PREDICTING NEXT TRADING DAY CLOSING PRICE OF QATAR EXCHANGE INDEX USING TECHNICAL INDICATORS AND ARTIFICIAL NEURAL NETWORKS

Accurate prediction of stock market price is of great importance to many stakeholders. Artificial neural networks (ANNs) have shown robust capability in predicting stock price return, future stock price and the direction of stock market movement. The major aim of this study is to predict the next trading day closing price of the Qatar Exchange (QE) Index using historical data from 3 January 2010 to 31 December 2012. A multilayer perceptron ANN architecture was used as a prediction model with 10 market technical indicators as input variables. The experimental results indicate that ANNs are an effective modelling technique for predicting the QE Index with high accuracy, outperforming the well‐established autoregressive integrated moving average models. To the best of our knowledge, this is the first attempt to use ANNs to predict the QE Index, and its performance results are comparable to, and sometimes better than, many stock market predictions reported in the literature. The ANN model also revealed that the weighted and simple moving averages are the most important technical indicators in predicting the QE Index, and the accumulation/distribution oscillator is the least important such indicator. The analysis results also indicated that the ANNs are resilient to stock market volatility. Copyright © 2014 John Wiley & Sons, Ltd.     

PRICING AND HEDGING SHORT STERLING OPTIONS USING NEURAL NETWORKS

This paper compares the performance of artificial neural networks (ANNs) with that of the modified Black model in both pricing and hedging short sterling options. Using high‐frequency data, standard and hybrid ANNs are trained to generate option prices. The hybrid ANN is significantly superior to both the modified Black model and the standard ANN in pricing call and put options. Hedge ratios for hedging short sterling options positions using short sterling futures are produced using the standard and hybrid ANN pricing models, the modified Black model, and also standard and hybrid ANNs trained directly on the hedge ratios. The performance of hedge ratios from ANNs directly trained on actual hedge ratios is significantly superior to those based on a pricing model, and to the modified Black model. Copyright © 2012 John Wiley & Sons, Ltd.     

A predictive system integrating intrinsic mode functions,artificial neural networks,and genetic algorithms for forecasting S&P500 intra‐day data

There is an abundant literature on the design of intelligent systems to forecast stock market indices. In general, the existing stock market price forecasting approaches can achieve good results. The goal of our study is to develop an effective intelligent predictive system to improve the forecasting accuracy. Therefore, our proposed predictive system integrates adaptive filtering, artificial neural networks (ANNs), and evolutionary optimization. Specifically, it is based on the empirical mode decomposition (EMD), which is a useful adaptive signal‐processing technique, and ANNs, which are powerful adaptive intelligent systems suitable for noisy data learning and prediction, such as stock market intra‐day data. Our system hybridizes intrinsic mode functions (IMFs) obtained from EMD and ANNs optimized by genetic algorithms (GAs) for the analysis and forecasting of S&P500 intra‐day price data. For comparison purposes, the performance of the EMD‐GA‐ANN presented is compared with that of a GA‐ANN trained with a wavelet transform's (WT's) resulting approximation and details coefficients, and a GA‐general regression neural network (GRNN) trained with price historical data. The mean absolute deviation, mean absolute error, and root‐mean‐squared errors show evidence of the superiority of EMD‐GA‐ANN over WT‐GA‐ANN and GA‐GRNN. In addition, it outperformed existing predictive systems tested on the same data set. Furthermore, our hybrid predictive system is relatively easy to implement and not highly time‐consuming to run. Furthermore, it was found that the Daubechies wavelet showed quite a higher prediction accuracy than the Haar wavelet. Moreover, prediction errors decrease with the level of decomposition.     

Artificial Neural Networks Applied to Ratio Analysis in the Analytical Review Process

Experts claim that artificial neural network (ANN) technology can outperform standard statistical methods when applied to examine actual financial data. Researchers have used ANNs to analyze bankruptcy prediction, bond rating and the going-concern problem. Financial firms have employed ANNs commercially to predict commercial bank failures, detect credit card fraud and verify signatures. For accounting and auditing problems, however, application of ANN technology has been limited. Preliminary experiments tested whether an ANN offered improved performance in recognizing material misstatements during the analytical review process of auditing. Four years of audited financial data from a medium-sized distributor were input as data streams to calibrate the ANN across fifteen financial accounts. Researchers compared a presumed lack of actual errors and certain seeded material errors with signals from the ANN analytical review process to evaluate performance. Results were compared to analyses where financial ratios and regression methods were employed as analytical review techniques. Results tentatively suggest that the ANN method recognized patterns within financial accounts more effectively than did financial ratio and regression methods. ANNs applied as a forecasting tool seem useful for identifying patterns that can indicate potential investigations of a firm's unaudited financial data in the current year.     

