A firm's book equity is a measure of the value held by a firm's ordinary shareholders. Increasingly, it is being reported as a negative number. Since the firm's limited liability structure means that shareholders' value cannot be negative value, negative book equity has no obvious interpretation. Consequently, both practitioners and academics typically omit such stocks. While these stocks are small in number they are disproportionately represented in extreme value/growth sectors, and therefore can have an impact on applications where "value" is defined in terms of book equity. We propose a new approach that classifies negative book equity stocks across the value/growth spectrum by considering how close their returns correspond to stocks that fit more obviously into these classifications. We find that this new value factor, which includes negative book equity stock, is economically and statistically different from the old value factor that excludes such stocks. Although we illustrate how this approach can be used to classify negative book equity stock, the approach is quite general and may be used whenever particular accounting data are unavailable or otherwise suspect. ABSTRACTA firm's book equity is a measure of the value held by a firm's ordinary shareholders. Increasingly, it is being reported as a negative number. Since the firm's limited liability structure means that shareholders' value cannot be negative value, negative book equity has no obvious interpretation. Consequently, both practitioners and academics typically omit such stocks. While these stocks are small in number they are disproportionately represented in extreme value/growth sectors, and therefore can have an impact on applications where "value" is defined in terms of book equity. We propose a new approach that classifies negative book equity stocks across the value/growth spectrum by considering how close their returns correspond to stocks that fit more obviously into these classifications. We find that this new value factor, which includes negative book equity stock, is economically and statistically different from the old value factor that excludes such stocks. Although we illustrate how this approach can be used to classify negative book equity stock, the approach is quite general and may be used whenever particular accounting data are unavailable or otherwise suspect.

The provision of imputation tax credits can in principle lower the returns that investors require on equity. Whether in practice imputation credits lower the returns that investors require depends in large part on the impact of foreign investors on equity prices. This is because foreign investors in general cannot use the credits that domestic equities provide. We use a range of pricing models and monthly data from July 1987 to December 2009 to test whether, holding risk constant, equity returns are related to credit yields. We find no evidence that the provision of imputation tax credits lowers the returns investors require on equity.

A hybrid neural network is used to predict the difference between the conventional option-pricing model and observed intraday option prices for stock index option futures. Confidence intervals derived with bootstrap methods are used in a trading strategy that only allows trades outside the estimated range of spurious model fits to be executed. Whilst hybrid neural network option pricing models can improve predictions they have bias. The hybrid option-pricing bias can be reduced with bootstrap methods. A modified bootstrap predictor is indexed by a parameter that allows the predictor to range from a pure bootstrap predictor, to a hybrid predictor, and finally the bagging predictor. The modified bootstrap predictor outperforms the hybrid and bagging predictors. Greatly improved performance was observed on the boundary of the training set and where only sparse training data exists. Finally, bootstrap bias estimates were studied.

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