Exploring the financial consequences of the servitization of manufacturing

Commentators suggest that to survive in developed economies manufacturing firms have to move up the value chain, innovating and creating ever more sophisticated products and services, so they do not have to compete on the basis of cost. While this strategy is proving increasingly popular with policy makers and academics there is limited empirical evidence to explore the extent to which it is being adopted in practice. And if so, what the impact of this servitization of manufacturing might be. This paper seeks to fill a gap in the literature by presenting empirical evidence on the range and extent of servitization. Data are drawn from the OSIRIS database on 10,028 firms incorporated in 25 different countries. The paper presents an analysis of these data which suggests that: [i] manufacturing firms in developed economies are adopting a range of servitization strategies-12 separate approaches to servitization are identified; [ii] these 12 categories can be used to extend the traditional three options for servitization-product oriented Product-Service Systems, use oriented Product-Service Systems and result oriented Product-Service Systems, by adding two new categories "integration oriented Product-Service Systems" and "service oriented Product-Service Systems"; [iii] while the manufacturing firms that have servitized are larger than traditional manufacturing firms in terms of sales revenues, at the aggregate level they also generate lower profits as a % of sales; [iv] these findings are moderated by firm size (measured in terms of numbers of employees). In smaller firms servitization appears to pay off while in larger firms it proves more problematic; and [v] there are some hidden risks associated with servitization-the sample contains a greater proportion of bankrupt servitized firms than would be expected. © Springer Science + Business Media, LLC 2009.

Dynamic Covariance Models for Multivariate Financial Time Series

The accurate prediction of time-changing covariances is an important problem in the modeling of multivariate financial data. However, some of the most popular models suffer from a) overfitting problems and multiple local optima, b) failure to capture shifts in market conditions and c) large computational costs. To address these problems we introduce a novel dynamic model for time-changing covariances. Over-fitting and local optima are avoided by following a Bayesian approach instead of computing point estimates. Changes in market conditions are captured by assuming a diffusion process in parameter values, and finally computationally efficient and scalable inference is performed using particle filters. Experiments with financial data show excellent performance of the proposed method with respect to current standard models.

A Radial Basis Function Approach to Financial Time Series Analysis

Nonlinear multivariate statistical techniques on fast computers offer the potential to capture more of the dynamics of the high dimensional, noisy systems underlying financial markets than traditional models, while making fewer restrictive assumptions. This thesis presents a collection of practical techniques to address important estimation and confidence issues for Radial Basis Function networks arising from such a data driven approach, including efficient methods for parameter estimation and pruning, a pointwise prediction error estimator, and a methodology for controlling the "data mining'' problem. Novel applications in the finance area are described, including customized, adaptive option pricing and stock price prediction.

The impact of interconnectedness in a triadic relationship between a financial services provider, IFAs, and customers.

The aim of this study is to investigate the impact of interconnectedness between a long-term savings and investments provider, Independent Financial Advisers (IFAs) and customers. Ritter’s (2000) framework of the effect of interconnectedness was used to analyse this triadic relationship. Conceptual studies of triadic business relationships are scarce in marketing and organisational research (Blakenburg & Johanson, 1992; Havila, Johnson & Thilenius, 2004; Ritter, 2000). However, the applicability of a triadic relationship has been tested in a number of case studies (Andersson & Mattsson, 2004; Cunningham & Pyatt, 1989; Jaaskelainen, Kuivalainen & Saarenketo, 2000; Narayandas, 2002; Odorici & Corrado, 2004; Pardo & Salle, 1994; Trimachi, 2002). This study was conducted in collaboration with one of the UK’s largest long-term savings and investments providers. A substantial proportion of the provider’s business is conducted through IFAs and thus their significance as a major stakeholder. Indeed, the majority of sales in the long-term savings and investments industry in the UK are realised through IFAs. Academic studies (Gough, 2005; Gough & Nurullah, 2009) have indicated that IFAs are the strongest distribution channel in the industry. Thus, by analysing the impact of the interconnectedness in this relationship, a strategy that can increase the relationship performance can be proposed. However, to the best of the authors’ knowledge, a study that investigates the effect of the interconnectedness in this triadic relationship has not been established. In addition, the regulatory environment which continues to face change such as the recent implementation of Retail Distribution Review (RDR) on 1st January 2013 will make the relationship more rather than less complex.

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.

Financial modelling with 2-EPT probability density functions

The class of all Exponential-Polynomial-Trigonometric (EPT) functions is classical and equal to the Euler-d’Alembert class of solutions of linear differential equations with constant coefficients. The class of non-negative EPT functions defined on [0;1) was discussed in Hanzon and Holland (2010) of which EPT probability density functions are an important subclass. EPT functions can be represented as ceAxb, where A is a square matrix, b a column vector and c a row vector where the triple (A; b; c) is the minimal realization of the EPT function. The minimal triple is only unique up to a basis transformation. Here the class of 2-EPT probability density functions on R is defined and shown to be closed under a variety of operations. The class is also generalised to include mixtures with the pointmass at zero. This class coincides with the class of probability density functions with rational characteristic functions. It is illustrated that the Variance Gamma density is a 2-EPT density under a parameter restriction. A discrete 2-EPT process is a process which has stochastically independent 2-EPT random variables as increments. It is shown that the distribution of the minimum and maximum of such a process is an EPT density mixed with a pointmass at zero. The Laplace Transform of these distributions correspond to the discrete time Wiener-Hopf factors of the discrete time 2-EPT process. A distribution of daily log-returns, observed over the period 1931-2011 from a prominent US index, is approximated with a 2-EPT density function. Without the non-negativity condition, it is illustrated how this problem is transformed into a discrete time rational approximation problem. The rational approximation software RARL2 is used to carry out this approximation. The non-negativity constraint is then imposed via a convex optimisation procedure after the unconstrained approximation. Sufficient and necessary conditions are derived to characterise infinitely divisible EPT and 2-EPT functions. Infinitely divisible 2-EPT density functions generate 2-EPT Lévy processes. An assets log returns can be modelled as a 2-EPT Lévy process. Closed form pricing formulae are then derived for European Options with specific times to maturity. Formulae for discretely monitored Lookback Options and 2-Period Bermudan Options are also provided. Certain Greeks, including Delta and Gamma, of these options are also computed analytically. MATLAB scripts are provided for calculations involving 2-EPT functions. Numerical option pricing examples illustrate the effectiveness of the 2-EPT approach to financial modelling.