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Journal of the Academy of Marketing Science

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01-06-2010 | Original Empirical Research

Product competitiveness and beating analyst earnings target

Author: Xueming Luo

Published in: Journal of the Academy of Marketing Science | Issue 3/2010

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Abstract

While Wall Street closely watches financial analysts’ earnings forecasts, Main Street often scrutinizes product quality relative to competition. Do firms with superior product competitiveness enjoy greater likelihood of beating analyst earnings target? And if so, is there contingency in this impact? We show that positive changes in product competitiveness contribute to the firm’s likelihood of beating analyst earnings target, while negative changes in product competitiveness account for missed earnings target. In addition, this impact of product competitiveness on the likelihood of beating analyst earnings target is more positive in the situations of high firm future growth opportunity and low financial environment uncertainty. These findings innovatively use analyst forecast metrics to reinforce the relationship between product quality, competitive advantage, and financial performance. Our study also cultivates a contingency theory of the marketing-finance interface and allows marketing and finance executives to find common ground in strategic discourse. Overall, this research offers brand new financial analysts-based implications of product competitiveness.

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This line of reasoning is also based on efficient market hypothesis (EMH) theory because this theory suggests that financial markets are at least semi strongly efficient and that investors only react to surprises (i.e., positive/negative earnings surprises or, in our context, beating/missing financial analysts’ earnings forecasts).

An implicit assumption is that contingency theory motivates our moderating hypotheses, beyond the main effect hypotheses. For contingency variables, we select firm-level factor (future growth opportunity) because quality programs may benefit more in firms with more growth opportunity, industry-level factor (industry concentration) because of the importance of industry competition for most marketing initiatives, and analyst-level factor (forecast dispersion) due to asymmetric information theory and the fact that higher dispersion can infer more noisy and uncertain financial environment on Wall Street.

Note that our data on product competitiveness is not about other marketing variables such as customers’ perceptions/attitudes or brand recognition. But rather, each year, Fortune polls more than 10,000 senior executives, financial analysts, and investors to determine the perceived product competitiveness. In this sense, we also feel that product competitiveness information may enter the investment marketplace in such a fashion as to help investors make choices. Further, we check the world top 100 brand value ratings (2001-2005) from BusinessWeek and run a correlation between product competitiveness and brand values. We find that the two variables have relatively low but significant correlation (r = .17, p < .05). Product competitiveness and brand values should be correlated (though not too highly correlated as they are separate constructs), because superior product quality is an antecedent of brand equity. Thus, these steps help establish the validity of the Fortune data for product competitiveness.

Following the accounting literature (Bartov et al. 2002; Lim and Tan 2008), we use the latest (or most recent) analysts’ median consensus forecasts that are no earlier than 2 months before the date of earnings announcements. In addition, COMPUSTAT provides more accurate data on firms’ reported earnings per share than I/B/E/S does.

The robust covariance matrix is: \( \hat\sum {HW} = \frac{T}{T - k}\left( {X\prime X} \right)^{- 1} \Omega \left( {X\prime X} \right)^{- 1} \), \( \Omega = \frac{T}{T - k}\left[ {\sum\limits_{t = 1}^T {u_t^2 x_t x_t^{\prime } + \sum\limits_{v = 1}^q {\left( {\left( {1 - \frac{v}{q + 1}} \right)\sum\limits_{t = v + 1}^T {\left( {x_t u_t u_{{t - \nu }} x_{t - v}^{\prime } + x_{t - v} u_{t - v} u_t x_t^{\prime } } \right)} } \right)} } } \right] \), and q (the truncation lag) = floor (4(T/100) ^2/9 (Maddala 1983).

We acknowledge one anonymous reviewer for bringing this implication to our attention.

We focus here on healthy practices (i.e., quality improvement) of earnings surprises management rather than misguided earnings management (under- or over-accounting reporting) which may be unhealthy for firm shareholder value.

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Title: Product competitiveness and beating analyst earnings target
Author: Xueming Luo
Publication date: 01-06-2010
Publisher: Springer US
Published in: Journal of the Academy of Marketing Science / Issue 3/2010
Print ISSN: 0092-0703
Electronic ISSN: 1552-7824
DOI: https://doi.org/10.1007/s11747-009-0158-9

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