Intelligent Systems in Accounting, Finance and Management最新文献

In this paper, we focus on the French cancel order tax implemented on 1 August 2012. We question the effectiveness of the modified tax with no exemptions and we analyze its impact on market quality, measured by liquidity, volatility and efficiency. Additionally, this paper raises the question whether this tax leads to a reduction of high-frequency trading (HFT) activities and a decline in trading volume. Based on our findings we report that introduction of cancel order tax only slightly reduces HFT activities, but it significantly affects market liquidity, increases market volatility and leads to deteriorating market efficiency. We conclude that it is difficult to dissuade investors from entering into unproductive trades and eliminate negative outputs of HFT (such as price manipulations) through tax, without altering the benefits of HFT like liquidity provision and efficient price discovery.

Cruise tourism represents a strategic sector for the economic growth of several countries, impacting on different direct and indirect markets. The arrival of cruises in a city represents an unmissable opportunity to increment its tourist market penetration. Nevertheless, the management of an unforeseen number of passengers that need to visit a city in a short time may also have a negative impact, so reducing the expected benefits. This is mainly due to the difficulty of taking the right decisions when organizing the dispatching of passengers in different city areas since these decisions depend on several conditions that can also dynamically occur, and may have an impact on different city sectors. Here, a decision support system is proposed to help involved stakeholders to make decisions to plan passengers' transportation in the city and also to evaluate the consequences for the city if the plans are really implemented. The system is designed according to the multi-agent paradigm, so allowing one to easily manage the necessary coordination among different entities and data sources that are usually distributed and need to cooperate to provide useful suggestions. In addition, a prototype of a web-based application is provided to end users, so that it can run on heterogeneous platforms, and it can be easily accessed by different users from different devices, as it is the case for the considered application domain.

Prediction of exchange rates has been a topic for debate in economic literature since the late 1980s. The recent development of machine learning techniques has spurred a plethora of studies that further improves the prediction models for currency markets. This high-tech progress may create challenges for market efficiency along with information asymmetry and irrationality of decision-making. This technological bias emerges from the fact that recent innovative approaches have been used to solve trading tasks and to find the best trading strategies. This paper demonstrates that traders can leverage technological bias for financial market forecasting. Those traders who adapt faster to the changes in market innovations will get excess returns. To support this hypothesis we compare the performance of deep learning methods, shallow neural networks with baseline prediction methods and a random walk model using daily closing rate between three currency pairs: Euro and US Dollar (EUR/USD), British Pound and US Dollar (GBP/USD), and US Dollar and Japanese Yen (USD/JPY). The results demonstrate that deep learning achieves higher accuracy than alternate methods. The shallow neural network outperforms the random walk model, but cannot surpass ARIMA accuracy significantly. The paper discusses possible outcomes of the technological shift for financial market development and accounting conforming also to adaptive market hypothesis.

A two-step system is presented to improve prediction of telemarketing outcomes and to help the marketing management team effectively manage customer relationships in the banking industry. In the first step, several neural networks are trained with different categories of information to make initial predictions. In the second step, all initial predictions are combined by a single neural network to make a final prediction. Particle swarm optimization is employed to optimize the initial weights of each neural network in the ensemble system. Empirical results indicate that the two-step system presented performs better than all its individual components. In addition, the two-step system outperforms a baseline one where all categories of marketing information are used to train a single neural network. As a neural networks ensemble model, the proposed two-step system is robust to noisy and nonlinear data, easy to interpret, suitable for large and heterogeneous marketing databases, fast and easy to implement.

Business models are economic models that describe the rationale of why organizations create and deliver value. These models focus on what organizations offer and why. Business process models capture business activities and the ways in which they are accomplished (i.e. their coordination). They explain who is involved in the activities, and how and when these activities should be performed. This paper discusses the alignment between business models and business process models. It proposes a novel systematic method for extracting a value chain (i.e. business model) expressed in the Resources, Events, Agents (REA) ontology from a business process model expressed in Business Process Model and Notation™. Our contribution is twofold: (1) from a theoretical standpoint we identified a set of structural and behavioural patterns that enable us to infer the corresponding REA value chain; (2) from a pragmatic perspective, our approach can be used to derive useful knowledge about the business process and serve as a starting point for business analysis.

