Linear Accounting Valuation When Abnormal Earnings Are AR(2)

The Ohlson (1995) model assumes that abnormal earnings follow an AR(1) process primarily for reasons of mathematical tractability. However, the empirical literature on the Garman and Ohlson (1980) model finds that the data support an AR(2) lag structure for earnings, book values and dividends. Moreover, the AR(2) process encompasses a far richer variety of time series patterns than does the AR(1) process and includes the AR(1) process as a special case. This paper solves the Ohlson model directly for an AR(2) abnormal earnings dynamic. The model is estimated on a time series firm-level basis following the approach used by Myers (1999). It is found that, like the Ohlson AR(1) model, the Ohlson AR(2) model severely underestimates market prices even relative to book values. These results further bring into question the empirical validity of the Ohlson model.     

Dynamic Efficiency in the East European Emerging Markets

The paper re-investigates the efficiency of the East European emerging markets of the Czech Republic, Hungary, Poland and Russia analyzed by Rockinger and Urga (2000, 2001) based on the data from September, 1995 through December, 2004. We propose a first-order autoregressive (AR (1)) type time varying parameter model with a non-stochastic linear time trend including the random walk (RW) type model as a special case. The observed data rejects the RW type model for the AR (1) type one. The markets exhibit dynamic efficiency for all the four countries in the sense that the linear time trend approaches to zero over time. The empirical result for the Russian markets differs from that of Rockinger and Urga (2000). JEL Classifications: G14, G15     

Predictive regression with order-p autoregressive predictors

Studies of predictive regressions analyze the case where y_t is predicted by x_t ? 1 with x_t being first-order autoregressive, AR(1). Under some conditions, the OLS-estimated predictive coefficient is known to be biased. We analyze a predictive model where y_t is predicted by x_t ? 1, x_t ? 2,… x_t ? p with x_t being autoregressive of order p, AR(p) with p > 1. We develop a generalized augmented regression method that produces a reduced-bias point estimate of the predictive coefficients and derive an appropriate hypothesis testing procedure. We apply our method to the prediction of quarterly stock returns by dividend yield, which is apparently AR(2). Using our method results in the AR(2) predictor series having insignificant effect, although under OLS, or the commonly assumed AR(1) structure, the predictive model is significant. We also generalize our method to the case of multiple AR(p) predictors.     

Predicting Recurrent Financial Distresses with Autocorrelation Structure: An Empirical Analysis from an Emerging Market

The dynamic logit model (DLM) with autocorrelation structure (Liang and Zeger Biometrika 73:13–22, 1986) is proposed as a model for predicting recurrent financial distresses. This model has been applied in many examples to analyze repeated binary data due to its simplicity in computation and formulation. We illustrate the proposed model using three different panel datasets of Taiwan industrial firms. These datasets are based on the well-known predictors in Altman (J Financ 23:589–609, 1968), Campbell et al. (J Financ 62:2899–2939, 2008), and Shumway (J Bus 74:101–124, 2001). To account for the correlations among the observations from the same firm, we consider two different autocorrelation structures: exchangeable and first-order autoregressive (AR1). The prediction models including the DLM with independent structure, the DLM with exchangeable structure, and the DLM with AR1 structure are separately applied to each of these datasets. Using an expanding rolling window approach, the empirical results show that for each of the three datasets, the DLM with AR1 structure yields the most accurate firm-by-firm financial-distress probabilities in out-of-sample analysis among the three models. Thus, it is a useful alternative for studying credit losses in portfolios.     

Predictability of nonlinear trading rules in the U.S. stock market

Most of the existing technical trading rules are linear in nature. This paper investigates the predictability of nonlinear time series model based trading strategies in the U.S. stock market. The performance of the nonlinear trading rule is compared with that of the linear model based rules. It is found that the self-exciting threshold autoregressive (SETAR) model based trading rules perform slightly better than the AR rules for the Dow Jones and Standard and Poor 500, while the AR rules perform slightly better in the NASDAQ market. Both the SETAR and the AR rules outperform the VMA rules. The results are confirmed by bootstrap simulations.     

The co-creation of regional futures: Facilitating action research in regional foresight

This article presents the interconnected phases of a regional-foresight process in an AR perspective within the context of a complex regional dynamic of actors with distinct local and regional political presences. The analysis is based on a Norwegian case of AR on regional foresight. The article reflects upon and develops the concept of action research (AR) as it relates to regional-foresight practices in connection with regional planning strategies (policies), according to Norway's new Planning and Building Act (PBA, 2008). Both AR and regional foresight are broad terms within a number of domains, and several contributions have sought to show how these are interlinked Ramos (2006). The focus is on how AR and action researchers both contribute to the co-creation of regional and sub-regional formulation of planning by regional-foresight processes, which are driven by decision-making regional bodies. This article contributes to the understanding of how an AR strategy of ‘strategic facilitation’ may improve the overall foresight capacity of all regional actors, both in concert and as single stakeholders. Also it furthers the understanding of how an AR approach may assist in transforming the foresight practices and the strategic decision-making into a more transparent process.     

