Michael Falta

The Impact of Exponential Growth Variables on Regression Models of the Market–Accounting Relation

Exponential growth of market value, book value, and earnings is a basic characteristic of many firms. We propose a simple theory linking market value to a multiplicative relation of accounting variables and a lognormal error term, when model variables exhibit this characteristic. Implications for the interpretation of estimated coefficients in commonly used additive linear models of the market–accounting relation in both levels and returns are discussed. We test the theory using a selected panel of 30 of some of the largest long-lived U.S. firms over a 50-year period. Annual cross-section and firm-specific dynamic models of market regressed on accounting values are estimated in levels. Multiplicative models of levels data produce markedly improved statistical specifications compared with additive forms. Lags are also shown to be necessary to produce well-specified dynamic models. A multiplicative returns model based on error correction principles is derived from the multiplicative levels model providing for a straightforward interpretation of the magnitude of coefficients relating market to accounting values. Estimates from deflated returns models are reinterpreted in the light of these results as approximations to estimates derived from multiplicative levels models.

Using Forecasting Criteria to Identify Value Relevance in the Relationship between Accounting Numbers and Market Value

We explain and demonstrate a disciplined and systematic approach to repeatable modelling using forecast criteria, in addition to the usual statistical estimation criteria, to identify value relevance in regressions of the market-accounting relation. The method was used in Cooke et al. (2009). It is illustrated here in the case of a single firm over a 59-year period. Market and accounting data for the U.S. firm Abbott Laboratories Inc. from 1955 are modelled using a testing-down, error correction approach. Hold-out samples of 10 to 15 years are used to assess forecasting performance relative to a random walk. Emphasis is placed upon the use of simple, directly observable and theory-independent model variables that can be replicated with other sample data. In this case, logarithmic transformations of all variables have to be computed in order to achieve correct statistical specification, implying a multiplicative relationship in the raw data. The strongest cointegrating accounting variable with forecasting ability for Abbotts market value is earnings. The model parameters exhibit long-run stability and the accounting regressor marginally improves forecasts of market value compared to a random walk, demonstrating ‘value relevance’.

A Statistical Activity Cost Analysis of Engineering Assets

In this paper, Statistical Activity Cost Analysis (SACA) is used to identify the interaction of mutually dependent physical and financial aspects of a fixed asset-like system configuration. The novelty of the approach is, having established a rational description of the uncertainty inherent in both domains, the analysis of their interaction. Little research to date has investigated the duality of engineering and accounting aspects, in a statistical setting. Our approach is conceptual rather than empirical. We use an illustrative 4-component model, a) to explain the concept of SACA by means of a software demonstration tool, b) to relate financial issues of cost to engineering asset capacity to perform specified tasks, and c) to demonstrate how to produce quantified measures of return and risk, both of which are relevant in areas of life-cycle analysis, budgeting and planning decision-making.

Unintended consequences of regulatory reporting requirements for small and medium size construction entities: Australian evidence

The Queensland Building Services Authority (QBSA) regulates the construction industry in Queensland, Australia, with licensing requirements creating differential financial reporting obligations, depending on firm size. Economic theories of regulation and behaviour provide a framework for investigating effects of the financial constraints and financial reporting requirements imposed by QBSA licensing. Data are analysed for all small and medium construction entities operating in Queensland between 2001 and 2006. Findings suggesting that construction licensees are categorizing themselves as smaller to avoid the more onerous and costly financial reporting of higher licensee categories are consistent with US findings from the 2002 Sarbanes-Oxley (SOX) regulation which created incentives for small firms to stay small to avoid the costs of compliance with more onerous financial reporting requirements. Such behaviour can have the undesirable economic consequences of adversely affecting employment, investment, wealth creation and financial stability. Insights and implications from the analysed QBSA processes are important for future policy reform and design, and useful to be considered where similar regulatory approaches are planned.

Parameter Heterogeneity in the Market-Accounting Relation

In this paper firm parameter heterogeneity in cross section regression analysis in capital market research (CMR) is investigated. Using panel data for 30 large US firms over the period 1955 to 2004, a well-specified common form of dynamic model for each firm is identified. Average parameter estimates from these models are compared to average parameter estimates from 50 annual cross section models having the same functional form. The dynamic parameters are mostly stable over time but variation in individual firm parameters is apparent. Analysis shows that even well-specified annual cross section models using large samples of data cannot guarantee valid and reliable estimates of the parameters of interest. Firm-level dynamic analysis is necessary to avoid this problem. We show how a fixed effects panel analysis of the sample data can be used to approximate the average data generating process of the firms in the sample. Although the impact of accounting variables is slight, compared to the autoregressive component in market value, it is systematic. There is weak evidence of cointegration between market and accounting data in most firms in the sample. Consequently, it is possible to construct the cross section analogue of the dynamic error correction model. Book value of net assets is used to illustrate the role of accounting variables. Other variables could be used, but single variable, multiplicative noise models perform best when judged by joint explanatory power and forecast ability criteria.

Cross-Section Regression Models of the Long-Run Relationship Between Market and Accounting Values - The Case for a Log-Linear Model

Basic annual cross sections of market on accounting values are estimated in levels and returns using a 30 US firm by 50 year panel of data. Log transformations of the levels data are shown to produce an improved statistical specification. Deflated returns models are shown to suffer from other problems of inference. The nature of the implicit assumption in cross section analysis that firms in the sample have a common data generating processes is discussed.