I don’t usually post research on the site, but thought that this was a great article, just take a look at this chart.
http://ftalphaville.ft.com/blog/2008/07/15/14504/a-trillion-dollar-mean-reversion/
Abstract
Any admissible portfolio performance measure should satisfy four minimal conditions: it assigns zero performance to each reference portfolio and it is linear, continuous, and nontrivial. Such an admissible measure exists if and only if the securities market obeys the law of one price. A positive admissible measure exists if and only if there is no arbitrage. This article characterizes the (infinite) set of admissible performance measures. It is shown that performance evaluation is generally quite arbitrary. A mutual fund
data set is also used to demonstrate how the measurement method developed here can be applied.
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Abstract
We study time variation in expected excess bond returns. We run regressions of one-year excess returns on initial forward rates. We find that a single factor, a single tent-shaped linear combination of forward rates, predicts excess returns on one- to five-year maturity bonds with R2 up to 0.44. The return-forecasting factor is countercyclical and forecasts stock returns. An important component of the returnforecasting factor is unrelated to the level, slope, and curvature movements described by most term structure models. We document that measurement errors do not affect our central results. (JEL G0, G1, E0, E4)
Abstract.
In this article we revisit the classic problem of tatonnement in price formation from a microstructure point of view, reviewing a recent body of theoretical and empirical work explaining how fluctuations in supply and demand are slowly incorporated into prices. Because revealed market liquidity is extremely low, large orders to buy or sell can only be traded incrementally, over periods of time as long as months. As a result order flow is a highly persistent long-memory process. Maintaining compatibility with market efficiency has profound consequences on price formation, on the dynamics of liquidity, and on the nature of impact. We review a body of theory that makes detailed quantitative predictions about the volume and time dependence of market impact, the bid-ask spread, order book dynamics, and volatility. Comparisons to data yield some encouraging successes. This framework suggests a novel interpretation of financial information, in which agents are at best only weakly informed and all have a similar and extremely noisy impact on prices. Most of the processed information appears to come from supply and demand itself, rather than from external news. The ideas reviewed here are relevant to market microstructure regulation, agent-based models, cost-optimal execution strategies, and understanding market ecologies.
Ann Devitt, Khurshid Ahmad
Abstract
Text is not unadulterated fact. A text can make you laugh or cry but can it also make you short sell your stocks in company A and buy up options in company B? Research in the domain of finance strongly suggests that it can. Studies have shown that both the informational and affective aspects of news text affect the markets in profound ways, impacting on volumes of trades, stock prices, volatility and even future firm earnings. This paper aims to explore a computable metric of positive or negative polarity in financial news text which is consistent with human judgments and can be used in a quantitative analysis of news sentiment impact on financial markets. Results from a preliminary evaluation are presented and discussed.
Note:
A colleage of mine is into NLP, he sent me two article to get up here.
Second one will come tomorrow.
Costantino, Richard G. Morgan, Russell J. Collingham, Roberto Garigliano
Abstract
Quantitative financial data are today largely analyzed by automatic computer programs based on traditional or artificial intelligence techniques. Differently, qualitative data and, in particular, articles from on-line news agencies or from financial newspapers are not yet successfully processed. As a result , financial operators suffer from qualitative data-overload. This paper addresses the issue of the use of Natural Language Processing and, in particular, information extraction, for processing qualitative financial data. The financial information extraction system under development at the University of Durham can identify specific kinds of information within a source article, producing a set of relevant templates which represent the most important information in the article and therefore reducing the operators’ qualitative data-overload. The application has been designed in close contact with experts of the financial sector and can be fully customized by the user who can add new templates to the existing ones/
Ekkehart Boehmer, Charles M. Jones, Xiaoyan Zhang
This is a great article, there is a new version out published in Journal of Finance, Vol63: Issue 2, April 2008 couldn’t find it though.
Abstract
We use a long, recent panel of proprietary system order data from the New York Stock Exchange to examine the incidence and information content of various kinds of short sale orders. Since 2000, more than 12.9% of NYSE volume involves a short seller, suggesting that many market participants are able to surmount any short-sale constraints that might be present. As a group, these short sellers are extremely well-informed. Stocks with relatively heavy shorting underperform lightly shorted stocks by a risk-adjusted average of 1.25% in the following 20 days of trading (16.9% on an annualized basis). We partition short sales by account type: individual, institutional, member-firm proprietary, and other, and we can tell if a short sale is part of a program trade. Institutional non-program short sales are the most informative. Compared to stocks that are lightly shorted by institutions, a portfolio of stocks most heavily shorted by institutions underperforms by a risk-adjusted average of 1.54% in the next month (over 21% annualized). Large short sale orders are the most informative. In contrast, when more of the short sales are small (less than 500 shares), stocks tend to rise in the following month, indicating that these orders are uninformed. The reported alphas do not account for the cost of shorting, and they cannot be achieved by outsiders, because the internal NYSE data that we use are not generally available to market participants. But these findings indicate that institutional short sellers have identified and acted on important value-relevant information that has not yet been impounded into price. The results are strongly consistent with the emerging consensus in financial economics that short sellers possess important information, and their trades are important contributors to more efficient stock prices.
Guojin Gong , Henock Louis , Amy X. Sun
Abstract
We provide evidence suggesting that both the post-repurchase long-term abnormal returns and the reported improvement in operating performance documented in prior studies are driven, at least partly, by pre-repurchase downward earnings management, rather than genuine growth in profitability. The average firm reports significantly negative abnormal accruals prior to open-market repurchases. The extent of the downward earnings management increases with the percentage of the company that managers repurchase and CEO ownership. The pre-repurchase abnormal accruals are also significantly negatively associated with both future operating performance and future stock performance, and the negative associations are driven almost exclusively by those firms that report the largest income-decreasing abnormal accruals prior to the repurchases. The study suggests that one reason firms experience post-repurchase abnormal returns is that the post-repurchase realized earnings growth exceeds expectations formed on the basis of the pre-repurchase deflated earnings numbers.
Haven’t read it yet.
Remarks by Mr Ben S Bernanke, Chairman of the Board of Governors of the US Federal Reserve System, at the US Chamber Education and Workforce Summit, Washington DC, 24 September 2007.
On most dimensions the U.S. economy appears to be performing well. Output growth has returned to healthy levels, the labor market is firming, and inflation appears to be well controlled. However, one aspect of U.S. economic performance still evokes concern among economists and policymakers: the nation’s large and growing current account deficit. In 2004, the U.S. external deficit stood at $666 billion, or about 5-3/4 percent of the U.S. gross domestic product (GDP). Corresponding to that deficit, U.S. citizens, businesses, and governments on net had to raise $666 billion on international capital markets.1 The current account deficit has been on a steep upward trajectory in recent years, rising from a relatively modest $120 billion (1.5 percent of GDP) in 1996 to $414 billion (4.2 percent of GDP) in 2000 on its way to its current level. Most forecasters expect the nation’s current account imbalance to decline slowly at best, implying a continued need for foreign credit and a concomitant decline in the U.S. net foreign asset position.