vi

LIST OF FIGURES vii

ABSTRACT viii

INTRODUCTION AND MOTIVATION 1

How Efficient Do We Think Us Stock Markets Are And Does It Really Matter? 3

What Really Matters When Buying and Selling Stock? 5

So You Discovered an Anomaly…Gonna Publish It? 6

Outline of Dissertation 9

HOW EFFICIENT DO WE THINK US STOCK MARKETS ARE AND DOES IT REALLY MATTER? 10

Introduction 10

Background 11

Subjects, Surveys, and Response Rate 13

How Efficient are US Markets? 19

Assessing Views of Market Efficiency Based on Investing Objectives 23

Does Market Efficiency Even Matter? 24

Conclusion 35

WHAT REALLY MATTERS WHEN BUYING AND SELLING STOCKS? 46

Introduction 46

Background 47

Surveys in Finance Literature 52

Survey Subjects, Description, and Distribution 53

Results 56

Conclusion 67

SO YOU DISCOVERED AN ANOMALY…GONNA PUBLISH IT? 84

Introduction 84

The Theory and Assumptions 86

Model Implications 92

Analysis of Empirical Implications – Data and Methods 94

Analysis of Empirical Implications – Results 101

Conclusion and Discussion 110

CONCLUSION 125

APPENDIX 127

REFERENCES 138

BIOGRAPHICAL SKETCH 144

LIST OF TABLES

3 Market Efficiency Specialists’ Opinions About Market Efficiency 39

4 Respondents’ Propensities to Actively Invest by Rank 40

5 Respondents’ Propensities to Actively Invest by Specialty 41

6 The Congruence of Respondents’ Opinions and Investment Objectives 42

7 Respondents’ Investment Objectives as a Function of Their Opinions and Confidence 43

8 Explaining Investment Objectives – Ordered Probit Analysis 44

11 Relative Importance of 43 Individual Variables 71

15 Ordered Probit Analysis of Investment Experience 77

17 What Groups of Variables Matter to Active Investors 80

20 Differences in Means and Medians: Anomaly vs Matched Authors 113

25 Multicollinearity Mitigated Regression Analyses 121

LIST OF FIGURES

Related to the first question, I discover that finance professors agree that US stock markets are weak form efficient but not strong form efficient However, there is much disagreement about the semi-strong form efficiency of US stock markets Their investing behavior, though, suggests that finance professors accept markets as semi-strong form efficient; twice as many finance professors passively invest than actively invest

Surprisingly, their opinion about market efficiency has very little to do with their

investing behavior Instead, their investing behavior seems primarily driven by their confidence in their own abilities to beat the market, regardless of how efficient they perceive US stock markets to be

Related to the second question, I present three main findings First, traditional valuation techniques and asset-pricing models commonly used in research and taught in

the classroom are universally unimportant to finance professors when they buy and sell

stocks Second, the most important information to finance professors when considering stock purchases and sales are firm characteristics (PE ratio and market capitalization) and momentum related information (the stock’s return over the past six to 12 months and 52-week high and low) Third, finance professors have less real-world investing experience than one might expect – the median professor has bought an individual stock between 10 and 19 times, and 14.5% have never done so

Related to the third question, I find that finance professors are, in fact, acting rationally when they publish market anomalies The theory I develop suggests it is rational for researchers to publish market anomalies if they have relatively few previous publications or have lesser reputations Accordingly, the theory implies that the

likelihood of publishing an anomaly and the profitability of published anomalies should

be inversely related to the authors’ previous publications and reputation These

implications are empirically corroborated providing evidence for the theory and

supporting the notion that researchers are behaving rationally when they publish Sadly, this also suggests that it is very likely that profitable anomalies have been discovered but not published so that the discoverer can exploit the anomaly, which provides indirect evidence of market inefficiency

CHAPTER 1 INTRODUCTION AND MOTIVATION

Finance professors are compensated to read and perform high-level research on the subject of investing Further, they teach the subject to undergraduate and graduate students In addition to teaching and researching the topic, they also participate as

individual investors directing the allocation of their own personal portfolios Some of them are also privileged to participate at an even higher level by acting as professional managers

Hence, finance professors occupy a unique and privileged position They are both researchers and participants in the arena of investing They are abundantly educated, well informed, and highly sophisticated individuals who have the opportunity to apply the knowledge and sophistication gained through their research and teaching activities by participating in the very market they study Additionally, the research they perform actually influences the arena in which they participate

Finance professors, then, represent a strikingly unique group Yet to date they have almost been entirely ignored in their unique joint role as both researchers and

participants I can locate only one article that acknowledges and studies the dual role of finance professors – Haddad and Redman (2005) They survey finance, accounting, and economics professors to study four areas related to academics as investors: (1) current asset allocation, (2) expected sources of retirement income, (3) expected retirement asset allocation, and (4) types of financial instruments used

Considering the fact that only one article has explicitly recognized the important dual role of finance professors and that this article had a relatively limited scope, I see ample opportunity for productive and useful research in this area Similar to Haddad and Redman (2005), I begin by recognizing the unique positions of finance professors as researchers and participants in the arena of investing The topic of my work, however, substantially diverges from theirs My dissertation is a compilation of three essays

studying the subject of market efficiency and market anomalies through an in depth

analysis of finance professors in their joint role both as researchers and as market

participants

The first two essays are based on a survey distributed to all finance professors at accredited, four-year universities and colleges in the United States In the first essay, I concentrate on the issue of market efficiency I ask finance professors five questions to assess how efficient they believe US stock markets truly are I also empirically test, and ultimately refute, the notion that an investor’s decision to actively or passively invest is based on his perception of the efficiency of the market in which he is considering

investing In the second essay, I ask finance professors to identify which asset-valuation techniques, asset-pricing models, market anomalies, and other information are most and least important when they invest In the second essay, I also analyze how much real-world investing experience finance professors possess

I am not the first to survey finance professors to assess their opinions on these matters Welch (2000) surveys financial economists to develop a consensus estimate of the equity premium over a set of future horizons As a secondary (arguably tertiary) matter, he also asks them a set of questions about “issues that are commonly debated in the academic literature.” Included in his survey are two broad questions about market efficiency and the stationarity of certain firm characteristics His cursory treatment of the topics of this dissertation underscore their importance and also motivate the need for an in-depth study such as the one I offer

In the third essay, I address a perplexing question: why do finance professors publish highly profitable market anomalies? Considerable evidence exists that the

profitability of market anomalies substantially decreases and oft-times entirely disappears once the anomaly is published (see Dimson and Marsh (1999) and Marquering, Nisser, and Valla (2006)) Given their role as market participants, finance professors could easily choose not to publish highly profitable anomalies and instead use them in their own trading It doesn’t seem economically rational for a finance professor to publish a market anomaly, and yet our literature is replete with articles introducing and analyzing market anomalies (see Russell and Torbey (2002)) In the third essay, I present and empirically test a theory that explains this ostensibly irrational behavior I further

motivate each of the three essays below

How Efficient Do We Think Us Stock Markets Are And Does It Really Matter?

