Daniel Suh
West Virginia University
Department of Finance
Division of Economics and Finance
Morgantown, WV 26506-6025
EMAIL: Daniel.Suh@mail.wvu.edu
Home:
3812 Bates Street
Pittsburgh, PA 15213
412-607-5757 (Cell)
Citizenship: U.S.
Education
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Ph.D., Financial Economics, West Virginia University, May 2009 (expected)
o Dissertation: “The Correlations and Volatilities of Stock Returns: The CAPM Beta
and the Fama-French Factors”
o Advisor: Dr. Ashok Abbott
M.B.A., Finance, University of Florida, 1999
M.A., Economics, State University of New York, Stony Brook, 1979
B.A., Economics, Seoul National University, Korea, 1971
Academic Appointments
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Visiting Assistant Professor, Department of Finance, Division of Economics and Finance,
West Virginia University, Spring 2008
Adjunct Faculty, Master of Business Administration, Seton Hill University, Greensburg,
PA Summer 2003- Spring 2004
Research & Teaching Interests
Research Interests: Asset Pricing, Corporate Finance, Investments, Derivative Securities
Teaching interests also include Portfolio Management, Capital Markets, Governance/Ethics
Research Papers___________________
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“The Correlations and Volatilities of Stock Returns: The CAPM Beta and the FamaFrench Factors” to be presented at the Midwestern Finance Association Conference,
March 6, 2009 and also at the Eastern Finance Association Conference, May 2, 2009
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“Industry Restructuring, Market Risk, and the Cost of Capital: A case study of the
electric power industry” to be presented at the Rutgers University, Annual Conference
of the Center for Research in Regulated Industries, May 14, 2009
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“Stock Returns, Volatilities, and CAPM Beta: Evidence from the Electric Power
Industry,” presented at the Southern Finance Association Conference in November
2007
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“The Term Structure of Interest Rates: Has the expectations theory recently become
more valid?“ presented at the Southern Finance Association Conference in November
2007
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Academic Teaching
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West Virginia University
o Investments (two sessions), Spring 2008
o Financial Statement Analysis, Spring 2008
o Advanced Corporate Finance (a capstone course), Fall 2007
o Investments (Invited substitute lectures) Spring 2007
o Corporate Finance (TA and substitute lectures), Fall 2006 and Spring 2007
o Energy Economics (Invited guest lectures) Fall 2006
Seton Hill University, Greensburg, Pennsylvania
o M.B.A. Financial Management, Summer and Fall 2003 and Spring 2004
o M.B.A. Economics, Spring 2004
o Finance, undergraduate, Fall 2003
Robert Morris University, Pittsburgh, Pennsylvania
o C.F.A and C.P.A. candidate training in Finance and Economics, Summer 2004
Allegheny County Community College, Pittsburgh, Pennsylvania
o Microeconomics, Spring 2003
o Business Statistics, Fall 2003
Frostburg University, Maryland
o M.B.A. Financial Management, Summer 2001
Chartered Financial Analyst (CFA) Examinations
Have passed the first two levels of examinations
References*:
Dr. Ashok B. Abbott (Chair)
Associate Professor of Finance
Division of Economics and Finance
College of Business and Economics
West Virginia University
Morgantown, WV 26506-6025
(304) 293-7886
aabbott@wvu.edu
Dr. Victor Chow
Professor of Finance
Division of Economics and Finance
College of Business and Economics
West Virginia University
Morgantown, WV 26506-6025
(304) 293-7888
kchow@wvu.edu
Dr. Strafford Douglas
Associate Professor of Economics
Division of Economics and Finance
College of Business and Economics
West Virginia University
Morgantown, WV 26506-6025
(304) 293-7863
douglas@wvu.edu
Dr. Alexander Kurov
Assistant Professor of Finance
Division of Economics and Finance
College of Business and Economics
West Virginia University
Morgantown, WV 26506-6025
(304) 293-7892
alkurov@mail.wvu.edu
Dr. Alexei Egorov
Assistant Professor of Economics
Division of Economics and Finance
College of Business and Economics
West Virginia University
Morgantown, WV 26506-6025
(304) 293-7868
alexei.egorov@mail.wvu.edu
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Abstracts of Research Papers
“The Correlations and Volatilities of Stock Returns: The CAPM Beta and the Fama-French
Factors”
This paper conducts time-series tests on the Capital Asset Pricing Model (CAPM) and the FamaFrench three-factor (FF3) model in the context of market beta estimation for the cost of equity
capital. This paper focuses on the data generating process of the three risk factor loadings (the
market index, SMB, and HML) with an objective to identify the underlying economic sources of
factor loadings. The main focus of this paper is on the stock return correlations, as well as on the
relative volatility of stock returns with the market portfolio volatilities. This paper attempts to
find answers to the following research questions: (1) how stock returns and volatilities are
correlated with the three risk factors and potentially other factors, (2) whether the CAPM beta
provides economically consistent and statistically significant estimates for the cost of equity of a
firm inter-temporally and cross-sectionally, or whether the CAPM beta provides a quantitatively
reasonable and qualitatively useful reference in the estimation of the cost of equity capital of a
firm, and (3) how much the Fama-French 3-factor model improves the CAPM beta estimates in
terms of economic consistency, statistical explanatory power, and the significance of estimates, or
how much the FF3 model adds useful information to the CAPM for the estimation of the cost of
equity for a firm.
