Particle swarm optimization approach to portfolio construction

Particle swarm optimization (PSO) is an artificial intelligence technique that can be used to find approximate solutions to extremely difficult or impossible numeric optimization problems. Recently, PSO algorithms have been widely used in solving complex financial optimization problems. This paper presents a PSO approach to solve a portfolio construction problem, since this methodology is a population-based heuristic algorithm that is suitable for solving high-dimensional constrained optimization problems. The computational results show that PSO algorithms have advantages in optimizing the Sortino ratio, especially in speed, when the size of the portfolio is large.     

智能财务分析与诊断机器人的开发及实证检验——来自我国A股上市公司的经验证据

近年来,随着人工智能技术的发展,金融、财务、投资、审计与会计的智能化问题引起广泛关注。本文应用人工智能技术,依据财务分析的基本理论和方法,结合中国上市公司财务特征,率先尝试开发了"智能财务分析与诊断机器人",用于评价上市公司的综合财务绩效。该款智能机器人具有"速度快""智能化程度较高""专业能力较强""相对公正客观"和"实用性较强"五大特点。本文应用该款机器人设计了两个投资决策实验,个股和组合实验结果均表明该款智能机器人能有效地评价和区分上市公司的财务绩效类型。根据其输出的综合绩效,买入高绩效型股票或组合,或买入绩效成长型的股票或组合,具有显著的超额收益;买入高绩效型股票或组合,比买入绩效成长型的股票或组合,具有更高和更稳定的超额收益。可见,基于人工智能的财务分析与诊断机器人具有稳健和有效的择股能力。     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.     

“The reports of my death are greatly exaggerated”—Artificial intelligence research in accounting

Gray et al. (2014) examined the productivity of expert systems/artificial intelligence research in accounting and came to the conclusion that both research on and practice use of expert systems/artificial intelligence had waned since the late 1990s. In our study, we reconsider these findings based on a broader view that is ‘artificial intelligence’ centric versus ‘expert systems’ centric. The results show that while there was a bit of a lull in the late 1990s, artificial intelligence research in accounting has continued to steadily increase over the past 30 years. Further consideration of artificial intelligence techniques as embedded modules in integrated audit support systems also suggest that use by practice continues to be robust. Based on these findings, we make a call for much more research on the usability, and use, of artificial intelligence techniques in accounting domains. Contrary to earlier perceptions, the research domain remains vibrant and holds great potential for AIS researchers to take a leadership role in advancing the field.     

Regulatory technology (Reg-Tech) in financial stability supervision: Taxonomy,key methods,applications and future directions

Financial regulation is the basic requirement for financial stability. Recently, regulatory technology (Reg-Tech) has become one of the main research topics in financial stability regulation. Reg-Tech aims to use artificial intelligence technologies to realize intelligent identification and early risk warning. It is a powerful tool for assisting financial regulation informatization and high efficiency. This study aims to comprehensively review the application of smart technology in financial stability regulation, and analyze the objects and results of the technology's applications. We build a framework for the application of complex networks, knowledge graphs, machine learning, and dynamic systems in Reg-Tech. The aim is to form a clear context for its development, and serve as the support and development foundation for financial stability research. Finally, we summarize the limitations and shortcomings of current Reg-Tech developments, and discuss future research and development directions.     

A Hybrid-Based Expert System for Personal Pension Planning in the UK

While advanced computing technology, and particularly the use of artificial intelligence in the form of expert systems, could not necessarily be said to be common in the US financial planning domain, it is certainly not unheard of. This situation is significantly different from that found in the comparable UK domain. This paper is based on a project to look at the use of computing technology to support the role of the personal financial adviser in the UK—a domain in which little published research work has been undertaken. It briefly describes the current UK marketplace and details the construction of a small hybrid-based expert system, built to support the selection of personal pension plans, to illustrate the inherent value in developing such technology in this domain. The paper discusses the benefits of using hybrid representational techniques as opposed to single representations in creating an expert system in a financial planning domain.     

2019年金融信息化10件大事

2019年,在新一轮科技革命加速变革的背景下,我国金融业积极响应国家战略要求,推动行业数字化转型,围绕架构转型、技术升级、金融与科技融合发展等方面进行了深入探索与实践,信息化成果丰硕。在经历了近两个月的初选和复选两个阶段的专家评审,“2019年金融信息化10件大事”评审结果成功揭晓,分别是:(1)中国人民银行发布《金融科技(FinTech)发展规划(2019-2021年)》;(2)中国工商银行发布智慧银行生态系统ECOS1.0。     