Market efficiency has been given tremendous attention in academic literature One reason for this is that the actually efficiency of securities markets presumably greatly influences the strategies investors adopt Burton G Malkiel in his book A Random Walk Down Wall Street explained the matter very eloquently He remarked that if securities markets are efficient, “a blindfolded monkey throwing darts at a newspaper’s financial pages could select a portfolio that would do just as well as one carefully selected by experts.”

Clearly, understanding the efficiency of securities markets is valuable

information Unfortunately, the literature on the subject has not definitively identified how efficient securities markets really are

There is much empirical evidence demonstrating efficiency in our stock markets This is demonstrated by the myriad event studies that report the impounding of

information into prices in an impressively short time period On top of the academic evidence, the well-known Wall Street Journal Dartboard Contest seemed to convincingly validated Malkiel’s statement (see Adams and Cyree (2004) for summary results)

In direct opposition to the efficient-market hypothesis, there is a large body of literature documenting short-term and long-term return anomalies (see Schwert (2002) and Russell and Torbey (2002) for insightful surveys of the subject) Market anomalies suggest that markets are not purely efficient and that investors may be able to construct strategies that consistently earn abnormal returns

There is obviously room for debate on the subject of the efficiency of US stock markets But in spite of the many published anomalies, the ability of most investors to earn consistent abnormal returns with real-world investment dollars seems dubious Even professional money managers and other sophisticated and informed investors struggle to beat the market (see Adams and Cyree (2004), Gruber (1996), Carhart (1997), Roll (1994), and Wermers (2000))

In the face of mounting evidence that sophisticated investors struggle to beat the market, investors increasingly appear to be merely passively investing Battacharya and

Galpin (2005) present evidence that stock picking is declining around the globe and particularly in America, suggesting that investors in general are accepting the argument that it is extremely difficult to beat the market on a consistent basis Instead of

attempting to do so, more and more investors are simply passively investing their money

in an attempt to mirror market returns

Battacharya and Galpin (2005) also note, however, that as more and more

investors passively invest, the Grossman-Stiglitz (1980) paradox suggests markets might become more inefficient I.e., the more investors accept and act as if markets are

efficient, the more inefficient they may become, creating opportunities for above-market returns without having to incur commensurate risk levels

So it could be that as more and more people accept markets as efficient, the markets drift toward less efficiency My objective in the first essay is to determine the collective opinion of the researchers on the subject through the use of a comprehensive survey instrument Further, I aim to discover whether an investor’s perception of market efficiency really is a fundamental determinant of his investment objectives as many of us presume it is?

I offer a brief preview of the results I find that finance professors generally strongly agree that US stock markets are weak form efficient but that they are not strong form efficient There is much disagreement, however, about the semi-strong form efficiency of US stock markets The investing behavior of respondents helps to clear up the disagreement, though Roughly twice as many respondents passively invest than actively invest, which suggests finance professors generally accept markets as semi-strong form efficient Surprisingly, however, I discover that a finance professor’s

opinion regarding the efficiency of US stock markets has little to do with his investment behavior and objectives Using robust methodologies, including ordered Probit analysis and structural equation modeling, I discover that a respondent’s confidence in his own abilities to beat the market drive his behavior, not his opinion about the efficiency of the markets in which he invests, which motivates the need for further work investigating the role of overconfidence in investing such as that by Barber and Odean (2001)

What Really Matters When Buying and Selling Stock?

Similar to market efficiency, the question of what really matters when buying and selling stock has been the subject of many articles in finance literature In fact, any article that deals with market efficiency or market anomalies necessarily addresses this question However, once again, there seems to be little consensus about what truly matters when buying and selling stock

There exists a broad array of valuation techniques, asset-pricing models, and anomalies to market efficiency , which all suggest that some unique variable or factor that is highly relevant when buying or selling stocks But the collection of literature and lack of consensus on the matter can be dizzying Is β really the only thing that matters, as CAPM suggests? Or perhaps a stock’s correlations with the Fama and French (1993) factors are also extremely important What about Carhart’s (1995) momentum factor? Could a stock’s dividend-yield also be important (Fama and French (1998) and Shiller (1998))? Or should an investor also look at a stock’s market capitalization (Banz

(1981)), PE ratio (Basu (1977)), book-to-market equity (Stattman (1980)), or 52-week high and low (George and Hwang (2004))? What about it’s return over the past six

months (Jegadeesh and Titman (1993))? Past 12 months? Is it the stock’s past returns or the industry’s past returns that matter (Moskowitz and Grinblatt (1999))? And, what

about analysts – recommendations, target prices, earnings forecasts, etc.? Should an investor care what they say? What’s an investor to do? What matters most? What matters least?

My objective in the second essay is to discover what valuation techniques, pricing models, market anomalies, firm characteristics, corporate events, seasonal

asset-variables, and other information are most and least important to finance professors when they are considering buying and selling a stock In the process, I aim to uncover how much real-world investing experience finance professors possess Surely those reading their research and listening to their lectures would be interested to know whether finance professors represent a vastly experienced group on the subject of investing Or, are they largely a group of theorists in an ivory tower with little real-world investing experience?

I offer a brief preview of the results The most surprising finding is that the traditionally accepted and most widely taught valuation techniques and asset pricing models (such as dividend valuation models and CAPM) are strikingly unimportant in the eyes of finance professors when they invest Instead, respondents indicate that the most important information in making their stock purchase and sale decisions are firm

characteristics (especially the PE ratio and market capitalization) along with momentum related information (returns over the past six and 12 months and a stock’s 52-week high and low) I also find, somewhat surprisingly, that finance professors have less investing experience than one might expect The average respondent had purchased an individual stock between 10 and 19 times, and 14.5% of all respondents had never purchased an individual stock

So You Discovered an Anomaly…Gonna Publish It?

The third essay addresses a single critical question: If finance faculty have the opportunity to transform their education and especially their research into abnormal returns for themselves and investors whose money they manage, might they be inclined

to withhold their most significant findings from publication to preserve the possibility of capturing the abnormal returns as long as possible?