An Experimental Study of Auditor Analytical Review Judgements

This paper provides evidence as to how five factors highlighted in the current UK auditing standard are taken into account by auditors in analytical review (AR) judgements. While the relative importance of particular cues was generally found to be consistent with the standard, certain factors were taken into account only to a marginal extent. Little evidence of configural cue usage was identified. The study also provides evidence of a tendency towards conservatism in the way auditors approach AR. The results suggest both that the potential to substitute AR for other substantive procedures may be realised only imperfectly and that the issue of configural reasoning should be addressed in the auditing standard.     

An Experimental Study of Auditor Analytical Review Judgements

This paper provides evidence as to how five factors highlighted in the current UK auditing standard are taken into account by auditors in analytical review (AR) judgements. While the relative importance of particular cues was generally found to be consistent with the standard, certain factors were taken into account only to a marginal extent. Little evidence of configural cue usage was identified. The study also provides evidence of a tendency towards conservatism in the way auditors approach AR. The results suggest both that the potential to substitute AR for other substantive procedures may be realised only imperfectly and that the issue of configural reasoning should be addressed in the auditing standard.     

Stock price distributions with stochastic volatility: an analytic approach

We study the stock price distributions that arise when pricesfollow a diffusion process with a stochastically varying volatilityparameters. We use analytic techniques to derive an explicitclosed-form solution for the case when volatility is drivenby an arithmetic Ornstein-Uhlenbeck (or AR1) process. We thenapply our results to two related problems in the finance literature:(i) options pricing in a world of stochastic volatility, and(ii) the relationship between stochastic volatility and thenature of 'fat tails' in stock price distributions.     

Summary annual reports: length,readability and content

This paper examines summary annual reports (SAR) as an alternative channel for communicating financial information. We use a sample of New Zealand local governments (councils) that are required to report audited SAR. Using various measures, we compare document length and readability of SAR and annual reports (AR). We find that SAR are approximately 10 percent of the AR length and both have a ___very difficult___ readability score. We then use a disclosure index to examine the relation between SAR report length and the level of disclosures. After controlling for other factors that impact the level of disclosure, we conclude that the reduction in SAR is driven by lower levels of content. We then discuss the policy implications of our findings.     

The performance of the gph estimator of the fractional difference parameter: Simulation results

This paper investigates the behavior of the GPH estimator of the fractional difference parameter suggested by Geweke and Porter-Hudak (1983), through Monte Carlo simulations. The simulation results indicate that when considering a stationary AR(1) generating process the GPH estimator of the fractional difference parameter has serious bias which increases with the value of the autoregressive parameter, even for relatively large samples. The results suggest that tests and point estimates based on this procedure can be seriously misleading with hypothesis ests yielding incorrect inferences and thus must be used with great care.     

Option pricing under linear autoregressive dynamics,heteroskedasticity, and conditional leptokurtosis

Daily returns of financial assets are frequently found to exhibit positive autocorrelation at lag 1. When specifying a linear AR(1) conditional mean, one may ask how this predictability affects option prices. We investigate the dependence of option prices on autoregressive dynamics under stylized facts of stock returns, i.e. conditional heteroskedasticity, leverage effect, and conditional leptokurtosis. Our analysis covers both a continuous and discrete time framework. The results suggest that a non-zero autoregression coefficient tends to increase the deviation of option prices from Black and Scholes prices caused by stochastic volatility.     

Term Structure Movements and Pricing Interest Rate Contingent Claims

This paper derives an arbitrage-free interest rate movements model (AR model). This model takes the complete term structure as given and derives the subsequent stochastic movement of the term structure such that the movement is arbitrage free. We then show that the AR model can be used to price interest rate contingent claims relative to the observed complete term structure of interest rates. This paper also studies the behavior and the economics of the model. Our approach can be used to price a broad range of interest rate contingent claims, including bond options and callable bonds.     

Why long horizons? A study of power against persistent alternatives

This paper studies tests of predictability in regressions with a given AR(1) regressor and an asset return dependent variable measured over a short or long horizon. The paper shows that when there is a persistent predictable component in the return, an increase in the horizon may increase the R² statistic of the regression and the approximate slope of a predictability test. Monte Carlo experiments show that long-horizon regression tests have serious size distortions when asymptotic critical values are used, but some versions of such tests have power advantages remaining after size is corrected.     

