Antti J. Kanto

Decomposing the value of department store shopping into utilitarian, hedonic and social dimensions: Evidence from Finland

– The purpose of this paper is to decompose total customer value as perceived by department store shoppers into utilitarian, hedonic and social dimensions, and empirically test this conceptualization in a Finnish department store shopping context., – Data were collected by a questionnaire administered over three days at a department store that generates the second largest turnover in Finland. A total of 364 shoppers completed the questionnaire., – Empirical evidence supports our tripartite conceptualization of total customer value. In particular, social value is an independent construct. Further, social value varies by day‐of‐week, with a significant increase on Saturday (versus weekdays) when the store is more crowded, whereas no such differences in utilitarian and hedonic values were detected., – The principal contribution is a tripartite conceptualization of total customer value that incorporates utilitarian, social and hedonic value dimensions in a department store shopping context. Individually these dimensions are all well rooted in streams of consumer behavior literature, albeit mostly at the product or brand, not the store, level. Increasing our understanding of these softer aspects of shopping, particularly the social dimension, is important because they represent possible differentiating factors in the highly competitive and often commoditized retail markets.

Expertise effects on prechoice decision processes and final outcomes

The purpose of this study was to determine the effect of expertise on prechoice decision processes and final outcomes. By decomposing verbal protocols collected from 90 individuals who made one complex, mortgage loan decision, we could compare the frequency and type of elementary information processes evoked. We found that experts, relative to less knowledgeable decision makers, made a greater number of problem framing statements; made more references to why an option was being retained for further consideration; and used more compensatory decision rules. In addition, we found that misunderstanding externally provided information mediates the expertise‐choice relationship. Novices were significantly more likely to misunderstand information than were more knowledgeable decision makers. As a result, there was greater variance in novices’ final choices than was the case with experts’. The deleterious effect of mis‐understandings is disconcerting because consumers frequently miscomprehend print communications.

Characteristic times in stock market indices

In this study we analyze the Standard and Poors 500 index data of the New York Stock Exchange for more than 32 years. Using a simple random walk model we demonstrate that the proper variable to look at is the logarithmic return. In the statistical analysis we have done fittings to the Levy distribution using either the index data as such or pre-processing it with ARCH, GARCH or IGARCH methods, which tend to remove the time-dependent variance. For short times the truncated Levy distribution is found to fit the data quite well. Since this is not a stable distribution, the scaling behavior observed for short times should brake down for longer times. We demonstrate that the characteristic time where this cross-over starts is of the order of one day.

A multidimensional model for the disclosure policy of a firm

It is suggested in this article that a firms mandatory disclosure policy can be summarized in a single factor characterizing the type of firm. For voluntary disclosure policy a firm-size variable should be included in the model, in addition to the firm-type factor. The fit of the capital structure and growth factors is tested for the basic saturated model. In the next phase, the possibility of combining these two factors into a single factor, resulting in a more parsimonious model, is tested. The test in favour of the one-factor model is clearly passed. The results add to our current understanding of the determinants of interim reports in a relatively new interim reporting practice, namely in Finland. Compared to conventional regression models with many independent variables, this study reports a model that reduces the number of parameters. Furthermore, valuable insight is gained into the differences between the determinants of mandatory and voluntary disclosures. This, in turn, should be able to help legislators and regulators in their work.

Evaluating multivariate GARCH models in the Nordic electricity markets

ABSTRACT This article considers a variety of specification tests for multivariate GARCH models that are used for dynamic hedging in electricity markets. The test statistics include the robust conditional moments tests for sign-size bias along with the recently introduced copula tests for an appropriate dependence structure. We consider this effort worthwhile, since quite often the tests of multivariate GARCH models are omitted and the models become selected ad hoc depending on the results they generate. Hedging performance comparisons, in terms of unconditional and conditional ex-post variance portfolio reduction, are conducted.

On utility function estimation of racetrack bettors

Abstract Racetrack betting makes possible the empirical study of peoples behavior in decision making under uncertainty and risk in laboratory-like but still natural conditions. Hence, it gives an opportunity to empirically test the classical assumptions of rational behavior. In this paper it is found that empirical data from a Finnish racetrack do not support risk-aversion or risk-neutrality of bettors. This gives rise to the question of under exactly which cultural and other circumstances various assumptions regarding attitudes toward risk actually apply.

Conditional Value-at-risk Estimation Using Non-integer Values of Degrees of Freedom in Students t-distribution

This study provides an analytical formula for CVAR calculated using t-distributions with non-integer degrees of freedom. We generalize standard formulas, calculated on the assumption of normal log-returns without compromising on the difficulty of the calculation procedure involved. We also extend the results of Heikkinen and Kanto (2002) to show the impact of kurtosis on values of CVAR. The results are summarized in a closed-form formula that can, with little effort, be used by risk managers in the evaluation of risk exposures for a family of heavy-tailed distributions.

The impact of disclosure on the market response to reported earnings

There is a wealth of evidence of a certain delay in the markets adjustment to published earnings information. However, there is a shortage of studies focusing on whether this behaviour can be explained at least partially by the level and quality of disclosures released together with earnings. This paper explores whether the degree of disclosure is related to the market reaction, and in particular whether the quantity and quality of disclosure affects the adjustment of security prices to interim earnings announcements. Evidence on the pricing of disclosures is also presented. The data comprises interim reports submitted to the Helsinki Exchanges in the period 1985-93. Interim reports are used because they relate to a specific event conveying new and previously unpublished material to the market, in contrast to annual reports which primarily document the history of the previous year. It is found that both disclosure and earnings are important in explaining drift, and our results indicate that the drift is associated with disclosure. These results augment the non-US market evidence of this drift.

A characterization of the inverse autocorrelation function

The inverse autocorrelation function of a weakly stationary stochastic process Xt at lag h, γi h, is shown to equal the negative of the partial correlation between random variables Xt and Xt+h after elimination of the influence of random variables Xk, k≠t5,t+h.

Computational Examples of a New Method for Distribution Selection in the Pearson System

Abstract A considerable problem in statistics and risk management is finding distributions that capture the complex behaviour exhibited by financial data. The importance of higher order moments in decision making has been well recognized and there is increasing interest in modelling with distributions that are able to account for these effects. The Pearson system can be used to model a wide scale of distributions with various skewness and kurtosis. This paper provides computational examples of a new easily implemented method for selecting probability density functions from the Pearson family of distributions. We apply this method to daily, monthly, and annual series using a range of data from commodity markets to macroeconomic variables.