Simple laws of economics suggest that any activity that provides abnormal returns will attract attention As the attention grows and entry occurs, the abnormal profitability

of the activity will gradually dissipate, until the activity offers only normal profits Similarly, if an investor discovers a strategy that results in consistent abnormal profits, he might reasonably expect that his strategy will receive increased attention As his strategy receives increased attention, he should expect to observe other investors mimicking his strategy, until the abnormal profits it once provided likewise dissipate The dissipation of profits would be expedited further if the investor made his strategy readily available to the general public

This brings up a heretofore-overlooked conundrum in the academic circles of finance Perhaps given the opportunities for personal financial enrichment, finance faculty may be withholding from publication advancements in asset pricing models or

profitable anomalies to market efficiency in order to prevent their abnormal profits from dissipating Perhaps finance professors are keeping the best for themselves and only sharing (publishing) the moderately interesting but ultimately unprofitable models,

anomalies, and empirical results from their research I am unaware of any literature addressing this possibility, which further underscores the importance of this dissertation

It is informative that Roll and Ross Asset Management states the following on its

website:

Roll and Ross was founded with a simple philosophy: Manage both risk and return To

control for risk, the firm relies on its proprietary APT risk control technology

It is no surprise that an asset management company started by Stephen Ross would use some form of the APT model, but I am struck by the fact that they use a

“proprietary” variation of the model I suppose many would shrug their shoulders and find nothing out of the ordinary about this situation – an asset management firm using

“proprietary” models in its investing What is interesting is that the father of the APT started this firm, which is using a proprietary version of his model It’s important to recognize that one cannot infer from that statement alone whether Ross had developed the proprietary APT risk control technology when he was still actively publishing and chose not to publish that particular model or whether this proprietary model came after he had decided to start Roll and Ross Asset Management

LSV Asset Management similarly admits to the use of proprietary models:

LSV Asset Management (LSV) is a quantitative value equity manager providing active

management for institutional investors through the application of proprietary investment

models

Again, one cannot infer whether Lakonishok, Shleifer, and Vishny developed their proprietary models during their peak publishing years and chose not to publish their proprietary models or if they developed the models after forming LSV Asset

management But these examples motivate a study into the possibility of academics deciding against publishing their most prized work in order to capitalize on it in the markets

Even though principles of economics advises against publishing valuable

anomalies, the literature has been filled with articles discussing specific market anomalies

(see Schwert (2002) and Russell and Torbey (2002) for a survey of the subject of market anomalies and Roll (1983) and Jegadeesh and Titman (1993) for examples of typical market-anomaly articles), how profitable they are (see, for instance, George and Hwang (2004)), how persistent they are (for example, see Dimson and Marsh (1999), Jegadeesh and Titman (2001), and Schwert (2002)), and what does or does not explain them (see, for instance, Grundy and Martin (2001) and Cooper, Guitierrez, and Hameed (2004)), but there are no articles that address the question of why finance professors are willing to publish them in the first place

To me, what’s more perplexing than the existence of market anomalies is the fact that anyone is willing to publish them once they are discovered Considering the fact that

we rely upon finance professors to discover and report market anomalies and the

possibility that publishing an anomaly may prevent its discoverer from profitably

exploiting it, it seems logical and necessary to explore the reasons why finance professors publish anomalies and especially the conditions under which finance professors may be motivated not to publish the anomalies they discover

One of the significant contributions of my third essay is to propose a theory that helps explain when or why a professor would or would not publish a profitable market anomaly he discovers It ultimately predicts that the most accomplished finance

professors have economic incentive not to publish profitable anomalies to market

efficiency and advances in asset pricing As I develop the theory, I explain the necessary conditions and then discuss the empirical implications of the model

The results ultimately corroborate the theory and suggest that authors who publish anomalies have fewer publications, especially fewer top publications per year, than non-anomaly authors publishing in the same journal Additionally, authors who publish anomalies have been in the field for a shorter period of time than their non-anomaly counterparts The profitability of an anomaly is inversely related to the number of

publications that an author has at the time of the publication of the anomaly and the number of years the author has been in the field Moreover, the profitability of an

anomaly is strongly inversely related to the first principal component of the previous publications of the author and the number of years the author has been in the field If this principal component may be interpreted as reputation, I can conclude that authors with

lesser reputations are much more likely to publish highly profitable anomalies, a

conclusion that is consistent with the theory outlined in the paper

This implies that some of the brightest minds in our field, some of the most

widely published authors in finance, have the highest incentive not to publish any market

anomalies they may find So while finance professors appear to be rational utility

maximizers, we should not expect to see the kingpins of our field publishing highly profitable anomalies, unless their utility functions are highly skewed toward reputation The empirically supported implication that some researchers discover but do not publish market anomalies also provides indirect evidence of inefficiency in the markets

Outline of Dissertation

The remainder of the dissertation proceeds as follows Chapter 2 contains the first essay entitled, “How Efficient Do We Think US Markets Are And Does It Really Matter? Chapter 3 contains the second essay entitled, “What Really Matters When Buying and Selling Stock?” Chapter 4 contains the third essay entitled, “So You Discovered an Anomaly…Gonna Publish It?” Each of the three essays is designed to be a self-

contained publication-ready document, however, I offer brief concluding remarks on all three of the essays in Chapter 5

CHAPTER 2 HOW EFFICIENT DO WE THINK US STOCK MARKETS ARE AND

DOES IT REALLY MATTER?

Introduction

One of the most fundamental questions related to investing is whether one should actively or passively invest Surely, the first factor to consider in making the active vs passive investing decision is the efficiency of the market in which one is considering investing Actively investing in a perfectly efficient market will not yield consistent abnormal returns, so why bother?

This partially explains why so many academic articles in finance address the subject of market efficiency Unfortunately, the voluminous work on the subject has not conclusively determined the actual efficiency of US stock markets In light of the

empirical disagreement on the subject, I take a unique approach to assessing the current efficiency of US stock markets I survey the experts in the field to assess their opinions

on the subject Specifically, I invite all finance professors at accredited, four-year universities in the US to respond to a survey in which I inquire about their opinions on the efficiency of US stock markets

I ultimately find that finance professors strongly agree that US stock markets are not strong form efficient To a slightly lesser degree they also concur that US stock markets are weak form efficient However, they show little agreement regarding the semi-strong form efficiency of the markets In spite of their ostensible disagreement on the matter, their investing objectives suggest they generally lean towards the belief that markets are, in fact, semi-strong form efficient: twice as many finance professors

passively invest than those who actively invest

Although my primary objective is to determine the collective opinions of finance professors on the actual efficiency of US stock markets, I come to a surprising

conclusion: a finance professor’s opinion of the efficiency of US stock markets does not really influence whether he actively or passively invests Instead of basing their

finance professors base their investing decisions on their confidence in their own abilities

to beat the market, not the general efficiency of the markets This contradicts the

fundamental notion that the active vs passive decision starts with an assessment of the efficiency of US stock markets This finding questions the practical relevance of the literature on market efficiency and provides motivation for further work on the role of overconfidence in investing

The remainder of the essay proceeds as follows Section II provides background

on the topic, including a review of relevant literature Section III describes the subjects, survey, and response rate Section IV presents finance professors’ explicit opinions about the efficiency of US stock markets Section V analyzes professors’ opinions of market efficiency through an analysis of their investing objectives Section VI explores the question of whether a respondent’s decision to actively or passively invest is related to his perceptions of the market’s efficiency Section VII concludes

