How should forward-looking managers plan advertising if they envision a product-harm crisis in the future? To address this question, Professor Olivier Rubel, Professor Prasad Naik and Professor Shuba Srinivasan propose a dynamic model of brand advertising in which, at each instant, a nonzero probability exists for the occurrence of a crisis event that damages the brand’s baseline sales and may enhance or erode marketing effectiveness when the crisis occurs. Because managers do not know when the crisis will occur, its random time of occurrence induces a stochastic control problem, which they solve analytically in closed form. More importantly, the envisioning of a possible crisis alters managers’ rate of time preference: anticipation enhances impatience.

Current models posit that awareness of advertising declines immediately and gradually once it is over, although anecdotal evidence from managers suggests that awareness stays constant for a while and then decays rapidly. This pattern arises because consumers remember advertisements for a finite time before they forget.

This paper by Professors Prasad Naik, Chih-Ling Tsai and co-author Dan Weiss from Tel Aviv University proposes a new index to extract forward-looking information from security prices and infer market participants’ expectations of future earnings. The index, called market-adapted earnings (MAE), utilizes stock returns and fundamental accounting signals to estimate market expectations of future earnings at the firm level. MAE outperforms time-series models (e.g., random-walk) in predicting future earnings. Results demonstrate the usefulness of MAE for firms that have no analyst following.

Companies invest millions of dollars in various forms of marketing communications to impact customers’ awareness, attitudes, purchases, and, ultimately, profitability. An important question for marketers and shareholders alike is: what effects do marketing investments have on market performance?

In this paper, Professors Prasad Naik and Chih-Ling Tsai, with co-author Peide Shi from Nuclear Safety Solutions Ltd., examine the problem of jointly selecting the number of components and variables in finite mixture regression models.

In Markov-switching regression models, Professors Prasad Naik, Chih-Ling Tsai and co-author Aaron Smith from the UC Davis Department of Agricultural and Resource Economics use Kullback–Leibler (KL) divergence between the true and candidate models to select the number of states and variables simultaneously.

Companies spend millions of dollars on advertising to boost a brand’s image and simultaneously spend millions of dollars on promotion that many believe calls attention to price and erodes brand equity. We believe this paradoxical situation exists because both advertising and promotion are necessary to compete effectively in dynamic markets. Consequently, brand managers need to account for interactions between marketing activities and interactions among competing brands.

Many advertisers adopt the integrated marketing communications perspective that emphasizes the importance of synergy in planning multimedia activities. However, the role of synergy in multimedia communications is not well understood.

in this paper, Professors Prasad Naik and Chih-Ling Tsai derive a new model selection criterion for single-index models, AIC, by minimizing the expected Kullback-Leibler distances between the true and candidate models.

The pro-posed criterion selects not only relevant variables but also the smoothing parameter for an unknown link function. Thus, it is a general selection criterion that provides a unifies approach to model selection across both parametric and nonparametric functions. Monte Carlo studies demonstrate that AIC performs satisfactorily in most situations.

In data-rich marketing environments (e.g., direct marketing or new product design), managers face an ever-growing need to reduce the number of variables effectively. To accomplish this goal, Professors Prasad Naik and Chih-Ling Tsai and co-author Michael Hagerty introduce a new method called sliced inverse regression (SIR), which finds factors by taking into account the information contained in both the dependent and independent variables.