Financial Knowledge and “Don't Know” Response

We investigate the effect of group characteristics and educational interventions on young respondents' objective financial knowledge level. We examine six questions about personal finance and covariates selected from the 2015 National Financial Capability Study. Because these questions include “Don't Know” or “Refuse” (DK/RF) responses, a simple regression model could cause researchers to reach misleading conclusions if DK/RF responses are not random. Thus, we suggest a binomial‐latent regression model to evaluate the effect of educational interventions and group differences that are hidden in DK/RF responses. The estimation result shows that rejecting financial education opportunities is disadvantageous to obtaining proper financial knowledge. In addition, both formal and informal financial education are less effective in improving objective financial knowledge in our preferred model. We also find few or no gender, income, and age differences in young adults' objective financial knowledge level after controlling for financial education interventions.     

The Emergence of Individual Knowledge in a Group Setting: Mitigating Cognitive Fallacies

Research in psychology has found that subjects regularly exhibit a conjunction fallacy in probability judgments. Additional research has led to the finding of other fallacies in probability judgment, including disjunction and conditional fallacies. Such analyses of judgments are critical because of the substantial amount of probability judgment done in accounting, business and organizational settings. However, most previous research has been conducted in the environment of a single decision maker. Since business and other organizational environments also employ groups, it is important to determine the impact of groups on such cognitive fallacies. This paper finds that groups substantially mitigate the impact of probability judgment fallacies among the sample of subjects investigated. The key finding of this paper is the analysis of the apparent manner in which groups make such decisions. A statistical analysis, based on a binomial distribution, suggests that groups investigated here did not use consensus. Instead, if any one member of the group has correct knowledge about the probability relationships, then the group uses that knowledge and does not exhibit fallacy in probability judgment. Having a computational model of the group decision making process provides a basis for developing computational models that can be used to simulate “mirror worlds” of reality or model decision making in real world settings.     

Predictable forward performance processes: Infrequent evaluation and applications to human-machine interactions

We study discrete-time predictable forward processes when trading times do not coincide with performance evaluation times in a binomial tree model for the financial market. The key step in the construction of these processes is to solve a linear functional equation of higher order associated with the inverse problem driving the evolution of the predictable forward process. We provide sufficient conditions for the existence and uniqueness and an explicit construction of the predictable forward process under these conditions. Furthermore, we find that these processes are inherently myopic in the sense that optimal strategies do not make use of future model parameters even if these are known. Finally, we argue that predictable forward preferences are a viable framework to model human-machine interactions occurring in automated trading or robo-advising. For both applications, we determine an optimal interaction schedule of a human agent interacting infrequently with a machine that is in charge of trading.     

GRAPHICAL MODELS FOR CORRELATED DEFAULTS

A simple graphical model for correlated defaults is proposed, with explicit formulas for the loss distribution. Algebraic geometry techniques are employed to show that this model is well posed for default dependence: it represents any given marginal distribution for single firms and pairwise correlation matrix. These techniques also provide a calibration algorithm based on maximum likelihood estimation. Finally, the model is compared with standard normal copula model in terms of tails of the loss distribution and implied correlation smile.     

Market versus Corporate Structure in Plant-Level Innovation Performance

This paper examines the effect which market and corporate structure have on the extent of innovation for a sample of circa 300 manufacturing plants located in Scotland. Innovation is defined as the introduction of a commercially significant new product at the establishment level. The theoretical model of Geroski (1990) is extended to incorporate plant- level variables such as size, multiplant operation, the presence of R&D facilities and external/indigenous ownership. A distinction is made between the direct and indirect effects of these variables. Negative binomial estimations indicate that corporate structure influences are more important in determining the number of innovations than market structure and barrier to entry variables. Plant size, foreign ownership and the presence of R&D are all positively associated with innovation. Direct effects greatly outweigh indirect effects. Tobit estimations on the number of innovations per employee support the findings of Acs and Audretsch (1988) that smaller enterprises are more innovation intensive than larger enterprises, at least up to a limit of around 1200 employees. The positive effect of R&D arises principally from increasing the probability of a plant becoming an innovator, rather than from making a plant more innovation intensive. By contrast, the importance of size lies principally in encouraging further innovations among plants which are already innovators, but less than proportionately with the increase in employment size.     