Background

I am not the first to survey academics to gather a reading on their collective opinion on market efficiency Welch (2000) surveyed academics to assess their

prediction for the equity premium over a set of future horizons As a secondary matter,

he also took the opportunity to survey academics on a broad range of topics of interest Included in his survey was a single sweeping question about the efficiency of stock markets, which led him to conclude, “financial economists feel that, by and large,

financial markets are efficient.” The lack of a clear empirical consensus on the actual efficiency of US markets moved Welch to include a question on the matter in his survey, which was fundamentally about a very different subject

There is much empirical evidence supporting a large measure of efficiency in our stock markets This is demonstrated by the myriad event studies that report the

impounding of information into prices in an impressively short time period With

mounting evidence of efficiency, Burton G Malkiel in his book A Random Walk Down Wall Street explained the argument: “a blindfolded monkey throwing darts at a

newspaper’s financial pages could select a portfolio that would do just as well as one

carefully selected by experts.” Malkiel’s statement spawned the well-known Wall Street Journal Dartboard Contest, which convincingly validated his opinion (see Adams and Cyree (2004) for summary results)

In direct opposition to the efficient-market hypothesis, there is a large body of literature documenting short-term and long-term return anomalies (see Schwert (2002) and Russell and Torbey (2002) for insightful surveys of the subject) Market anomalies suggest that markets are not purely efficient and that investors may be able to construct strategies that consistently earn abnormal returns

However, many of the anomalies that have given cause for optimism to investors seeking to beat the market have been discredited for a variety of reasons: the anomalies may simply be practically unprofitable (see Jensen (1978) and Roll (1994)); the

anomalies may derive most of their hypothetical profits from the short-side of a investment portfolio (Chan (2003)), which may be constrained in reality; the anomalies may have been identified and promoted using erroneous or incomplete methodologies that do not fully consider changes to risk or relevant explanatory variables (see Fama (1998), Mitchell and Stafford (2000), Brav, Geczy and Gompers (2000), Eckbo, Masulis, and Norli (2000), Boehme and Sorescu (2002), and many others)); or the anomalies may have dissipated or even disappeared since their initial identification (see Dimson and Marsh (1999), Schwert (2002), and Marquering, Nisser, and Valla (2006))

zero-Clearly, there is room for debate on the subject of the efficiency of US stock markets In spite of the many published anomalies, the ability of most investors to earn consistent abnormal returns with real-world investment dollars seems dubious Even professional money managers and highly sophisticated and informed investors struggle to beat the market (see Adams and Cyree (2004), Gruber (1996), Carhart (1997), Roll (1994), and Wermers (2000))

Consistent with the increasing volume of work suggesting that it is difficult for investors to beat the market consistently on a risk-adjusted basis, Battacharya and Galpin (2005) present evidence that stock picking is declining around the globe and particularly

in America Leveraging on the theoretical insight from Lo and Wang (2000), they posit that if everyone in the world holds only a combination the risk-free asset and the market portfolio, the trading volume of a stock should be entirely explained by its market

capitalization It follows then that 1 – R2 from the cross-sectional regression of volume

on market capitalization represents a reasonable measure of deviation from the indexing philosophy suggested by the two-fund separation theorem In more precise language, 1 –

R2 from the said regression proxies for the degree of stock picking in a given market

They find that this measure has been decreasing steadily throughout the world, and in the US in particular Their paper suggests that investors are increasingly accepting the argument that it is extremely difficult to beat the market on a consistent basis Instead

of attempting to do so, more and more investors are simply passively investing their money in an attempt to mirror market returns

Battacharya and Galpin (2005) also note, however, that as more and more

investors passively invest, the Grossman-Stiglitz (1980) paradox suggests markets might become more inefficient I.e., the more investors accept markets as efficient, the more inefficient they may become, creating opportunities for above-market returns without having to incur commensurate risk levels

So it could be that as more and more people accept markets as efficient, the markets drift toward less efficiency My objective is to determine the collective opinion

of the researchers on the subject through the use of a comprehensive survey instrument Further, I aim to discover whether an investor’s perception of market efficiency really is

a fundamental driver of his investment objectives?

Subjects, Surveys, and Response Rate

Surveys in Finance Literature

The use of survey instruments has historically been infrequent in finance literature for three main reasons First, as Friedman (1953) articulated and Brav, Graham, Harvey, and Michaely (2005) reiterated, economic models are not conditional on the underlying agents’ understanding why they do what the do As long as statistical inference using measurable data is able to adequately support or refute economic models and hypotheses, surveying the agents is unnecessary Second, finance is privileged with access to an abundance of archival data, which provides a number of statistically desirable benefits in formal hypothesis testing Therefore, use of the archival data is generally preferable to

survey-based data Third, as Welch (2000) articulated, almost all surveys have

shortcomings and flaws, which create skepticism regarding inferences from survey data

However, recent articles in respected journals such as Journal of Finance, Journal

of Financial Economics, and Journal of Accounting and Economics have employed survey instruments as critical components of their research methodologies Specifically, Brau and Fawcett (2006) survey CFOs to determine the primary motives behind firms’ decisions to go public, Brav, et al (2005) survey financial executives to assess the

determinants of dividend and share repurchase decisions, and Graham, Harvey, and Rajgopal (2005) survey executives to illuminate the factors motivating reported earnings and disclosure decisions Further, the Journal of Applied Finance published two survey articles (Hartikainen and Torstila (2004), and Jorgensen and Wingender (2004)) in a single 2004 issue Earlier articles based on surveys include Pinegar and Wilbricht

(1989), Trahan and Gitman (1995), Welch (2000), Graham and Harvey (2001), and Krigman, Shaw, and Womack (2001) So although surveys are infrequent in finance literature, they have been published in some of the top journals in the field, are gaining greater acceptance, and have provided useful insights on a variety of subjects, particularly

in bridging the theory-practice gap

The Subjects

The subjects of my study are finance professors in the United States To identify the professors for the survey, I use the list of all regionally accredited U.S universities compiled by the University of Texas at Austin.1 For each four-year university or college,

I hand collect the names and email addresses of all professors of finance by visiting the relevant academic college and department websites at the university or college

The Surveys

The survey contains questions that fall into the following five categories:

conditioning variables (mostly comprised of demographic variables), indicators of

opinion on market efficiency, opinion on market efficiency, propensity to passively

invest, and investment strategies This essay primarily focuses on the portions dedicated

to opinion of market efficiency and propensity to passively invest

Beta Testing

An important part of successful survey construction is beta testing, in which initial drafts of the survey are administered to test groups to determine the intelligibility, reliability, and validity of the questions on the survey I beta test the survey on my

colleagues - PhD students in the College of Business at Florida State University After analyzing results from the beta testing and receiving feedback from the beta subjects, I modify the survey in close consultation with my dissertation committee members

