Book-Tax Differences as an Indicator of Financial Distress

2013 ◽  
Vol 27 (3) ◽  
pp. 469-489 ◽  
Author(s):  
Tracy J. Noga ◽  
Anne L. Schnader
Keyword(s):  
Financial Distress ◽  
Hazard Model ◽  
Time Frame ◽  
Bankruptcy Prediction ◽  
Related Information ◽  
Ex Ante ◽  
Out Of Sample ◽  
Sample Testing

SYNOPSIS: We contend that tax-related information, which has not yet been considered by extant research, can significantly improve bankruptcy prediction. We investigate the association between abnormal changes in book-tax differences (BTDs) and bankruptcy using a hazard model and out-of-sample testing as in Shumway (2001). We find that information regarding abnormal changes in BTDs significantly increases our ability to ex ante identify firms that have an increased likelihood of going bankrupt in the coming five-year period. The information provided by BTDs significantly adds information to traditional models for predicting bankruptcy, such as that proposed by Ohlson (1980), and also expands the prediction window beyond the traditional two-year time frame.

scholarly journals Machine Learning in Futures Markets

2021 ◽  
Vol 14 (3) ◽  
pp. 119
Author(s):  
Fabian Waldow ◽  
Matthias Schnaubelt ◽  
Christopher Krauss ◽  
Thomas Günter Fischer
Keyword(s):  
Machine Learning ◽  
Futures Markets ◽  
Learning Models ◽  
Cross Sectional ◽  
Data Set ◽  
Statistical Arbitrage ◽  
Out Of Sample ◽  
Sample Testing ◽  
Arbitrage Strategy ◽  
Machine Learning Models

In this paper, we demonstrate how a well-established machine learning-based statistical arbitrage strategy can be successfully transferred from equity to futures markets. First, we preprocess futures time series comprised of front months to render them suitable for our returns-based trading framework and compile a data set comprised of 60 futures covering nearly 10 trading years. Next, we train several machine learning models to predict whether the h-day-ahead return of each future out- or underperforms the corresponding cross-sectional median return. Finally, we enter long/short positions for the top/flop-k futures for a duration of h days and assess the financial performance of the resulting portfolio in an out-of-sample testing period. Thereby, we find the machine learning models to yield statistically significant out-of-sample break-even transaction costs of 6.3 bp—a clear challenge to the semi-strong form of market efficiency. Finally, we discuss sources of profitability and the robustness of our findings.

Αποτίμηση κινδύνων των ευρωπαϊκών τραπεζών και συστήματα έγκαιρης προειδοποίησης

2018 ◽  
Author(s):  
Παντελής Σταυρούλιας
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Cross Validation ◽  
Multinomial Logistic Regression ◽  
Classification Tree ◽  
Probit Regression ◽  
Macroprudential Policy ◽  
Policy Makers ◽  
Out Of Sample ◽  
Sample Testing

