Intelligent Systems in Accounting, Finance and Management最新文献

{"title":"Predicting carbon and oil price returns using hybrid models based on machine and deep learning","authors":"Jesús Molina-Muñoz,&nbsp;Andrés Mora-Valencia,&nbsp;Javier Perote","doi":"10.1002/isaf.1563","DOIUrl":"https://doi.org/10.1002/isaf.1563","url":null,"abstract":"<div>\u0000 \u0000 <p>Predicting carbon and oil prices is recently gaining relevance in the climate change literature. This is due to the fact that conventional energy market analysis and the design of mechanisms for climate change mitigation constitute key variables for artificial carbon markets. Yet, modelling non-linear effects in time series remains a major challenge for carbon and oil price forecasting. Hence, hybrid models seem to be appealing alternatives for this purpose. This study evaluates the performance of 12 hybrid models, which weigh results from random forest, support vector machine, autoregressive integrated moving average and the non-linear autoregressive neural network models. The weights are determined by (i) assuming equal weights, <span>(</span>ii) using a neural network to optimise individual weights and (iii) employing deep learning techniques. The findings of our work confirm the salient characteristics of modelling the non-linear effects of time series and the potential of hybrid models based on neural networks and deep learning in predicting carbon and oil price returns. Furthermore, the best results are obtained from hybrid models that combine machine learning and traditional econometric techniques as inputs, which capture the linear and non-linear effects of time series.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"31 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141264581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward an extended framework of exhaust data for predictive analytics: An empirical approach","authors":"Daniel E. O'Leary","doi":"10.1002/isaf.1554","DOIUrl":"https://doi.org/10.1002/isaf.1554","url":null,"abstract":"<p>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.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"31 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.1554","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140641971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Internet financial reporting disclosure index of e-commerce businesses on social media","authors":"Diyah Probowulan,&nbsp;Ardianto Ardianto","doi":"10.1002/isaf.1550","DOIUrl":"https://doi.org/10.1002/isaf.1550","url":null,"abstract":"<div>\u0000 \u0000 <p>The study measured the Internet Financial Reporting (IFR) disclosure index and compared the results across three continents of the global e-commerce business. In addition, it documents various social media platforms used by e-commerce. We use content analysis with a scoring matrix based on content, timeliness, technology, and support used in websites and a one-way ANOVA. The findings identified an average IFR e-commerce disclosure index of 0.735, which is of good quality as it approaches the value of 1. There is no difference in index IFR between the three continental zones overall, but slightly different from non-e-commerce companies. The results also prove that websites and blog media still dominate the use of social media, while other social media platforms have not provided financial information. Researchers in accounting have not conducted research topics on social media, so there are still limited references and narrow analytical content. This research will interest the e-commerce business industry and compile their financial reporting through the website to improve the quality of their IFR and financial access. Since the e-commerce business is an internet-based company growing significantly, it can use other social media to reveal its reporting as decent work and economic growth. This subject is relatively innovative because none of the IFR disclosure index studies focuses on e-commerce businesses on social media. It fills the research gap related to the characteristics of e-commerce businesses, where almost all activities are internet-based.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"31 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140348589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural stochastic agent-based limit order book simulation with neural point process and diffusion probabilistic model","authors":"Zijian Shi,&nbsp;John Cartlidge","doi":"10.1002/isaf.1553","DOIUrl":"https://doi.org/10.1002/isaf.1553","url":null,"abstract":"<p>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.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"31 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.1553","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140348588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nowcasting directional change in high frequency FX markets","authors":"Edward P. K. Tsang,&nbsp;Shuai Ma,&nbsp;V. L. Raju Chinthalapati","doi":"10.1002/isaf.1552","DOIUrl":"https://doi.org/10.1002/isaf.1552","url":null,"abstract":"<p>Directional change (DC) is an alternative to time series in recording transactions: it only records the transactions at which price changes to the opposite direction of the current trend by a threshold specified by the observer. DC can only be confirmed in hindsight: one does not know that direction has changed until it is confirmed by a later transaction. The transaction in which the price confirms a DC is called a DC confirmation point. DC nowcasting is an attempt to recognize DC before the DC confirmation point. Accurate DC nowcasting will benefit trading. In this paper, we propose a method for DC nowcasting. This method is entirely data driven: it is based on the historical distribution of DC-related indicators. Empirical results suggest that DC nowcasting is possible, even under a naïve rule. This opens the door to a promising research direction on an important topic.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.1552","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140123717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accounting journal entries as a long-term multivariate time series: Forecasting wholesale warehouse output","authors":"Mario Zupan","doi":"10.1002/isaf.1551","DOIUrl":"https://doi.org/10.1002/isaf.1551","url":null,"abstract":"<p>Less than 2 years ago, many small entrepreneurs in the commodities trading business faced price volatility, which had not been the case for the last few decades. Generally, the income section of the profit and loss statement was not the main problem, especially in building material commodities trading, due to the recent growth in real estate demand. Logistic disorders, raw material shortages, inflation, and interest rate growth caused difficulties in supply management and warehouse balancing, which were reflected in a particular significant expense called the cost of goods sold. The real problem of its forecasting was identified, and data from accounting books likely contain information about previous warehouse dynamics. This paper presents how accounting data are prepared and shaped into time series suitable for machine learning algorithms, the relevant literature that helped in algorithm selection, and the development and description of the forecasting model, as well as its benchmarking with traditional forecasting models. Visualization and mean squared error loss measured on unseen data show that the model has proven more successful than expected. Based on data from four journal accounts spanning over 14 years, the model predicts the debit and credit sides of the wholesale warehouse for 150 working days.