SMEs have been recognized as important actors of commercial activity most specifically since the second half of the 20th century. Although understanding its customer potential has been named a top priority among many sectors, there is a limited body of knowledge regarding the buying behaviour of SME’s. Many studies and industry practices frame the context within corporate or individual buying behaviour, however few researchers mention SME’s. When mentioned they tend to tie with elitist attributes and consider them as small versions of big enterprises. Studies and industry practices are conformist and assume that SME’s buying behaviour consists of nothing but -ignoring impulsive tendencies- ‘normative’ or ‘conservative’ typologies. However, the lack of application makes this dichotomy untested, which is also what this book aims to scrutinize.

Financial time series including high frequency structures like jumps, spikes and stochastic volatility are usually modeled in an ad-hoc manner by stochastic differential equations together with Levy processes. Estimating the parameters and determining the jump size distributions do not have a precise and universally accepted method. Under these circumstances, complexities and confusions usually arise. This book, approaches to this issue from a very different angle through introducing an autocorrelation one process together with finite number of Fourier series terms. Introduction of Fourier series to estimate the dynamics of the process is not done in an ad-hoc manner or as done before in dealing with seasonality. Here the moving average is transformed to a “moving and fluctuating” average by the help of Fourier series. Instead of adding jump structures to the model which makes the parameter estimation quite cumbersome, our model in discrete time can easily be transformed to a well-known mean reverting continuous time process. Moreover, our alternative model turned out to be a quite powerful and accurate forecasting technique.