Intelligent Systems in Accounting, Finance and Management最新文献

This paper is focused on one of the fundamental problems in financial time-series analysis; namely, the identification of the historical bull and bear phases. We start with the proof that the trend-cycle can be well estimated using the technique of a higher degree fuzzy transform. Then, we suggest a mathematical definition of the bull and bear phases and provide a novel technique for their identification. As a consequence, the turning points (i.e. the points where the market changes its phase) are detected. We illustrate our methodology on several examples.

The business models of banks are often seen as the result of a variety of simultaneously determined managerial choices, such as those regarding the types of activities, funding sources, level of diversification, and size. Moreover, owing to the fuzziness of data and the possibility that some banks may combine features of different business models, the use of hard clustering methods has often led to poorly identified business models. In this paper we propose a framework to deal with these challenges based on an ensemble of three unsupervised clustering methods to identify banking business models: fuzzy c-means (which allows us to handle fuzzy clustering), self-organizing maps (which yield intuitive visual representations of the clusters), and partitioning around medoids (which circumvents the presence of data outliers). We set up our analysis in the context of the European banking sector, which has seen its regulators increasingly focused on examining the business models of supervised entities in the aftermath of the twin financial crises. In our empirical application, we find evidence of four distinct banking business models and further distinguish between banks with a clearly defined business model (core banks) and others (non-core banks), as well as banks with a stable business model over time (persistent banks) and others (non-persistent banks). Our proposed framework performs well under several robustness checks related with the sample, clustering methods, and variables used.

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.

In financial trading, technical and quantitative analysis tools are used for the development of decision support systems. Although these traditional tools are useful, new techniques in the field of machine learning have been developed for time-series forecasting. This paper analyses the role of attribute selection on the development of a simple deep-learning ANN (D-ANN) multi-agent framework to accomplish a profitable trading strategy in the course of a series of trading simulations in the foreign exchange market. The paper evaluates the performance of the D-ANN multi-agent framework over different time spans of high-frequency (HF) intraday asset time-series data and determines how a set of the framework attributes produces effective forecasting for profitable trading. The paper shows the existence of predictable short-term price trends in the market time series, and an understanding of the probability of price movements may be useful to HF traders. The results of this paper can be used to further develop financial decision-support systems and autonomous trading strategies for the financial market.

Money exchange is one of the most common day-to-day activities performed by humans in the daily market. This paper presents an approach to money tracking through a blockchain. The proposed approach consists of three main components: serial number localization, serial number recognition, and a blockchain to store all transactions and ownership transfers. The approach was tested with a total of 110 banknotes of different currency types and achieved an average accuracy of 91.17%. We conducted a user study in real-time with 21 users, and the mean accuracy across all users was 86.42%. Each user gave us feedback on the proposed approach, and most of them welcomed the idea.

The literature on corporate governance (CG) has been expanding at an unprecedented rate since major corporate scandals surfaced, such as Enron, WorldCom, and HealthSouth. Corresponding with accounting's important role in CG, accounting scholars increasingly have investigated CG in recent years, so the body of literature is growing. Although previous attempts have been made to summarize extant literature on CG via reviews, none of these attempts has utilized recent developments in text analyses and natural language processing. This study uses latent semantic and topic analyses to address this research gap by analysing abstracts from 1,399 articles in all accounting journals that the Australian Business Deans Council (ABDC) has rated A and A*. The ABDC journal list is widely recognized as a journal-quality indicator across many universities worldwide. The analyses revealed 10 distinct research topics on CG in the ABDC's top accounting journals. The results presented include the five most representative articles for each topic, as distinguished by topic scores. This study carries important practice and policy implications, as it reveals major research streams and exhibits how researchers respond to various CG problems.

We implement an efficient methodology for extracting themes from Securities Exchange Commission 13D filings using aspects of human-assisted active learning and long short-term memory (LSTM) neural networks. Sentences from the ‘Purpose of Transaction’ section of each filing are extracted and a randomly chosen subset is labelled based on six filing themes that the existing literature on shareholder activism has shown to have an impact on stock returns. We find that an LSTM neural network that accepts sentences as input performs significantly better, with precision of 77%, than an alternately specified neural network that uses the common bag of words approach. This indicates that both sentence structure and vocabulary are important in classifying SEC 13D filings. Our study has important implications, as it addresses the recent cautions raised in the literature that analysis of finance and accounting-related text sources should move beyond bag-of-words approaches to alternatives that incorporate the analysis of word sense and meaning reflecting context.

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.