1,012 research outputs found
Multimodal Generative Models for Bankruptcy Prediction Using Textual Data
Textual data from financial filings, e.g., the Management's Discussion &
Analysis (MDA) section in Form 10-K, has been used to improve the prediction
accuracy of bankruptcy models. In practice, however, we cannot obtain the MDA
section for all public companies, which limits the use of MDA data in
traditional bankruptcy models, as they need complete data to make predictions.
The two main reasons for the lack of MDA are: (i) not all companies are obliged
to submit the MDA and (ii) technical problems arise when crawling and scrapping
the MDA section. To solve this limitation, this research introduces the
Conditional Multimodal Discriminative (CMMD) model that learns multimodal
representations that embed information from accounting, market, and textual
data modalities. The CMMD model needs a sample with all data modalities for
model training. At test time, the CMMD model only needs access to accounting
and market modalities to generate multimodal representations, which are further
used to make bankruptcy predictions and to generate words from the missing MDA
modality. With this novel methodology, it is realistic to use textual data in
bankruptcy prediction models, since accounting and market data are available
for all companies, unlike textual data. The empirical results of this research
show that if financial regulators, or investors, were to use traditional models
using MDA data, they would only be able to make predictions for 60% of the
companies. Furthermore, the classification performance of our proposed
methodology is superior to that of a large number of traditional classifier
models, taking into account all the companies in our sample
Machine Learning in Management Accounting Research : Literature Review and Pathways for the Future
This paper explores the possibilities of employing machine learning (ML) methods and new data sources in management accounting (MA) research. A review of current accounting and related research reveals that ML methods in MA are still in their infancy. However, a review of recently published ML research from related fields reveals several new opportunities to utilize ML in MA research. We suggest that the most promising areas to employ ML methods in MA research lie in (1) the exploitation of the rich potential of various textual data sources; (2) the quantification of qualitative and unstructured data to create new measures; (3) the creation of better estimates and predictions; and (4) the use of explainable AI to interpret ML models in detail. ML methods can play a crucial role in MA research by creating, developing, and refining theories through induction and abduction, as well as by providing tools for interventionist studies.© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.fi=vertaisarvioitu|en=peerReviewed
A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications
Enterprise financial risk analysis aims at predicting the enterprises' future
financial risk.Due to the wide application, enterprise financial risk analysis
has always been a core research issue in finance. Although there are already
some valuable and impressive surveys on risk management, these surveys
introduce approaches in a relatively isolated way and lack the recent advances
in enterprise financial risk analysis. Due to the rapid expansion of the
enterprise financial risk analysis, especially from the computer science and
big data perspective, it is both necessary and challenging to comprehensively
review the relevant studies. This survey attempts to connect and systematize
the existing enterprise financial risk researches, as well as to summarize and
interpret the mechanisms and the strategies of enterprise financial risk
analysis in a comprehensive way, which may help readers have a better
understanding of the current research status and ideas. This paper provides a
systematic literature review of over 300 articles published on enterprise risk
analysis modelling over a 50-year period, 1968 to 2022. We first introduce the
formal definition of enterprise risk as well as the related concepts. Then, we
categorized the representative works in terms of risk type and summarized the
three aspects of risk analysis. Finally, we compared the analysis methods used
to model the enterprise financial risk. Our goal is to clarify current
cutting-edge research and its possible future directions to model enterprise
risk, aiming to fully understand the mechanisms of enterprise risk
communication and influence and its application on corporate governance,
financial institution and government regulation
Multimodal Document Analytics for Banking Process Automation
In response to growing FinTech competition and the need for improved
operational efficiency, this research focuses on understanding the potential of
advanced document analytics, particularly using multimodal models, in banking
processes. We perform a comprehensive analysis of the diverse banking document
landscape, highlighting the opportunities for efficiency gains through
automation and advanced analytics techniques in the customer business. Building
on the rapidly evolving field of natural language processing (NLP), we
illustrate the potential of models such as LayoutXLM, a cross-lingual,
multimodal, pre-trained model, for analyzing diverse documents in the banking
sector. This model performs a text token classification on German company
register extracts with an overall F1 score performance of around 80\%. Our
empirical evidence confirms the critical role of layout information in
improving model performance and further underscores the benefits of integrating
image information. Interestingly, our study shows that over 75% F1 score can be
achieved with only 30% of the training data, demonstrating the efficiency of
LayoutXLM. Through addressing state-of-the-art document analysis frameworks,
our study aims to enhance process efficiency and demonstrate the real-world
applicability and benefits of multimodal models within banking.Comment: A Preprin
Bankruptcy in Indian Private Sector Banks: A Neural Network Analysis
This paper aims to predict the bankruptcy in Indian private banks using financial ratios such as ROA, GNPA, EPS, PAT, and GNP of the country. This paper also explains the importance of Ohlson’s number, Graham’s number and Zmijewski number as the major predictors of bankruptcy while developing a model using neural networks. For the prediction, the financial data for private sector banks of India such as HDFC, HDFC, ICICI, AXIS, YES bank, KOTAK MAHINDRA Bank, FEDERAL BANK, INDUSIND Bank, RBL and KARUR VYSYA for the last 10 years from 2010-2019 have been analysed. The model developed during the research will help the financial institutions and banks in India to understand the economic condition of the banking industry
Non-conventional data and default prediction: the challenge of companies’ websites
Small and Medium Enterprises (SMEs) contribution to the European Union
economy has always been relevant, for both value added and the creation of
jobs. That is why the prediction of their survival is considered one of the
economic pillars UE keeps under observation. Default prediction models,
accounting for SMEs idiosyncratic traits, are based on several types of data,
mainly accounting indicators. Balance sheet data, indeed, are considered the
standard predictors for classification models in this field, although they do not
allow to completely overcome the information opacity that is one of the main
barriers preventing these firms from accessing credit. In our work, we explore
the possibility of complementing accounting information with data scraped
from the firms’ websites. We modeled the data using a nonlinear discriminant
analysis and we benchmarked the results with the Logistic Regression. The
evidence of our study is promising although the combination of online and
offline data shows better results in case of survival firms than for defaulted
companies
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