Alliance portfolio reconfiguration following a technological discontinuity

Research summary : We study how technological discontinuities generate first‐ and second‐order effects on alliance formation and termination, leading to reconfiguration of firms' alliance portfolios. Following technological shocks, we argue that firms often seek alliances that provide new resources while also having incentives to form alliances for reinforced and challenged resources that complement the new resources. In parallel, alliance terminations, even involving resources otherwise unaffected by the discontinuity, increase due to limits in firms' alliance carrying capacity. We study biopharmaceutical firms between 1990 and 2000, which faced a technological discontinuity in 1995 in the form of combinatorial chemistry and high‐throughput screening. We improve understanding of how technological discontinuities affect the value of resources and how firms reconfigure alliance portfolios in response. Managerial summary : When firms form alliances to gain new resources during technological discontinuities that disrupt their industry, they cannot consider only the focal new partnerships. Instead, new alliances create complementarity and substitution pressures that lead to broader reconfiguration of the firms' alliance portfolios: (1) complementarity creates incentives to also form alliances for resources that the technological discontinuity reinforces or challenges in order to improve the collective value of co‐specialized assets; (2) substitution creates incentives to terminate existing alliances, even if their value is otherwise unaffected by the discontinuity, in order to create carrying capacity for new alliances. Thus, one new alliance can generate a cascade of reconfiguration that challenges the balance between the benefits of stability and the need for change in an alliance portfolio. Copyright © 2016 John Wiley & Sons, Ltd.     

Tail dependence analysis of stock markets using extreme value theory

Financial risk modelling frequently uses the assumption of a normal distribution when considering the return series which is inefficient if the data is not normally distributed or if it exhibits extreme tails. Estimation of tail dependence between financial assets plays a vital role in various aspects of financial risk modelling including portfolio theory and hedging amongst applications. Extreme Value Theory (EVT) provides well established methods for considering univariate and multivariate tail distributions which are useful for forecasting financial risk or modelling the tail dependence of risky assets. The empirical analysis in this article uses nonparametric measures based on bivariate EVT to investigate asymptotic dependence and estimate the degree of tail dependence of the ASX-All Ordinaries daily returns with four other international markets, viz., the S&P-500, Nikkei-225, DAX-30 and Heng-Seng for both extreme right and left tails of the return distribution. It is investigated whether the asymptotic dependence between these markets is related to the heteroscedasticity present in the logarithmic return series using GARCH filters. The empirical evidence shows that the asymptotic extreme tail dependence between stock markets does not necessarily exist and rather can be associated with the heteroscedasticity present in the financial time series of the various stock markets.     

Information Content of Offer Date Revelations: A Fresh Look at Seasoned Equity Offerings

Besides the offer price discount, investment bankers use revisions in offer size from the amount originally filed to signal the issuer's quality to their buy‐side clients. Unlike the offer price discount, offer size revision not only relates to the offer date price reaction, it also predicts post‐SEO (seasoned equity offering) performance. Improved SEOs, whose offer size exceeds the amount originally registered, experience significantly positive returns during the registration period and on the offer date. More importantly, they do not underperform post‐issuance. Their complement, regular SEOs, exhibit significantly negative returns during the registration period, on the offer date, and underperform their benchmark following issuance.