Reported Earnings and Analyst Forecasts as Competing Sources of Information: A New Approach

We study information flows between earnings and forecasts, using suitably adapted Granger causality tests. This approach complements existing cross-sectional studies by abstracting from stock market reactions to information, and focussing on dynamic interactions between information flows instead. We find bi-directional causality in timeseries of analyst earnings forecasts and reported earnings, supporting our expectation that forecasts contribute to information that is reflected in future reports. Further, our evidence of feedback suggests that past reports and forecasts are both reflected in future forecasts, implying that the information in reports has inherent value, and that forecasts do not fully substitute for reports.

Oil prices and stock returns : nonlinear links across sectors

We present evidence of an asymmetric relationship between oil prices and stock returns. The two regime multivariate Markov switching vector autoregressive (MSVAR) model allow us to capture the state shifts in the relationship between regional stock markets and sectors. Results suggest that oil price risk is significantly priced in the sample used. The impact is asymmetric with respect to market phases, and regimes have been associated with world economic, social and political events. Our study also suggests asymmetric responses of sector stock returns to oil price changes and different transmission impacts depending on the sector analyzed. There is a high causality from oil to sectors like Industrials and Oil & Gas. Companies inside the Utilities sector were more able to hedge against oil price increases between 2007 and 2012. Historical crisis events between 1992–1998 and 2003–2007 do not seem to have affected the relationship between oil and sector stock returns, given the higher probability of remaining smoother. For all sectors there seems to be a turn back to stability from 2012 onwards. Finally, investors gain more through portfolio diversification benefits built across, rather than within sectors.info:eu-repo/semantics/publishedVersio

Stock price reaction to profit warnings: The role of time-varying betas

This study investigates the role of time-varying betas, event-induced variance and conditional heteroskedasticity in the estimation of abnormal returns around important news announcements. Our analysis is based on the stock price reaction to profit warnings issued by a sample of firms listed on the Hong Kong Stock Exchange. The standard event study methodology indicates the presence of price reversal patterns following both positive and negative warnings. However, incorporating time-varying betas, event-induced variance and conditional heteroskedasticity in the modelling process results in post-negative-warning price patterns that are consistent with the predictions of the efficient market hypothesis. These adjustments also cause the statistical significance of some post-positive-warning cumulative abnormal returns to disappear and their magnitude to drop to an extent that minor transaction costs would eliminate the profitability of the contrarian strategy

Measuring and Modeling Risk Using High-Frequency Data

Measuring and modeling financial volatility is the key to derivative pricing, asset allocation and risk management. The recent availability of high-frequency data allows for refined methods in this field. In particular, more precise measures for the daily or lower frequency volatility can be obtained by summing over squared high-frequency returns. In turn, this so-called realized volatility can be used for more accurate model evaluation and description of the dynamic and distributional structure of volatility. Moreover, non-parametric measures of systematic risk are attainable, that can straightforwardly be used to model the commonly observed time-variation in the betas. The discussion of these new measures and methods is accompanied by an empirical illustration using high-frequency data of the IBM incorporation and of the DJIA index

Acidic Extracellular pH Promotes Activation of Integrin αvβ3

Acidic extracellular pH is characteristic of the cell microenvironment in several important physiological and pathological contexts. Although it is well established that acidic extracellular pH can have profound effects on processes such as cell adhesion and migration, the underlying molecular mechanisms are largely unknown. Integrin receptors physically connect cells to the extracellular matrix, and are thus likely to modulate cell responses to extracellular conditions. Here, we examine the role of acidic extracellular pH in regulating activation of integrin [alpha]v[beta]3. Through computational molecular dynamics simulations, we find that acidic extracellular pH promotes opening of the [alpha]v[beta]3 headpiece, indicating that acidic pH can thereby facilitate integrin activation. This prediction is consistent with our flow cytometry and atomic force microscope-mediated force spectroscopy assays of integrin [alpha]v[beta]3 on live cells, which both demonstrate that acidic pH promotes activation at the intact cell surface. Finally, quantification of cell morphology and migration measurements shows that acidic extracellular pH affects cell behavior in a manner that is consistent with increased integrin activation. Taken together, these computational and experimental results suggest a new and complementary mechanism of integrin activation regulation, with associated implications for cell adhesion and migration in regions of altered pH that are relevant to wound healing and cancer.National Institute of Biomedical Imaging and Bioengineering (U.S.) (Award Number T32EB006348)Massachusetts Institute of Technology (Collamore-Rogers Fellowship)National Institutes of Health (U.S.) (NIH Cell Migration Consortium Grant U54-GM069668)National Science Foundation (U.S.) (CAREER Award)Singapore-MIT Alliance for Research and Technology (BioSystem and Micromechanics (BioSyM) Interdisciplinary Research Group