Dinamikus pénzügyi mutatószámok alkalmazása a csődelőrejelzésben = Application of dynamic financial variables in bankruptcy prediction

A csődelőrejelző modellek a vállalkozások jövőbeli fizetőképességét próbálják előrejelezni objektív információk alapján statisztikai (adatbányászati) módszerek felhasználásával. E modellek jellemzően a vállalatok pénzügyi kimutatásaiból (mérleg, eredménykimutatás) számítható hányados típusú pénzügyi mutatószámokat használják magyarázó változóként. A tudományterület kutatása közel 50 éves múltra tekint vissza. Ennek ellenére számos nyitott kutatási kérdés található a szakirodalomban, melynek köszönhetően folyamatos az érdeklődés a témakör iránt. E kérdések közül az egyik legrégebben ismert probléma a csődelőrejelző modellek statikus jellegéből adódik. Ennek lényege, hogy a modellek magyarázó változói közt csak legaktuálisabb adatokat használják fel és figyelmen kívül hagyják a pénzügyi mutatószámok időbeli trendjéből kinyerhető információkat. A probléma megoldására főként bonyolultabb módszertani megoldások születtek, melyek – vélhetően komplexitásuk miatt – nem terjedtek el általánosan az ígéretes eredmények ellenére sem. Értekezésemben arra törekedtem, hogy a modellek dinamizálását bonyolultabb módszertani megoldások nélkül valósítsam meg. Erre a célra egy olyan mutatószámot javasoltam, amely lehetővé teszi a pénzügyi mutatók időbeli tendenciájának figyelembe vételét a szakirodalomban és a gyakorlati modellezésben általánosan elterjedt „hagyományos” módszerek keretei közt. E mutatószám azt tükrözi, hogyan viszonyul egy érintett vállalkozás legaktuálisabb pénzügyi mutatója az adott vállalkozás azonos mutatójának korábbi években megfigyelt értékeihez

A Back Propagation Neural Network Model with the Synthetic Minority Over-Sampling Technique for Construction Company Bankruptcy Prediction

Improving model accuracy is one of the most frequently addressed issues in bankruptcy prediction. Several previous studies employed artificial neural networks (ANNs) to improve the accuracy at which construction company bankruptcy can be predicted. However, most of these studies use the sample-matching technique and all of the available company quarters or company years in the dataset, resulting in sample selection biases and between-class imbalances. This study integrates a back propagation neural network (BPNN) with the synthetic minority over-sampling technique (SMOTE) and the use of all of the available company-year samples during the sample period to improve the accuracy at which bankruptcy in construction companies can be predicted. In addition to eliminating sample selection biases during the sample matching and between-class imbalance, these methods also achieve the high accuracy rates. Furthermore, the approach used in this study shows optimal over-sampling times, neurons of the hidden layer, and learning rate, all of which are major parameters in the BPNN and SMOTE-BPNN models. The traditional BPNN model is provided as a benchmark for evaluating the predictive abilities of the SMOTE-BPNN model. The empirical results of this paper show that the SMOTE-BPNN model outperforms the traditional BPNN

Magyar vállalkozások felszámolásának előrejelzése pénzügyi mutatóik idősorai alapján

A vállalatok felszámolásának előrejelzésében általános gyakorlat a számviteli adatokból kapott hányados típusú pénzügyi mutatók használata. E mutatókat általában csak az utolsó lezárt üzleti év adatai alapján kalkulálják. Az így felépített modellek azonban statikus jellegűek, s nem veszik figyelembe a vállalati gazdálkodás folyamatjellegét. E hiányosság kiküszöbölésére korábban Nyitrai [2014a] tett kísérletet a statikus pénzügyi mutatószámok idősoraiból képzett, úgynevezett dinamikus pénzügyi mutatók használatával – azonban számos, önkényesnek tűnő feltételezéssel élt, amelyek közül tanulmányunkban kettőt feloldunk. Az idézett cikk csak döntési fák segítségével vizsgálta a pénzügyi mutatók időbeli trendjét kifejező változó hatékonyságát. Most e megközelítés hatását a modellek előrejelző képességére a – szakirodalomban általánosan elterjedt – logisztikus regresszió keretei között vizsgáljuk meg. Nyitrai [2014a] a pénzügyi mutatók teljes idősorait felhasználta, ennek szükségessége kérdéses lehet, ezért megnézzük a csődmodellek előrejelző képességét annak függvényében, hogy hány évre visszamenően vesszük figyelembe a pénzügyi mutatók értékeit. Journal of Economic Literature (JEL) kód: C52, C53, G33

