Maxwell Stevenson

Development of a Method to Assess the Relevant Variables and the Probability of Financial Distress

This study develops a method to select variables, and to specify the relative importance of those variables, as input for failure models. In addition, our method provides estimates of the failure probabilities of organisations at a point in time. The results of tests of our method on Australian credit unions are intuitively appealing, given the recent experiences of the Australian financial market generally, and the credit union industry in particular.

Surviving Chapter 11 Bankruptcies: Duration and Payoff?

Three of the authors previously developed a model to predict the duration of Chapter 11 bankruptcy and the payoff to shareholders (Partington et al., 2001). This work augments that study using a much larger sample to re-estimate the model and assess its stability. It also provides an opportunity for out-of-sample testing of predictive accuracy. The resulting models are based on Coxs proportional hazards model and the current article points to the need to test two important assumptions underlying the model. First, that the hazards are proportional and, second, that censoring is independent of the event studied. Using the extended data set, all the previously significant accounting variables drop out of the model and only two covariates of the original model remain significant. These are the market wide credit spread and the market capitalization of the firm, both measured immediately prior to the firms entry to Chapter 11. Receiver operating characteristic curves are then used to assess the predictive accuracy of the original and extended models. The results show that Lachenbruch tests can provide a misleading indication of predictive ability out of sample. Using the Lachenbruch method of in-sample testing, both models show predictive power, but in a true out-of-sample test they fail dismally. The lessons of this work are relevant to better predicting the gains and losses likely to accrue to shareholders of companies in Chapter 11 bankruptcy and in similar administrative arrangements in other jurisdictions.

Hot or Cold?: A Comparison of Different Approaches to the Pricing of Weather Derivatives

This article reviews six different temperature forecasting models proposed by prior literature for pricing weather derivatives. Simulation of these models is used to estimate daily temperature and, as a consequence, the metrics used for pricing temperature derivatives. The models that rely on an auto-regressive moving average (ARMA) process exhibit a better goodness-of-fit than those that are established under Monte Carlo simulations. However, the superiority of ARMA-type models is not reflected over the forecast horizon. Over that period, the models that rely on Monte Carlo simulations exhibit a tendency to over-forecast the monthly accumulated heating degree day (AccHDD) index and to under-forecast the monthly accumulated cooling degree day (AccCDD) index. Alternatively, models established under the ARMA approach both under-forecast and over-forecast the monthly accumulated indices. All models consistently over-forecast the average daily temperature. The most appropriate pricing model varies between cities and months. Finally, the models examined in this study generate a more accurate AccHDD futures price than the price traded on the market. However, the ability of these models to estimate the AccCDD futures price is significantly poorer than that of the market.

Run length and the predictability of stock price reversals

Survival analysis is used to estimate time-varying probabilities of price reversals using daily data for the Australian All Ordinaries Price Index. Lagged price changes lead to persistence (shortening) in a price run if they are of the same (opposite) sign as the run. An increase in the number of runs observed in the previous 30 days also increases the probability of price reversal. The predictive accuracy of the models is assessed using a probability scoring rule. Consistent with market efficiency, the estimated models are less accurate than the random walk model in predicting the length of individual price runs out-of-sample.

Weather Derivatives: An Attractive Additional Asset Class

This article demonstrates that companies from a wide range of industries are able to hedge against the volatility of their revenues more efficiently by resorting to non-standardized weather derivative contracts. In addition, including weather derivatives contracts as an additional asset class produces significant diversification benefits for conventional portfolios. This study proposes that institutional investors write non-standardized contracts for their corporate clients, repackage them, and offer them as an additional asset class. This strategy would help to mitigate the lack of liquidity inherent in non-standardized contracts and, simultaneously, provide significant diversification benefits for the conventional portfolio.

A Hybrid Approach to Combining CART and Logistic Regression for Stock Ranking

The performance of a stock relative to a suitable peer group is often influenced by a multitude of factors and their interactions. Traditional parametric models, albeit very useful, are often inadequate in capturing complicated relationships. In contrast, the nonparametric decision tree technique, such as classification and regression trees (CART), is more capable of capturing any nonlinearities and high-order interactions among stock characteristics, with the additional convenience of graphically representing the model, but discontinuous and coarse-grained responses produced by CART are potentially undesirable. In contrast, traditional regression models such as logistic regression produce a smooth response surface, which is more tractable in practice. The authors use a hybrid approach that takes advantage of the strengths in both parametric (logistic regression) and nonparametric models (CART). An application of this sophisticated technique to North American defensive stocks demonstrates its usefulness.

Price and Volume Behavior around the Ex‐dividend Day: Evidence on the Value of Dividends from American Depositary Receipts and their Underlying Australian Stocks*

Australian residents are tax-advantaged, relative to American investors, in their access to imputation tax credits on Australian stocks. This paper provides evidence consistent with a difference in dividend valuations between Australian stocks and their American Depositary Receipts (ADRs). The ex-dividend drop-off ratio is lower for ADRs relative to their underlying Australian stocks and this difference is most pronounced for stocks that have imputation tax credits and high dividend yields. Consistent with dividend capture trading in the Australian market, the difference in drop-off ratios is driven by both temporarily higher Australian cum-prices and temporarily lower Australian ex-prices. Abnormal trading volume about the ex-day is present in both markets and in the Australian market the abnormal volume is greater for dividends with imputation tax credits. Dividend-related trading leads to price differences across the markets on the ex-dividend day. Price differences are also observed when the stock and the ADR trade with different dividend entitlements due to different ex-dividend dates.

Predicting the directional change in consumer sentiment

The Consumer Sentiment Index (CSI) is a widely monitored economic indicator. The index measures consumer expectations, which contain information about potential future changes in consumer spending. Thus, any change in the dynamics of the sentiment index should contain important signals about future consumer behaviour, as well as changes in the real economy and stock returns. In this paper we use Cox’s proportional hazards model to estimate dynamic transition probabilities for a reversal in the CSI. We predict the probability of both positive and negative runs in sentiment surviving in the same state. Lagged changes in consumer sentiment and the frequency of recent reversals of sentiment are found to be significant predictors of a change in state. The model has substantial predictive ability, as measured by both categorical accuracy and a probability scoring rule, the Brier Score (Brier, 1950) out of sample. The predictive accuracy of the model is robust to the impact of the global financial crisis.

Modelling the Longitudinal Properties of Financial Ratios of European Firms

The use of financial ratios by analysts to compare the performance of firms from one accounting period to the next is of growing importance with continued European economic integration. Recent studies suggest that the individual component series of financial ratios exhibit nonstationarity which is not eliminated by the ratio transformation. In this paper, w e derive a generalised model that incorporates stochastic and deterministic trends and allows for restricted and unrestricted proportionate growth in the ratio numerator and denominator. When the individual firm series are included in a panel structure with large N and small T, we are unable to reject convincingly a joint hypothesis of nonstationarity, whilst in about one third of the individual firm panels there is no evidence of a unit root. Although the components of financial ratios are correlated variables, our estimates show that any cointegrating effects decay rapidly.