The efficacy of red flags in predicting the SEC's targets: an artificial neural networks approach

This paper illustrates the application of artificial neural networks (ANNs) to test the ability of selected SAS No. 53 red flags to predict the targets of the SEC investigations. Investors and auditors desire to predict SEC targets because substantial losses in equity value are associated with SEC investigations. The ANN models classify the membership in target (investigated) versus control (non-investigated) firms with an average accuracy of 81%. One reason for the relative success of the ANN models is that ANNs have the ability to ‘learn’ what is important. The participants in financial reporting frauds have incentives to appear prosperous as evidenced by high profitability. In contrast to conventional statistical models with static assumptions, the ANNs use adaptive learning processes to determine what is important in predicting targets. Thus, the ANN approach is less likely to be affected by accounting manipulations. Our ANN models are biased against achieving predictive success because we use only publicly available information. The results confirm the value of red flags, i.e. financial ratios available from trial balance in conjunction with non-financial red flags such as the turnover of CEO, CFO and auditors do have predictive value. © 2000 John Wiley & Sons, Ltd.     

Black–Scholes versus artificial neural networks in pricing FTSE 100 options

This paper compares the performance of Black–Scholes with an artificial neural network (ANN) in pricing European‐style call options on the FTSE 100 index. It is the first extensive study of the performance of ANNs in pricing UK options, and the first to allow for dividends in the closed‐form model. For out‐of‐the‐money options, the ANN is clearly superior to Black–Scholes. For in‐the‐money options, if the sample space is restricted by excluding deep in‐the‐money and long maturity options (3.4% of total volume), then the performance of the ANN is comparable to that of Black–Scholes. The superiority of the ANN is a surprising result, given that European‐style equity options are the home ground of Black–Scholes, and suggests that ANNs may have an important role to play in pricing other options for which there is either no closed‐form model, or the closed‐form model is less successful than is Black–Scholes for equity options. Copyright © 2005 John Wiley & Sons, Ltd.     

Making trading decisions for financial‐engineered derivatives: a novel ensemble of neural networks using information content

Over the last decades, there has been a growing interest in applying artificial intelligence techniques to solve a spectrum of financial problems. A number of studies have shown promising results in using artificial neural networks (ANNs) to guide investment trading. Given the expanding role of ANNs in financial trading, this paper proposes the use of a hybrid neural network, which consists of two independent ANN architectures, and comparatively evaluates its performance against independent ANNs and econometric models in the trading of a financial‐engineered (synthetic) derivative composed of options on foreign exchange futures. We examine the financial profitability and the market timing ability of the competing neural network models and statistically compare their attributes with those based on linear and nonlinear statistical projections. A random walk model and the option pricing method are also included as benchmarks for comparison. Our empirical investigation finds that, for each of the currencies analysed, trading strategies guided by the proposed dual network are financially profitable and yield a more stable stream of investment returns than the other models. Statistical results strengthen the notion that diffusion of information contents and cross‐validation between the independent components within the dual network are able to reduce bias and extreme decision making over the long run. Moreover, the results are robust with respect to different levels of transaction costs. Copyright © 2009 John Wiley & Sons, Ltd.     

Accounting education literature review (1991–1997), part II: students,educational technology,assessment and faculty issues

This paper updates literature reviews by Rebele et al. (1991)and Rebele and Tiller (1986)by reviewing a subset of the accounting education literature published during the period 1991–1997. In a preceding paper (Part I), Rebele et al. (1998)reviewed accounting education articles related to two topics: curriculum and instructional approaches. In this paper (Part II), we review articles related to the topics of students, educational technology, assessment and faculty issues, published primarily in the following five journals: Journal of Accounting Education, Issues in Accounting Education, The Accounting Educators' Journal, Accounting Education: A Journal of Theory, Practice and Research and Accounting Perspectives. Recommendations for future research are offered at the end of each major section. An appendix identifies instructional cases and educational resources published in accounting education journals during the 1991–1997 period.     

