The FCC’s Net Neutrality Decision and Stock Prices

More than a year after a court invalidated its “net neutrality” rules on broadband Internet service providers (ISPs), the Federal Communications Commission (FCC) decided to extend public-utility (Title II) regulation on broadband services. This paper uses traditional event analysis of the movements in the values of major communications and media companies’ equities at key moments in the FCC’s path to this decision to estimate the financial market’s assessment of the likely effects of regulation on ISPs, traditional media companies, and new digital media companies. The results are surprising: the markets penalized only three large cable companies to any extent, and even these effects appear to have been short-lived. The media companies, arguably the intended beneficiaries of the regulations, were unaffected.     

Ethnic Minority Analysts’ Participation in Public Earnings Conference Calls

We investigate ethnic minority and nonminority sell-side analysts’ participation in public earnings conference calls. We find that minority analysts are underrepresented in conference call Q&A sessions, and minority analysts who do participate on the calls experience lower levels of prioritization than do nonminority analysts. Minority analysts’ lower participation rates are partially but not fully mediated by characteristics such as experience, work environment, and stock rating favorability. Additionally, firm and conference call fixed effects mediate approximately half the magnitude of lower minority participation rates. Extroverted minority analysts participate at higher rates, but the negative association between minority status and conference call participation is exacerbated when calls are more time constrained, when executive teams are less diverse, and when analysts are from less prestigious brokerage houses. Overall, we document the underrepresentation of minority analysts on earnings conference calls and provide evidence suggesting both analysts’ and managers’ choices influence minority analysts’ participation rates.     

Managerial myopia and the unintended real consequences of conditional conservatism

We examine whether the demand for conditional conservatism produces unintended real consequences that are exacerbated by managerial incentives to report higher earnings. We document a robust positive association between conditional conservatism and real earnings management (REM), particularly for firms whose CEOs face greater compensation incentives and capital market incentives to report higher earnings. Using mediation analyses, we find that conservatism has a negative indirect relation with future returns via REM over the next 1–3 years. In additional tests, we find that the relation between conservatism and REM is attenuated for firms with higher debt-to-equity, which suggests that debtholders moderate the negative relation between conditional conservative reporting and REM. Our findings suggest that, in contrast to its monitoring benefit, conditional conservatism can exacerbate managerial myopia, resulting in negative consequences for future firm value.     

A NOTE ON BIAS IN CAPITAL BUDGETING INTRODUCED BY STOCHASTIC LIFE

This note extends work by Young and Contreras and by Rosenthal on the present worth of cash flows under uncertain timing. In capital budgeting analysis, the use of the expected life of a project instead of the life distribution of the project biases the estimate of its expected net present value. In most situations the bias results in an overestimate of the expected net present value of the project. When the exact life distribution is unknown, the bias can be approximated by Taylor series expansion. The sensitivity of the bias to the discount rate, to cash flow patterns, and to income taxes is also investigated.     

The evolution of the seven steps of selling

The traditional seven steps of selling is perhaps the oldest paradigm in the sales discipline. The seven steps model has served as a basic framework in sales training, personal selling textbooks, and teaching personal selling classes. Very little has changed in this framework since the turn of the 20th century. This article reviews the traditional seven steps of selling, examines transformative factors that have led to changes in each step, and presents an evolved seven steps process. While the traditional seven steps reflected a selling orientation on the part of a firm, the evolved selling process reflects more of a customer orientation in that the focus is on relationship selling—that is, securing, building, and maintaining long-term relationships with profitable customers.     

Modeling default risk with support vector machines

Predicting default risk is important for firms and banks to operate successfully. There are many reasons to use nonlinear techniques for predicting bankruptcy from financial ratios. Here we propose the so-called Support Vector Machine (SVM) to predict the default risk of German firms. Our analysis is based on the Creditreform database. In all tests performed in this paper the nonlinear model classified by SVM exceeds the benchmark logit model, based on the same predictors, in terms of the performance metric, AR. The empirical evidence is in favor of the SVM for classification, especially in the linear non-separable case. The sensitivity investigation and a corresponding visualization tool reveal that the classifying ability of SVM appears to be superior over a wide range of SVM parameters. In terms of the empirical results obtained by SVM, the eight most important predictors related to bankruptcy for these German firms belong to the ratios of activity, profitability, liquidity, leverage and the percentage of incremental inventories. Some of the financial ratios selected by the SVM model are new because they have a strong nonlinear dependence on the default risk but a weak linear dependence that therefore cannot be captured by the usual linear models such as the DA and logit models.     

Basket trading under co-integration with the logistic mixture autoregressive model

In this paper, we propose a co-integration model with a logistic mixture auto-regressive equilibrium error (co-integrated LMAR), in which the equilibrium relationship among cumulative returns of different financial assets is modelled by a logistic mixture autoregressive time series model. The traditional autoregression (AR) based unit root test (ADF test), used in testing co-integration, cannot give a sound explanation when a time series passes the ADF test. However, its largest root in the AR polynomial is extremely close to, but less than, one, which is most likely the result of a mixture of random-walk and mean-reverting processes in the time series data. With this background, we put an LMAR model into the co-integration framework to identify baskets that have a large spread but are still well co-integrated. A sufficient condition for the stationarity of the LMAR model is given and proved using a Markovian approach. A two-step estimating procedure, combining least-squares estimation and the Expectation-Maximization (EM) algorithm, is given. The Bayesian information criterion (BIC) is used in model selection. The co-integrated LMAR model is applied to basket trading, which is a widely used tool for arbitrage. We use simulation to assess the model in basket trading strategies with the statistical arbitrage feature in equity markets. Data from several sectors of the Hong Kong Hang Seng Index are used in a simulation study on basket trading. Empirical results show that a portfolio using the co-integrated LMAR model has a higher return than portfolios selected by traditional methods. Although the volatility in the return increases, the Sharpe ratio also increases in most cases. This risk–return profile can be explained by the shorter converging period in the co-integrated LMAR model and the larger volatility in the ‘mean-reverting’ regime.     

The affine Heston model with correlated Gaussian interest rates for pricing hybrid derivatives

In this article we define a multi-factor equity–interest rate hybrid model with non-zero correlation between the stock and interest rate. The equity part is modeled by the Heston model and we use a Gaussian multi-factor short-rate process. By construction, the model fits in the framework of affine diffusion processes, allowing fast calibration to plain vanilla options. We also provide an efficient Monte Carlo simulation scheme.     

Variance swap dynamics

We compare several parametric and non-parametric approaches for modelling variance swap curves by conducting an in-sample and an out-of-sample analysis using market prices. The forecasted Heston model gives the best overall performance. Moreover, the static Heston model highlights some problems of stochastic volatility models in option pricing of forward starting products.