Not all family firms are equal: The moderating effect of family involvement on the political risk exposure of the foreign direct investment portfolio. Preliminary evidence from Spanish multinational enterprises

We investigate the effect of political risk (PR) exposure and family control on the internationalization strategy of multinational enterprises (MNEs) using social capital theory. Our results from a negative binomial cross‐sectional analysis in 2007 of Spanish MNEs show family ownership or the limited presence of family members on the board has no effect on internationalization. However, when the conceptualization of family firms (FFs) includes majority ownership and board presence, we find a direct negative effect on their internationalization scope but a positive moderating effect on the relationship between the exposure to PR and internationalization scope. FFs have some specific advantages suitable to be employed in their corporate political activity allowing them to develop long‐lasting relationships with relevant political actors. By disentangling the effects of family control on internationalization and PR, this article explains how FFs can be simultaneously risk‐willing and risk‐averse.     

When consumers can return digital products: Influence of firm- and consumer-induced communication on the returns and profitability of news articles

The news industry is being massively disrupted by the digital distribution of news. Consequently, publishers have revised their business models and integrated pay-per-article options. To reduce pre-purchase uncertainty, consumers can use information from firm-induced (e.g., newsletters), or consumer-induced communication (e.g., likes). These communication activities avoid purchases with poor fit but also increase customer expectations. Consequently, their effect on sales, returns, and profitability is unclear. For digital products, these effects are even less clear because product quality is difficult to evaluate pre-purchase, and products can be returned at almost no cost, even after consumption. In this study, we investigate the effects of firm- and consumer-induced communication on digital returns in the context of news articles on a major online platform (Blendle). We rely on a multi-equation model to quantify the effect of firm- and consumer-induced communication activities (i.e., newsletter promotions sent out by the platform and consumer likes from readers) on sales and returns and calculate their profitability impact. Our results show that newsletters decrease returns but do not significantly affect sales. In contrast, consumer likes have a twofold effect by increasing sales and decreasing returns. A simulation shows that both newsletters and likes increase profitability and that likes have a higher potential. Our results offer much needed guidance for online aggregators of digital products (e.g., audiobooks, e-books or news articles), as well as for online publishers based on pay-per-unit business models.     

Predicting future purchases with the Poisson log-normal model

The negative binomial distribution (NBD) has been widely used in marketing for modeling purchase frequency counts, particularly in packaged goods contexts. A key managerially relevant use of this model is Conditional Trend Analysis (CTA)—a method of benchmarking future sales utilizing the NBD conditional expectation. CTA allows brand managers to identify whether the sales change in a second period is accounted for by previous non-, light, or heavy buyers of the brand. Although a useful tool, the conditional prediction of the NBD suffers from a bias: it under predicts what the period-one non-buyer class will do in period two and over predicts the sales contribution of existing buyers. In addition, the NBD's assumption of a gamma-distributed mean purchase rate lacks theoretical support—it is not possible to explain why a gamma distribution should hold. This paper therefore proposes an alternative model using a log-normal distribution in place of the gamma distribution, hence creating a Poisson log-normal (PLN) distribution. The PLN distribution has a stronger theoretical grounding than the NBD as it has a natural interpretation relying on the central limit theorem. Empirical analysis of brands in multiple categories shows that the PLN distribution gives better predictions than the NBD.     

A study of factors affecting intersection crash frequencies using random-parameter multivariate zero-inflated models

Recent research demonstrates the appropriateness of multivariate regression models in crash count modelling when one specific type of crash counts needs to be analysed, since they can better handle the correlated issues in multiple crash counts. In this paper, a random-parameter multivariate zero-inflated Poisson (RMZIP) regression model is proposed as an alternative multivariate methodology for jointly modelling crash counts simultaneously. Using this RMZIP model, we are able to account for the heterogeneity due to the unobserved roadway geometric design features and traffic characteristics. Our formulation also has the merit of handling excess zeros in correlated crash counts, a phenomenon that is commonly found in practice. The Bayesian method is employed to estimate the model parameters. We use the proposed modelling framework to predict crash frequencies at urban signalized intersections in Tennessee. To investigate the model performances, three models – a fixed-parameter MZIP model, a random-parameter multivariate negative binomial (RMNB) model, and a random-parameter multivariate zero-inflated negative binomial (RMZINB) model – have been employed as the comparison methods. The comparison results show that the proposed RMZIP models provide a satisfied statistical fit with more variables producing statistically significant parameters. In other word, the RMZIP models have the potential to provide a fuller understanding of how the factors affect crash frequencies on specific roadway intersections. A variety of variables are found to significantly influence the crash frequencies by varying magnitudes. These variables result in random parameters and thereby their effects on crash frequencies are found to vary significantly across the sampled intersections.     