Affect and marketing stimuli in consumer loyalty decisions to automobile insurers

Most insurance loyalty studies assume largely cognitive psychological underpinnings. A study is conducted that investigates the relative contributions of cognitive versus affective influences on the desire to be loyal to an automobile insurer after a poor service experience. Three hundred and fifteen respondents were introduced to scenarios that manipulated the response to an advertisement to switch insurers by price- and brand-related marketing influences to investigate their influence on anticipated regret (AR). The results support the conclusions that both cognitive and affective considerations are important to consumer judgment and decision-making (J/DM) processes in the context of car insurance, that male and female customers may vary in their J/DM processes with car insurance, and that insurance modelers of J/DM should consider outside influences on consumers in studies, particularly marketing-related brand and price perceptions. Failure to do so can lead to a failure to correctly predict potential action versus inaction effects associated with AR.     

Prediction accuracy and sloppiness of log-periodic functions

We show that log-periodic power-law (LPPL) functions are intrinsically very hard to fit to time series. This comes from their sloppiness, the squared residuals depending very much on some combinations of parameters and very little on other ones. The time of singularity that is supposed to give an estimate of the day of the crash belongs to the latter category. We discuss in detail why and how the fitting procedure must take into account the sloppy nature of this kind of model. We then test the reliability of LPPLs on synthetic AR(1) data replicating the Hang Seng 1987 crash and show that even this case is borderline regarding the predictability of the divergence time. We finally argue that current methods used to estimate a probabilistic time window for the divergence time are likely to be over-optimistic.     

Are Underwriting Cycles Real and Forecastable?

Speculative efficiency often requires that future changes in a series cannot be forecast. In contrast, series with a cyclical component would seem to be forecastable with decreases, possibly relative to a trend, during the upper part of the cycle and increases during the lower part. On the basis of autoregressive model (AR) estimates, it is considered that there is strong evidence of cycles in insurance underwriting performance as measured by the premium‐to‐loss ratio. Indeed, a large literature attempts to explain this documented cyclicality. First, we show that the parameter estimates from AR models do not lead to any such inference and that in the contrary, the evidence in the data is consistent with no cyclicality at all. Second, we show that a number of different filters lead to the same conclusion: that there is no evidence of in‐sample or out‐of‐sample predictability in annual insurance underwriting performance in the United States.     

Structural change in the forward discount: Implications for the forward rate unbiasedness hypothesis

It is a well-accepted empirical result that forward exchange rate unbiasedness is rejected in tests using the “differences regression” of the change in the logarithm of the spot exchange rate on the forward discount. We model the forward discount as an AR(1) process and argue that its persistence is exaggerated due to the presence of structural breaks. We show using a stochastic multiple break model that the forward discount persistence is substantially less if one allows for multiple structural breaks in the mean of the process. We argue that these breaks could be identified as monetary shocks to the central bank's reaction function. Using Monte Carlo simulations, we show that if we do not account for structural breaks that are present in the forward discount process, the forward discount coefficient in the “differences regression” is severely biased downward, away from its true value of 1.     

Measuring consumption smoothing in CEX data

A new method of measuring the degree of consumption smoothing is proposed and implemented using data from the Consumer Expenditure Survey. The structure of this Survey is such that estimators previously used in the literature are inconsistent, simply because income is measured annually and consumption is measured quarterly. An AR(1) structure is imposed on the income process to obtain a proxy for quarterly income through a projection on annual income. By construction, this proxy gives rise to a measurement error which is orthogonal to the proxy itself—as opposed to the unobserved regressor—leading to a consistent estimator. Our estimates are contrasted with the output of two estimators used in the literature. This comparison shows that while the first (OLS) estimator tends to overstate the degree of risk sharing, the second (IV) estimator grossly understates it.     

Why Do Analysts use a Zero Forecast for Other Comprehensive Income?

Accounting theory and accounting researchers stress the importance of clean surplus accounting and comprehensive income to corporate valuation. However, casual observation suggests that sell-side equity analysts routinely ignore other comprehensive income (OCI) in their forecasts and instead focus on forecasting earnings (before OCI). Using a sample of analyst reports, I first confirm that analysts normally omit forecasts of OCI or comprehensive income from their reports, consistent with analysts forecasting OCI as zero. I then predict and find that a zero forecast for OCI generally produces lower forecasting errors than alternative time-series models, such as a random walk or AR(1) model, suggesting a rational reason why analysts take this approach. Finally, I predict and find that although analysts’ point forecasts of future OCI are usually zero, their implied cost of equity estimates are consistent with analysts forecasting a positive variance for OCI.