abstract

In the classical finance literature, the pricing kernel (or
stochastic discount factor) represents the unified framework for
asset pricing (see Cochrane 2001). Two results from finance theory
imply the centrality of the pricing kernel. First, in the absence of
riskfree arbitrage, prices can always be written as expected
payoffs weighted by the pricing kernel (see Duffie 1988). Second, in
many representative agent models the pricing kernel can be easily
linked to investor's preferences when these satisfy the expected
utility theory of von Neumann and Morgenstern (1944). Behavioural
finance identifies several pricing anomalies or puzzles, i.e.,
empirical observations that cannot be understood using classical
models for asset pricing. However, pricing anomalies generally do
not imply riskfree arbitrage investment opportunities, thus the
pricing kernel remains the unified framework for asset pricing also
in the context of behavioral asset pricing. The question of how the
investor sentiment, i.e., the way people form beliefs in practice
(thus also their biases), impacts the pricing kernel has recently
attracted the attention of researchers; see, for example, De Bondt
and Thaler (1985), Lee, Shleifer, and Thaler (1991), Swaminathan
(1996), Neal and Wheatley (1998), Shefrin (1999), Han (2004) and
Baker and Wurgler (2006). Shefrin (2001) and De Giorgi and Post
(2008) propose behavioral asset pricing models where the link
between the pricing kernel and the investor sentiment is formalized.
In De Giorgi and Post (2008) the pricing kernel corresponds to a
distortion of markets' returns which implies a pessimistic view on
the market, i.e., investors attach higher probabilities to negative
returns compared to the true distribution. The aim of this project
is to contribute to this growing literature on behavioral asset
pricing. More specifically, we want to (1) Extend the asset pricing
model of De Giorgi and Post (2008) along several directions, in
order to derive a formal link between the pricing kernel and the
investor sentiment under more general assumptions on the market and
on investors' preferences, compared to De Giorgi and Post (2008).
(2) Use advanced techniques from computational statistics to
calibrate the model from point (1) on market data, in particular the
dynamics of the pricing kernel and the link to a set of behavioral
risk factors. (3) Derive directly from market data a
modelindependent estimation of the pricing kernel and link it to a
set of behavioral risk factors. (4) Develop a timeseries model for
the pricing kernel. (5) Apply the results from the previous points
to price options and to derive behavioral investment strategies.

keywords

Behavioral finance, Regression trees, Investor sentiment, Computational statistics, Portfolio selection, Pricing kernel, Asset pricing, Boosting, Machine learning 
topics

Behavioral finance, Regression trees, Investor sentiment,
Computational statistics, Portfolio selection, Pricing kernel, Asset
pricing, Boosting, Machine learning
