Intelligent Systems in Accounting, Finance and Management最新文献

The evaluation of ESG ratings by ESG rating agencies is time-consuming and requires the participation of numerous human specialists. In this paper, we propose a method for creating proxies of ESG scores by collecting corporate ESG news and publicly available ESG-related data using data crawling techniques and deep learning-based classification technology while minimizing human involvement. To validate the effectiveness of the proposed approach, we suggest three hypotheses. Two of them are related to the connection between open-source information and ESG ratings, while one concerns the link between proxy ESG rating and firm performance. To validate the effectiveness of the proposed approach, we conduct an empirical analysis based on 976 unique companies listed by the Korean Corporate Governance Agency (KCGS) from 2016 to 2019. Initially, we gather ESG indicators from open sources including disclosures and firms' news articles from a news portal site. We utilize Bidirectional Encoder Representations from Transformers (BERT) to classify news articles into environment, social, and governance categories and determine their sentiments. We confirm that ESG news sentiment and variables extracted from open-source data are related to ESG ratings. Furthermore, we find a significantly positive relationship between E, S, and G ratings predicted based on open-source data and Tobin's Q.

Metaverse is an emerging digital space that uses innovative technologies to allow users to facilitate building relationships virtually and to create new interaction opportunities. Even, the financial sector has been disrupted by the metaverse involving digital assets, cryptocurrencies, blockchain technology, and decentralized finance. The objective of this paper is to focus on novel intelligent systems technologies with the potential for application in the financial area to have a better knowledge of the current research topics, challenges, and future directions. A systematic literature review was conducted analyzing papers on technological innovation of the metaverse in financial sector. Following the PRISMA methodology, we have selected 29 primary studies from five scientific databases to be included in the review. The results show that 11 types of innovative metaverse technologies are applied in the financial sector, developing financial innovations, among which the most discussed is cryptocurrency. Among the opportunities that the use of the metaverse brings to the financial sector, the reduction of transaction costs is the most discussed. Finally, five open challenges in the use of metaverse technologies in the financial sector have been identified, relating to the use of data, the application of technologies, social integration, financial innovation, and regulatory compliance. Based on this study, recommendations on future research directions are provided to the scientific community.

Liquidity planning and forecasting are essential activities in corporate financial planning team. Traditionally, empirical models and techniques based on in-house expertise have been used to navigate the numerous challenges of this forecasting activity. These challenges become more complex when the forecasting activities are extended to subsidiaries of a large firm. This paper presents a structured approach that utilizes 240 covariates to predict net liquidity, customer receipts, and payments to suppliers to improve the accuracy and efficiency of liquidity forecasting in subsidiaries and at the corporate level. The approach is empirically validated on a large corporation headquartered in Germany, with average annual revenue from 6 to 7 billion Euro spanning 80 countries. The proposed approach demonstrated superior performance over existing methods in six out of nine forecasts using the data from 2014 to 2018. These findings suggest that a firm's classical approach to liquidity forecasting can be effectively challenged and outperformed by the algorithmic approach.

Although the financial audit controls in companies have advanced over the years, the number of corporate fraud instances is growing, thus raising the need for investigating the factors that can be used as early warning signals and developing effective systems for identifying financial fraud. In this paper, financial statements from 133 Greek companies listed in the Athens Stock Exchange over the period 2014 to 2019 are investigated, based on the fraud diamond theory. Financial data and corporate governance variables are used as inputs to data mining techniques to develop models that can identify patterns of irregularities in a company's financial reports. To this end, popular machine learning classification algorithms are employed in a novel multi-label classification setting that not only identifies fraudulent cases but also considers the nature of the auditors' comments. The results indicate that the proposed multi-label approach provides enhanced results compared to binary classification algorithms, avoiding inconsistent outputs with respect to the existence of different forms of manipulation of financial statements.

We investigate applying and extending an exhaust data framework, using an empirical analysis to explore and compare different predictive analytic capabilities of both internal and external exhaust data for estimating sales. We use internal exhaust data that explores the relationship between app usage and web traffic data and estimation of sales and find the ability to predict sales at least 4 days ahead. We also develop predictive models of sales, using external data of Google searches, extending the previous research to include additional macroeconomic Google variables and Wikipedia pageviews, finding that we can predict at least 4 months ahead, suggesting a portfolio of exhaust data be used. We introduce the roles of internal and external exhaust data, direct and indirect exhaust data and transformed exhaust data, into an exhaust data framework. We examine what appear to be different levels of information fineness and predictability from those exhaust data sources. We also note the importance of the types of devices (e.g., mobile) and the types of commerce (e.g., mobile commerce) in creating and finding different types of exhaust. Finally, we apply an existing exhaust data framework to develop macroeconomic data exhaust variables, as the means of capturing inflation and unemployment information, using Google searches.

Modern financial exchanges use an electronic limit order book (LOB) to store bid and ask orders for a specific financial asset. As the most fine-grained information depicting the demand and supply of an asset, LOB data are essential in understanding market dynamics. Therefore, realistic LOB simulations offer a valuable methodology for explaining the empirical properties of markets. Mainstream simulation models include agent-based models (ABMs) and stochastic models (SMs). However, ABMs tend not to be grounded on real historical data, whereas SMs tend not to enable dynamic agent-interaction. More recently, deep generative approaches have been successfully implemented to tackle these issues, whereas its black-box essence hampers the explainability and transparency of the system. To overcome these limitations, we propose a novel hybrid neural stochastic agent-based model (NS-ABM) for LOB simulation that incorporates a neural stochastic trader in agent-based simulation, characterised by (1) representing the aggregation of market events' logic by a neural stochastic background trader that is pre-trained on historical LOB data through a neural point process model; (2) learning the complex distribution for order-related attributes conditioned on various market indicators through a non-parametric diffusion probabilistic model; and (3) embedding the background trader in a multi-agent simulation platform to enable interaction with other strategic trading agents. We instantiate this hybrid NS-ABM model using the ABIDES platform. We first run the background trader in isolation and show that the simulated LOB can recreate a comprehensive list of stylised facts that demonstrate realistic market behaviour. We then introduce a population of ‘trend’ and ‘value’ trading agents, which interact with the background trader. We show that the stylised facts remain and we demonstrate order flow impact and financial herding behaviours that are in accordance with empirical observations of real markets.