Эксоцман
на главную поиск contacts
Всего публикаций в данном разделе: 37

Последние поступления:

Опубликовано на портале: 26-11-2011
А.Р. Махмутов Экономические науки. 2009.  № 9. С. 61-64. 
В статье раскрыто понятие производных финансовых инструментов (деривативов), определены их место и роль на финансовом рынке, связь между их общей стоимостью и величиной денежной массы. Рассмотрен вопрос о росте стоимости ценных бумаг как инфляции финансовых активов.
ресурс содержит прикрепленный файл

Noise [статья]
Опубликовано на портале: 03-12-2007
Fisher Black Journal of Finance. 1986.  Vol. 21. P. 529-543. 
The effects of noise on the world, and on our views of the world, are profound. Noise in the sense of a large number of small events is often a causal factor much more powerful than a small number of large events can be. Noise makes trading in financial markets possible, and thus allows us to observe prices for financial assets. Noise causes markets to be somewhat inefficient, but often prevents us from taking advantage of inefficiencies. Noise in the form of uncertainty about future tastes and technology by sector causes business cycles, and makes them highly resistant to improvement through government intervention. Noise in the form of expectations that need not follow rational rules causes inflation to be what it is, at least in the absence of a gold standard or fixed exchange rates. Noise in the form of uncertainty about what relative prices would be with other exchange rates makes us think incorrectly that changes in exchange rates or inflation rates cause changes in trade or investment flows or economic activity. Most generally, noise makes it very difficult to test either practical or academic theories about the way that financial or economic markets work. We are forced to act largely in the dark
ресурс содержит полный текст, либо отрывок из него

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.
ресурс содержит полный текст, либо отрывок из него

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.
ресурс содержит полный текст, либо отрывок из него

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
ресурс содержит полный текст, либо отрывок из него

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
ресурс содержит полный текст, либо отрывок из него

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
ресурс содержит полный текст, либо отрывок из него

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.
ресурс содержит полный текст, либо отрывок из него

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.
ресурс содержит полный текст, либо отрывок из него ресурс содержит гиперссылку на сайт, на котором можно найти дополнительную информацию

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.
ресурс содержит полный текст, либо отрывок из него

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.
ресурс содержит полный текст, либо отрывок из него

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.
ресурс содержит полный текст, либо отрывок из него

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.
ресурс содержит полный текст, либо отрывок из него

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.
ресурс содержит полный текст, либо отрывок из него

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.
ресурс содержит полный текст, либо отрывок из него

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.
ресурс содержит полный текст, либо отрывок из него ресурс содержит гиперссылку на сайт, на котором можно найти дополнительную информацию

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.
ресурс содержит полный текст, либо отрывок из него

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.
ресурс содержит полный текст, либо отрывок из него

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.
ресурс содержит полный текст, либо отрывок из него

Опубликовано на портале: 22-10-2007
Hakan Berument, Ercan Balaban Journal of Economics & Finance. 2001.  Vol. 25. No. 2. P. 181-193. 
This study tests the presence of the day of the week effect on stock market volatility by using the S&P 500 market index during the period of January 1973 and October 1997. The findings show that the day of the week effect is present in both volatility and return equations. While the highest and lowest returns are observed on Wednesday and Monday, the highest and the lowest volatility are observed on Friday and Wednesday, respectively. Further investigation of sub-periods reinforces our findings that the volatility pattern across the days of the week is statistically different.
ресурс содержит полный текст, либо отрывок из него