Modeling the Audience's Banner ad Exposure for Internet Advertising Planning

Exposure models focusing on reach and frequency where an audience is exposed to an ad message are the foundation of audience measurement. Given that a certain fundamental assumption of conventional exposure models is not relevant in the Internet advertising context, this paper suggests that the audience's on-line banner exposure can be best analyzed by its Web site visits. The negative binomial distribution (NBD) model, having long been applied in analyzing repeat behaviors, is proposed to serve as a banner ad exposure model. An empirical validation indicates that the model performs well in both data fitting and prediction.     

Cross-category sales promotion effects

We introduce a multivariate binomial logit model measuring cross-category dependence and sales promotion effects of a retail assortment. This model requires as data both the market baskets of individual shoppers and the categories currently promoted in a retail outlet. A special section describes the stepwise procedure used to estimate parameters of this model. Its application is demonstrated analyzing 6147 purchases that were acquired in a medium-sized supermarket. We finally discuss the managerial relevance of this model for sales promotion decisions of retail firms.     

Erratum on the MBG/NBD Model

This erratum clarifies an inconsistency in the equation provided by Batislam, Denizel, and Filiztekin [Batislam, E.P., Denizel, M., & Filiztekin, A., (2007). Empirical validation and comparison of models for customer base analysis. International Journal of Research in Marketing, 24 (3), 201–209] for the conditional expected number of future transactions used in their modified beta geometric/negative binomial distribution model. The derivation of the appropriate expression is carried out by means of parameter updates, which are an elegant alternative to integral calculus.     

Brand and consumer engagement behaviors on Facebook brand pages: Let's have a (positive) conversation

This paper examines consumer-brand engagement on social media, focusing on the dynamic interplay between brands and consumers as well as among consumers themselves. In addition to liking and sharing behaviors, we look at what influences consumers to provide feedback to brands by commenting on brand posts, connect with other consumers by replying to their comments and agree with other consumers by liking their comments and replies. Drawing on a large longitudinal dataset from 2740 Facebook brand pages across 25 industries, a multilevel multivariate autoregressive zero-inflated negative binomial model is used to examine how brand post and response behaviors are related to specific consumer engagement behaviors. Results show that the amount of brand posts indirectly affects consumer comments through increased liking and sharing, but with diminishing returns. Liking is key as it increases positive and attenuates negative feedback, whereas sharing is associated with both positive and negative consumer-brand comments. Posting photos and videos encourages liking, with photos having increasing returns. Videos are the only format with a direct positive influence on consumer-brand comments, though with decreasing effect. Brand replies to consumer comments, even delayed ones, have little effect on negative conversations but prompt replies would be more beneficial as they bolster positive consumer comments. Moreover, our findings provide evidence for both a “positivity spiral” and “negativity spiral”, with the negativity spiral stronger than the positivity one. Taken together, these findings shed light on how marketers can stimulate positive conversations between brands and consumers on social media.     

Bilateral trade wars

The paper constructs a three-country, two-good general equilibrium model to analyze the welfare effects of bilateral trade wars. The presence of a third country (or a number of countries) pursuing free trade policies alters several results based on a two-country framework: Regardless of whether tariffs or quotas are used, bilateral trade wars need not eliminate trade between the two retaliating countries; even a “small” retaliating country can win a bilateral trade war; and quotas can be welfare-superior to tariffs under bilateral retaliation.     

Immediate and enduring effects of digital badges on online content consumption and generation

Awarding users digital badges for their contributions such as generating valuable content and participating in community activities has become an increasingly popular practice on user-generated content (UGC) platforms to incentivize user engagement. Beyond their immediate effects, digital badges can have enduring effects on UGC consumption and generation by affecting users’ underlying engagement states. To account for both effects, the authors develop a bivariate negative binomial hidden Markov model of content consumption and generation and estimate the model using a panel data set obtained from a large UGC platform. They find that users increase their content generation but decrease their content consumption immediately after being awarded a digital badge. Furthermore, being awarded a digital badge has a positive enduring effect on both content consumption and content generation behaviors of a user. This positive enduring effect mainly applies to users in relatively engaged states but not to users in the least engaged state. Finally, the authors demonstrate the value of the proposed model with respect to assessing consumer responses to the design of digital badges.     

