Business implications of manufacturing innovation : the experience of increasing automation in smaller to medium sized companies.

Despite the considerable potential of advanced manufacturing technologies (AMT) for improving the economic performance of many firms, a growing body of literature highlights many instances where realising this potential has proven to be a more difficult task than initially envisaged. Focussing upon the implementation of new manufacturing technologies in several smaller to medium sized enterprises (SME), the research examines the proposition that many of these problems can be attributed in part to inadequate consideration of the integrated nature of such technologies, where the effects of their implementation are not localised, but are felt throughout a business. The criteria for the economic evaluation of such technologies are seen as needing to reflect this, and the research develops an innovative methodology employing micro-computer based spreadsheets, to demonstrate how a series of financial models can be used to quantify the effects of new investments upon overall company performance. Case studies include: the development of a prototype machine based absorption costing system to assist in the evaluation of CNC machine tool purchases in a press making company; the economics and strategy of introducing a flexible manufacturing system for the production of ballscrews; and analysing the progressive introduction of computer based printing presses in a packaging and general print company. Complementary insights are also provided from discussion with the management of several other companies which have experienced technological change. The research was conducted as a collaborative CASE project in the Interdisciplinary Higher Degrees Scheme and was jointly funded by the SERC and Gaydon Technology Limited and later assisted by PE-Inbucon. The findings of the research shows that the introduction of new manufacturing technologies usually requires a fundamental rethink of the existing practices of a business. In particular, its implementation is seen as ideally needing to take place as part of a longer term business and manufacturing strategy, but that short term commercial pressures and limited resources often mean that firms experience difficulty in realising this. The use of a spreadsheet based methodology is shown to be of considerable assistance in evaluating new investments, and is seen as being the limit of sophistication that a smaller business is willing to employ. Several points for effective modelling practice are also given, together with an outline of the context in which a modelling approach is most applicable.

Modelling financial time series with switching state space models

The deficiencies of stationary models applied to financial time series are well documented. A special form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for financial markets. We use a dynamic switching (modelled by a hidden Markov model) combined with a linear dynamical system in a hybrid switching state space model (SSSM) and discuss the practical details of training such models with a variational EM algorithm due to [Ghahramani and Hilton,1998]. The performance of the SSSM is evaluated on several financial data sets and it is shown to improve on a number of existing benchmark methods.

Dynamical local models for segmentation and prediction of financial time series

In the analysis and prediction of many real-world time series, the assumption of stationarity is not valid. A special form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for financial markets. We introduce a new model which combines a dynamic switching (controlled by a hidden Markov model) and a non-linear dynamical system. We show how to train this hybrid model in a maximum likelihood approach and evaluate its performance on both synthetic and financial data.

Have the GIPSI settled down? Breaks and multivariate stochastic volatility models for, and not against, the European financial integration

We investigate the integration of the European peripheral financial markets with Germany, France, and the UK using a combination of tests for structural breaks and return correlations derived from several multivariate stochastic volatility models. Our findings suggest that financial integration intensified in anticipation of the Euro, further strengthened by the EMU inception, and amplified in response to the 2007/2008 financial crisis. Hence, no evidence is found of decoupling of the equity markets in more troubled European countries from the core. Interestingly, the UK, despite staying outside the EMU, is not worse integrated with the GIPSI than Germany or France. © 2013 Elsevier B.V.

Non-zero mean Gaussian process prior wind field models

This report outlines the derivation and application of a non-zero mean, polynomial-exponential covariance function based Gaussian process which forms the prior wind field model used in 'autonomous' disambiguation. It is principally used since the non-zero mean permits the computation of realistic local wind vector prior probabilities which are required when applying the scaled-likelihood trick, as the marginals of the full wind field prior. As the full prior is multi-variate normal, these marginals are very simple to compute.

