- Asset Pricing and Learning Effects
This research is concerned with explicitly modeling
how agents form expectations of future payoffs when
the parameters of the payoff distribution, as well as
its functional form, are unknown to investors. The research
takes off from the standard assumption of rational expectations
and then considers asset prices under adaptive and Bayesian
learning rules. My most recent research in this area
focuses on solving for asset prices in the presence
of structural breaks in the dividend process and studies
learning effects in asset prices immediately after such
structural breaks. This work also characterizes analytically
the dynamic properties of asset prices in the presence
of learning effects.
Collaborators on this work include Massimo Guidolin
from UCSD.
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Cyclical Variations in Stock Returns
A large literature in finance shows that stock returns
is, to some extent, predictable by means of forecasting
variables such as dividend yields, interest rates and
macroeconomic variables with a clear business cycle
component. However, standard forecasting models essentially
assume that the same forecasting specification stays
in effect throughout the entire sample.
If stock prices are predictable, simple notions of
market efficiency would suggest that they should not
be predictable for very long periods of time. The challenge
is to identify the 'pockets in time' where predictability
prevails. Predictability is likely to be linked to the
state of the economy. In periods with abundant liquidity
investors will exploit any predictable patterns, while
in periods with low liquidity, such as during economic
recessions, predictable patterns may still be present
in stock prices.
Part of this research focuses on forecasting models
that separate the data on stock prices into two states,
namely a recession state and an expansion state. Both
first-moment predictability ass well as predictability
of volatility, skewness, kurtosis and the full density
of returns are considered in the context of time-varying
mixtures of normal distributions. I find that the best
forecasting model is very different during recessions
and expansions. Forecasting variables such as interest
rate shocks and default premia have a large impact on
stock prices during recessions and a much smaller impact
during expansions. This research also finds that stock
returns can be predicted only during the very short
periods around turning points of the economic cycle.
Lastly, evidence on how to exploit predictability in
a trading strategy is reported and used to discuss the
consequences of the evidence for market timing strategies.
The research also considers whether stock returns are
predictable even after accounting for model specification
uncertainty and possible shifts in the underlying forecasting
equation. Finally we test the predictions of recent
imperfect capital market theories with regard to cyclical
asymmetries in the stock returns of small and large
firms.
Collaborators on this research include Hashem Pesaran,
University of Cambridge, and Gabriel Perez-Quiros, New
York Federal Reserve Bank.
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Data-snooping
High frequency asset returns appear to be predictable
by means of technical trading rules and simple deterministic
calendar effects. However, both findings are the outcome
of the financial research community's extensive search
across hundreds of possible forecasting models. Since
none of the technical trading rules or calendar effects
were predicted ex ante by theory, it is quite possible
that the findings reported in the finance literature
are simply the result of extensive data-snooping, i.e.,
the fact that the same data set was used to formulate
the hypothesis (that a given forecasting rule is successful)
and test it. This research applies a new bootstrap procedure
to assess the performance of the best forecasting model
in the context of the full set of forecasting models
under consideration. Our findings suggest that data-snooping
effects can be very significant. For example, it can
be strongly rejected that the best calendar rule, when
viewed in isolation, does not possess predictive power
over daily stock returns. Once data-snooping effects
are accounted for, however, the best calendar rule no
longer produces returns that are statistically significantly
different from those of a simple passive investment
strategy. Studying the predictive power of a large set
of technical trading rules on daily Dow Jones data after
1986, we similarly find that, after accounting for data-snooping
effects, there is no evidence that the best trading
rule outperforms a simple benchmark.
Collaborators on this work are Ryan Sullivan and Halbert
White, both from UCSD.
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Mutual Fund and Pension Fund Performance
This research investigates various aspects of the performance
of mutual funds in the UK. Questions addressed include
the risk-adjusted performance of funds, the persistence
of their performance, evidence of performance clustering
across mutual fund managers, returns to strategic asset
allocation, market timing and security selection, and
mutual fund managers' performance incentives.
Collaborators on this work are David Blake, Birkbeck
College, and Bruce Lehmann, UCSD.
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Duration Analysis of Financial Data sets
One part of this research models the hazard of mutual
fund managers. It computes the conditional probability
of fund closure given a fund's age and the performance
of the sector in which the fund operates as well as
the risk-adjusted performance of the fund itself. These
are important to understanding the incentives under
which mutual fund managers operate. For example, if
a small under-performance strongly increases a young
fund's chances of being closed down, then this would
lead managers to follow very conservative investment
strategies. In fact we find that the effect of under-performance
on fund hazard rates are very large; we also find that
very young and very old funds are less likely to be
closed down.
Future research also plans to study duration dependence
in bull and bear markets.
Collaborators include Asger Lunde, University of Aarhus
and David Blake, Birkbeck College.
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Forecasting with non-linear (and possibly non-stationary)
models
My interest in this area originates from the observation
in financial markets, that many time-series not only
are dominated by highly non-linear effects but also
are non-stationary. For example, a simple notion of
market efficiency suggests that if returns on a given
asset is predictable at a given point in time, then
such predictability should rapidly dissipate as a result
of investors allocation of capital into exploiting the
resulting investment opportunity. Hence conditional
forecasting relations cannot be expected to be stable
in the financial markets, unless of course they simply
reflect time-varying risk premia. Some of my current
work addresses the issue of modeling predictability
of asset returns in 'pockets in time'.
Collaborators include Hashem Pesaran, University of
Cambridge, and Gabriel Perez-Quiros of Federal Reserve
Bank of New York.