Risk assessment in internal auditing: a neural network approach

Risk assessment is a systematic process for integrating professional judgments about relevant risk factors, their relative significance and probable adverse conditions and/or events leading to identification of auditable activities (IIA, 1995, SIAS No. 9). Internal auditors utilize risk measures to allocate critical audit resources to compliance, operational, or financial activities within the organization (Colbert, 1995). In information rich environments, risk assessment involves recognizing patterns in the data, such as complex data anomalies and discrepancies, that perhaps conceal one or more error or hazard conditions (e.g. Coakley and Brown, 1996; Bedard and Biggs, 1991; Libby, 1985). This research investigates whether neural networks can help enhance auditors’ risk assessments. Neural networks, an emerging artificial intelligence technology, are a powerful non‐linear optimization and pattern recognition tool (Haykin, 1994; Bishop, 1995). Several successful, real‐world business neural network application decision aids have already been built (Burger and Traver, 1996). Neural network modeling may prove invaluable in directing internal auditor attention to those aspects of financial, operating, and compliance data most informative of high‐risk audit areas, thus enhancing audit efficiency and effectiveness. This paper defines risk in an internal auditing context, describes contemporary approaches to performing risk assessments, provides an overview of the backpropagation neural network architecture, outlines the methodology adopted for conducting this research project including a Delphi study and comparison with statistical approaches, and presents preliminary results, which indicate that internal auditors could benefit from using neural network technology for assessing risk. Copyright © 1999 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.     

An ontological artifact for classifying social media: Text mining analysis for financial data

In this paper we utilize a structured natural language processing implementation of the Financial Industry Business Ontology (FIBO) to extract financial information from the unstructured textual data of the social media platform Twitter regarding financial and budget information in the public sector, namely the two public-private agencies of the Port Authority of NY and NJ (PANYNJ), and the NY Metropolitan Transportation Agency (MTA). This research initiative uses the Design Science Research (DSR) perspective to develop an artifact to classify tweets as being either relevant to financial bonds or not. We apply a frame and slot approach from the artificial intelligence and natural language processing literature to operationalize this artifact. FIBO provides standards for defining the facts, terms, and relationships associated with financial concepts. We show that FIBO grammar can be used to mine semantic meaning from unstructured textual data and that it provides a nuanced representation of structured financial data. With this artifact, social media such as Twitter may be accessed for the knowledge that its text contains about financial concepts using the FIBO ontology. This process is anticipated to be of interest to bond issuers, regulators, analysts, investors, and academics. It may also be extended towards other financial domains such as securities, derivatives, commodities, and banking that relate to FIBO ontologies, as well as more generally to develop a structured knowledge representation of unstructured data through the application of an ontology.     

Research on optimization of an enterprise financial risk early warning method based on the DS-RF model

The financial risk early warning process of enterprises faces problems such as uncertainty and complexity. In the big data environment, scholars and enterprises that continue to use traditional evaluation methods will face large challenges. It is essential for an enterprise's sustainable operation to combine artificial intelligence algorithms, dynamically monitor its financial risks, and carry out financial risk early warning processes accurately and effectively. This study proposes an early warning method for corporate financial risks based on the evidence theory-random forest (DS-RF) model. The classic algorithm of machine learning—random forest was introduced into the framework of evidence theory to construct a random forest model with four dimensions: profitability, asset quality, debt risk, and operating growth. While predicting the risk, the credibility of the evidence was determined, and then the D-S synthesis rule was used for information fusion. An example was analyzed, taking JS Reclamation Group as the study subject. The comparison with the early warning results of the random forest algorithm and the traditional model shows that the DS-RF model proposed in this paper has a higher early warning accuracy and the results are presented more comprehensively and systematically, which effectively improves the efficiency of enterprise financial risk early warning and helps managers to make relevant decisions efficiently and scientifically.     

Case-based Reasoning and Multiple-agent Systems for Accounting Regulation Systems with Extensions

One of the areas of judgment research in accounting and financial applications is that of accounting regulation. Previously, artificial intelligence efforts at modeling human judgment in accounting regulation systems have concentrated on rule-based expert systems. In those systems, general heuristic knowledge was captured using ‘if … then …’ rules in order to model particular decision processes. Recent developments in artificial intelligence have focused on case-based reasoning (CBR) and multiple-agent intelligent systems (MAIS). The ideas behind CBR are that ‘if it worked once then remember to use it again’ and ‘if it did not work before, then remember to not use it again‘. MAIS assumes that many organizational systems can be treated as computational models of multiple-interacting intelligent agents. Typically, solutions may be derived using some form of negotiations between agents to accomplish single global or separate individual interacting goals. This paper argues that many accounting regulation judgment processes can be modeled using CBR and MAIS. As a result, it summarizes some examples of both CBR and MAIS useful in accounting regulation and extends those to other accounting applications. In addition, it describes the results of some previously developed systems that employ CBR or MAIS.     

Learning risk preferences from investment portfolios using inverse optimization

The level of risk an investor can endure, known as risk-preference, is a subjective choice that is tightly related to psychology and behavioral science in decision making. This paper presents a novel approach of measuring risk preference from existing portfolios using inverse optimization on mean–variance portfolio allocation framework. Our approach allows the learner to continuously estimate real-time risk preferences using concurrent observed portfolios and market price data. We demonstrate our methods on robotic investment portfolios and real market data that consists of 20 years of asset pricing and 10 years of mutual fund portfolio holdings. Moreover, the quantified risk preference parameters are validated with two well-known risk measurements currently applied in the field. The proposed methods could lead to practical and fruitful innovations in automated/personalized portfolio management, such as Robo-advising, to augment financial advisors’ decision intelligence in a long-term investment horizon.     