STAR and ANN models: forecasting performance on the Spanish “Ibex-35” stock index

This paper studies whether it is possible to exploit the nonlinear behaviour of daily returns on the Spanish Ibex-35 stock index returns to improve forecasts over short and long horizons. In this sense, we examine the out-of-sample forecast performance of smooth transition autoregression (STAR) models and artificial neural networks (ANNs). We use one-step (obtained by using recursive and nonrecursive regressions) and multi-step-ahead forecasting methods. The forecasts are evaluated with statistical and economic criteria. In terms of statistical criteria, we compared the out-of-sample forecasts using goodness of forecast measures and various testing approaches. The results indicate that ANNs consistently surpass the random walk model and, although the evidence for this is weaker, provide better forecasts than the linear AR model and the STAR models for some forecast horizons and forecasting methods. In terms of the economic criteria, we assess the relative forecast performance in a simple trading strategy including the impact of transaction costs on trading strategy profits. The results indicate a better fit for ANN models, in terms of the mean net return and Sharpe risk-adjusted ratio, by using one-step-ahead forecasts. These results show there is a good chance of obtaining a more accurate fit and forecast of the daily stock index returns by using one-step-ahead predictors and nonlinear models, but that these are inherently complex and present a difficult economic interpretation.     

Corporate Reporting in Central and Eastern Europe: Issues,Challenges and Research Opportunities

The purpose of this paper, building upon the papers included in this special section of Accounting in Europe on Corporate reporting in CEE countries and on our knowledge of the region, is to broaden out and open up dialogue and debate about how local institutions are evolving and impact the corporate reporting practices in this under-researched region. We begin by discussing the institutional context for conducting research on corporate reporting by entities in Central and Eastern Europe (CEE), within the broader context of emerging, transitional economies. We also reflect on how research conducted on CEE countries can make a relevant contribution to the international literature, and exemplify by summarizing the research questions and findings of the papers included in the special section. A future research agenda emerges, given the gaps in the international literature and the future research implications suggested in the papers constituting the special section.     

Connecting earnings management to the real World:What happens in the black box of the boardroom?

This British Accounting & Finance Association (BAFA) distinguished academic 2020 plenary address marries the researcher's two main research areas – financial reporting and corporate governance. Like Vivien Beattie (BAFA 2011 distinguished academic), the researcher commenced in the positivist tradition but was increasingly drawn to more qualitative, interdisciplinary perspectives, influencing the paper's positioning.“Accounting choice”, “income smoothing”, “earnings management”, and “earnings manipulation” are terms frequently used in the academic literature. This paper reviews these terms, highlighting the resonances and dissonances between them, and attempts to reconcile varied perspectives in the prior literature. The paper critiques taken-for-granted assumptions underlying this stream of research. The paper then examines prior earnings management research using alternative methodologies to deepen understanding of the four terms in praxis (best practice in practice). The paper reviews prior research on boards of directors using alternative methodologies to those adopted in mainstream corporate governance research, to provide a menu of opportunities to research earnings management inside the “black box” of the boardroom, including proposed research questions for future research. The paper concludes by considering the implications for policymakers and standard setters.     

A comparison of the relative costs of financial distress models: artificial neural networks,logit and multivariate discriminant analysis

This study uses two artificial neural networks (ANNs), categorical learning/instar ANNs and probabilistic (PNN) ANNs, suitable for classification and prediction type issues, and compares them to traditional multivariate discriminant analysis (MDA) and logit to examine financial distress one to three years prior to failure. The results indicate that traditional MDA and logit perform best with the lowest overall error rates. However, when the relative error costs are considered, the ANNs perform better than traditional logit or MDA. Also, as the time period moves farther away from the eventual failure date, ANNs perform more accurately and with lower relative error costs than logit or MDA. This supports the conclusion that for auditors and other evaluators interested in early warning techniques, categorical learning network and probabilistic ANNs would be useful. © 1997 John Wiley & Sons, Ltd.     