The major findings on the stock return correlations and volatility correlations with risk factors
include the following. Stock returns are most consistently and strongly correlated with the
market index, and next with the industry. The correlations of stock returns with the market and
the industry are much more consistent and stronger than with SMB and HML. By design, FamaFrench portfolio return correlations with the market index returns are inter-temporally and crosssectionally most homogenous and highest among the six stock groups or portfolios. Realized
return data often contradict the intuitive, standard theory of trade-off between expected total risk
and return, particularly during high market volatilities. In a highly volatile market particularly in
a market downturn, the correlations of individual stocks with the market index breakdown. When
the market volatility is high, the market returns go to extremes, either very high or deeply
negative; when market volatility is low, market returns also are generally low; and when market
volatility is intermediate, market returns are widely dispersed. The contradictions between the
theory and the realized return data confirm a fundamental problem of empirical test: estimation of
expected value. (Merton, 1980) The relative volatilities of individual stocks generally remained
more or less steady including the information technology bubble-and-burst period, except for the
early 1990s when the relative volatilities were consistently higher (contrary to Campbell et al.,
2001). Correlations of an individual stock appear to be an important determinant for intertemporal patterns of stock returns and the market beta estimates. The relative volatilities of
individual stocks appear to be a main determinant for total stock returns and the levels of the
market beta estimates. These characteristics of stock returns, correlations, and volatilities provide
insight into the underlying process of market beta generation.
The main findings and conclusions on the test of risk factor loadings include the following: The
market index is by far the most consistent and powerful systematic risk factor throughout the
sample period, for both large- and micro-cap stocks, in FF3 model specifications, and across
industry sectors. The market index beta largely provides economically reasonable estimates for
market risk, cross-sectionally (by industry) and inter-temporally. Consistent with a fundamental
finance theory of non-zero relations of stock returns with systematic risk, the time-series market
beta estimates are mostly positive, except during some portions of a period of high market
volatility and deeply negative market returns. Consistent with the theory, most alpha estimates
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are statistically zero, a core test for the model specification. However, wide ranges of beta and
alpha estimates indicate instability of the coefficients, model misspecification, or regime
switching of beta. The estimated alpha, although statistically insignificant, often is negatively
related with the estimated beta, another indication of model misspecifications. The implied crosssectional relations (the security market line, SML) of test results between the estimated beta and
mean returns are generally positive except during high market volatilities, although the relations
are weak and often statistically insignificant. However, the SML test is not necessarily a test of
the CAPM. (Kandel and Stambaugh, 1995; Roll, 1977, 1978; Roll and Ross, 1994) The above
findings all apply to both models. Market volatilities are critical for beta estimates; when the
market is highly volatile, beta estimates breakdown as do correlations of stock returns with the
market index. SMB and HML stabilize the market beta during periods of high market volatilities.
The two factors also enhance the statistical power for micro-cap stocks. However, the SMB and
HML add little to the CAPM during a stable market environment or for large-cap stocks in terms
of market beta estimates and statistical significance. The CAPM beta predominates as a reference
for systematic risk.