Semi-supervised learning of hierarchical latent trait models for data visualisation

An interactive hierarchical Generative Topographic Mapping (HGTM) ¸iteH_GTM has been developed to visualise complex data sets. In this paper, we build a more general visualisation system by extending the HGTM visualisation system in 3 directions: bf (1) We generalize HGTM to noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM) developed in ¸iteKaban_pami. bf (2) We give the user a choice of initializing the child plots of the current plot in either em interactive, or em automatic mode. In the interactive mode the user interactively selects ``regions of interest'' as in ¸iteH_GTM, whereas in the automatic mode an unsupervised minimum message length (MML)-driven construction of a mixture of LTMs is employed. bf (3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualisation plots, since they can highlight the boundaries between data clusters. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. We illustrate our approach on a toy example and apply our system to three more complex real data sets.

Monetary models of exchange rates and sweep programs

Numerous studies find that monetary models of exchange rates cannot beat a random walk model. Such a finding, however, is not surprising given that such models are built upon money demand functions and traditional money demand functions appear to have broken down in many developed countries. In this article, we investigate whether using a more stable underlying money demand function results in improvements in forecasts of monetary models of exchange rates. More specifically, we use a sweep-adjusted measure of US monetary aggregate M1 which has been shown to have a more stable money demand function than the official M1 measure. The results suggest that the monetary models of exchange rates contain information about future movements of exchange rates, but the success of such models depends on the stability of money demand functions and the specifications of the models.

Predicting returns in U.S. financial sector indices

This study focuses on: (i) the responsiveness of the U.S. financial sector stock indices to foreign exchange (FX) and interest rate changes; and, (ii) the extent to which good model specification can enhance the forecasts from the associated models. Three models are considered. Only the error-correction model (ECM) generated efficient and consistent coefficient estimates. Furthermore, a simple zero lag model in differences which is clearly mis-specified, generated forecasts that are better than those of the ECM, even if the ECM depicts relationships that are more consistent with economic theory. In brief, FX and interest rate changes do not impact on the return-generating process of the stock indices in any substantial way. Most of the variation in the sector stock indices is associated with past variation in the indices themselves and variation in the market-wide stock index. These results have important implications for financial and economic policies.

Executive remuneration, financial performance and corporate governance in UK and Spanish listed firms

This study examines the relationship between executive directors’ remuneration and the financial performance and corporate governance arrangements of the UK and Spanish listed firms. These countries’ corporate governance framework has been shaped by differences in legal origin, culture and backgrounds. For example, the UK legal arrangements can be defined as to be constituted in common-law, whereas for Spanish firms, the legal arrangement is based on civil law. We estimate both static and dynamic regression models to test our hypotheses and we estimate our regression using Ordinary Least Squares (OLS) and the Generalised Method of Moments (GMM). Estimated results for both countries show that directors’ remuneration levels are positively related with measures of firm value and financial performance. This means that remuneration levels do not lead to a point whereby firm value is reduced due to excessive remuneration. These results hold for our long-run estimates. That is, estimates based on panel cointegration and panel error correction. Measures of corporate governance also impacts on the level of executive pay. Our results have important implications for existing corporate governance arrangements and how the interests of stakeholders are protected. For example, long-run results suggest that directors’ remuneration adjusts in a way to capture variation in financial performance

A stochastic frontier approach to modelling financial constraints in firms:an application to India

We propose the use of stochastic frontier approach to modelling financial constraints of firms. The main advantage of the stochastic frontier approach over the stylised approaches that use pooled OLS or fixed effects panel regression models is that we can not only decide whether or not the average firm is financially constrained, but also estimate a measure of the degree of the constraint for each firm and for each time period, and also the marginal impact of firm characteristics on this measure. We then apply the stochastic frontier approach to a panel of Indian manufacturing firms, for the 1997–2006 period. In our application, we highlight and discuss the aforementioned advantages, while also demonstrating that the stochastic frontier approach generates regression estimates that are consistent with the stylised intuition found in the literature on financial constraint and the wider literature on the Indian credit/capital market.

