Soft computing in investment appraisal

Standard financial techniques neglect extreme situations and regards large market shifts as too unlikely to matter. Such approach accounts for what occurs most of the time in the market, but does not reflect the reality, as major events happen in the rest of the time and investors are ‘surprised’ by ‘unexpected’ market movements. An alternative fuzzy approach permits fluctuations well beyond the probability type of uncertainty and allows one to make fewer assumptions about the data distribution and market behaviour. Fuzzifying the present value criteria, we suggest a measure of the risk associated with each investment opportunity and estimate the project’s robustness towards market uncertainty. The procedure is applied to thirty-five UK companies traded on the London Stock Exchange and a neural network solution to the fuzzy criterion is provided to facilitate the decision-making process. Finally, we suggest a specific evolutionary algorithm to train a fuzzy neural net - the bidirectional incremental evolution will automatically identify the complexity of the problem and correspondingly adapt the parameters of the fuzzy network

Applied Computational Intelligence for finance and economics

This article introduces some relevant research works on computational intelligence applied to finance and economics. The objective is to offer an appropriate context and a starting point for those who are new to computational intelligence in finance and economics and to give an overview of the most recent works. A classification with five different main areas is presented. Those areas are related with different applications of the most modern computational intelligence techniques showing a new perspective for approaching finance and economics problems. Each research area is described with several works and applications. Finally, a review of the research works selected for this special issue is given.Publicad

Neural Networks and their application in the fields of corporate finance

This article deals with the usefulness of neuronal networks in the area of corporate finance. Firstly, we highlight the initial applications of neural networks. One can distinguish two main types: layer networks and self organizing maps. As Altman al. (1994) underlined, the use of layer networks has improved the reclassifying rate in models of bankruptcy forecasting. These first applications improved bankruptcy forecasting by showing a relationship between capital structure and corporate performance. The results highlighted in our second part, show the pertinence of the use of the algorithm of Kohonen applied to qualitative variables (KACM). More particularly, in line with Altman (1968, 1984), one can suggest the coexistence of negative and positive effects of financial structure on performance. This result allows us to question scoring models and to conclude as to a non-linear relationship. In a larger framework, the methodology of Kohonen has allowed a better perception of the factors able to explain the leasing financing (Cottrell et al., 1996). The objective is here to explain the factors of the choice between leasing and banking loans. By using different variables, we highlight the characteristics of firms which most often use leasing. The corporate financing policy could be explained by: the cost of the financing, advantages of leasing or by the minimization of agency costs in leasing, we highlight a relationship between resorting to leasing and credit rationing.neural netwoks, SOM, corporate finance

Applying fuzzy theory concepts to the analysis of employment diversification of farm households: methodological considerations

The Deliverable 7.2 (D7.2) of the SCARLED project provides methodological considerations for applying fuzzy set theory to the analysis of employment diversification of farm households. It presents a Mamdani's type fuzzy inference model and describes its application within the project's framework. The model consists of ten variables that are grouped into the four factors: (i) necessity to diversify, (ii) internal preconditions, (iii) external preconditions, and (iv) attitudes. The coherence of these four factors with the integrated framework for the analysis of nonfarm rural employment is discussed. The model will be realised in the Fuzzy Logic Toolbox from MATLAB®. Forty four membership functions and 138 rules are going to be implemented, tested, and adapted with survey data from the five countries: Bulgaria, Hungary, Poland, Romania, and Slovenia. The final model will be used to assess the diversification potential of 15 regions in these countries. --

"General Conclusions: From Crisis to A Global Political Economy of Freedom"

In this chapter I sum up the basic problems for a new theory of 21st century financial crises in light of the Asian and other subsequent crises. My conclusion is that there are indeed deep structural causes at work in the global markets that affect the political economy of countries and regions. Methodologically, new concepts, models and theories are constructed, at ;least partially, to conduct further meaningful empirical work leading to relevant policy conclusions. This book belongs to the beginning of intellectual efforts in this direction. Political economic analyses at the country level, CGE modeling within a new theoretical framework, and neural network approach to learning in a bounded rationality framework point to a role for reforms at the state, firm and regional level. A new type of institutional analysis called the 'extended panda's thumb approach' leads to the recommendation that path dependent hybrid structures need to be constructed at the local, national, regional and global level to lead to a new global financial architecture for the prevention--- and if prevention fails--- management of financial crises.

Investment Risk Appraisal

Standard financial techniques neglect extreme situations and regards large market shifts as too unlikely to matter. This approach may account for what occurs most of the time in the market, but the picture it presents does not reflect the reality, as the major events happen in the rest of the time and investors are ‘surprised’ by ‘unexpected’ market movements. An alternative fuzzy approach permits fluctuations well beyond the probability type of uncertainty and allows one to make fewer assumptions about the data distribution and market behaviour. Fuzzifying the present value criteria, we suggest a measure of the risk associated with each investment opportunity and estimate the project’s robustness towards market uncertainty. The procedure is applied to thirty-five UK companies and a neural network solution to the fuzzy criterion is provided to facilitate the decision-making process. Finally, we discuss the grounds for classical asset pricing model revision and argue that the demand for relaxed assumptions appeals for another approach to modelling the market environment

Investment-cash flow sensitivities, credit rationing and financing constraints

The controversy over whether investment-cash flow sensitivity is a good indicator of financing constraints is still unresolved. We tackle it from several different angles and cross-validate our analysis with both balance sheet and qualitative data on self-declared credit rationing and financing constraints. Our qualitative information shows that (self-declared) credit rationing is (weakly) related to both traditional a priori factors – such as firm size, age and location – and lenders’ rational decisions based on their credit risk models. We use our qualitative information on firms that were denied credit to provide evidence relevant to the investment-cash flow sensitivity debate. Our results show that self-declared credit rationing significantly discriminates between firms that do and do not have such sensitivity, whereas a priori criteria do not. The same result does not apply when we consider the wider group of financially constrained firms (which do not seem to have a higher investment-cash flow sensitivity), which supports the more recent empirical evidence in this direction.financing constraints; credit rationing; investment/cash flow sensitivity

The determinants of vulnerability to crisis: Country-specific factors versus regional factors

We investigate the determinants of exchange market pressures (EMP) for some new EU member states in two dimensions of national and regional levels, where macroeconomic and financial variables are considered as potential sources. The regional common factors are extracted from national levels of these variables by using the dynamic factor analysis. In a dynamic linear model, we find the statistically significant impact of the regional factor only in stock prices on the EMP for most of these economies. Overall, it highlights the importance of country-specific factors to defend against vulnerability in their external sector

The determinants of vulnerability to currency crises: country-specific factors versus regional factors

We investigate the determinants of exchange market pressures (EMP) for some new EU member states at both the national and regional levels, where macroeconomic and financial variables are considered as potential sources. The regional common factors are extracted from these variables by using dynamic factor analysis. The linear empirical analysis, in general, highlights the importance of country-specific factors to defend themselves against vulnerability in their external sectors. Yet, given a significant impact of the common component in credit on EMP, a contagion effect is apparent through the conduit of credit market integration across these countries under investigation