Distribution

Finance literature incorporating surveys have largely depended on hard-copy distributions of surveys to potential respondents Brau and Fawcett (2006) carry out three waves of mailings, which include a personalized envelope, personalized signed cover letter, the survey, a postage-paid return envelope and glossary of terms They also enter respondents’ names into a drawing in which one respondent would receive $1,000 Their response rate is 18% Brav et al (2005) administer hard copies of their survey in person

at two conferences and also distribute electronic copies of their survey via e-mail To stimulate response they offer an advanced copy of the results and entry into a drawing in which two respondents would receive $500 Their total response rate is 16% (8% for the electronic delivery mechanism) Graham and Harvey (2001) distribute hard copies of their survey in two waves of mailings and faxes with follow up phone calls and faxes from a team of MBA students They offer an advanced copy of results as incentive and experience a response rate of 9%

Paper surveys, however, are largely being replaced by electronic surveys

distributed and collected via the Internet Electronic surveys offer a number of

advantages compared with traditional paper surveys (see Wright (2005), Van Selm and Jankowski (2006), Medlin, Roy, and Chai (1999), and Schaefer and Dillman (1998))

The primary advantages of electronic distribution and receipt are efficiency, expediency, accuracy, and cost savings Specifically, electronic distribution and receipt

allows for distribution to a greater number of potential respondents in a shorter period of time, with faster and more complete responses, and without the expenses of envelopes, paper, and stamps Additionally, the data are collected in electronic format, which

removes the need for hand coding data and, thus, substantially reduces the possibility of measurement error introduced by mistakes in the transcription process

The primary disadvantages of this method of distribution and receipt are

unrepresentative sampling and lower response rates The unrepresentative sampling issue arises when some portion of the population of interest does not have access to the survey

or when some portion of the population is less inclined to respond to the survey due to the delivery mechanism The lower relative response rates (see Crawford, Couper, and Lamias (2001), which discusses Kwak and Radler (2000), Guterbock, Meekins, Weaver, and Fries (2000), and Medlin and Chai (1999)) may be a more troubling issue as

demonstrated by recent finance literature Brau et al (2006) achieve an admirable

response rate of 18% using personalized envelopes and personalized cover letters, while Brav et al (2005) achieve a response rate of only 8% using the impersonal e-mail

delivery mechanism But in spite of the above contrast, there is encouraging evidence that response rates to electronic surveys are, in fact, not significantly different from response rates to paper surveys (see Schaefer and Dillman (1998))

In light of the considerable advantages it offers, I choose the electronic method of survey distribution and collection The unrepresentative sampling concern should not be

a problem in this essay since virtually every professor in the United States has access to the Internet and has an email account that is checked regularly and since the sample is relatively homogeneous And although there may be some group that is less inclined to respond to an electronic survey, this issue is not unique to the electronic delivery format Further, Schaefer and Dilman (1998) demonstrate that multi-mode (a combination of electronic and paper) contact with respondents did not significantly increase response rates I also take comfort in the fact that if I can duplicate an 8% response rate, the

sample in this study should be sufficiently large for testing I take further comfort in the finding of Brav et al (2005) that the responses to their electronic survey did not differ from those obtained through the in-person delivery mechanism

I choose to create and distribute my surveys electronically using surveyZ.com and qualtrics.com2 I incorporate strategies that have proven helpful in increasing response rates to electronic surveys Schaefer and Dillman (1998) state on p 380 that, “the most powerful determinant of response rates is the number of attempts made to contact a sample unit.” They also argue that personalization increases response rates, suggesting the need to send emails addressed to the potential respondent rather than to a mailing list Crawford et al (2001) suggest that, in the electronic environment, reminders subsequent

to the initial invitation to participate in the survey are more effective when sent two days after the initial invitation as opposed to one week as suggested by Dillman (1978) for mail surveys They also find that offering an accurate estimate (as opposed to an

inaccurately low estimate) of the time it takes to complete the survey leads to a higher number of completed surveys.3 They further argue that including a progress bar in the survey is recommended.4 Bosnjak and Tuten (2003) provide evidence that of four

possible incentive payment structures (pre-paid, post-paid, prize-drawing, and no

incentive), the prize-drawing incentive structure is most effective in eliciting completed responses (Brau and Fawcitt (2006) and Brav et al (2005) use this incentive structure)

Following the suggestions of the above literature, I implement a series of emails inviting participation in the study Two days before (day – 2) distributing the official invitation to participate electronically, I send an email, the “pre-mail,” to all potential respondents The pre-mail explains (a) that they will be receiving an electronic invitation

to respond to the survey, (b) that their responses will be strictly confidential, (c) the purpose of the survey, (d) when they can expect to receive the invitation email, (e) an estimate of how long it will take to complete the survey, (f) and the incentives for

responding The incentive for responding is entry into a drawing for $500 The premail

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They actually found that including a progress bar led to lower completion rates, but they argued their results were an artifact of the survey structure, which included many open ended questions in the beginning stages of the survey In spite of their results, they endorse the inclusion of a progress bar

also contains a link to the survey and gives the recipients the option to take the survey at that time if they prefer

Two days after the pre-mail, I send the official invitation email (day 0) including the link to the survey hosted at qualtrics.com and repeating the relevant information from the pre-mail Two days after the initial invitation survey (day 2), I send a “post-mail” to remind potential respondents of the opportunity to fill out the survey A transcript of the survey, along with copies of the pre-mail, invitation email, and post-mail are contained in Appendices A, B, C, and D respectively

To further increase response rates, emails are sent in a manner such they are addressed individually to each potential respondent Also, a progress bar is included at the bottom of each page of the survey

The premail was sent on February 19, 2007, the invitation email was sent on February 21, 2007, and the postmail was sent on February 23, 2007 The survey was deactivated on February 26, 2007

Response Rate

Emails inviting participation in the survey were sent to 4,525 professors 60 of the email addresses were invalid 1,183 professors started the survey, which is a started response rate of 26.49% 870 professors completed the survey – i.e., they answered the final question on the survey – which represents a completion rate of 73.54% and a

completed response rate of 19.48%

In order to enter the final data set, I require a respondent to meet five criteria: 1) s/he must answer yes to the consent question, 2) s/he must be a finance professor, thus eliminating professors of law, economics, and other disciplines,5 3) s/he must hold a Ph.D or DBA, 4) s/he must be of the rank of assistant professor, associate professor, full professor, endowed chair, or eminent scholar, and 5) s/he must answer at least one of the five questions on the last page of the survey There were two exceptions to this final rule Two respondents answered well over half the questions on the survey but did not answer the last five I allowed these two respondents to enter the final data set Aside from these

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Some of the websites from which email addresses were collected made it impossible to distinguish

finance professors from professors of other fields, such as business law and economics Because of this,

outliers, the respondents who failed to answer one of the last five questions universally answered less than half the questions on the survey, which casts doubt on the credibility

of their responses to the questions they did answer I also removed three professors whose responses contained glaringly inconsistent answers to questions.6

Table 1 presents the summary statistics on the responses to the survey The final data set consists of 642 respondents Of the 642, 197 are assistant professors, 197 are associate professors, 171 are full professors, 71 are endowed chairs, and 6 are eminent scholars Almost 15% of respondents are female Slightly more than 83% of respondents are married The median age of respondents is between 40 and 49 The median 9-month salary of respondents is $110,000 to $119,999 Respondents have an average of 13.37 articles published in peer reviewed journals and an average of 1.84 articles published in the Journal of Business, Journal of Finance, Journal of Financial Economics, Journal of Financial and Quantitative Analysis, or Review of Financial Studies

How Efficient are US Markets?