Οι έγκυρες προβλέψεις χρηματοοικονομικών κρίσεων διασφάλιζαν ανέκαθεν την σταθερότητα τόσο ολόκληρου του χρηματοοικονομικού οικοδομήματος γενικότερα, όσο και του τραπεζικού τομέα ειδικότερα. Με την παρούσα διατριβή επιτυγχάνεται η πρόβλεψη συστημικών τραπεζικών κρίσεων για χώρες της EE-14 αρκετά τρίμηνα προτού αυτές γίνουν αντιληπτές με την χρησιμοποίηση των πιο διαδεδομένων μεταβλητών (μακροοικονομικών, τραπεζικών και αγοράς) μέσω δύο προσεγγίσεων, της δυαδικής και της πολυεπίπεδης. Ακολουθώντας τη δυαδική προσέγγιση, εξάγονται μοντέλα ταξινόμησης με την εφαρμογή της Διακριτής Ανάλυσης (Discriminant Analysis), της Γραμμικής Παλινδρόμησης (Linear Regression), της Λογιστικής Παλινδρόμησης (Logistic Regression) και της Παλινδρόμησης Πιθανοομάδας (Probit Regression), για την έγκαιρη πρόβλεψη των κρίσεων -12 έως -7 τρίμηνα πριν την εμφάνισή τους. Επιπροσθέτως, συγκρίνεται η απόδοση της ανωτέρω ανάλυσης χρησιμοποιώντας τις νεότερες και πλέον υποσχόμενες μεθόδους του Δέντρου Ταξινόμησης (Classification Tree), του Τυχαίου Δάσους (Random Forest) και της C5. Ταυτόχρονα προτείνεται ένα νέο μέτρο επιλογής κατωφλίων και απόδοσης προσαρμογής (GoF) των μοντέλων πρόβλεψης και μια νέα συνδυαστική (combined) μέθοδος ταξινόμησης. Προκειμένου να διερευνηθεί η απόδοση της ανωτέρω ανάλυσης, χρησιμοποιείται ο εκτός του δείγματος έλεγχος (out-of-sample testing) με τη μέθοδο της ανά χώρα σταυρωτής επικύρωσης (country-blocked cross validation). Σύμφωνα με τη μέθοδο αυτή, πραγματοποιείται η ανάλυση και εξάγονται τα μοντέλα πρόβλεψης με τη χρήση των δεκατριών από τις δεκατέσσερις χώρες του δείγματος (in-sample), εφαρμόζονται τα εξαγόμενα μοντέλα για την δέκατη τέταρτη χώρα που είχε εξαιρεθεί από το αρχικό δείγμα (out-of-sample) και ελέγχονται τα αποτελέσματα πρόβλεψης με τα πραγματικά δεδομένα της χώρας αυτής. Η παραπάνω διαδικασία επαναλαμβάνεται δεκατέσσερις φορές, αφήνοντας δηλαδή κάθε φορά μια χώρα εκτός δείγματος και τελικά εξάγεται ο μέσος όρος των επαναλήψεων. Στην παρούσα διατριβή, και χρησιμοποιώντας τον εκτός του δείγματος έλεγχο, επιτυγχάνεται η κατά 82.4% σωστή ταξινόμηση (Ακρίβεια – Accuracy), 78.4% ποσοστό Αληθινών Θετικών (Τrue Ρositive Rate - TPR) και 80.6% ποσοστό Θετικής Τιμής Πρόβλεψης (Positive Predictive Value - PPV). Σύμφωνα με την πολυεπίπεδη προσέγγιση, διακρίνονται δύο επίπεδα-περίοδοι πρόβλεψης των Συστημικών Τραπεζικών Κρίσεων. Το πρώτο επίπεδο ονομάζεται έγκαιρη πρόβλεψη (early warning) και αφορά περίοδο -12 έως -7 τρίμηνα πριν την έλευση της κρίσης ενώ το δεύτερο επίπεδο ονομάζεται καθυστερημένη πρόβλεψη (late warning) και αφορά περίοδο -6 έως -1 τρίμηνα πριν την έλευση της κρίσης. Για την πολυεπίπεδη αυτή ταξινόμηση, γίνεται χρήση των Νευρωνικών Δικτύων (Neural Networks), της Πολυωνυμικής Λογιστικής Παλινδρόμησης (Multinomial Logistic Regression) και της Πολυεπίπεδης Γραμμικής Διακριτής Ανάλυσης (Multinomial Discriminant Analysis). Εφαρμόζοντας τον ίδιο εκτός του δείγματος έλεγχο με την πρώτη προσέγγιση επιτυγχάνεται η κατά 85.7% σωστή ταξινόμηση με την βέλτιστη μέθοδο που αποδεικνύεται ότι είναι η Πολυεπίπεδη Γραμμική Διακριτή Ανάλυση. Εφαρμόζοντας την ανωτέρω ανάλυση, οι ενδιαφερόμενοι φορείς άσκησης πολιτικής (policy makers) μπορούν να ανιχνεύσουν την ύπαρξης κρίσης σε βάθος χρόνου έως τριών ετών με τα προτεινόμενα μοντέλα, χρησιμοποιώντας μόνο δεδομένα που υπάρχουν ελεύθερα προσβάσιμα στο κοινό, ασκώντας με τον τρόπο αυτό την κατάλληλη ανά περίπτωση μακροπροληπτική πολιτική (macroprudential policy).

scholarly journals Is it possible to register the ideas, concerns and expectations behind the reason for encounter as a means of classifying patient preferences with ICPC-2?