Knowledge Representation Issues in Complex Decision Domains: an Example from Inherent Audit Risk Assessment

There has been significant discussion in artificial intelligence and expert systems concerning different representation systems for complex domain knowledge. This article discusses the strengths, weaknesses and psychological validity of two common systems, associational or rule- and model-based, and presents a computer model which incorporates both approaches into a hybrid system. The computer model reasons in the complex decision domain of inherent audit risk assessment.     

金融科技助推供应链金融风险控制研究

传统信贷业务中,商业银行通常将风险评估重点放在企业的评级和财务状况上。在供应链金融业务中,授信因交易而存在,资金流、信息流、物流是评判业务可行性的基础。供应链金融最大的风险点在于不完整信息下的信用风险和不对称信息下的道德风险。防范化解供应链金融风险,除了运用常规风险管理手段,更要积极引用大数据、物联网、区块链、人工智能等金融科技手段来提升供应链金融的风险管理能力。本文通过分析供应链金融面临的风险,以基于B2B平台的供应链金融模式为例,运用Logistic回归方法构建中小企业信用评估模型,对供应链金融风控体系建设进行实证研究,并围绕金融科技提升商业银行供应链金融风控能力提出建议。     

Disclosures about algorithmic decision making in the corporate reports of Western European companies

Over the last decade, the use of different artificial intelligence (AI) tools has increased. To shed some light on the emerging trend of AI disclosure, the aim of this paper is to analyse the current practices of major Western European companies regarding the automated decision-making (ADM) disclosure in their annual or sustainability reports. This paper proposes a methodology based on bigrams that enables the automatic extraction of the information on ADM that companies disclose. The sample consisted of 962 annual/sustainability reports, published in 2018 and 2019, of 337 companies listed on 13 Western European countries’ stock markets. Our findings show that ADM disclosure is still at an early stage and that the first adopters are mostly companies operating in the financial sector.     

The application of artificial intelligence to medicine

This article reviews current developments in artificial intelligence as they apply to medicine. Initial applications of this approach to medicine are being actively pursued in medical diagnosis, interpretation of data from chemical studies, and the development of computer models of human behavioural processes. Of special interest is a new research programme established at Stanford University called SUMEX, one of whose major goals is the application of artificial intelligence to medicine. Within the framework of SUMEX, research is actively under way in a number of aspects of biomedical research and clinical medicine. Some of the work reviewed includes the DENDRAL and META-DENDRAL programs, the Protein Crystallography System, SECS, MYCIN, DIALOG, CASNET, the Present Illness Program, PARRY, and Believer. Suggestions for future applications of artificial intelligence to medicine will include clinical patient-record information systems, pharmacology, prosthetics, gerontology, and radiology.     

A quarterly time-series classifier based on a reduced-dimension generated rules method for identifying financial distress

Predicting financial distress has been and will remain an important and challenging issue. Many methods have been proposed to predict bankruptcies and detect financial crises, including conventional approaches and techniques involving artificial intelligence (AI). Financial distress information influences investor decisions, and investors depend on analysts’ opinions and subjective judgements in assessing such information, which sometimes results in investors making mistakes. In the light of the foregoing, this paper proposes a novel quarterly time series classifier, which reduces the sheer volume of high-dimensional data to be analysed and provides decision-makers with rules that can be used as a reference in assessing the financial situation of a company. This study employs the following six attribute selection methods to reduce the high-dimensional data: (1) the chi-square test, (2) information gain, (3) discriminant analysis, (4) logistic regression (LR) analysis, (5) support vector machine (SVM) and (6) the proposed Join method. After selecting attributes, this study utilises the rough set classifier to generate the rules of financial distress. To verify the proposed method, an empirically collected financial distress data-set is employed as the experimental sample and is compared with the decision tree, multilayer perceptron and SVM under Type I error, Type II error and accuracy criteria. Because financial distress data are quarterly time series data, this study conducts non-time series and time series (moving windows) experiments. The experimental results indicate that the LR and chi-square attribute selection combined with the rough set classifier outperform the listing methods under Type I, Type II error and accuracy criteria.     

RegTech—the application of modern information technology in regulatory affairs: areas of interest in research and practice

We provide a high-level view on topics addressed in scientific articles about regulatory technology (RegTech), with a particular focus on technologies used. For this purpose, we first explore different denominations for RegTech and derive search queries to search relevant literature portals. From the hits of that information retrieval process, we select 55 articles outlining the application of information technology in regulatory affairs with an emphasis on the financial sector. In comparison, we examine the technological scope of 347 RegTech companies and compare our findings with the scientific literature. Our research reveals that ‘compliance management’ is the most relevant topic in practice, and ‘risk management’ is the primary subject in research. The most significant technologies as of today are ‘artificial intelligence’ and distributed ledger technologies such as ‘blockchain’.