Всего публикаций в данном разделе: 220
Опубликовано на портале: 03-11-2007
Dong-Hyun Ahn, Jacob Boudoukh, Matthew Richardson, Robert Whitelaw
Review of Financial Studies.
2002.
Vol. 15.
No. 2.
P. 655-689.
We investigate the relation between returns on stock indices and their corresponding
futures contracts to evaluate potential explanations for the pervasive yet anomalous
evidence of positive, short-horizon portfolio antocorrelations. Using a simple theoretical
framework, we generate empirical implications for both microstructure and partial
adjustment models. The major findings are (i) return autocorrelations of indices
are generally positive even though futures contracts have autocorrelations close to zero,
and (ii) these autocorrelation differences are maintained under conditions favorable for
spot-futures arbitrage and are most prevalent during low-volume periods. These results
point toward microstructure-based explanations and away from explanations based on
behavioral models.
Опубликовано на портале: 03-11-2007
Iwaisako Tokuo
Discussion Paper Series of Hitotsubashi University.
2007.
Following Lo and MacKinlay's work on the U.S. market (1988, 1990), this paper investigates
the autocorrelation of the market index and the cross-autocorrelations of size-sorted
portfolios in the Japanese market. The structure of the cross-autocorrelations in
the Japanese market is very similar to that of the U.S. in the sense that there are
lead-lag relations running from larger stocks to smaller stocks, which will create
positive autocorrelation in the market index. Although we have found no autocorrelation
in the popular Japanese TOPIX market index, it is because TOPIX puts much more weight
on larger stocks compared to the CRSP index for the U.S. market. However, such a
cross-autocorrelation structure disappeared during the latter half of the 1990s,
as the largest stocks in the Japanese market began to exhibit negative autocorrelation.
The possibility of a serious financial crisis during this period provides an explanation
for negative autocorrelation. Some empirical evidence is provided for this explanation.
Опубликовано на портале: 02-11-2007
Gregory N. Mankiw, Stephen P. Zeldes
Journal of Financial Economics.
1991.
Vol. 29.
No. 1.
P. 97-112.
Only one-fourth of U.S. families own stock. This paper examines whether the consumption
of stockholders differs from the consumption of non-stockholders and whether these
differences help explain the empirical failures of the consumption-based CAPM. Household
panel data are used to construct time series on the consumption of each group. The
results indicate that the consumption of stockholders is more volatile than that
of non-stockholders and is more highly correlated with the excess return on the stock
market. These differences help explain the size of the equity premium, although they
do not fully resolve the equity premium puzzle
Опубликовано на портале: 02-11-2007
John Y. Campbell, John H. Cochrane
Journal of Political Economy.
1999.
Vol. 107.
No. 2.
P. 205-251.
We present a consumption-based model that explains the procyclical variation of stock prices, the long-horizon predictability of excess stock returns, and the countercyclical variation of stock market volatility. Our model has an i.i.d. consumption growth driving process, and adds a slow-moving external habit to the standard power utility function. The latter feature produces cyclical variation in risk aversion, and hence in the prices of risky assets Our model also predicts many of the difficulties that beset the standard power utility model, including Euler equation rejections, no correlation between mean consumption growth and interest rates, very high estimates of risk aversion, and pricing errors that are larger than those of the static CAPM. Our model captures much of the history of stock prices, given only consumption data. Since our model captures the equity premium, it implies that fluctuations have important welfare costs. Unlike many habit-persistence models, our model does not necessarily produce cyclical variation in the risk free interest rate, nor does it produce an extremely skewed distribution or negative realizations of the marginal rate of substitution
Опубликовано на портале: 02-11-2007
Chris I. Telmer
Journal of Finance.
1993.
Vol. 48.
No. 5.
P. 1803-32.
The representative agent theory of asset pricing is modified to incorporate heterogeneous agents and incomplete markets. The model features two types of agents who differ up to a nontradable, idiosyncratic component in their endowment processes. Numerical solutions indicate that individuals are able to diversify a substantial portion of their idiosyncratic income risk through riskless borrowing and lending alone. Restrictions on the variability of intertemporal marginal rates of substitution are used to argue that incomplete markets, as modeled here, cannot account for the properties of asset returns that are anomalous from the perspective of representative agent theory
Price Momentum and Trading Volume [статья]
Опубликовано на портале: 02-11-2007
Charles M.C. Lee, Bhaskaran Swaminathan
Journal of Finance.
