2,123 research outputs found
A transformer-based model for default prediction in mid-cap corporate markets
In this paper, we study mid-cap companies, i.e. publicly traded companies
with less than US $10 billion in market capitalisation. Using a large dataset
of US mid-cap companies observed over 30 years, we look to predict the default
probability term structure over the medium term and understand which data
sources (i.e. fundamental, market or pricing data) contribute most to the
default risk. Whereas existing methods typically require that data from
different time periods are first aggregated and turned into cross-sectional
features, we frame the problem as a multi-label time-series classification
problem. We adapt transformer models, a state-of-the-art deep learning model
emanating from the natural language processing domain, to the credit risk
modelling setting. We also interpret the predictions of these models using
attention heat maps. To optimise the model further, we present a custom loss
function for multi-label classification and a novel multi-channel architecture
with differential training that gives the model the ability to use all input
data efficiently. Our results show the proposed deep learning architecture's
superior performance, resulting in a 13% improvement in AUC (Area Under the
receiver operating characteristic Curve) over traditional models. We also
demonstrate how to produce an importance ranking for the different data sources
and the temporal relationships using a Shapley approach specific to these
models.Comment: to be published in the European Journal of Operational Researc
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
An approach to securitisation in Europe NPLs- machine learning model field lab project Nova SBE | moodyâs analytics
As a consulting project, we were proposed to develop a neural network (NN) to predict mortgage states in one year, based on the paper âDeep Learning for Mortgage Riskâ by Justin A. Sirignano, Apaar Sadhwani, Kay Giesecke (2018). We developed a neural network model with the aim of being able to capture the relationships between the different variables, with respect to each other and to the response variable (the loan status in 12 months), better than traditional classification methods, such as logistic regressions, which constitute the benchmark set. Data was provided by Moodyâs, relating borrower, property and loan/financing characteristics for several mortgages over several periods in time (over 350 thousand mortgages). The purpose of our model is to predict the probabilities to transition to different states at a certain point in time. The best results were obtained with a 10 layer, 500 nodes per layer network. The model can identify a large portion of defaults. At the cost, however, of a general overestimation of the default rate over the years. The capability of identifying loans that will be in arrears is also acceptable, with, again, an overestimation of the verified rate. Variables relating to borrower characteristics and history as well as financing are found to be the most significant
A twoâstage Bayesian network model for corporate bankruptcy prediction
We develop a Bayesian network (LASSO-BN) model for firm bankruptcy prediction. We select fnancial ratios via the Least Absolute Shrinkage Selection Operator (LASSO), establish the BN topology, and estimate model parameters. Our empirical results, based on 32,344 US firms from 1961-2018, show that the LASSO-BN model outperforms most alternative methods except the deep neural network. Crucially, the model provides a clear interpretation of its internal functionality by describing the logic of how conditional default probabilities are obtained from selected variables. Thus our model represents a major step towards interpretable machine learning models with strong performance and is relevant to investors and policymakers
Comparing the Performance of Deep Learning Methods to Predict Companies' Financial Failure
This work was supported in part by the Ministerio de Ciencia, Innovacion y Universidades under Project RTI2018-102002-A-I00, in part by the Ministerio de Economia y Competitividad under Project TIN2017-85727-C4-2-P and Project PID2020-115570GB-C22, in part by the Fondo Europeo de Desarrollo Regional (FEDER) and Junta de Andalucia under Project B-TIC-402-UGR18, and in part by the Junta de Andalucia under Project P18-RT-4830.One of the most crucial problems in the eld of business is nancial forecasting. Many
companies are interested in forecasting their incoming nancial status in order to adapt to the current
nancial and business environment to avoid bankruptcy. In this work, due to the effectiveness of Deep
Learning methods with respect to classi cation tasks, we compare the performance of three well-known
Deep Learning methods (Long-Short Term Memory, Deep Belief Network and Multilayer Perceptron model
of 6 layers) with three bagging ensemble classi ers (Random Forest, Support Vector Machine and K-Nearest
Neighbor) and two boosting ensemble classi ers (Adaptive Boosting and Extreme Gradient Boosting) in