Although Welch (2000) asks a question similar in spirit to mine, my reading on finance professors’ opinion about market efficiency offers a richer assessment of the matter It is important to acknowledge that assessing professors’ opinions of market efficiency was not his primary research interest Hence, it was naturally given less

treatment

My survey improves upon his in three primary ways First, his survey was

presented to a limited target population, which resulted in 226 total responses My

survey was sent to virtually every finance faculty member at accredited, four-year

universities in the United States, which resulted in 642 useable responses The increased sample size resulting from the broader distribution represents an opportunity for richer results

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For instance, one professor responded that he had published a grand total of one article in peer-reviewed journals, but he indicated later that he had published over 50 articles in peer-reviewed journals that support market efficiency

Second, I ask respondents a number of demographic and conditioning questions that allow me to form groups of professors who may be more or less knowledgeable about market efficiency For instance, I ask respondents to identify their specific areas of specialty, which allows me to zero in on the opinions of finance professors who

specialize specifically in market efficiency Welch asked similar questions but with much less specificity These types of categorizing questions should provide another opportunity for richer results

Third and most importantly, I ask five questions related to market efficiency whereas Welch asks two Welch asks respondents how strongly they agree or disagree with the following statements:

1 I believe that, by and large, public securities market prices are efficient

2 I believe that, by and large, public securities market prices offer arbitrage opportunities

In order to gauge finance professors’ opinions of the efficiency of US stock markets, I ask respondents to indicate how strongly they agree or disagree with the

following statements The scale is from 1 (strongly agree) to 7 (strongly disagree):

1 It is possible to predict future returns to US stocks using only past returns

2 It is possible to predict future returns to US stocks using only past returns and publicly available information

3 It is possible to predict future returns to US stocks using only past returns, publicly available information, and private information

4 Investment returns are solely a compensation for risk

5 Investment strategies exist that consistently beat average market returns without taking average risk

above-It is worth noting at this point that no survey will meet everyone’s approval Some respondents took issue with the wording of one or more of the above questions For example, Questions 1 – 3 above are intended to represent inquiries about weak, semi-strong, and strong form market efficiency One respondent, however, pointed out in an email that he believes future returns can be predicted using past returns merely by

employing the CAPM, which is not a statement regarding the inefficiency of the market This possible interpretation was discussed with my dissertation committee prior to

distributing the survey, but we decided on the above wording Our reasoning is simple: if

we asked respondents how strongly they agreed or disagreed with the statement that US

markets are weak form efficient, we leave the term weak form efficient open to

interpretation Hence, we decided to use more concrete language

The point is regardless of how the questions are constructed, someone will always think it is poorly worded This is precisely why we ask five questions about market efficiency, instead of just asking the catchall question – how strongly do you agree or disagree the statement that US stock markets are efficient I believe there is rich

information to glean from professors’ responses to the above five questions, in spite of possible wording and interpretation disputes

Results

Table 2 shows the mean responses to the five questions regarding market

efficiency The table reports overall means and medians as well as means and medians across rank Finance professors generally agree that US stock markets are weak form efficient Respondents disagreed (mean response of 5.3) with the statement that future stock returns could be predicted using only past returns Conversely, finance professors strongly agree that US stock markets are not strong form efficient Respondents

noticeably agreed (mean response of 2.68) with the statement that future returns to US stocks could be predicted using only past returns, publicly available information, and private information They seem much less decided on the matter of semi-strong form efficiency Respondents were relatively neutral (mean response of 4.46) about the

statement that future stock returns could be predicted using only past returns and publicly available information

Regarding the other two questions on the subject, respondents were relatively neutral Mean responses to the statements that (1) investment returns are solely a

compensation for risk and (2) investment strategies exist that consistently beat average market returns without taking above-average risk are 4.29 and 4.46, respectively

Some of the respondents may be more qualified than their peers to offer an

opinion on the efficiency of US stock markets Specifically, finance professors who specialize in market efficiency should be particularly well qualified to make a statement

on the matter As a part of my survey, I ask respondents to indicate their specific areas of specialty One of the options is “market efficiency and anomalies to market efficiency.”

I expect those finance professors who specialize in market efficiency to be most the most informed on the subject I report the responses of this group in Table 3 The table shows the number and percentage of respondents who specialize in market efficiency who responded 1 through 7 to the five questions

Finance professors who specialize in market efficiency overwhelmingly agree that

US stock markets are not strong form efficient 67% of these participants showed their agreement by responding either 1 or 2, compared to only 9% responding 6 or 7, to the statement that future stock returns could be predicted using past returns, publicly

available information, and private information, indicating they clearly do not accept US stock markets as strong form efficient Although not quite to the same degree, these experts also seem largely convinced that US stock markets are weak form efficient Only 14% of this group responded 1 or 2, compared to 49% responding 6 or 7, to the statement that future returns could be predicted using only past returns, indicating their general disagreement with the statement

Their opinion on semi-strong form efficiency, however, is much less polarized 29% responded 1 or 2, while 24% responded 6 or 7 to the statement that future returns can be predicted using past returns and publicly available information This suggests the experts lean slightly to the side of US markets not being semi-strong form efficient, but mostly it suggests the experts are undecided or ambivalent about the semi-strong form efficiency of US markets This is somewhat expected given the conflicting empirical evidence on the matter

Respondents who specialize in market efficiency also generally disagree that investment returns are solely a compensation for risk (35% responded 6 or 7 compared to only 15% who answered 1 or 2) However, they also generally disagree with the notion that strategies exist that consistently beat average market returns without taking above average risk (30% responded 6 or 7 compared to only 18% answering 1 or 1)

Both overall results and results from focusing specifically on market efficiency experts indicate finance professors are fairly strongly convinced that markets are not strong form efficient To a slightly lesser degree, they seem to largely agree that US stock markets are weak form efficient However, they seem almost perfectly split in their opinions of semi-strong form efficiency