2000.
Vol. 55.
No. 5.
P. 2017-2069.
This study shows that past trading volume provides an important link between ‘momentum’ and ‘value’ strategies. Specifically, we find that firms with high (low) past turnover ratios exhibit many glamour (value) characteristics, earn lower (higher) future returns, and have consistently more negative (positive) earnings surprises over the next eight quarters. Past trading volume also predicts both the magnitude and persistence of price momentum. Specifically, price momentum effects reverse over the next five years, and high (low) volume winners (losers) experience faster reversals. Collectively, our findings show that past volume helps to reconcile intermediate-horizon ‘underreaction’ and long-horizon ‘overreaction’ effects
Опубликовано на портале: 01-11-2007
Cheng Hua
SSRN Working Papers.
2006.
We develop a dynamic model in which traders have differential information about the
true value of the risky asset and trade the risky asset with proportional transaction
costs. We show that without additional assumption, trading volume can not totally
remove the noise in the pricing equation. However, because trading volume increases
in the absolute value of noisy per capita supply change, it provides useful information
on the asset fundamental value which cannot be inferred from the equilibrium price.
We further investigate the relation between trading volume, price autocorrelation,
return volatility and proportional transaction costs. Firstly, trading volume decreases
in proportional transaction costs and the influence of proportional transaction costs
decreases at the margin. Secondly, price autocorrelation can be generated by proportional
transaction costs: under no transaction costs, the equilibrium prices at date 1 and
2 are not correlated; however under proportional transaction costs, they are correlated
- the higher (lower) the equilibrium price at date 1, the lower (higher) the equilibrium
price at date 2. Thirdly, we show that return volatility may be increasing in proportional
transaction costs, which is contrary to Stiglitz 1989, Summers & Summers 1989’s
reasoning but is consistent with Umlauf 1993 and Jones & Seguin 1997’s empirical
results
Опубликовано на портале: 01-11-2007
Michael D. Atchison, Kirt C. Butler, Richard R. Simonds
Journal of Finance.
1987.
Vol. 42.
No. 1.
P. 111-118.
The theoretical portfolio autocorrelation due solely to nonsynchronous trading is estimated
from a derived model. This estimated level is found to be substantially less than
that observed empirically. The theoretical and empirical relationship between portfolio
size and autocorrelation also is investigated. The results of this study suggest that other
price-adjustment delay factors in addition to nonsynchronous trading cause the high
autocorrelations present in daily returns on stock index portfolios
Опубликовано на портале: 01-11-2007
Jennifer Conrad, Mustafa N. Gultekin, Gautam Kaul
Journal of Business and Economic Statistics.
1997.
Vol. 15.
No. 3.
P. 379-386.
In recent years, several researchers have argued that the stock market consistently overreacts to new
information, which, in turn, results in price reversals. Lehmann and others showed that a contrarian
can make substantial profits in the short run by simply buying losers and selling winners. We,
however, demonstrate that these profits are largely generated by the bid-ask bounce in transaction
prices; accounting for this "bounce" by using bid prices eliminates all profits from price reversals for
NASDAQ-NMS stocks and most of the profits for NYSE/AMEX stocks. Moreover, any remaining
profits (regardless of their source) disappear at trivial levels of transactions costs.
Опубликовано на портале: 26-10-2007
Yakov Amihud, Haim Mendelson
Journal of Finance.
1987.
Vol. 42.
No. 3.
P. 533-553.
This paper examines the effects of the mechanism by which securities are traded on
their price behavior. We compare the behavior of open-to-open and close-to-close
returns on NYSE stocks, given the differences in execution methods applied in the
opening and closing transactions. Opening returns are found to exhibit greater dispersion,
greater deviations from normality and a more negative and significant autocorrelation
pattern than closing returns. We study the effects of the bid-ask spread and the
price-adjustment process on the estimated return variances and covariances and discuss
the associated biases. We conclude that the trading mechanism has a significant effect
on stock price behavior.