Nonconvergence in the Variation of the Hedging Strategy of a European Call Option

In this paper we consider the variation of the hedging strategy of a European call option when the underlying asset follows a binomial tree. In a binomial tree model the hedging strategy of a European call option converges to a continuous process when the number of time points increases so that the price process of the underlying asset converges to a Brownian motion, the Bachelier model. However, the variation of the hedging strategy need not converge to the variation of the limit process. In fact, it is shown that the asymptotic variation of the hedging strategy may be of any order.     

Walking Inside the Potential Tax Evader’s Mind: Tax Morale Does Matter

We conduct an empirical study on the determinants of the psychological costs of tax evasion, also known as tax morale. As a preliminary step, we build a model of tax evasion including non-monetary considerations, show the relationship between tax compliance and tax morale. In the empirical analysis of tax morale we find, using a binomial logit model, that the justification of tax evasion can be explained by the presence of grievance in absolute terms (those who feel that taxes are too high, those who feel that public funds are wasted, and those who accept underground economic activities); and grievances in relative terms (the suspected level of others’ tax evasion). The sense of duty and the level of solidarity are also relevant factors, but to a lesser extent.     

Optimal No‐Arbitrage Bounds on S&P 500 Index Options and the Volatility Smile

This article shows that the volatility smile is not necessarily inconsistent with the Black–Scholes analysis. Specifically, when transaction costs are present, the absence of arbitrage opportunities does not dictate that there exists a unique price for an option. Rather, there exists a range of prices within which the option's price may fall and still be consistent with the Black–Scholes arbitrage pricing argument. This article uses a linear program (LP) cast in a binomial framework to determine the smallest possible range of prices for Standard & Poor's 500 Index options that are consistent with no arbitrage in the presence of transaction costs. The LP method employs dynamic trading in the underlying and risk‐free assets as well as fixed positions in other options that trade on the same underlying security. One‐way transaction‐cost levels on the index, inclusive of the bid–ask spread, would have to be below six basis points for deviations from Black–Scholes pricing to present an arbitrage opportunity. Monte Carlo simulations are employed to assess the hedging error induced with a 12‐period binomial model to approximate a continuous‐time geometric Brownian motion. Once the risk caused by the hedging error is accounted for, transaction costs have to be well below three basis points for the arbitrage opportunity to be profitable two times out of five. This analysis indicates that market prices that deviate from those given by a constant‐volatility option model, such as the Black–Scholes model, can be consistent with the absence of arbitrage in the presence of transaction costs. © 2001 John Wiley & Sons, Inc. Jrl Fut Mark 21:1151–1179, 2001     

Enhancing the Efficiency of Water Supply—Product Market Competition Versus Trade

In most developed countries, the provision of water is organized at a local level. The costs and tariffs vary significantly, even between adjacent water utilities. Such heterogeneity is an obvious indication of the sector??s overall inefficiency and stresses a need for institutional adjustments. We show that cooperation by water trade and the introduction of competition by common carriage between adjacent utilities are valuable alternatives to improve the industry??s efficiency, even when mergers are not feasible. Because both approaches require the physical connection of neighboring networks, they may have similar effects. This paper analyzes and compares the relevant welfare gains and shows that production efficiency and retail prices may differ depending on the initial cost differential, the application of regulations and the distribution of bargaining power. Using a theoretical model, we show that at higher initial production cost differentials, welfare is higher under competitive conditions, even in a lower-bound benchmark case without any regulation.     

Cost of capital and public loan guarantees to small firms

In this paper, we study the determinants of the spread charged by banks under a UK policy intervention scheme, aimed at supporting access to the credit market for small firms through guarantee backed loans. We exploit a unique dataset containing data on 29,266 guarantee backed loans under the UK SFLG scheme over the period 2000 to 2005. Results suggest that lower spreads are offered for loans of larger amounts and higher durations, for service firms, for larger firms, and for those located in the most advanced regions. Higher spreads are applied to high-tech manufacturing firms and to loans issued for working capital purposes. We also find that the presence of other extant debt is associated with a relatively higher spread and that this effect is especially significant for the subset of firms that have reached a maximum debt capacity based on collateralized assets. Further, we also find that the higher the incidence of the publicly guaranteed debt over the total amount of outstanding loans, the lower, on average, the spread. However, an increase in the guaranteed coverage leads to a contraction in the spread only for loans aimed at covering working capital needs rather than investments.