The semi-strong form efficiency of a market in which one is considering investing

is arguably the most important of the three forms to those faced with the active vs

passive decision It is unfortunate, though not unexpected, that the experts are almost perfectly split on the matter Perhaps an analysis of their investing goals and behavior will help to clarify where they truly stand on the issue

Assessing Views of Market Efficiency Based on Investing Objectives

Considering the general lack of consensus regarding the semi-strong form

efficiency of US markets, it may be informative to explore respondents’ investment behavior to infer information about their views on market efficiency It seems reasonable

to expect that finance professors who passively invest are more convinced of the

efficiency of US stock markets than those who actively invest I ask the following

question to determine the propensity of finance professors to passively or actively invest

“Please indicate how strongly you agree or disagree with the following statement: When I invest, my goal is to beat the market.” The scale is from 1 (strongly agree) to 7 (strongly disagree)

Results

Table 4 shows the responses of all participants to the question asking respondents

to indicate how strongly they agreed or disagreed with the statement, “When I invest, my goal is to beat the market.” The table separates respondents by rank and shows the

number and percentage of respondents in each rank who responded 1 through 7 to the question

The most salient feature of the table is the fact that 42% of all participants

responded either 6 or 7, compared to only 18% responding 1 or 2 to the statement This may be interpreted to mean that twice as many finance professors admit to passively investing than those who admit to actively investing This trend largely holds across all ranks: almost twice as many respondents within each rank passively invest than those who actively invest I interpret this to mean that although professors’ opinions appear largely undecided about the semi-strong form efficiency of US stock markets, their

investment objectives suggest they are more convinced of the markets’ efficiency than they admit

Again, I am particularly interested in the group of respondents who specialize in market efficiency I report their responses to the statement, “When I invest, my goal is to beat the market” in Table 5 For comparison purposes, I also include five other

specialties in the table: (1) asset pricing, (2) behavioral finance, (3) capital structure, (4) corporate governance, and (5) derivatives I include capital structure and corporate governance, because these are distinctly corporate finance subspecialties I am interested

to see how this group behaves I include the other three specialties because they

represent respondents who may be more inclined to believe either (a) markets are

inefficient or (b) regardless of market efficiency, they have the skills to beat the market

The primary result from Table 5 is that those who specialize in market efficiency behave much like the overall sample Twice as many of these experts passively invest compared to those who actively invest (40% to 20%) Again, this group indicated earlier

it is largely undecided about the semi-strong form efficiency of US markets, but their investment objectives suggest they accept US stock markets as more efficient than they are willing to admit

The professors who specialize in the other areas shown in the table generally differ from the overall sample in predictable ways Respondents specializing in corporate finance (capital structure and corporate governance) show a much stronger propensity to passively invest than the overall sample Three times as many professors in these fields passively invest than those who actively invest Professors specializing in behavioral finance and derivatives, however, shower a stronger propensity to actively invest than the overall sample The proportion of these professors actively investing is nearly identical

to the proportion passively investing Professors who specialize in asset pricing show a propensity to passively invest that is similar to the overall sample

Does Market Efficiency Even Matter?

I asserted earlier that the first factor to consider in making the active vs passive investing decision is the efficiency of the market in which one is considering investing

If this statement holds, I expect a finance professor’s investment objectives to be strongly correlated with his opinion of the efficiency of US stock markets

To begin the analysis, in Table 6 I double sort respondents based on their opinions

of market efficiency and their investment objectives To represent a respondent’s opinion

of market efficiency, I take the average of the participants’ responses to the three

questions related to weak, semi-strong, and strong form efficiency To represent their investment objectives, I use participants’ response to the statement, “When I invest, my goal is to beat the market.” Panel A reports raw numbers, while Panel B reports

percentages

The portions of the table highlighted in gray represent respondents whose

investment objectives are highly congruent with their opinions of market efficiency (those who believe markets are inefficient and are trying to beat the market or those who believe markets are efficient and are not trying to beat the markets) The portions

highlighted in black represent respondents whose investment objectives are highly

incongruent with their opinions of market efficiency (those who believe markets are efficient and yet are trying to beat the market or those who believe markets are inefficient but are not trying to beat the markets)

The table demonstrates that the portion of respondents whose investment

objectives are congruent with their opinions of efficiency is much higher than the portion whose objectives are incongruent The investment objectives of 264 (42%) respondents are highly congruent with their opinions of market efficiency, while the investment objectives of only 137 (22%) are incongruent with their opinions of market efficiency This simple table suggests that a respondent’s opinion of market efficiency is, in fact, highly correlated with his investment objectives

However, there is a considerable portion of the sample (22%) who behave in a

manner that is seemingly incongruent with their opinion of the actual efficiency of the markets The inconsistency between a professor’s beliefs about the efficiency of the markets and his stated investment objectives may be explained by his level of confidence

in his own ability to beat the market I.e., the inconsistencies may be explained by the response to a question on the survey in which I asked respondents to indicate how

strongly they agreed or disagreed with the following statement, “Given sufficient time

and resources, I could implement an investing strategy that would consistently beat the market.”

In effect, there are two factors at work that may heavily influence an investor’s propensity to passively or actively invest: (1) his perceptions of the general efficiency of the markets in which he will invest and (2) his confidence in his own abilities to beat the market It is easy to conceive of an investor who believes markets are inefficient but who does not believe he can develop a strategy to capitalize on the inefficiencies In this case, the investor would likely passively invest, which would create one of the perceived inconsistencies discussed above – an investor who believes markets are not efficient but who still passively invests

To explore this possibility, I double sort respondents based on their opinions of market efficiency and their belief in their own abilities to implement an investing strategy that would consistently beat the market For each subgroup, I report the mean response

to the statement, “When I invest, my goal is to beat the market.” I expect those whose believe markets are inefficient and who believe they could personally implement a

market-beating strategy to be the most likely to try to beat the market, while I expect those who believe markets are efficient and who do not believe they could implement a market-beating strategy to be the most likely to passively invest

Table 7 reports this information Panel A uses the response to the question about the weak form efficiency of US stock markets as the measure of each respondent’s

opinion of market efficiency Panel B uses the response to the question about strong form efficiency to represent each respondent’s opinion of market efficiency

semi-The table reveals a somewhat surprising result A professor’s opinion on the general efficiency of US stock markets has much less influence on his investment

objectives than his confidence in his own abilities to implement a market beating

strategy Within confidence groupings (within columns), there is no monotonic pattern as respondents’ opinion of market efficiency changes However, within the opinion

groupings (within rows), there is an obvious monotonic pattern as a respondent’s

confidence in his own investing abilities changes This suggests that one’s opinion of market efficiency has very little to do with the decision of whether to actively or

passively invest What matters is merely a person’s confidence in his own abilities

