Combining the wavelet transform and forecasting models to predict gas forward prices

This paper presents a forecasting technique for forward energy prices, one day ahead. This technique combines a wavelet transform and forecasting models such as multi- layer perceptron, linear regression or GARCH. These techniques are applied to real data from the UK gas markets to evaluate their performance. The results show that the forecasting accuracy is improved significantly by using the wavelet transform. The methodology can be also applied to forecasting market clearing prices and electricity/gas loads.

Leading edge forecasting techniques for exchange rate prediction

This paper describes how modern machine learning techniques can be used in conjunction with statistical methods to forecast short term movements in exchange rates, producing models suitable for use in trading. It compares the results achieved by two different techniques, and shows how they can be used in a complementary fashion. The paper draws on experience of both inter- and intra-day forecasting taken from earlier studies conducted by Logica and Chemical Bank Quantitative Research and Trading (QRT) group's experience in developing trading models.

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.

Forecasting exchange rates with linear and nonlinear models

In this paper, the exchange rate forecasting performance of neural network models are evaluated against the random walk, autoregressive moving average and generalised autoregressive conditional heteroskedasticity models. There are no guidelines available that can be used to choose the parameters of neural network models and therefore, the parameters are chosen according to what the researcher considers to be the best. Such an approach, however,implies that the risk of making bad decisions is extremely high, which could explain why in many studies, neural network models do not consistently perform better than their time series counterparts. In this paper, through extensive experimentation, the level of subjectivity in building neural network models is considerably reduced and therefore giving them a better chance of Forecasting exchange rates with linear and nonlinear models 415 performing well. The results show that in general, neural network models perform better than the traditionally used time series models in forecasting exchange rates.

A comparison of linear forecasting models and neural networks:an application to Euro inflation and Euro Divisia

Linear models reach their limitations in applications with nonlinearities in the data. In this paper new empirical evidence is provided on the relative Euro inflation forecasting performance of linear and non-linear models. The well established and widely used univariate ARIMA and multivariate VAR models are used as linear forecasting models whereas neural networks (NN) are used as non-linear forecasting models. It is endeavoured to keep the level of subjectivity in the NN building process to a minimum in an attempt to exploit the full potentials of the NN. It is also investigated whether the historically poor performance of the theoretically superior measure of the monetary services flow, Divisia, relative to the traditional Simple Sum measure could be attributed to a certain extent to the evaluation of these indices within a linear framework. Results obtained suggest that non-linear models provide better within-sample and out-of-sample forecasts and linear models are simply a subset of them. The Divisia index also outperforms the Simple Sum index when evaluated in a non-linear framework. © 2005 Taylor & Francis Group Ltd.

Inverse forecasting:a new approach for predictive modelling

In the last two decades there have been substantial developments in the mathematical theory of inverse optimization problems, and their applications have expanded greatly. In parallel, time series analysis and forecasting have become increasingly important in various fields of research such as data mining, economics, business, engineering, medicine, politics, and many others. Despite the large uses of linear programming in forecasting models there is no a single application of inverse optimization reported in the forecasting literature when the time series data is available. Thus the goal of this paper is to introduce inverse optimization into forecasting field, and to provide a streamlined approach to time series analysis and forecasting using inverse linear programming. An application has been used to demonstrate the use of inverse forecasting developed in this study. © 2007 Elsevier Ltd. All rights reserved.

Model specification and forecasting foreign exchange rates with vector autoregressions

This study examines the forecasting accuracy of alternative vector autoregressive models each in a seven-variable system that comprises in turn of daily, weekly and monthly foreign exchange (FX) spot rates. The vector autoregressions (VARs) are in non-stationary, stationary and error-correction forms and are estimated using OLS. The imposition of Bayesian priors in the OLS estimations also allowed us to obtain another set of results. We find that there is some tendency for the Bayesian estimation method to generate superior forecast measures relatively to the OLS method. This result holds whether or not the data sets contain outliers. Also, the best forecasts under the non-stationary specification outperformed those of the stationary and error-correction specifications, particularly at long forecast horizons, while the best forecasts under the stationary and error-correction specifications are generally similar. The findings for the OLS forecasts are consistent with recent simulation results. The predictive ability of the VARs is very weak.

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.