Chris Charalambous

Predicting corporate failure: empirical evidence for the UK

The main purpose of this study is to examine the incremental information content of operating cash flows in predicting financial distress and thus develop reliable failure prediction models for UK public industrial firms. Neural networks and logit methodology were employed to a dataset of fifty-one matched pairs of failed and non-failed UK public industrial firms over the period 1988–97. The final models are validated using an out-of-sample-period ex-ante test and the Lachenbruch jackknife procedure. The results indicate that a parsimonious model that includes three financial variables, a cash flow, a profitability and a financial leverage variable, yielded an overall correct classification accuracy of 83% one year prior to the failure. In summary, our models can be used to assist investors, creditors, managers, auditors and regulatory agencies in the UK to predict the probability of business failure.

Pricing and trading European options by combining artificial neural networks and parametric models with implied parameters

We compare the ability of the parametric Black and Scholes, Corrado and Su models, and Artificial Neural Networks to price European call options on the S&P 500 using daily data for the period January 1998 to August 2001. We use several historical and implied parameter measures. Beyond the standard neural networks, in our analysis we include hybrid networks that incorporate information from the parametric models. Our results are significant and differ from previous literature. We show that the Black and Scholes based hybrid artificial neural network models outperform the standard neural networks and the parametric ones. We also investigate the economic significance of the best models using trading strategies (extended with the Chen and Johnson modified hedging approach). We find that there exist profitable opportunities even in the presence of transaction costs.

The Prediction of Earnings Using Financial Statement Information: Empirical Evidence With Logit Models and Artificial Neural Networks

In the past three decades, earnings have been one of the most researched variables in accounting. Empirical research provided substantial evidence on its usefulness in the capital markets but evidence in predicting earnings has been limited, yielding inconclusive results. The purpose of this study is to validate and extend prior research in predicting earnings by examining aggregate and industry-specific data. A sample of 10,509 firm-year observations included in the Compustat database for the period 1982–91 is used in the study. The stepwise logistic regression results of the present study indicated that nine earnings and non-earnings variables can be used to predict earnings. These predictor variables are not identical to those reported in prior studies. These results are also extended to the manufacturing industry. Two new variables are identified to be significant in this industry. Moreover, an Artificial Neural Network (ANN) approach is employed to complement the logistic regression results. The ANN models performance is at least as high as the logistic regression models predictive ability.

Application of feature extractive algorithm to bankruptcy prediction

This study uses the feature selection algorithm proposed by Setiono and Liu (1997) to select the most relevant features for the bankruptcy prediction problem. The method uses a feedforward neural network with one hidden layer to decide which features to be removed. Our data consists of 139 matched pair of bankrupt and nonbankrupt US firms for the period 1983-1994. The results of this study indicate that the final neural network obtained with reduced number of inputs gives significantly better prediction results than the one that uses all initial features.

Flexible working arrangements in context: An empirical investigation through self-organizing maps

This is one of the first studies to utilize Kohonens self-organizing maps on flexible work arrangements (FWAs), employee turnover and absenteeism within different national contexts and an array of organizational factors. While the majority of FWAs did not reduce significantly employee turnover or absenteeism, country and industry were significant contextual variables in FWA use: we deciphered six main country regions, where service and manufacturing organizations were important to FWA preferences. We found a curvilinear relationship between turnover and shift-work among manufacturing firms regardless of country: turnover decreases at low levels and increases at high levels of shift-work. We also found strong positive relationships between weekend work and turnover among manufacturing firms regardless of country and firms in the region comprising of Germany, Austria, Sweden, Finland, Denmark, Czech Republic and Belgium. Finally, we found consistently high concentration of organizations with low absenteeism throughout certain industries and countries: noteworthy are service organizations in the Netherlands and manufacturing organizations in Australia. The results demonstrate the contextuality of FWA use across countries and industries, and the usefulness of SOMs for research within human resource management.

European Option Pricing by Using the Support Vector Regression Approach

We explore the pricing performance of Support Vector Regression for pricing S&P 500 index call options. Support Vector Regression is a novel nonparametric methodology that has been developed in the context of statistical learning theory, and until now it has not been widely used in financial econometric applications. This new method is compared with the Black and Scholes (1973) option pricing model, using standard implied parameters and parameters derived via the Deterministic Volatility Functions approach. The empirical analysis has shown promising results for the Support Vector Regression models.

Implied non-recombining trees and calibration for the volatility smile

In this paper we capture the implied distribution from option market data using a non-recombining (binary) tree, allowing the local volatility to be a function of the underlying asset and of time. The problem under consideration is a non-convex optimization problem with linear constraints. We elaborate on the initial guess for the volatility term structure and use nonlinear constrained optimization to minimize the least squares error function on market prices. The proposed model can accommodate European options with single maturities and, with minor modifications, options with multiple maturities. It can provide a market-consistent tree for option replication with transaction costs (often this requires a non-recombining tree) and can help pricing of exotic and Over The Counter (OTC) options. We test our model using options data for the FTSE 100 index obtained from LIFFE. The results strongly support our modelling approach.

Critical Assessment of Option Pricing Methods Using Artificial Neural Networks

In this paper we compare the predictive ability of the Black-Scholes Formula (BSF) and Artificial Neural Networks (ANNs) to price call options by exploiting historical volatility measures. We use daily data for the S&P 500 European call options and the underlying asset and furthermore, we employ nonlinearly interpolated risk-free interest rate from the Federal Reserve board for the period 1998 to 2000. Using the best models in each sub-period tested, our preliminary results demonstrate that by using historical measures of volatility, ANNs outperform the BSF. In addition, the ANNs performance improves even more when a hybrid ANN model is utilized. Our results are significant and differ from previous literature. Finally, we are currently extending the research in order to: a) incorporate appropriate implied volatility per contract with the BSF and ANNs and b) investigate the applicability of the models using trading strategies.

Hybrid artificial neural networks for efficient valuation of real options and financial derivatives

Abstract.A hybrid valuation methodology is proposed and tested for improving the efficiency of contingent claims pricing by combining Artificial Neural Networks (ANN) and conventional parametric option pricing techniques. With one application on financial derivatives and one on real options the method’s superiority is demonstrated. The resulting efficiency is instrumental for real time applications.