Directions in scenario planning literature - A review of the past decades

This paper provides a systematized overview of patterns in the scenario planning literature published in the last decades. Recently, scenario planning has enjoyed a revival, apparent in the ‘boom’ in published research on the matter. Consequently, a major issue that needs to be addressed is how to organize the literature along precise lines. A number of reviews that describe the current status of the body of literature and knowledge on scenario planning have made attempts to respond to such requirements. These studies agree that systematizing the existing literature is a necessary step in developing the field. This paper aims to contribute to this purpose. The review of the academic literature here conducted is thought to be useful for both academics and practitioners. For researchers, this systematic overview will be constructive not only in providing an analysis of the directions of published research but also in setting up a research agenda for the future. For managers and practitioners, it provides a clear outline of firm-related articles and discusses their contribution from a managerial point of view. It also raises awareness with regard to future analytical methods, and in particular, to scenario planning and its potential contribution to the competitiveness of firms. The research was carried out under the research Project Enterprise of the Future of the University of Aveiro.     

Management control packages: a literature review and guidelines for public sector research

ABSTRACT

The effectiveness of a management control (MC) element, such as performance measurement, may depend on other MC elements, such as organizational values and training activities. This is why MC elements should be studied ‘as a package’, instead of as isolated elements. Although examining MC elements as a package has the potential to make significant contributions to ongoing debates, this approach is largely absent in the academic public sector management literature. This paper reviews the MC package literature, examines its implications for public sector research and practice, and aids future research by discussing research design choices and research directions.     

Betriebswirtschaftliche Forschungsbeiträge zum Management in gesetzlichen Krankenkassen – ein Überblick

This paper reviews all published business research into the management of German statutory health insurance. A comprehensive systematic search of literature was done from 1996 to September 2008. Notably, almost all identified studies were published in health or insurance specific journals or as practical book contributions. In general management journals, research into the health insurance sector is seldom. All investigated management fields of the health insurance are substantially lacking research. Findings from general economic research or research into health insurance in other countries cannot be transferred easily. Therefore research has to adapt concepts from general economic literature or develop new concepts for future studies of the health insurance sector in Germany.     

Human information processing research in accounting: The state of the art

Accountant's interest in decision making is resulting in a growing body of research based upon theories and models from the psychology of information processing. This paper presents a classification of information processing variables which have been studied by psychologists which serves as a framework for organizing information processing research in accounting. It then reviews and evaluates the existing accounting literature based upon three research approaches: the lens model, probabilistic judgment, and cognitive style. Finally, directions for future research in this area are suggested.     

Accounting education literature review (2010–2012)

This review of the accounting education literature includes 291 articles and 104 instructional cases published over the 3-year period, 2010–2012, in six journals: (1) Journal of Accounting Education, (2) Accounting Education: An International Journal, (3) Advances in Accounting Education, (4) Global Perspectives on Accounting Education, (4) Issues in Accounting Education, and (6) The Accounting Educators’ Journal. This article updates prior literature reviews by organizing and summarizing recent additions to the accounting education literature. These reviews are categorized into four sections corresponding to traditional lines of inquiry: (1) curriculum, assurance of learning (AOL), and instruction; (2) educational technology; (3) faculty issues; and (4) students. Suggestions for educational research in all content areas are presented. For the first time in this series of literature reviews, we assess the data collection and empirical analysis methods and recommend adoption of more rigorous techniques moving forward. Articles presenting teaching materials and educational cases published in the same six journals during 2010–2012 are presented in an appendix, categorized by the courses for which they are appropriate.     

Two Decades of Research on Comparative Management Accounting – Achievements and Future Directions

Comparative Management Accounting research has explored the applications, transferability and cross‐country adaptation of management accounting practices since the early 1990s. These efforts have been triggered by findings indicating that the adoption of foreign management accounting techniques generates comparative advantages in environments characterised by increasing global business competition. However, the literature on Comparative Management Accounting is heterogeneous in terms of its methods and theory and tends to focus on a great variety of practices and instruments. Therefore, this paper reviews the existing literature, elaborating on the dimensions of comparative research in management accounting. In addition, it provides insights into shortcomings and developments in the field and identifies directions for future research and implications for corporate practice.