“Industry Restructuring, Market Risk, and the Cost of Capital: A case study of the electric
power industry”
This paper conducts a focused test on the firms of the electric power industry, which has been
going through a restructuring and deregulation process since the 1990s. The main objective of
this chapter is to test for inter-temporal and cross-sectional regime-switching of beta and to
identify the economic sources as the market and industry environment changes and a firm’s
investment model and financing strategy evolve. This paper examines the industry background,
the restructuring process at the market, government policy, and corporate investment and
financing strategy levels to gain conditioning information; the information provides a basis for
the design of research and sample data and for interpretation of test results. The sample period is
divided into subsamples and firms are grouped by investment model and financing strategy. The
main findings include the following. The market betas of the electric power industry switch their
regimes to reflect the expected and realized changes in system risk. As the electric power
industry becomes restructured and deregulated, the beta steadily has become higher over time,
except during the period of Internet bubble-burst. Beta estimates during regulation are stable
between 0.4-0.5, the lowest among industries. Beta estimates for recent years are near twice as
high, or 0.7-0.9. Correlations of the industry with the market have increased while return
volatilities of individual firms diverged. Structural breakpoint tests show unique structural breaks
of the industry in terms of stock returns and correlation with the market index. Tests identify two
major breakpoints for the industry: the middle of 2002 at the depths of the industry crisis and the
early 1998 when California became the first state to deregulate the electric power industry and
open its market to competition. On the other hand, no such clear breaks in other industry sectors
of large- and micro-cap stocks are found. Market beta estimates of merchant power firms are
consistently higher than regulated utilities. The merchant power investment model exposes firms
to uncertain commodity price spread, and high financial leverage to finance capital-intensive
investment increased a systematic risk (or interest rate risk).
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“The Term Structure of Interest Rates: Has the expectations theory become more valid?”
Has the expectations theory of the term structure of interest rates recently become more
valid? If so, what kinds of recent economic conditions and monetary policy may have
contributed to it? What are the implications to the economic activities and policy? To address
the above questions, I focus on a test of the expectations theory for short-term interest rates and
briefly discuss the economic conditions and monetary policy factors that may have contributed to
the increased validity of the theory. My initial test using Treasury bill data of the latest period
finds evidence that the expectations theory has become increasingly valid since the early 1990s.
The initial analysis also shows that the economic environment and monetary policy recently have
become increasingly supportive of the validity of the theory. This paper concludes with an
additional research plan to test a fuller range of term structure and more extensively investigate
the contributing economic and policy factors and the implications.
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Graduate-level Study in Finance and Economics
MBA Finance (Finance study at the University of Florida)
Financial Statement Analysis
Financial Decision Making (Corporate Finance)
Measuring and Managing Value (Valuation)
Studies in Valuation
Investment Concepts
Derivative Securities
Portfolio Management
Investment Banking I & II
Venture Finance
Secondary Mortgage Markets
International Finance
Ph.D Courses (at the West Virginia University)
Advanced Microeconomics I & II
Advanced Macroeconomics I & II
Econometrics II, III
Portfolio Theory
Asset Pricing
Seminar in Finance
International Finance
Microeconomics of Banking
Monetary economics
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Industry Research Experience
Economic and Financial Analysis
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Conducted Financial Due Diligence, Financial Valuation, and Strategic Analysis on the
competitors and potential merger/purchase targets. The process included analysis of the
cost of capital, discounted cash flows, external financing required, current and long-term
assets, inter-corporate investment, business combinations/consolidations, off-balancesheet activities, economic value added, financial performance measurement.
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Directed an economic forecasting team in the development of econometric forecasting
systems, economic analysis, statistical analysis/inference, sample design, regression
modeling, and econometric forecasting.
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Conducted regular market analysis/research and consumer survey analysis to analyze
consumer characteristics such as demographics, income, housing, appliance saturations,
consumption, etc.
Competitive Analysis and Strategic Study
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Conducted corporate strategic planning. Actively participated in the company’s
development of corporate strategies, which required a broad analysis of the economy, the
industry, the competition, and the company business, as well as a fundamental
understanding of financial accounting principles and business valuation in the context of
a specific industry environment and company strategies.
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Initiated competitive intelligence activities and published regular reports on strategically
critical issues at an early stage of electric power industry deregulation. Also initiated a
weekly publication of “Industry News” to disseminate strategically critical information to
the senior management.
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Teaching Philosophy and Methods
Daniel Suh
My primary objective of teaching is to promote life-long application and benefits for my students.
I constantly throw out thought-provoking questions for creative thinking and use illustrations
familiar and realistic to students. I strive to provide a learning experience that is not only easy-tounderstand and instructive, but also enjoyable and long-lasting in application. An ultimate goal is
to help students appreciate the value of theoretical concepts and application.
