The Effect of Leverage on Bidding Behavior: Theory and Evidence from the FCC Auctions

This is an exploration of how bidding behavior of firms in variousauctions is affected by their capital structure. The theoreticalmodel considers a first-price sealed bid and an English auction.We find that as debt levels increase, firms tend to reduce theirbids. The lower bids give the competition incentives to reducetheir bids as well. These results are investigated empiricallyusing data from the 1994–1995 FCC spectrum auctions. Consistentwith the theoretical model, higher debt levels of the biddingfirm and of the competition tend to lead to lower bids. Additionaldeterminants of bidding behavior in these auctions are alsoanalyzed.     

Established companies diversifying into young industries: A comparison of firms with different levels of performance

Differences in performance among established firms diversifying into young industries were investigated, with hypotheses concerning 11 ‘corporate level’ strategic and organizational variables being examined. Performance was found to be associated with firm size and financial strength, time of entry, and the maturity of the firm's markets. The importance of several variables examined also appears to change as an industry evolves.     

Election predictions are arbitrage-free: response to Taleb

Taleb [Election predictions as martingales: An arbitrage approach. Quant. Finance, 2018, 18, 1–5] claimed a novel approach to evaluating the quality of probabilistic election forecasts via no-arbitrage pricing techniques and argued that popular forecasts of the 2016 U.S. Presidential election had violated arbitrage boundaries. We show that under mild assumptions all such political forecasts are arbitrage-free and that the heuristic that Taleb's argument was based on is false.     

A RE-EXAMINATION OF THE PROXY HYPOTHESIS

In this study we examine whether the proxy hypothesis can explain the puzzling negative relation between real stock returns and expected inflation. Our study improves upon previous tests that suffer from model misspecification and are incomplete. With correctly specified models, we provide a comprehensive test using data from four major industrialized nations during the period of floating exchange rates. Results do not support the proxy hypothesis.     

Why hard-nosed executives should care about management theory

Theory often gets a bum rap among managers because it's associated with the word "theoretical," which connotes "impractical." But it shouldn't. Because experience is solely about the past, solid theories are the only way managers can plan future actions with any degree of confidence. The key word here is "solid." Gravity is a solid theory. As such, it lets us predict that if we step off a cliff we will fall, without actually having to do so. But business literature is replete with theories that don't seem to work in practice or actually contradict each other. How can a manager tell a good business theory from a bad one? The first step is understanding how good theories are built. They develop in three stages: gathering data, organizing it into categories highlighting significant differences, then making generalizations explaining what causes what, under which circumstances. For instance, professor Ananth Raman and his colleagues collected data showing that bar code-scanning systems generated notoriously inaccurate inventory records. These observations led them to classify the types of errors the scanning systems produced and the types of shops in which those errors most often occurred. Recently, some of Raman's doctoral students have worked as clerks to see exactly what kinds of behavior cause the errors. From this foundation, a solid theory predicting under which circumstances bar code systems work, and don't work, is beginning to emerge. Once we forgo one-size-fits-all explanations and insist that a theory describes the circumstances under which it does and doesn't work, we can bring predictable success to the world of management.     

Predicting MBA no-shows and graduation success with discriminate analysis

This paper uses discriminate analysis to examine five years of MBA admission records in order to separate no-shows from the successful program graduates. The study used traditional numeric data such as age, length of time with current employer, undergraduate GPA and GMAT scores—as well as dummy variables for sex, full- or part-time status, race, the public or private nature of the undergraduate institution, and in-state tuition eligibility. The analysis correctly separated the no-shows with a 94.2 percent classification rate based entirely on the use of dummy variables. Unlike other studies, undergraduate GPAs, GMAT scores, and other numeric variables played no role in the final classification. The results suggest that more attention be given to the use of dummy variables when it comes to predicting the success of MBA program graduates.The authors would like to thank the unfailingly efficient efforts of Geetanjali Goel, who helped retrieve and organize the data examined in this study.