Beta and firm age

We document a robust pattern of beta declining over the age of a firm. We find that changes in systematic risk via firm characteristics and life-cycle stages are insufficient to explain this pattern. Moreover, standard proxies for the quantity and quality of information also explain this pattern only partially. To fully explain this pattern we rely on the increasingly important role of familiarity in financial decision making: familiarity is a determinant of beta and firm age is a proxy for the degree of familiarity that investors feel toward individual stocks. To illustrate the implication of our findings, we document that when we control for firm age there is support for the CAPM and its use as an input for the cost of equity capital calculation.     

Valuation uncertainty,home and host market uncertainty,and cross-border seasoned equity offerings

This study examines how market timing can affect host market reaction to cross-border seasoned equity offerings (SEOs), an event generally viewed unfavorably by investors. We assume that firms engage in market timing in response to valuation uncertainty (VU), home market uncertainty (HMU) and/or host market uncertainty (HSU), and that raising capital abroad faces higher scrutiny costs and familiarity bias from host market investors. We conjecture that timing strategies provide signals that vary in strength to host market investors and that dual-timing strategies may strengthen an existing signal. Our hypotheses are tested on a sample of 190 cross-border SEOs that were issued on the U.S. stock market between 1990 and 2017 by firms from 29 countries. Using event study methodology, we find that market timing based on VU is negatively related to host market valuation and that a dual-timing strategy with HMU or HSU generally produces a stronger signal. Our results have practical relevance for stock markets that suffer from high uncertainty; we estimate that a high VU firm with a $1 billion valuation suffers a drop of $31.3 million in market valuation during a high host market uncertainty (high HSU) compared with low host market uncertainty (low HSU).     

Turning local: Home-bias dynamics of relocating foreigners

Using comprehensive data from Denmark, we study private investors’ preferences for domestic stocks. We compare the equity home bias of foreigners recently relocated to Denmark to the equity home bias of other investors. We find that home bias of recently relocated foreigners is lower than home bias of other investors. Our main result is that when relocated foreigners’ duration of stay increases, their home bias also increases. After 7–8 years, home bias of relocated foreigners does not differ from home bias of other investors. Our results imply that familiarity with domestic stocks develops dynamically with the length of stay in a given country. We discuss implications for explanations of the home-bias puzzle building on information asymmetries.     

Geographic dissemination of information

Urban companies are located near millions more potential investors and sophisticated money managers than non-urban companies. More investors are familiar with urban companies and have access to informal information about them. The stock of urban companies is also more liquid than the stock of non-urban companies. We hypothesize that these factors lead information to be spread from urban companies to other companies. Urban stock returns lead rural/small city stock returns even controlling for size, industry, and analyst coverage. Closer examination of the lead–lag relation reveals that urgent trades, which are likely to reflect short-lived information, are much more common for urban firms. Information appears to be uncovered through informal means more easily available to people physically near a company. We discuss the corporate finance implications of our findings.     

Is algorithm aversion WEIRD? A cross-country comparison of individual-differences and algorithm aversion

Although algorithms offer superior performance over humans across many tasks, individuals often exhibit algorithm aversion, resisting algorithmic advice in favour of human recommendations. However, most algorithm aversion studies rely on American samples, potentially limiting the generalisability of the findings. Given the increasing adoption of algorithms globally, we explore if the impact of two crucial factors driving algorithm aversion, uniqueness neglect and familiarity, differ between culturally different countries. Drawing on the individualism-collectivism cultural dimension, we conducted two online studies comparing algorithm aversion between people in India and the United States in medical and financial services scenarios. While our results suggest that there is no difference in the degree of algorithm aversion between Indians and Americans at an aggregate level, we find important cross-cultural differences: Uniqueness neglect strengthens algorithm aversion for Americans more than Indians, while familiarity weakens algorithm aversion more for Indians than Americans. Thus, our results reveal generalisability issues within the algorithm aversion literature, as factors influencing algorithm aversion can be culturally dependent.     