companies' nancial failure prediction. Because of the inherent nature of the problem addressed, three
extremely imbalanced datasets of Spanish, Taiwanese and Polish companies' data have been considered in
this study. Thus, ve oversampling balancing techniques, two hybrid balancing techniques (oversamplingundersampling)
and one clustering-based balancing technique have been applied to avoid data inconsistency
problem. Considering the real nancial data complexity level and type, the results show that the Multilayer
Perceptron model of 6 layers, in conjunction with SMOTE-ENN balancing method, yielded the best
performance according to the accuracy, recall and type II error metrics. In addition, Long-Short Term
Memory and ensemble methods obtained also very good results, outperforming several classi ers used in
previous studies with the same datasets.Ministerio de Ciencia, Innovacion y Universidades RTI2018-102002-A-I00Spanish Government TIN2017-85727-C4-2-P
PID2020-115570GB-C22European Commission B-TIC-402-UGR18Junta de Andalucia B-TIC-402-UGR18
P18-RT-483
Modelling Credit Risk for SMEs in Saudi Arabia
The Saudi Governmentâs 2030 Vision directs local banks to increase and improve credit for the Small and Medium Enterprises (SMEs) of the economy (Jadwa, 2017). Banks are, however, still finding it difficult to provide credit for small businesses that meet Baselâs capital requirements. Most of the current credit-risk models only apply to large corporations with little constructed for SMEs applications (Altman and Sabato, 2007). This study fills this gap by focusing on the Saudi SMEs perspective.
My empirical work constructs a bankruptcy prediction model based on logistic regressions that cover 14,727 firm-year observations for an 11-year period between 2001 and 2011. I use the first eight years data (2001-2008) to build the model and use it to predict the last three years (2009-2011) of the sample, i.e. conducting an out-of-sample test. This approach yields a highly accurate model with great prediction power, though the results are partially influenced by the external economic and geopolitical volatilities that took place during the period of 2009-2010 (the world financial crisis).
To avoid making predictions in such a volatile period, I rebuild the model based on 2003-2010 data, and use it to predict the default events for 2011. The new model is highly consistent and accurate. My model suggests that, from an academic perspective, some key quantitative variables, such as gross profit margin, days inventory, revenues, days payable and age of the entity, have a significant power in predicting the default probability of an entity. I further price the risks of the SMEs by using a credit-risk pricing model similar to Bauer and Agarwal (2014), which enables us to determine the risk-return tradeoffs on Saudiâs SMEs
The Financial Crisis of 2008 and the Developing Countries
Following the financial crisis that broke in the US and other Western economies in late 2008, there is now serious concern about its impact on the$financial crisis, developing countries, development finance, financial development
Debt Sustainability in Emerging Markets: A Critical Appraisal
This paper critically assesses the standard IMF analytical framework for debt sustainability in emerging markets. It focuses on complementarities and trade-offs between fiscal and external sustainability, and interactions and feedbacks among policy and endogenous variables affecting debt ratios. It examines current fragilities in emerging markets and notes that domestic debt is of concern. Despite favourable conditions, many governments are unable to generate a large enough primary surplus to stabilize public debt ratios. Worsening global financial conditions may create difficulties for budgetary transfers, posing greater challenges to government debt management since restructuring often is more difficult for domestic than external debt.debt sustainability, emerging markets, crisis
WARNING: Physics Envy May Be Hazardous To Your Wealth!
The quantitative aspirations of economists and financial analysts have for
many years been based on the belief that it should be possible to build models
of economic systems - and financial markets in particular - that are as
predictive as those in physics. While this perspective has led to a number of
important breakthroughs in economics, "physics envy" has also created a false
sense of mathematical precision in some cases. We speculate on the origins of
physics envy, and then describe an alternate perspective of economic behavior
based on a new taxonomy of uncertainty. We illustrate the relevance of this
taxonomy with two concrete examples: the classical harmonic oscillator with
some new twists that make physics look more like economics, and a quantitative
equity market-neutral strategy. We conclude by offering a new interpretation of
tail events, proposing an "uncertainty checklist" with which our taxonomy can
be implemented, and considering the role that quants played in the current
financial crisis.Comment: v3 adds 2 reference
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