Relative accuracy and predictive ability of direct valuation methods, price to aggregate earnings method and a hybrid approach

In this paper, we assess the relative performance of the direct valuation method and industry multiplier models using 41 435 firm‐quarter Value Line observations over an 11 year (1990–2000) period. Results from both pricing‐error and return‐prediction analyses indicate that direct valuation yields lower percentage pricing errors and greater return prediction ability than the forward price to aggregated forecasted earnings multiplier model. However, a simple hybrid combination of these two methods leads to more accurate intrinsic value estimates, compared to either method used in isolation. It would appear that fundamental analysis could benefit from using one approach as a check on the other.     

Disclosure‐protected Inference Using Generalised Linear Models

Vast amounts of data that could be used in the development and evaluation of policy for the benefit of society are collected by statistical agencies. It is therefore no surprise that there is very strong demand from analysts, within business, government, universities and other organisations, to access such data. When allowing access to micro‐data, a statistical agency is obliged, often legally, to ensure that it is unlikely to result in the disclosure of information about a particular person or organisation. Managing the risk of disclosure is referred to as statistical disclosure control (SDC). This paper describes an approach to SDC for output from analysis using generalised linear models, including estimates of regression parameters and their variances, diagnostic statistics and plots. The Australian Bureau of Statistics has implemented the approach in a remote analysis system, which returns analysis output from remotely submitted queries. A framework for measuring disclosure risk associated with a remote server is proposed. The disclosure risk and utility of approach are measured in two real‐life case studies and in simulation.     

Mortgage Pricing: Some Provisional Empirical Results

We test some of the qualitative properties of mortgage pricing models. The models use option pricing techniques, focusing on prepayment as a call option. They imply a quite nonlinear relationship between mortgage price and coupon, interest rates and volatility. We test for both the first and second derivatives of the effects of these variables using data on Ginnie Mae mortgage backed securities. We find that the model is largely supported by the data.     

A Research Perspective: Artificial Intelligence,Management and Organizations

In recent years many companies have deployed Artificial Intelligence (AI), which has included neural networks, expert systems and voice-recognition systems. Yet managers and developers understand very little about how management and organizations affect or are affected by the technology. Using specific examples from practice and research, this paper discusses the interaction of AI, management and organizations, and describes some methodological approaches and theoretical models for studying those interactions. It provides direction for future research.