Historically, microfinance institutions (MFIs) have played a significant social role by helping people at the base of the socio-economic pyramid escape from social exclusion through the creation of microenterprises. However, international banks have recently started competing in the microfinance sector. In this adverse environment, MFI management tools should be more innovative and technologically advanced to increase efficiency, solvency and profitability and to compete with commercial banks on equal terms. This study therefore strives to develop a credit-risk management tool based on a multilayer perceptron (MLP) credit-scoring model for a Peruvian MFI, and to calculate the capital requirements and microcredit pricing on both internal ratings-based (IRB) and standardized approaches, analysing the impact of these models on the management of the MFI. Our findings show that the implementation of an IRB approach with default probabilities obtained from an MLP credit-scoring model produces the best benefit by the MFIs in terms of higher accuracy (reduction of misclassification costs by 13.78%), lower capital requirements (in the range of 8.5–78%) and the best risk-adjusted interest rates. Furthermore, with the establishment of interest rates adjusted to the real risk of each client, MFIs are fairer and more socially engaged by preventing economically viable low-risk projects from becoming unviable due to excessive interest rates. This leads to the creation of more small businesses by people from the base of the socio-economic pyramid and greater economic development and social cohesion. The IRB model should therefore be implemented to improve MFI solvency, profitability, efficiency, survival, management and social performance.

The scope of this manuscript is to present a new short-term financial forecasting and trading tool: the Gene Expression Programming (GEP) Trader Tool. It is based on the gene expression programming algorithm. This algorithm is based on a genetic programming approach, and provides supreme statistical and trading performance when used for modelling and trading financial time series. The GEP Trader Tool is offered through a user-friendly standalone Java interface. This paper applies the GEP Trader Tool to the task of forecasting and trading the future contracts of FTSE100, DAX30 and S&P500 daily closing prices from 2000 to 2015. It is the first time that gene expression programming has been used in such massive datasets. The model's performance is benchmarked against linear and nonlinear models such as random walk model, a moving-average convergence divergence model, an autoregressive moving average model, a genetic programming algorithm, a multilayer perceptron neural network, a recurrent neural network a higher order neural network. To gauge the accuracy of all models, both statistical and trading performances are measured. Experimental results indicate that the proposed approach outperforms all the others in the in-sample and out-of-sample periods by producing superior empirical results. Furthermore, the trading performances are improved further when trading strategies are imposed on each of the models.

The aim of this paper is to compare several predictive models that combine features selection techniques with data mining classifiers in the context of credit risk assessment in terms of accuracy, sensitivity and specificity statistics. The t-statistic, Battacharrayia statistic, the area between the receiver operating characteristic, Wilcoxon statistic, relative entropy, and genetic algorithms were used for the features selection task. The selected features are used to train the support vector machine (SVM) classifier, backpropagation neural network, radial basis function neural network, linear discriminant analysis and naive Bayes classifier. Results from three datasets using a 10-fold cross-validation technique showed that the SVM provides the best accuracy under all features selections techniques adopted in the study for all three datasets. Therefore, the SVM is an attractive classifier to be used in real applications for bankruptcy prediction in corporate finance and financial risk management in financial institutions. In addition, we found that our best results are superior to earlier studies on the same datasets.

We present a novel approach for analysing the qualitative content of annual reports. Using natural language processing techniques we determine if sentiment expressed in the text matters in fraud detection. We focus on the Management Discussion and Analysis (MD&A) section of annual reports because of the nonfactual content present in this section, unlike other components of the annual reports. We measure the sentiment expressed in the text on the dimensions of polarity, subjectivity, and intensity and investigate in depth whether truthful and fraudulent MD&As differ in terms of sentiment polarity, sentiment subjectivity and sentiment intensity. Our results show that fraudulent MD&As on average contain three times more positive sentiment and four times more negative sentiment compared with truthful MD&As. This suggests that use of both positive and negative sentiment is more pronounced in fraudulent MD&As. We further find that, compared with truthful MD&As, fraudulent MD&As contain a greater proportion of subjective content than objective content. This suggests that the use of subjectivity clues such as presence of too many adjectives and adverbs could be an indicator of fraud. Clear cases of fraud show a higher intensity of sentiment exhibited by more use of adverbs in the “adverb modifying adjective” pattern. Based on the results of this study, frequent use of intensifiers, particularly in this pattern, could be another indicator of fraud. Moreover, the dimensions of subjectivity and intensity help in accurately classifying borderline examples of MD&As (that are equal in sentiment polarity) into fraudulent and truthful categories. When taken together, these findings suggest that fraudulent MD&As in contrast to truthful MD&As contain higher sentiment content. Copyright © 2016 John Wiley & Sons, Ltd.

Digitization and the related datafication produce huge amounts of data. Organizations have started to exploit these new data in order to gain benefits. Exploring this ‘big data jungle’ is a new area for both scholars and practitioners, and the experiences of early adopters are valuable. This paper analyses big data use cases described in the academic literature by using computerized content analysis methods. Based on the analysis results, we have conceptualized themes and guidelines of big data in the context of an organization, thus contributing to the emerging research of big data. In addition to the realized benefits, the case studies reveal issues regarding technology, skills, organizational culture and decision-making processes. The paper also points out several new research avenues, acts as a reference collection to big data case studies found in academic sources, and bridges theory and practice by pointing out several topics that practitioners should consider. Copyright © 2016 John Wiley & Sons, Ltd.