Chen, Qingqing. 2009. Essays On Asset Pricing: Predictability, Information, And Liquidity.
Doctoral Dissertation, Cornell University.
This dissertation is a
collection of essays on Asset Pricing: Predictability, Information, and
Liquidity. The first chapter, Predictability of Equity Returns over Different
Time Horizons: A Non-parametric Approach? aims to test an important hypothesis
in financial economics: whether equity returns are predictable over various
horizons? We first propose a non-parametric test to examine the predictability
of equity returns, which can be interpreted as a signal-to-noise ratio test.
Our empirical results show that the short rate, dividend yields and earnings yields
have good predictability power for both short and long horizons, which is different
from both the conventional wisdom and Ang and Bekaert (2007). Also, using our
non-parametric test, a comprehensive in-sample and out-of-sample analysis
documents that the predictor variables (dividend yields, earnings yields,
dividend payout ratio, short rate, inflation, book-to-market ratio, investment
to capital ratio, corporate issuing activity, and consumption, wealth, and
income ratio) have predictability power on equity returns but this cannot be
well captured by linear prediction models. In addition, we use the
nonparametric test to compare the conventional long-horizon prediction
regression models on predictor variables with the historical mean model, where
there has exists a debate about which model has better forecasting power for
equity returns (Campbell and Thompson (2007) and Goyal and Welch (2007)). We find
that the prevailing prediction model has a better forecasting power than the
historical mean model because the former has a lower neglected signal-to-noise
ratio. Finally, we find that our nonparametric predictive models have lower
RMSE than the historical mean model at both short-horizon and long-horizon.
Using our non-parametric methods, both combined and individual forecast
outperform the historical average. The second chapter, An Intraday Analysis of
Related Investment Vehicles Traded in the NYSE and AMEX? undertakes an intraday
analysis of related, investment vehicles traded in the NYSE and AMEX. I investigate
how the trading behaviors of three related investment vehicles (American
Depository Receipt, Exchange-traded Fund, and Closed-end Fund) differ across
countries using high-frequency intraday data. I ?nd that ADRs trade at
transaction prices that are on average worse than ETFs and CEFs. The trading of
ADRs, ETFs, and CEFs follows positive feedback strategies. The buy and sell
trades of the three securities are driven by the net order imbalances and past
returns of three securities themselves. The correlated trading behaviors of the
three securities can be explained by momentum traders with a common information
set. The third chapter, “Endogenous Information Acquisition, Cost of Capital,
and Comovement of Equity Returns” investigates endogenous information
acquisition, , cost of capital, and comovement of equity returns. The
traditional asset pricing model cannot provide a good explanation for the
comovement of asset returns. This chapter introduces endogenous costly
information acquisition that generates comovement of asset returns in a
rational expectations framework. The private information signals observed by
many investors contain information not only about the value of the asset
itself, but also the value of many other assets. This common source of
information causes excessive covariance in their returns. If informed investors
acquire more private information, or more investors are informed, the
comovement of asset returns will increase. On the other hand, if informed
investors aggressively obtain abundant private information, the comovement will
decrease. We also find that both greater precision in private information and
higher cost of information will increase a company cost of capital’s.
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