General and multiplicative non-parametric corporate performance models with interval ratio data

The increasing intensity of global competition has led organizations to utilize various types of performance measurement tools for improving the quality of their products and services. Data envelopment analysis (DEA) is a methodology for evaluating and measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. All the data in the conventional DEA with input and/or output ratios assumes the form of crisp numbers. However, the observed values of data in real-world problems are sometimes expressed as interval ratios. In this paper, we propose two new models: general and multiplicative non-parametric ratio models for DEA problems with interval data. The contributions of this paper are fourfold: (1) we consider input and output data expressed as interval ratios in DEA; (2) we address the gap in DEA literature for problems not suitable or difficult to model with crisp values; (3) we propose two new DEA models for evaluating the relative efficiencies of DMUs with interval ratios, and (4) we present a case study involving 20 banks with three interval ratios to demonstrate the applicability and efficacy of the proposed models where the traditional indicators are mostly financial ratios. © 2011 Elsevier Inc.

An aggregate measure of financial ratios using a multiplicative DEA model

This paper examines the problems in the definition of the General Non-Parametric Corporate Performance (GNCP) and introduces a multiplicative linear programming as an alternative model for corporate performance. We verified and tested a statistically significant difference between the two models based on the application of 27 UK industries using six performance ratios. Our new model is found to be a more robust performance model than the previous standard Data Envelopment Analysis (DEA) model.

Semisupervised learning of hierarchical latent trait models for data visualization

Recently, we have developed the hierarchical Generative Topographic Mapping (HGTM), an interactive method for visualization of large high-dimensional real-valued data sets. In this paper, we propose a more general visualization system by extending HGTM in three ways, which allows the user to visualize a wider range of data sets and better support the model development process. 1) We integrate HGTM with noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM). This enables us to visualize data of inherently discrete nature, e.g., collections of documents, in a hierarchical manner. 2) We give the user a choice of initializing the child plots of the current plot in either interactive, or automatic mode. In the interactive mode, the user selects "regions of interest," whereas in the automatic mode, an unsupervised minimum message length (MML)-inspired construction of a mixture of LTMs is employed. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. 3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualization plots, since they can highlight the boundaries between data clusters. We illustrate our approach on a toy example and evaluate it on three more complex real data sets. © 2005 IEEE.

Evaluating spread models with a basket security

In this article we evaluate the most widely used spread decomposition models using Exchange Traded Funds (ETFs). These funds are an example of a basket security and allow the diversification of private information causing these securities to have lower adverse selection costs than individual securities. We use this feature as a criterion for evaluating spread decomposition models. Comparisons of adverse selection costs for ETF's and control securities obtained from spread decomposition models show that only the Glosten-Harris (1988) and the Madhavan-Richardson-Roomans (1997) models provide estimates of the spread that are consistent with the diversification of private information in a basket security. Our results are robust even after controlling for the stock exchange. © 2011 Copyright Taylor and Francis Group, LLC.

Modelling stock volatilities during financial crises:a time varying coefficient approach

We examine how the most prevalent stochastic properties of key financial time series have been affected during the recent financial crises. In particular we focus on changes associated with the remarkable economic events of the last two decades in the volatility dynamics, including the underlying volatility persistence and volatility spillover structure. Using daily data from several key stock market indices, the results of our bivariate GARCH models show the existence of time varying correlations as well as time varying shock and volatility spillovers between the returns of FTSE and DAX, and those of NIKKEI and Hang Seng, which became more prominent during the recent financial crisis. Our theoretical considerations on the time varying model which provides the platform upon which we integrate our multifaceted empirical approaches are also of independent interest. In particular, we provide the general solution for time varying asymmetric GARCH specifications, which is a long standing research topic. This enables us to characterize these models by deriving, first, their multistep ahead predictors, second, the first two time varying unconditional moments, and third, their covariance structure.