Evocation and experiential seduction: Updating choice-sets modelling

This paper operationalises discourses on affects-as-information in terms of destination imagining and choosing. Evoked sets are conceptualised not simply as destinations, but as destinations in terms of imagery, knowledge and familiarity; forming Unusual Selling Points (USPs) or their standardised equivalent, Standardised Selling Points (SSPs). USPs and SSPs are each in turn categorised threefold, as utility, experiential and symbolic selling points. Action sets are conceptualised not just as destinations but as destinations-as-propensity to visit. This conceptualisation of decision making is illustrated in terms of British consumers’ evocations of Scandinavia and their propensity to visit Scandinavian countries. One element of familiarity is found to be disproportionately important in the associations between evoked and action sets: experiential familiarity, namely seduction by experience.     

Antecedents of tourism destination reputation: The mediating role of familiarity

This study aims to test whether cognitive perception and affective evaluation of a tourism destination are significant antecedents of its reputation; and whether this relationship is mediated by familiarity. The proposed model was tested among 750 participants and results revealed that 40% of a destination's reputation is explained by familiarity. Familiarity in turn, is explained by cognitive perception (43%) and affective evaluation (14%). Findings confirmed the mediating role of familiarity in the relationship between cognitive perception, affective evaluation, and destination reputation. Given the important mediating role of familiarity, the various stakeholders in the tourism industry should strengthen familiarity in order to develop and enhance their reputation.     

Do investors hold that they know? Impact of familiarity bias on investor’s reluctance to realize losses: Experimental approach

In this article we investigate the impact of familiarity bias on the individual investor’s reluctance to realize losses. Our experimental approach reveals a strong correlation between familiarity and disposition effect. We conducted 714 tests in which different respondents could sell stocks of two types – winners and losers. One group of respondents “owned” familiar assets and another group operated anonymous portfolios. The results of the experiment show that an individual investor’s tendency to ride losers too long is more than twice as high in the case of unfamiliar stocks as it is when assets are familiar to the holder.     

Measuring the impact of positive and negative word of mouth on brand purchase probability

Using two methods, three measures, and data covering a large number of categories, we present findings on the respondent-assessed impact of positive and negative word of mouth (PWOM, NWOM) on brand purchase probability.For familiar brands, we find that:

1. The impact of PWOM is generally greater than NWOM. The pre-WOM probability of purchase tends to be below 0.5, which gives more latitude for PWOM to increase purchase probability than for NWOM to reduce it.

2. The impact of both PWOM and NWOM is strongly related to the pre-WOM probability of purchase, the strength of expression of the WOM, and whether the WOM is about the respondent's preferred brand.

3. PWOM and NWOM appear to be similar forms of advice-giving behavior, except for their opposed effects on choice.

4. Respondents resist NWOM on brands they are very likely to choose, and resist PWOM on brands they are very unlikely to choose.

In the Discussion section, we show how our methods could be used to construct a word-of-mouth metric.     

Market segmentation strategies for complex automotive products

ABSTRACT

With the advent of ‘big data’, the purpose of this empirical study was to take the opportunity to rethink conventional market segmentation strategies. This is particularly relevant for the automotive industry which is going through a period of rapid change with advanced technologies such as electric powered and autonomous vehicles, creating increased concerns as to how this complexity is communicated effectively.

A mixed methods approach was utilised to collect data from multiple sources, incorporating in-depth discussion groups, semi-structured interviews, an online survey, and data collection of communication processes through the attendance of new car product launches.

The results suggest that marketing departments should rethink their data capture methods to collect more relevant consumer information, not the contemporary trend of needs, attitude, and motivation variables that are difficult to identify and collect, but basic information on their level of familiarity with products through previous experience and exposure. The basic dimensions identified are characterised by a consumer’s expertise, involvement, and familiarity with a product. The findings are synthesised into a theoretical framework to define differing levels of product complexity, which would enable manufacturers to provide more closely defined market segmentation strategies when communicating new product information.