2017 ◽  
Vol 19 (01) ◽  
pp. 1-6 ◽  
Author(s):  
Diego Schrans ◽  
Pauline Boeckxstaens ◽  
An De Sutter ◽  
Sara Willems ◽  
Dirk Avonts ◽  
...  
Keyword(s):  
Primary Care ◽  
Patient Preferences ◽  
Time Frame ◽  
Specific Procedure ◽  
Reason For Encounter ◽  
Related Information ◽  
Almost All ◽  
Danger To Others ◽  
Do So

BackgroundFamily practice aims to recognize the health problems and needs expressed by the person rather than only focusing on the disease. Documenting person-related information will facilitate both the understanding and delivery of person-focused care.AimTo explore if the patients’ ideas, concerns and expectations (ICE) behind the reason for encounter (RFE) can be coded with the International Classification of Primary Care, version 2 (ICPC-2) and what kinds of codes are missing to be able to do so.MethodsIn total, 613 consultations were observed, and patients’ expressions of ICE were narratively recorded. These descriptions were consequently translated to ICPC codes by two researchers. Descriptions that could not be translated were qualitatively analysed in order to identify gaps in ICPC-2.ResultsIn all, 613 consultations yielded 672 ICE expressions. Within the 123 that could not be coded with ICPC-2, eight categories could be defined: concern about the duration/time frame; concern about the evolution/severity; concern of being contagious or a danger to others; patient has no concern, but others do; expects a confirmation of something; expects a solution for the symptoms without specification of what it should be; expects a specific procedure; and expects that something is not done.DiscussionAlthough many ICE can be registered with ICPC-2, adding eight new categories would capture almost all ICE.

scholarly journals Network controllability in transmodal cortex predicts psychosis spectrum symptoms

2020 ◽  
Author(s):  
Linden Parkes ◽  
Tyler M. Moore ◽  
Monica E. Calkins ◽  
Matthew Cieslak ◽  
David R. Roalf ◽  
...  
Keyword(s):  
Control Theory ◽  
Structural Connectivity ◽  
Prediction Performance ◽  
Network Control ◽  
Association Cortex ◽  
Out Of Sample ◽  
Sample Testing ◽  
Functional Hierarchy ◽  
Structural Connections ◽  
Network Controllability

ABSTRACTBackgroundThe psychosis spectrum is associated with structural dysconnectivity concentrated in transmodal association cortex. However, understanding of this pathophysiology has been limited by an exclusive focus on the direct connections to a region. Using Network Control Theory, we measured variation in both direct and indirect structural connections to a region to gain new insights into the pathophysiology of the psychosis spectrum.MethodsWe used psychosis symptom data and structural connectivity in 1,068 youths aged 8 to 22 years from the Philadelphia Neurodevelopmental Cohort. Applying a Network Control Theory metric called average controllability, we estimated each brain region’s capacity to leverage its direct and indirect structural connections to control linear brain dynamics. Next, using non-linear regression, we determined the accuracy with which average controllability could predict negative and positive psychosis spectrum symptoms in out-of-sample testing. We also compared prediction performance for average controllability versus strength, which indexes only direct connections to a region. Finally, we assessed how the prediction performance for psychosis spectrum symptoms varied over the functional hierarchy spanning unimodal to transmodal cortex.ResultsAverage controllability outperformed strength at predicting positive psychosis spectrum symptoms, demonstrating that indexing indirect structural connections to a region improved prediction performance. Critically, improved prediction was concentrated in association cortex for average controllability, whereas prediction performance for strength was uniform across the cortex, suggesting that indexing indirect connections is crucial in association cortex.ConclusionsExamining inter-individual variation in direct and indirect structural connections to association cortex is crucial for accurate prediction of positive psychosis spectrum symptoms.

scholarly journals Financial Distress Pada Perusahaan Yang Terdaftar Di Bursa Efek Indonesia

2021 ◽  
Vol 2 (2) ◽  
pp. 237-246
Author(s):  
Martinus Robert Hutauruk ◽  
Mansyur Mansyur ◽  
Muhammad Rinaldi ◽  
Yisar Renza Situru
Keyword(s):  
Financial Distress ◽  
Financial Analysis ◽  
Stock Exchange ◽  
Bankruptcy Prediction ◽  
Stock Index ◽  
Risk Level ◽  
Food And Beverage ◽  
Community Information ◽  
High Level ◽  
Prediction Approach

Companies engaged in the food and beverage business have a very high chance of success in running their business, given the increasingly high level of food and beverage consumption for the community. Information based on financial ratios needs to be improved in other forms of financial analysis to ascertain the future risk level. The purpose of this study is to analyze financial distress for food and beverage sub-sector companies listed on conventional stocks and Islamic stocks on the Indonesia Stock Exchange in the period 2015-2020. Financial distress analysis uses the Altman Z-Score bankruptcy prediction approach. The results of the study indicate that companies that experience accounting losses do not necessarily experience financial distress. Companies whose shares are listed on the Sharia stock index tend to experience healthier financial conditions and do not experience financial distress. Sharia shares of food and beverage sub-sector companies on the Indonesia Stock Exchange have good resistance to financial distress. This is supported by the high and stable value of Inti Agri Resources' shares compared to the shares of other companies.

