How Do Different Types of Investors React to New Financial Statement Information?

The study contributes to our understanding of the forces that drive the stock market by investigating how different types of investors react to new financial statement information. Using the extremely comprehensive official register of share holdings in Finland, we find that the majority of investors are more probable to sell (buy) stocks in a company after a positive (negative) earnings surprise, and show a bias towards buying after the disclosure of new financial statement information. Large investors, on the other hand, show behavior opposite to that of the majority of investors in the market. Further, foreign investors show behavior similar to that of domestic investors. We suggest investor overconfidence and asymmetric information as possible explanations for the documented behavior.

The Power of Bootstrap Tests of Cointegration Rank with Financial Time Series

Bootstrap likelihood ratio tests of cointegration rank are commonly used because they tend to have rejection probabilities that are closer to the nominal level than the rejection probabilities of the correspond- ing asymptotic tests. The e¤ect of bootstrapping the test on its power is largely unknown. We show that a new computationally inexpensive procedure can be applied to the estimation of the power function of the bootstrap test of cointegration rank. The bootstrap test is found to have a power function close to that of the level-adjusted asymp- totic test. The bootstrap test estimates the level-adjusted power of the asymptotic test highly accurately. The bootstrap test may have low power to reject the null hypothesis of cointegration rank zero, or underestimate the cointegration rank. An empirical application to Euribor interest rates is provided as an illustration of the findings.

Lack of Critical Slowing Down Suggests that Financial Meltdowns Are Not Critical Transitions, yet Rising Variability Could Signal Systemic Risk

Complex systems inspired analysis suggests a hypothesis that financial meltdowns are abrupt critical transitions that occur when the system reaches a tipping point. Theoretical and empirical studies on climatic and ecological dynamical systems have shown that approach to tipping points is preceded by a generic phenomenon called critical slowing down, i.e. an increasingly slow response of the system to perturbations. Therefore, it has been suggested that critical slowing down may be used as an early warning signal of imminent critical transitions. Whether financial markets exhibit critical slowing down prior to meltdowns remains unclear. Here, our analysis reveals that three major US (Dow Jones Index, S&P 500 and NASDAQ) and two European markets (DAX and FTSE) did not exhibit critical slowing down prior to major financial crashes over the last century. However, all markets showed strong trends of rising variability, quantified by time series variance and spectral function at low frequencies, prior to crashes. These results suggest that financial crashes are not critical transitions that occur in the vicinity of a tipping point. Using a simple model, we argue that financial crashes are likely to be stochastic transitions which can occur even when the system is far away from the tipping point. Specifically, we show that a gradually increasing strength of stochastic perturbations may have caused to abrupt transitions in the financial markets. Broadly, our results highlight the importance of stochastically driven abrupt transitions in real world scenarios. Our study offers rising variability as a precursor of financial meltdowns albeit with a limitation that they may signal false alarms.

Resale in Auctions with Financial Constraints

This paper analyzes auctions where bidders face nancial constraints that may force them to resell part of the property of the good (or subcontract part of a project) at a resale market. First we show that the ine¢ cient speculative equilibria of second- price auctions (Garratt and Tröger, 2006) generalizes to situations with partial resale where only the high value bidder is nancially constrained. However, when all players face nancial constraints the ine¢ cient speculative equilibria disappear. Therefore, for auctioning big facilities or contracts where all bidders are nancially constrained and there is a resale market, the second price auction remains a simple and appropriate mechanism to achieve an e¢ cient allocation.

Financial x-ray

An over view of the financial x-ray service. JL

Report on Knowledge Exchange workshop Price of keeping knowledge: financial streams for digital preservation

In contrast to cost modeling activities, the pricing of services must be simple and transparent. Calculating and thus knowing price structures, would not only help identify the level of detail required for cost modeling of individual instititutions, but also help develop a ”public” market for services as well as clarify the division of task and the modeling of funding and revenue streams for data preservation of public institutions. This workshop has built on the results from the workshop ”The Costs and Benefits of Keeping Knowledge” which took place 11 June 2012 in Copenhagen. This expert workshop aimed at: •Identifying ways for data repositories to abstract from their complicated cost structures and arrive at one transparent pricing structure which can be aligned with available and plausible funding schemes. Those repositories will probably need a stable institutional funding stream for data management and preservation. Are there any estimates for this, absolute or as percentage of overall cost? Part of the revenue will probably have to come through data management fees upon ingest. How could that be priced? Per dataset, per GB or as a percentage of research cost? Will it be necessary to charge access prices, as they contradict the open science paradigm? •What are the price components for pricing individual services, which prices are currently being paid e.g. to commercial providers? What are the description and conditions of the service(s) delivered and guaranteed? •What types of risks are inherent in these pricing schemes? •How can services and prices be defined in an all-inclusive and simple manner, so as to enable researchers to apply for specific amount when asking for funding of data-intensive projects?Please