USING NEURAL NETS TO COMBINE INFORMATION SETS IN CORPORATE BANKRUPTCY PREDICTION

We demonstrate that the use of a neural network (NN) model to combine information from corporate financial statements and equity markets provides improved predictive estimates of the probability of corporate bankruptcy. Using performance measures, based on the receiver operating characteristic curve, the forecast combinations from the NN models are demonstrated to outperform the forecasts derived from a forecast combination generated using a logistic regression approach. This result provides support for the use of forecast combinations generated from NN models in the estimation of corporate bankruptcy probabilities as it outperforms the standard approach of forming a hybrid forecasting model which includes all the explanatory variables. Copyright © 2012 John Wiley & Sons, Ltd.     

CORPORATE DIVIDEND POLICY DETERMINANTS: INTELLIGENT VERSUS A TRADITIONAL APPROACH

Dividend is the return that an investor receives when purchasing a company's shares. The decision to pay these dividends to shareholders concerns several other groups of people, such as financial managers, consulting firms, individual and institutional investors, government and monitoring authorities, and creditors, just to name a few. The prediction and modelling of this decision has received a significant amount of attention in the corporate finance literature. However, the methods used to study the aforementioned question are limited to the logistic regression method without any implementation of the advanced and expert methods of data mining. These methods have proven their superiority in other business‐related fields, such as marketing, production, accounting and auditing. In finance, bankruptcy prediction has the vast majority among data‐mining implementations, but to the best of the authors’ knowledge such an implementation does not exist in dividend payment prediction. This paper satisfies this gap in the literature and provides answers that help to understand the so‐called ‘dividend puzzle’. Specifically, this paper provides evidence supporting the hypothesis that data‐mining methods perform better in accuracy measures against the traditional methods used. The prediction of dividend policy determinants provides valuable benefits to all related parties, as they can manage, invest, consult and monitor the dividend policy in a more effective way. Copyright © 2013 John Wiley & Sons, Ltd.     

MULTIDIMENSIONAL DISTANCE‐TO‐COLLAPSE POINT AND SOVEREIGN DEFAULT PREDICTION

By focusing on sovereign defaults, this paper introduces a multidimensional distance‐to‐collapse point based on a two‐step procedure. The first step is nonparametric and provides an early warning system that signals a potential crisis whenever preselected leading indicators exceed specific thresholds. The second is parametric and incorporates the first‐step country default predictors within a probit specification. Such a two‐step procedure generalizes the distance‐to‐default à la Merton within a multidimensional setting, wherein we care about the distance of each indicator from its threshold. Empirical evidence about debt crises of emerging markets over the period 1975–2002 proves that our methodology predicts 80% of the total defaults and non‐defaults in and out of sample. Copyright © 2012 John Wiley & Sons, Ltd.     

HIGH‐FREQUENCY EXCHANGE‐RATE PREDICTION WITH AN ARTIFICIAL NEURAL NETWORK

This paper examines how market microstructure variables can be used to forecast foreign exchange (FX) rates at frequencies of one to several minutes. We use a unique FX dataset of global inter‐dealer electronic transactions and applied the artificial neural network (ANN) as the predicting model. The immediately preceding bid and ask prices are significant factors in these predictions, which is in keeping with market microstructure theory. These microstructure factors have not been tested in an ANN model before. High‐frequency trading strategies based on the ANN model are shown to be profitable even when transaction costs are included. Copyright © 2012 John Wiley & Sons, Ltd.     

DETERMINANTS OF CAPITAL STRUCTURE IN THE UK RETAIL INDUSTRY: A COMPARISON OF MULTIPLE REGRESSION AND GENERALIZED REGRESSION NEURAL NETWORK

Firms need to rely on different financing sources, but the question is how capital structure is determined for a particular industry. Our aim is to undertake an investigation into the factors which determine capital structure in the UK retail industry. Our initial sample consists of 163 (final sample: 100) UK retail companies, using data from 2000 in order to analyse capital structure from 2002 to 2006. Nonlinear models tend to be unduly neglected in capital structure research, and so we apply generalized regression neural networks (GRNNs), which are compared with conventional multiple regressions. We utilize a hold‐out sample for the multiple regressions to make them comparable with the GRNNs. Stability of the data is also confirmed. Our main findings are: net profitability and the depreciation‐to‐sales ratio are key determinants of capital structure based on GRNNs, while two more variables are added in the multiple regressions, namely size and quick ratio; there is strong support for the pecking‐order theory; both root‐mean‐square errors and mean absolute errors are much lower for the GRNNs than those for the multiple regressions for overall, training and testing datasets. The potential benefit of this research to financial managers and investors in the UK retail sector is the identification of the overriding role of net profitability in reducing the financial risk from high levels of gearing. Copyright © 2012 John Wiley & Sons, Ltd.     

Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms

Security indices are the main tools for evaluation of the status of financial markets. Moreover, a main part of the economy of any country is constituted of investment in stock markets. Therefore, investors could maximize the return of investment if it becomes possible to predict the future trend of stock market with appropriate methods. The nonlinearity and nonstationarity of financial series make their prediction complicated. This study seeks to evaluate the prediction power of machine‐learning models in a stock market. The data used in this study include the daily close price data of iShares MSCI United Kingdom exchange‐traded fund from January 2015 to June 2018. The prediction process is done through four models of machine‐learning algorithms. The results indicate that the deep learning method is better in prediction than the other methods, and the support vector regression method is in the next rank with respect to neural network and random forest methods with less error.