scholarly journals Family Businesses in Global Economy and Evaluating their Financial Health Using Bankruptcy Models

2021 ◽  
Vol 129 ◽  
pp. 03031
Author(s):  
Maria Truchlikova
Keyword(s):  
Financial Distress ◽  
Global Economy ◽  
Agricultural Sector ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Value Added ◽  
Business Environment ◽  
Bankruptcy Prediction ◽  
Family Businesses ◽  
Financial Health

Research background: Predicting and assessing financial health should be one of the most important activities for each business especially in context of turbulent business environment and global economy. The financial sustainability of family businesses has a direct and significant influence on the development and growth of the economy because they still represent the backbone of the economy and play an important role in national economies worldwide accounting. Purpose of the article: We used in this article the financial distress and bankruptcy prediction models for assessing financial status of family businesses in agricultural sector. The aim of the paper is to compare models developed by using three different methods to identify a model with the highest predictive accuracy of financial distress and assess financial health. Methods: The data was obtained from Finstat database. For assessing the financial health of selected family businesses bankruptcy models were used: Chrastinova’s CH-Index, Gurcik’s G-Index (defined for Slovak agricultural enterprises) and Altman Z-score. Findings & Value added: This article summarizes existing models and compares results of assessing financial health of family businesses using three different models.

Portfolio Optimization using Rank Correlation

2014 ◽  
pp. 1866-1879
Author(s):  
Chanaka Edirisinghe ◽  
Wenjun Zhou
Keyword(s):  
Portfolio Optimization ◽  
Rank Correlation ◽  
Financial Assets ◽  
Risk Averse ◽  
Out Of Sample ◽  
Return Distributions ◽  
Sample Testing ◽  
Correlation Measures ◽  
Computational Properties ◽  
Critical Challenge

A critical challenge in managing quantitative funds is the computation of volatilities and correlations of the underlying financial assets. We present a study of Kendall's t coefficient, one of the best-known rank-based correlation measures, for computing the portfolio risk. Incorporating within risk-averse portfolio optimization, we show empirically that this correlation measure outperforms that of Pearson's in our out-of-sample testing with real-world financial data. This phenomenon is mainly due to the fat-tailed nature of stock return distributions. We also discuss computational properties of Kendall's t, and describe efficient procedures for incremental and one-time computation of Kendall's rank correlation.

scholarly journals Quantifying the future lethality of terror organizations

2019 ◽  
Vol 116 (43) ◽  
pp. 21463-21468 ◽  
Author(s):  
Yang Yang ◽  
Adam R. Pah ◽  
Brian Uzzi
Keyword(s):  
Latent Variable ◽  
Warning Signal ◽  
Scientific Methods ◽  
Explanatory Variables ◽  
Modeling Techniques ◽  
Out Of Sample ◽  
Sample Testing ◽  
Explained Variance ◽  
Human Conflict ◽  
Require Data

As terror groups proliferate and grow in sophistication, a major international concern is the development of scientific methods that explain and predict insurgent violence. Approaches to estimating a group’s future lethality often require data on the group’s capabilities and resources, but by the nature of the phenomenon, these data are intentionally concealed by the organizations themselves via encryption, the dark web, back-channel financing, and misinformation. Here, we present a statistical model for estimating a terror group’s future lethality using latent-variable modeling techniques to infer a group’s intrinsic capabilities and resources for inflicting harm. The analysis introduces 2 explanatory variables that are strong predictors of lethality and raise the overall explained variance when added to existing models. The explanatory variables generate a unique early-warning signal of an individual group’s future lethality based on just a few of its first attacks. Relying on the first 10 to 20 attacks or the first 10 to 20% of a group’s lifetime behavior, our model explains about 60% of the variance in a group’s future lethality as would be explained by a group’s complete lifetime data. The model’s robustness is evaluated with out-of-sample testing and simulations. The findings’ theoretical and pragmatic implications for the science of human conflict are discussed.

Financial distress and bankruptcy prediction: An appropriate model for listed firms in Vietnam

2018 ◽  
Vol 42 (4) ◽  
pp. 616-624 ◽  
Author(s):  
Binh Pham Vo Ninh ◽  
Trung Do Thanh ◽  
Duc Vo Hong
Keyword(s):  
Financial Distress ◽  
Bankruptcy Prediction ◽  
Listed Firms
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