Artificial neural networks to predict share prices on the Johannesburg stock exchange

The use of historical data to build models for stock market prediction has been extensively researched. Artificial Neural Networks (ANNs) bring new opportunities for predicting stock markets, and is now one of the leading techniques used for time series and specifically stock market prediction. This study explored the application of ANNs to predict share prices in the banking sector of the South African Johannesburg Stock Exchange (JSE). This study used three companies, i.e. Standard Bank, Nedbank and First National Bank, listed on the JSE as case studies for the use of ANNs for predicting the closing share price for the next day, week and month. Historical share price data from the JSE was integrated with datasets of external factors that influence market. The external factors considered in this study include index data from NASDAQ, the JSE top 40 and all share indexes, the exchange rate and the business cycle indicator (BCI) values from the South African Reserve Bank. Comparative analysis were conducted between traditional regression models and ANN models using the lagged share price as input variable. The effect on prediction performance of using external factors as additional input variables was also explored. The ANN models using only the share price was found in general to perform better than both traditional models and ANNs that used the external factors as additional input variables. The average next month prediction model produced a noticeably smaller prediction error compared to the next week, and next day prediction models for all three banks. The results showed that the introduction of external factors as additional input variables did not lead to an improved prediction performance, over models that used only the share price. This study also highlights the importance of using an appropriate validation method and evaluating model stability for evaluating and developing ANN models for share price prediction in time series data. The results contribute to existing research that indicate that an ANN is more effective than a regression method for predicting banking share prices, and that these predictive models have potential for supporting investment decision making

Service Quality and Customer Loyalty in a Post-Crisis Context. Prediction-Oriented Modeling to Enhance the Particular Importance of a Social and Sustainable Approach

Research into the influence of service quality on customer loyalty has typically focused on confirming isolated direct causal influences regarding particular dimensions of quality, usually undertaken in the context of positive, firm-customer relations. The present study extends analysis of these factors through a new lens. First, the study was undertaken in a market context following a crisis that has had far-reaching consequences for customers’ relational behaviors. We explore the case of the Spanish banking industry, a sector that accurately reflects these new relational conditions, including a rising demand for more socially responsible banking. Second, we propose a holistic model that combines the effects of four key factors associated with service quality (outcome, personnel, servicescape and social qualities). We also apply an innovative predictive methodological technique using partial least squares (PLS) and qualitative comparative analysis (QCA) that enables us not only to determine the direct causal effects among variables, but also to consider different scenarios in which to predict customer loyalty. The results highlight the role of outcome and social qualities. The novelty of the social qualities factor helps to underscore the importance of social, ethical and sustainable practices to customer loyalty, although personnel and servicescape qualities must also be present to improve the predictive capability of service quality on loyalty

Priorities and Sequencing in Privatization: Theory and Evidence from the Czech Republic

While privatization of state-owned enterprises has been one of the most important aspects of economic transition from a centrally planned to a market system, no transition economy has privatized all its firms simultaneously. This raises the issue of whether governments strategically privatize firms. In this paper we examine theoretically and empirically the determinants of the sequencing of privatization. First, we develop new and adapt existing theoretical models in order to obtain testable predictions about factors that may affect the sequencing of privatization. In doing so, we characterize potentially competing government objectives as (i) maximizing sales revenue from privatization or public goodwill from transferring shares of firms to voters, (ii) increasing economic efficiency, and (iii) reducing political costs due to layoffs. Next, we use an enterprise-level data set from the Czech Republic to test the competing theoretical predictions about which firm characteristics affect the sequencing of privatization. We find strong evidence that more profitable firms were sold first. This suggests that the government sequenced the sale of firms in a way that is consistent with our theories of sale revenue maximization and/or maximizing public goodwill from subsidized share transfers to citizens. Our results are also consistent with Shleifer and Vishny's (1994) prescription for increasing efficiency when there are political costs to employment losses caused by privatization. We also find that the Glaeser-Scheinkman (1996) recommendations for increasing efficiency by privatizing first firms subject to large informational shocks are consistent with our results. Finally, our findings are inconsistent with the government pursuing a static Pareto efficiency objective. In addition to enhancing the general understanding of privatization, our evidence suggests that many empirical studies of the effects of privatization on firm performance may suffer from selection bias since privatized firms are likely to have observable and unobservable characteristics that make them more profitable than firms that remain under state ownership.http://deepblue.lib.umich.edu/bitstream/2027.42/39707/3/wp323.pd

Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises

The major success of fuzzy logic in the field of remote control opened the door to its application in many other fields, including finance. However, there has not been an updated and comprehensive literature review on the uses of fuzzy logic in the financial field. For that reason, this study attempts to critically examine fuzzy logic as an effective, useful method to be applied to financial research and, particularly, to the management of banking crises. The data sources were Web of Science and Scopus, followed by an assessment of the records according to pre-established criteria and an arrangement of the information in two main axes: financial markets and corporate finance. A major finding of this analysis is that fuzzy logic has not yet been used to address banking crises or as an alternative to ensure the resolvability of banks while minimizing the impact on the real economy. Therefore, we consider this article relevant for supervisory and regulatory bodies, as well as for banks and academic researchers, since it opens the door to several new research axes on banking crisis analyses using artificial intelligence techniques

Could early warning systems have helped to predict the sub prime crisis?

One of the features of the sub-prime crisis, that began in August 2007, was its unexpected nature. It came as a surprise not only to most financial market participants but also in some degree to the policy community. In this context, we seek to assess whether early warning systems based on the logit and binomial tree approaches on the UK and US economies could have helped to warn about the crisis. We also consider a “check list approach” of indicators based on history. Although not all of the complementary approaches are successful, we contend that our work suggests that a broadening of approaches of macroprudential analysis is appropriate

Statistical modelling to predict corporate default for Brazilian companies in the context of Basel II using a new set of financial ratios

This paper deals with statistical modelling to predict failure of Brazilian companies in the light of the Basel II definition of default using a new set of explanatory variables. A rearrangement in the official format of the Balance Sheet is put forward. From this rearrangement a framework of complementary non-conventional ratios is proposed. Initially, a model using 22 traditional ratios is constructed. Problems associated with multicollinearity were found in this model. Adding a group of 6 non-conventional ratios alongside traditional ratios improves the model substantially. The main findings in this study are: (a) logistic regression performs well in the context of Basel II, yielding a sound model applicable in the decision making process; (b) the complementary list of financial ratios plays a critical role in the model proposed; (c) the variables selected in the model show that when current assets and current liabilities are split into two sub-groups - financial and operational - they are more effective in explaining default than the traditional ratios associated with liquidity; and (d) those variables also indicate that high interest rates in Brazil adversely affect the performance of those companies which have a higher dependency on borrowing