Neural networks and the evolution of firms and industries: An application to UK SIC34 and SIC72

This paper considers whether neural networks might be used to analyse firm activity and the evolution of industries. The key findings of the simulation results used are summarised as follows. While efficiency seeking behaviour has growth advantages, compared to unchanged firms, these are small compared to the growth advantages that are displayed with firms that are able to exploit input use variability. In addition the two sectors analysed here (UK SIC34 and SIC72) show different profit implications of these growth advantages. In SIC34 an increase in firm growth caused by strategic flexibility coincides with an increase in profitability, whereas in SIC72 the increase in firm growth coincides with a profitability reduction. This difference is explained in terms of the differing market structures in the two sectors along with the differing effects of market shocks. Finally the market structure effects of differing firm types have been analysed. It is shown that factor flexibility generates relative growth advantages that benefit smaller firms. But strategic flexibility generates relative growth effects that benefit larger firms

Forecasting inflation with thick models and neural networks

This paper applies linear and neural network-based “thick” models for forecasting inflation based on Phillips–curve formulations in the USA, Japan and the euro area. Thick models represent “trimmed mean” forecasts from several neural network models. They outperform the best performing linear models for “real-time” and “bootstrap” forecasts for service indices for the euro area, and do well, sometimes better, for the more general consumer and producer price indices across a variety of countries. JEL Classification: C12, E31bootstrap, Neural Networks, Phillips Curves, real-time forecasting, Thick Models

Neural networks and the evolution of firms and industries: An application to UK SIC34 and SIC72.

This paper considers whether neural networks might be used to analyse firm activity and the evolution of industries. The key findings of the simulation results used are summarised as follows. While efficiency seeking behaviour has growth advantages, compared to unchanged firms, these are small compared to the growth advantages that are displayed with firms that are able to exploit input use variability. In addition the two sectors analysed here (UK SIC34 and SIC72) show different profit implications of these growth advantages. In SIC34 an increase in firm growth caused by strategic flexibility coincides with an increase in profitability, whereas in SIC72 the increase in firm growth coincides with a profitability reduction. This difference is explained in terms of the differing market structures in the two sectors along with the differing effects of market shocks. Finally the market structure effects of differing firm types have been analysed. It is shown that factor flexibility generates relative growth advantages that benefit smaller firms. But strategic flexibility generates relative growth effects that benefit larger firms.

Direction-of-Change Forecasting using a Volatility- Based Recurrent Neural Network

This paper investigates the profitability of a trading strategy, based on recurrent neural networks, that attempts to predict the direction-of-change of the market in the case of the NASDAQ composite index. The sample extends over the period 2/8/1971 \u2013 4/7/1998, while the sub-period 4/8/1998 - 2/5/2002 has been reserved for out-of-sample testing purposes. We demonstrate that the incorporation in the trading rule of estimates of the conditional volatility changes strongly enhances its profitability during `bear' market periods. This improvement is being measured with respect to a nested model that does not include the volatility variable as well as to a buy & hold strategy. We suggest that our findings can be justified by invoking either the `volatility feedback' theory or the existence of portfolio insurance schemes in the equity markets. Our results are also consistent with the view that volatility dependence produces sign dependence.

A Duration-Dependent Regime Switching Model for an Open Emerging Economy

We employ duration-dependent Markov-switching vector auto-regression (DDMSVAR) methodology to construct an economic cycle model for an emerging economy. By modifying the software codes for DDMSVAR methodology written by Pelagatti (2003), we show how to estimate the economic cycles in an emerging economy where macroeconomic shocks are suddenly observed and their levels are deep. The monthly values of net international reserves, domestic debt, inflation and industrial production in the Turkish economy from January 1989 to July 2007 are used for constructing the empirical analysis. Empirical evidence shows that DDMSVAR model can be successfully used in an emerging economy to estimate the cycles using basic macroeconomic indicators.duration dependent regime switching model, economic cycles, Markov models, Turkish economy

Using simple neural networks to analyse firm activity

Characteristically, in economics, the analysis of firm activity is based on a production function that defines a deterministic relationship between factor inputs and firm output. The analysis of the firm as an organisation takes a somewhat different approach. For instance, behavioural economics (for example Simon, 1955; March and Simon, 1958; Cyert and March, 1963), transaction cost theory (Williamson, 1975, 1985) and capabilities approaches (for example Foss and Loasby, 1998; Foss, 2005) emphasise that economic agents have inevitably incomplete information and knowledge and are at most boundedly or limitedly rational. The implication here is that while general principles governing intra-firm interaction can be specified, detailed organisational processes inside the firm are, for practical academic purposes, effectively unobservable. Hence, the usual analytical tools designed to analyse firm behaviour, based on production functions and optimising principles with full information, are in practice an oversimplification of firm activity (Loasby, 1999)