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140096686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Text-based sentiment analysis in finance: Synthesising the existing literature and exploring future directions","authors":"Andrew Todd,&nbsp;James Bowden,&nbsp;Yashar Moshfeghi","doi":"10.1002/isaf.1549","DOIUrl":"https://doi.org/10.1002/isaf.1549","url":null,"abstract":"<p>Advances in Deep Learning have drastically improved the abilities of Natural Language Processing (NLP) research, creating new state-of-the-art benchmarks. Two research streams at the forefront of NLP analysis are transformer architecture and multimodal analysis. This paper critically evaluates the extant literature applying sentiment analysis techniques to the financial domain. We classify the financial sentiment analysis literature according to the most used techniques in the area, with a focus on methods used to detect sentiment within corporate earnings conference calls, because of their dual modality (text-audio) nature. We find that the financial literature follows a similar path to NLP sentiment literature, in that more advanced techniques to define sentiment are being used as the field progresses. However, techniques used to determine financial sentiment currently fall behind state-of-the-art techniques used within NLP. Two future directions stem from this paper. Firstly, we propose that the adoption of transformer architecture to create robust representations of textual data could enhance sentiment analysis in academic finance. Secondly, the adoption of multimodal classifiers in finance represents a new, currently underexplored area of study that offers opportunities for finance research.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.1549","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139976454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cost-sensitive machine learning to support startup investment decisions","authors":"Ronald Setty,&nbsp;Yuval Elovici,&nbsp;Dafna Schwartz","doi":"10.1002/isaf.1548","DOIUrl":"https://doi.org/10.1002/isaf.1548","url":null,"abstract":"<p>In 2022, global startup investments exceeded US$445 billion, sourced from entities like venture capital (VC) funds, angel investors, and equity crowdfunding. Despite their role in driving innovation, startup investments often fall short of S&amp;P 500 returns. Surprisingly, the potential of artificial intelligence (AI) remains untapped by investors, despite AI's growing sway in financial decision-making. Our empirical analysis predicts the success of 10,000 Israeli startups, utilizing diverse machine learning models. Unlike prior research, we employ the MetaCost algorithm to convert models into cost-sensitive variants, minimizing total cost instead of total error. This innovative approach enables varied costs linked to different prediction errors. Our results underscore that these cost-sensitive machine learning models significantly reduce risk for VC funds and startup investors compared to traditional ones. Furthermore, these models provide investors with a distinct capability to tailor their risk profiles, aligning predictions with their risk appetite. However, while cost-sensitive machine learning reduces risk, it may limit potential gains by predicting fewer successful startups. To address this, we propose methods to enhance successful startup identification, including aggregating outcomes from multiple MetaCost models, particularly advantageous for smaller deal flows. Our research advances AI's role in startup investing, presenting a pivotal tool for investors navigating this domain.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.1548","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139732302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using large language models to write theses and dissertations","authors":"Daniel E. O'Leary","doi":"10.1002/isaf.1547","DOIUrl":"10.1002/isaf.1547","url":null,"abstract":"<p>There has been substantial discussion aimed at investigating the extent to which academic researchers can or should “use” large language models, such as ChatGPT and Bard, in their research papers. However, there seems to have been limited attention given to the extent to which students can use these tools for the development of theses, proposals and dissertations. This paper pushes the arguments from focusing on academic researchers, journal papers, and technical meetings to considering those theses and dissertations, raising several questions and concerns. Ultimately, university policies need to address these issues, but if publisher and editor responses and alternative business uses are a signal of that direction, consensus may be difficult to achieve.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"30 4","pages":"228-234"},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.1547","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138822612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Costs associated with exit or disposal activities: A topic modeling investigation of disclosure and market reaction","authors":"Charles P. Cullinan,&nbsp;Richard Holowczak,&nbsp;David Louton,&nbsp;Hakan Saraoglu","doi":"10.1002/isaf.1545","DOIUrl":"10.1002/isaf.1545","url":null,"abstract":"<div>\u0000 \u0000 <p>The Securities and Exchange Commission (SEC) mandates disclosure of exit or disposal activity events in 8-K filings. We use Latent Dirichlet Allocation (LDA), a topic modeling method from computational linguistics, to investigate the possibility that substantively different event types may be subsumed under Item 2.05, the SEC category for costs associated with exit or disposal activities. Our analysis reveals that four distinct topics are reported under the Item 2.05 umbrella category: (1) restructuring, (2) disposal of a line of business, (3) plant closings, and (4) layoffs/workforce reductions. We then investigate various aspects of these 8-K filings. We find that the market reacts most negatively to workforce reductions that are reported in the absence of a broader strategic initiative. Subsequent amendments to the Item 2.05 8-K filings are significantly more likely for restructuring initiatives, and significantly less likely for layoffs. Asset impairment charges most frequently accompany line-of-business disposals and plant closings. Our results demonstrate that there are meaningful differences between the event types reported within Item 2.05 filings and that LDA provides a useful means of differentiating among these event types.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"30 4","pages":"173-191"},"PeriodicalIF":0.0,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138597223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}