My first academic teaching sparked my long-dormant, youthful desire for academic education
and research. When I was teaching an MBA Financial Management course in Maryland in 2001,
two MBA students not in my class approached me and said that they would like to take my course
the next semester. They said that finance is a difficult subject but they heard from my students
that I was teaching the course in an easy-to-understand and practical way. That course was my
first teaching ever! I was teaching it while working full time. Alas, the two students never had
an opportunity because my company just had transferred me from Maryland to Pittsburgh in the
middle of the course and I was making a 370-mile round trip to teach twice a week. I began to
prepare myself to become a scholar, exploiting my teaching talent and research orientation.
The following two teaching examples demonstrate my teaching philosophy and methods.
On the first day of the Investments class last year, I began with a question:
“Have you ever made an investment in your life?”
One student immediately raised his hand and he talked about his stock investment. After a brief
discussion on his investment, I said to the class:
“I know EVERYONE in this class has made an investment, at least one investment with NO
exception. Can you think of an investment EVERYONE of you has made?”
After a moment of silence, two students raised hands. A student who said, “Education.”
“EDUCATION! That’s right, EDUCATION!” I responded. The second student said that she also
had the same answer.
Then I began to discuss why education is an investment. Investment is an action or a payment
made today for expected future payoffs. Investment requires a known sacrifice today for
unknown future potential or probable benefit. Investment is a temporal decision, where time is an
important decision factor, unlike economic theory students have learned. Education has the same
characteristics of investment; so every student has been an investor. Education is relatively less
uncertain about the future payoffs and so less risky than other investments. The payoffs are lifelong. Therefore, education is among the safest and the most valuable investment. And so on…
With a discussion of education as an example of investment, I achieve dual objectives: (1)
motivate students to exert themselves with today’s sacrifice including this investments course for
future benefit and (2) help students to have a life-long understanding of investment and its
application. Next I move on to an illustration to teach a second lesson: financial investment also
is a business investment decision.
On the first day class of the financial statement analysis, I used a real-life-like illustration to teach
two critical concepts: accrual vs. cash accounting and capitalization vs. expensing of
expenditures. Suppose you open a small business. You borrow $50K and put in your own
money (equity) of $50K. You pay $80K to purchase equipment and materials. Each of the first
two months, your sales are $150K with an expense of $10K for rent and wage.
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Then I asked:
What do you report in the first-month income statement? A loss of $75K (=$15K-$10K-$80K)?
Or some other numbers? Why? How about in the second month? A positive $5K (=$15K-$10K?
How about the cost of materials used up in the first two months, for which you had paid before
the opening of business but no payment during the two months of operation?
I ask a question at a time and conduct class discussions before moving to the next question. I let
students present common-sense logic as well as accounting principles for an answer. To help
students understand and remember accounting principles and rules I focus on logic, because
accounting principles and rules are based on common-sense logic and reporting purposes.
After discussions on those questions, I modified the $80K expense into $60K in cash payment
and $20K on credit. An ultimate objective for the first day is to naturally flow into another core
concepts of financial accounting and analysis: economic vs. accounting income, accounting
income and cash flows.
During my MBA, I was determined to become a well-rounded financial economist; I took 50%
more courses than the MBA requirements, including six more finance courses than required for
finance specialization. Afterwards I became fully engaged in strategic financial decision-making
in the industry. I also have prepared myself to become a scholar in finance who is well-balanced
in academic teaching and research, in theory and applications to corporate finance, investment
and financial accounting. My preparations so far include (1) Ph.D. study and research, (2)
teaching experience in MBA and undergraduate economics, advanced corporate finance, financial
management, investments, financial accounting analysis, and statistics, and (3) successful CFA
examinations in Levels I and II.
I believe that three factors have helped me enjoy and become successful in teaching. The first
factor is my passion for teaching and strong desire to help others. My teaching evaluations testify
to it; I will never compromise it. The second factor is my long experience of teaching and
training of others. Before coming to academia, I had taught and delivered speeches for 25 years
as an elder and overseer of both Korean- and English-speaking churches. Several times a year, I
also delivered speeches before an audience of thousand members at church conventions. The
teaching and speeches required not only to capture mental attention but also to move the heart. I
always attempt to incite students to heart-felt appreciation as well as mental understanding of
economic and finance concepts; a goal is to help students act on what they learn. The third factor
is my natural talent in teaching. My teaching and speeches are filled with thought-provoking
questions and real-life examples and illustrations familiar to the audience. A goal is to lead
students naturally to understanding theoretical concepts and applications.
Thank you very much.
Daniel Suh
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