Опубликовано на портале: 25-10-2007
Wilson Tong
The Journal of Financial Research.
2000.
Vol. 13.
No. 4.
P. 495-522.
Recent studies on the U.S. market find that the Monday effect is observed mainly
when the rettim on the previous Friday is negative or when the Monday falls within
the last two weeks of the month. I look for international evidence and examine whether
such properties of the Monday effect are related to another anomalous phenomenon—high
weekend correlation. By examining
twenty-three equity market indexes, I find that the negative Friday is, in general,
important to the Monday effect. Furthermore, Monday returns tend to be lowest on
the fourth week of the month. Although high weekend correlation is also common to
these markets, it seems not related to the bad-Friday factor and shows no seasonality
across weeks of the month.
Explaining Monday Returns [статья]
Опубликовано на портале: 25-10-2007
Paul Draper, Krishna Paudyal
The Journal of Financial Research.
2002.
Vol. 15.
No. 4.
P. 507-520.
The Monday effect is reexamined using two stock indexes and a sample of 452 individual
stocks that trade on the London Stock Exchange. The results based on conventional
test methods reveal a negative average return on Monday. Extending the analysis to
examine the effects of various possible influences simultaneously, the average Monday
return becomes positive and does not differ significantly from the average returns
of most other days of the week. Fortnight, ex-dividend day, account period, (bad)
news flow, trading activity, and bid-ask spread effects are all controlled for. The
results broadly support the trading time hypothesis.
A New Look at the Monday Effect [статья]
Опубликовано на портале: 25-10-2007
Ko Wang, John Erickson, Yuming Li
Journal of Finance.
1997.
Vol. 52.
No. 5.
P. 2171-2186.
It is well documented that expected stock returns vary with the day-of-the-week (the
Monday or weekend effect). In this article we show that the well-known Monday effect
occurs primarily in the last two weeks (fourth and fifth weeks) of the month. In
addition, the mean Monday return of the first three weeks of the month is not significantly
different from zero. This result holds for most of the subperiods during the 1962-1993
sampling period and for various stock return indexes. The monthly
effect reported by Ariel (1987) and Lakonishok and Smidt (1988) cannot fully explain
this phenomenon.
Опубликовано на портале: 25-10-2007
A. Craig MacKinlay, Andrew W. Lo
Princeton: Princeton University Press, 1999, 448 с.
For over half a century, financial experts have regarded the movements of markets
as a random walk--unpredictable meanderings akin to a drunkard's unsteady gait--and
this hypothesis has become a cornerstone of modern financial economics and many investment
strategies. Here Andrew W. Lo and A. Craig MacKinlay put the Random Walk Hypothesis
to the test. In this volume, which elegantly integrates their most important articles,
Lo and MacKinlay find that markets are not completely random after all, and that
predictable components do exist in recent stock and bond returns. Their book provides
a state-of-the-art account of the techniques for detecting predictabilities and evaluating
their statistical and economic significance, and offers a tantalizing glimpse into
the financial technologies of the future.
The articles track the exciting course of Lo and MacKinlay's research on the predictability of stock prices from their early work on rejecting random walks in short-horizon returns to their analysis of long-term memory in stock market prices. A particular highlight is their now-famous inquiry into the pitfalls of "data-snooping biases" that have arisen from the widespread use of the same historical databases for discovering anomalies and developing seemingly profitable investment strategies. This book invites scholars to reconsider the Random Walk Hypothesis, and, by carefully documenting the presence of predictable components in the stock market, also directs investment professionals toward superior long-term investment returns through disciplined active investment management.
The articles track the exciting course of Lo and MacKinlay's research on the predictability of stock prices from their early work on rejecting random walks in short-horizon returns to their analysis of long-term memory in stock market prices. A particular highlight is their now-famous inquiry into the pitfalls of "data-snooping biases" that have arisen from the widespread use of the same historical databases for discovering anomalies and developing seemingly profitable investment strategies. This book invites scholars to reconsider the Random Walk Hypothesis, and, by carefully documenting the presence of predictable components in the stock market, also directs investment professionals toward superior long-term investment returns through disciplined active investment management.