To add statistical meaning to the pattern presented in Table 7, I estimate an

ordered Probit model The dependent variable is the response to the statement, “When I invest, my goal is to beat the market.” The independent variables of interest include responses to the three statements about weak, semi strong, and strong form efficiency and the response to the statement, “Given sufficient time and resources, I could implement an investing strategy that would consistently beat the market.” Control variables include a gender binary variable, a marital status binary variable, the respondents’ rank and age, and dummy variables indicating the respondent specializes in asset pricing, behavioral finance, capital structure, corporate finance, derivatives, or market efficiency I estimate two iterations of the ordered Probit model In the first iteration I include the independent variables as listed above In the second iteration, I add three interaction terms in which a participant’s response to the statement about his confidence in his ability to implement a market beating strategy is interacted on his responses to the weak, semi-strong, and strong form efficiency questions

The results of the ordered Probit model estimations are reported in Table 8 Several points in the table stand out First, an investor’s opinion of market efficiency has little impact on his investing objectives A respondent’s opinion of the weak form

efficiency of the markets has no statistically significant relationship to his investment objectives A respondent’s opinion of the strong form efficiency of markets is inversely related to his investment objectives I.e., the less one accepts markets as strong form efficient, the more inclined he is to simply passively invest This is perhaps reasonable if the investor assumes most abnormal investing profits are driven by private information and if the investor does not consider himself privy to the necessary private information

While a respondent’s opinion of the semi-strong form efficiency of the markets is significantly directly related to his investment objectives in the first iteration, it becomes negative and insignificant when the interaction terms are added to the model, which suggests that all of the influence of a respondent’s opinion about semi-strong form

efficiency on his investing objectives is dependent on his confidence in his abilities to beat the market

This leads to the second main point from the table A respondent’s investment objectives are primarily driven by his confidence in his abilities to beat the market,

regardless of his opinion of market efficiency In the first iteration, the magnitude and statistical significance of the coefficient on the confidence variable exceeds that of all other Likert scale variables Importantly, the coefficient on the confidence variable remains strongly statistically significant even in the presence of the interaction terms, which suggests a person’s confidence in his own abilities to beat the market is a major driver of his investment objectives, regardless of his opinion of market efficiency This is further supported by the fact that the magnitude and statistical significance of the

coefficient on the confidence variable well exceeds those of any of the interaction terms It’s not an investor’s opinion of market efficiency that matters; rather, it’s his confidence

in his own abilities to beat the market, regardless of his opinion about market efficiency, that seems to matter

A third interesting result from the table is that of the six specialties included in the model, only derivatives is statistically significant Specifically, a professor who

specializes in derivatives is much more likely to actively invest than one who does not

The summary results to this point are as follows Finance professors agree that markets are weak form efficient They agree even more strongly that markets are not strong form efficient Conversely, they are conflicted about the semi-strong form

efficiency of US stock markets Their investment objectives, however, suggest that finance professors do generally accept markets as semi-strong form efficient, since about twice as many of them passively invest than those who actively invest But surprisingly, finance professors’ opinions about the efficiency of US stock markets have very little to

do with their decision of whether to actively or passively invest Regardless of their perception of market efficiency, their investment objectives are primarily driven by their confidence in their own abilities to implement a strategy that can beat the market

I began the paper by assuming that an investor’s opinion about the general

efficiency of the markets in which he considering investing is fundamental to his decision

of whether to actively or passively invest This seems refuted Instead, what matters is simply his confidence in his own abilities, regardless of how efficient he perceives the markets to be To analyze the robustness of this surprising result, I use structural

equation modeling, which is a methodology that has been widely employed in literature that commonly uses survey data

Structure Equation Modeling – General Information

SEM, although not new, is still uncommon in finance literature Even the studies using surveys do not employ this technique Therefore, I offer a brief explanation of SEM

SEM is a multivariate predictive technique that utilizes covariance matrices (correlation matrices may also be used, but this is not recommended since doing so changes the interpretation of the output and may result in biased parameter estimates), instead of observation matrices, and common estimation techniques, such as maximum likelihood, to simultaneously estimate a set of separate but related regression equations

It is particularly useful in estimating multiple dependent relationships where the

dependent variable in a given model serves as an independent variable in a related model (Hair, Black, Babin, Anderson, and Tatham (2006)) Although SEM is not typical in finance literature, it is one of the foundational methodologies in disciplines that regularly use survey data.7 For example, consider the following system:

endogenous variables Y1 and Y2

OLS estimation assumes that the disturbance term of the dependent variable is uncorrelated with the explanatory variables This is guaranteed when the explanatory variables are non-stochastic (not random) Clearly however, Y1 and Y2 as explanatory variables in the above system are stochastic If the system only included the equations

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For instance, Brady, Calantone, Ramirez, and Voorhees report in an untitled working paper that over the years of 1996 – 2005 at least 283 articles in the top seven marketing journals employed structural equation modeling in their methodologies

with Y1 and Y2 as dependent variables, the classic solution to this econometric problem is two-stage least-squares estimation (2SLS), which would estimate Y1 using X1 and then use the fitted value of Y1 as the explanatory variable in the second equation This special case of the instrumental variables approach has become a workhorse in finance in

resolving the above issue The inclusion of the third equation introduces further

difficulties It would require estimation of Y2 using X2, and the fitted value of Y1 Then the fitted values of Y1 and Y2 would be used to estimate Y3

Instead of working through the system in stages as does the instrumental variables approach, SEM solves the system simultaneously It does so through path analysis,

which estimates the theorized relationships between the variables in a system by

analyzing the correlations between the variables along all possible paths connecting the variables in the system Its ability to simultaneously solve the system hinges on

restrictions, usually zero restrictions, placed on the system This typically means that in order for SEM to work, the researcher must establish that some of the variables in the system are unrelated Each zero restriction adds a degree of freedom to the model, which increases statistical power

SEM also offers other advantages to traditional regression techniques Classical linear regression (CLR) techniques are designed to model observed variables, whereas SEM is designed to model observed and latent variables by incorporating a level of

confirmatory factor analysis In the process, SEM also reduces measurement error by using multiple observed variables to model the true latent variable, whereas CLR

typically uses a single observed variable to proxy for the true variable of interest

Further, SEM explicitly models measurement error, which if present in explanatory

variables creates a downward bias in coefficient estimates in CLR (see Hausman (2001))

Also, although SEM shares many common assumptions with CLR, it is more flexible in its ability to handle violations of the CLR assumptions Like CLR, SEM

assumes normality in the disturbance term of the dependent variable, specifically,

multivariate normality since there are multiple dependent variables Violation of this assumption can be handled with bootstrapping estimation techniques And although MLE is the primary estimation technique in SEM, it can also employ distribution-free estimation techniques to obtain estimates in the presence of violations of the multi-variate