Modelling conditional probability distributions for periodic variables

Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce two novel techniques for tackling such problems, and investigate their performance using synthetic data. We then apply these techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.

Modelling conditional probability distributions for periodic variables

Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we apply two novel techniques to the problem of extracting the distribution of wind vector directions from radar catterometer data gathered by a remote-sensing satellite.

Modelling conditional probability distributions for periodic variables

Most conventional techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce three related techniques for tackling such problems, and investigate their performance using synthetic data. We then apply these techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.

Estimating conditional probability densities for periodic variables

Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce three novel techniques for tackling such problems, and investigate their performance using synthetic data. We then apply these techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.

Estimating conditional volatility with neural networks

It is well known that one of the obstacles to effective forecasting of exchange rates is heteroscedasticity (non-stationary conditional variance). The autoregressive conditional heteroscedastic (ARCH) model and its variants have been used to estimate a time dependent variance for many financial time series. However, such models are essentially linear in form and we can ask whether a non-linear model for variance can improve results just as non-linear models (such as neural networks) for the mean have done. In this paper we consider two neural network models for variance estimation. Mixture Density Networks (Bishop 1994, Nix and Weigend 1994) combine a Multi-Layer Perceptron (MLP) and a mixture model to estimate the conditional data density. They are trained using a maximum likelihood approach. However, it is known that maximum likelihood estimates are biased and lead to a systematic under-estimate of variance. More recently, a Bayesian approach to parameter estimation has been developed (Bishop and Qazaz 1996) that shows promise in removing the maximum likelihood bias. However, up to now, this model has not been used for time series prediction. Here we compare these algorithms with two other models to provide benchmark results: a linear model (from the ARIMA family), and a conventional neural network trained with a sum-of-squares error function (which estimates the conditional mean of the time series with a constant variance noise model). This comparison is carried out on daily exchange rate data for five currencies.

Robust control of nonlinear stochastic systems by modelling conditional distributions of control signals

We introduce a novel inversion-based neuro-controller for solving control problems involving uncertain nonlinear systems that could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. In this work a novel robust inverse control approach is obtained based on importance sampling from these distributions. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The performance of the new algorithm is illustrated through simulations with example systems.

Conditional distribution modeling for estimating and exploiting uncertainty in control systems

This paper presents a general methodology for estimating and incorporating uncertainty in the controller and forward models for noisy nonlinear control problems. Conditional distribution modeling in a neural network context is used to estimate uncertainty around the prediction of neural network outputs. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localize the possible control solutions to consider. A nonlinear multivariable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non Gaussian distributions of control signal as well as processes with hysteresis.

Stock repurchases and treasury share sales: Do they stabilize price and enhance liquidity?

The article studies the impact of a firm’s trading in its own shares on the volatility and market liquidity of the firm’s stock in the Italian stock market. In the study, both stock repurchases and treasury share sales executed on the open market are defined as trading in own shares. The study finds that Italian firms can reduce the volatility of their stock and boost market liquidity by trading their own shares.

Using monthly returns to model conditional heteroscedasticity

This empirical study examines the extent of non-linearity in a multivariate model of monthly financial series. To capture the conditional heteroscedasticity in the series, both the GARCH(1,1) and GARCH(1,1)-in-mean models are employed. The conditional errors are assumed to follow the normal and Student-t distributions. The non-linearity in the residuals of a standard OLS regression are also assessed. It is found that the OLS residuals as well as conditional errors of the GARCH models exhibit strong non-linearity. Under the Student density, the extent of non-linearity in the GARCH conditional errors was generally similar to those of the standard OLS. The GARCH-in-mean regression generated the worse out-of-sample forecasts.

The quality of sales manager:salesperson exchanges perspective under problem resolution conditions

Building on social exchange theory and qualitative inquiry, managerial responsiveness, caring, and aggressiveness were uncovered as three key social exchange dimensions used by sales managers when dealing with problem situations in the salesforce. We used Australian data to develop measures of these three constructs. Results of the development process indicate that the measures show good validity. Further to this, we also provide examination of the relationship of the three exchange dimensions with key organizational outcomes. Overall the findings suggest that the three constructs are important in sales manager problem resolution exchanges, and that they may ultimately influence the success of sales organizations.

Sales manager and sales team determinants of salesperson ethical behaviour

Purpose – The purpose of this paper is to develop and test a model of the role managers and peers play in shaping salespeople's ethical behaviour. The model specifies that sales manager personal moral philosophies, whether sales managers themselves are rewarded according to the outcomes or behaviours of their salespeople, sales team job security, intra-team cooperation, and sales team tactical performance all influence sales team ethical standards. In turn, ethical standards influence the probability that sales team members will behave (un)ethically when faced with ethical dilemmas. Design/methodology/approach – The model is tested on a sample of 154 Finnish sales managers. Data were collected via mail survey. Analysis was undertaken using structural equation modelling. Findings – Ethical standards appear to be shaped by several factors; behaviour-based management controls increase ethical standards, relativist managers tend to manage less ethically-minded sales teams, job insecurity impedes the development of ethical standards, and sales teams' cooperation activity increases ethical standards. Sales teams are less likely to engage in unethical behaviour when the teams have strong ethical standards. Research limitations/implications – Cross-sectional data limits generalisability; single country data may limit the ability to generalise to different sales environments; additional measure development is needed; identification of additional antecedent factors would be beneficial. Practical implications – Sales managers should consciously develop high ethical standards in sales teams if they wish to reduce unethical behaviour. Ethical standards can be improved if sales managers change their own outward behaviour (exhibit a less relativistic ethical philosophy), foster cooperation amongst salespeople, and develop perceptions of job security. How sales managers are rewarded may shape how they approach the management of ethical behaviour in their sales teams. Originality/value – This paper appears to be the first to simultaneously examine both sales manager-specific and sales team-specific antecedents to sales team ethical standards and behaviours. As such, it provides an important base for research in this critical area.

Sales force social exchange in problem resolution situations

While sales managers spend much of their time resolving sales force-related problems, existing theory offers little insight into the social exchange processes which occur in problem resolution situations. Using a qualitative inquiry method rooted in grounded theory, we uncover three key social exchange contributions used by sales managers when dealing with problem situations in the sales force: sales manager responsiveness, caring, and aggressiveness. We then show that the extent to which managers use these exchange contributions in problem situations is a function of manager characteristics, problem-specific characteristics, and the situational context. We also show that the extent to which managers invest in these three social exchange contributions has implications for the quality for the interpersonal relationships between salespeople and their managers, and for the effectiveness of problem resolution activity.