Опубликовано на портале: 25-10-2007
David M. Cutler, James Michael Poterba, Lawrence H. Summers
American Economic Review.
1990.
Vol. 80.
No. 2.
P. 63-68.
This paper summarizes our earlier research documenting the characteristic speculative
dynamics of many asset markets and
suggests a framework for understanding them. Our model incorporates "feedback traders,"
traders whose demand is based on the
history of past returns rather than the expectation of future fundamentals. We use
this framework to describe ways in which
the characteristic return patterns might be generated, and also to address the long-standing
question of whether profitable
speculation stabilizes asset markets.
Опубликовано на портале: 25-10-2007
Kenneth R. French, Richard Roll
Journal of Financial Economics.
1986.
Vol. 17.
No. 1.
P. 5-26.
Asset prices are much more volatile during exchange trading hours than during non-trading
hours. This paper considers three explanations for this phenomenon:
(1) volatility is caused by public information which is more likely to arrive
during normal business hours;
(2) volatility is caused by private information which affects prices when informed
investors trade; and
(3) volatility is caused by pricing errors that occur during trading. Although
a significant fraction of the daily variance is caused by mispricing, the behavior
of returns around exchange holidays suggests that private information is the principle
factor behind high trading-time variances.
Portfolio Return Autocorrelation [статья]
Опубликовано на портале: 25-10-2007
Timothy S. Mech
Journal of Financial Economics.
1993.
Vol. 34.
No. 3.
P. 307-334.
This paper investigates whether portfolio return autocorrelation can be explained
by time-varying expected returns, nontrading, stale limit orders, market maker inventory
policy, or transaction costs. Evidence is consistent with the hypothesis that transaction
costs cause portfolio autocorrelation by slowing price adjustment. I develop a transaction-cost
model which predicts that prices adjust faster when changes in valuation are large
in relation to the bid-ask spread. Cross-sectional tests support
this prediction, but time-series tests do not.
Опубликовано на портале: 25-10-2007
Andrew W. Lo, A. Craig MacKinlay
Review of Financial Studies.
1988.
Vol. 1.
No. 1.
P. 41-66.
In this article we test the random walk hypothesis for weekly stock market returns
by comparing variance estimators derived from data sampled at different frequencies.
The random walk model is strongly rejected for the entire sampleperiod (1962-1985)
and for all subperiods for a variety of aggregate returns indexes and size-sorted
porfolios. Although the rejections are due largely to the behavior of small stocks,
they cannot be attributed completely to the effects of infrequent trading or timevarying
volatilities. Moreover, the rejection of the random walk for weekly returns does
not support a mean-reverting model of assetprices.
Опубликовано на портале: 25-10-2007
Jacob Boudoukh, Matthew Richardson, Robert Whitelaw
Review of Financial Studies.
1994.
Vol. 7.
No. 3.
P. 539-573.
This article reexamines the autocorrelation patterns of short-horizon stock returns.
We document empirical results which imply that these autocorrelations have been overstated
in the existing literature. Based on several new insights, we provide support for
a market efficiency-based explanation of the evidence. Our analysis suggests that
institutional factors are the most likely source of the autocorrelation patterns.
Stock Return Autocorrelation and Institutional Investors: The Case of American Depository
Receipt [статья]
Опубликовано на портале: 25-10-2007
Diane DeQing Li, Kennet Yung
Review of Accounting and Finance.
2006.
Vol. 5.
No. 1.
P. 45-58.
Though stock portfolio return autocorrelation is well documented in the literature,
its cause is still not clearly understood. Presently, evidence of private information
induced stock return autocorrelation is still very limited. The difficulty in obtaining
foreign country information by small investors makes the private information of institutional
investors in the ADR (American Depository Receipt) market more significant and influential.
As such, the ADR market provides a favorable environment for testing the effect of
private information on return autocorrelation. The purpose of this paper is to address
this issue.
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