Plenary
Lecture
Market Efficiency and Behavioral Finance: A Unifying
Stochastic Model of Stock Prices
Professor Sergio Bianchi
Rector's Delegate for Research and Benchmarking
Faculty of Economics - University of Cassino
Via S. Angelo, Campus Folcara - 03043 CASSINO (FR)
ITALY
E-mail:
sbianchi@eco.unicas.it
Abstract: Reams and reams have been written in
quantitative finance about the unsolved problem of the
stock markets efficiency. Starting from the seminal work
of Fama (1970), who defined the Efficient Market
Hypothesis in terms of expected values conditional to
the informative set ("financial assets fully reflect all
available information that is relevant to their
values"), a huge number of works have tried to address
the question whether real financial markets behave
efficiently. Roughly speaking, the EMH argues that
market does price assets broadly correctly, excluding
that deviations from equilibrium values could last for
long. So great was the consensus met by the EMH to set
one of the most impressive bodies of knowledge of the
20th century: the mathematical finance.
Nonetheless, the number of equilibrium theorems proved
under the assumptions of the EMH grew at the same rate
of the empirical evidence that made questionable the
validity of the EMH itself.
Many approaches have been followed in literature to test
the EMH. In spite of all the efforts, to date the
problem remains open and current more than ever,
basically because of two main reasons:
- the analyses, particularly those aiming at testing the
random walk model in its different specifications, often
provide non conclusive results;
- the real world dynamics, with their repeated financial
crises made of bubbles and crashes, seriously do
challenge the credibility of the EMH, to the extent that
a strand of skeptical thought, the behavioural finance,
has been booming. One of the most quoted works in this
context is due to DeBondt and Thaler (1985); they show
that using historical returns abnormal profits are
achievable in the long-run, simply going short a
portfolio of 'winner stocks' (i.e., stocks with good
performances in the past) and going long a portfolio of
'loser stocks' (i.e., stocks that performed badly in the
past). What causes these opposite profits, according to
the authors, is investors' excess of optimism and
pessimism, the so called overreaction to information.
Following this analysis, a plethora of contributions
provided evidence of reverse abnormal profits and
documented them in international markets and for short
time horizons. Other empirical results suggest that
prices underreact to information in a way to generate
the so called the "momentum" profit; the trading
strategy in this case consists in going long a portfolio
of extremely winner stocks in the past and going short a
portfolio of extremely loser stocks.
Results like those just recalled raise the question
whether a model exists able to make consistent all these
opposite findings. The paper concludes in favour of an
affirmative answer: a model is discussed that, recently
defined by Ayache and Taqqu (2005) in a general setting,
succeeds in reconciling efficiency and behavioural
finance. The model, named Multifractional Processes with
Random Exponent (MPRE), emanates from the well-known
fractional Brownian motion (fBm), the unique Gaussian
process, self-similar of order H, vanishing at the
origin with stationary increments, introduced to model
long-range dependence. The parameter H of the fBm is
informative of the intensity of dependence and of the
regularity of the process' paths. Yet, the constancy of
H is undesirable for many phenomena, whose complexity
requires the pointwise regularity to change over time,
even abruptly. When H is replaced by a random function
one gets the MPRE. The construction of this class of
processes considers (a) a Gaussian field depending on H
and the time domain, and (b) a random variable or a
stochastic process with values in an arbitrary fixed
compact interval. The resulting process is versatile
enough to describe very complex phenomena such as stock
price dynamics.
Brief Biography of the Speaker:
Sergio Bianchi, born on 25 September 1967, graduated in
1991 from University of Cassino (Italy) in Economics. He
earned his Ph.D. in Actuarial Science from University of
Rome "La Sapienza" (Italy). After teaching experiences
as invited professor at the Pontifical Gregorian
University (Vatican State) and at University of Sassari
(Italy), in 1998 was appointed assistant professor at
University of Cassino, where became associate professor
in 2001 and full professor of Mathematics and Financial
Mathematics in 2006. Since 2003 to 2005 he was
scientific responsible for the Computer Laboratory of
the Department "Economia e Territorio" at University of
Cassino. He also held the office of Head of the
Department "Istituzioni, Metodi Quantitativi e
Territorio" from 2005 to 2009, when he resigned after
the appointment as Rector's Delegate for Research and
Benchmarking. Member of the board of professors of the
Ph.D. in Economics at the same University, he is referee
for a number of international journals and the author of
more than fifty research papers concerning the modeling
of financial time series by (multi)fractional processes
and self-similar stochastic processes.
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