A Big Data analytics approach for construction firms failure prediction models

Using 693,000 datacells from 33,000 sample construction firms that operated or failed between 2008 and 2017, failure prediction models were developed using artificial neural network (ANN), support vector machine (SVM), multiple discriminant analysis (MDA) and logistic regression (LR). The accuracy of the models on test data surprisingly showed ANN to have only a slightly better accuracy than LR and MDA. The ANN’s number of units in the hidden layer and weight decay hyperparameters were consequently tuned using the grid search. Tuning process led to tedious machine computation that was aborted after many hours without completion. The state of art Big Data Analytics (BDA) technology was, for the first time in failure prediction, consequently employed and the tuning was completed in some seconds. Mean accuracy from cross-validation was used for selection of the model with best parameter values which were used to develop a new ANN model which outperformed all previously developed models on test data. Subsequent use of selected variables to develop new models led to reduced tuning computational cost but not improved performance. Since the real-life effect of a misclassification cost is greater than the tedious computation cost, it was concluded that BDA is the best compromise

Insolvency of Small Civil Engineering Firms: An Examination of Critical Strategic Factors

Construction industry insolvency studies have failed to stem the industry’s high insolvency tide because many focus on big civil engineering firms (CEF) when over 90% firms in the industry are small or micro (S&M). This study thus set out to uncover insolvency criteria of S&M CEFs and the underlying factors using mixed methods. Using convenience sampling, storytelling method was used to execute interviews of 16 respondents from insolvent firms. Narrative and thematic analysis were used to extract 17 criteria under 2 groups. Criteria were used to formulate questionnaire of which 81 completed copies were received and analysed using Cronbach’s alpha coefficient and relevance index score for reliability and ranking respectively. The five most relevant criteria are: economic recession, immigration, too many new firms springing up, collecting receivables and burden of sustainable construction. The 4 underlying factors established through factor analysis are: market forces, competence-based management, operations efficiency and other management issues and information management. The factors were in line with Mintzberg’s and Porters’ strategy theories. Results demonstrate that insolvency factors affecting big and small CEF can be quite different and sometimes, even opposite. This research will provide a unique resource on the ‘beware’ factors for potential owners of S&M CEF. The criteria are potential variables for insolvency prediction models for S&M CEFs

Mali Başarısızlık Tahminlemesinde Sektör Bazlı Bir Karşılaştırma1 A Sector-Based Comparison of Financial Distress Prediction

Firmalarda mali başarısızlık ile karşılaşılmasına neden olan faktörlerin bilinmesi ve mali başarısızlık yaşanabileceğinin öngörülmesi, düzenleyici önlemlerin alınması ile kayıplarının azaltılması noktasında önem arz etmektedir. Ekonominin paydaşları olarak sıralanabilecek yöneticiler, kredi verenler, yatırımcılar, bağımsız denetçiler ve devletler için mali başarısızlık tahminlemesi firmalara ve sektörlere ilişkin önemli bilgiler sunmaktadır. Bu noktada bu çalışma ile bilişim, imalat ve hizmet ana başlıklarında sınıflandırılabilecek üç farklı sektör bazında firmaların mali başarısızlık tahminlemeleri yapılarak mali başarısızlığın habercisi olarak nitelendirilebilecek değişkenlerin sektörden sektöre farklılaştıklarının ortaya konulması amaçlanmıştır. Lojistik regresyon analizi ile Borsa İstanbul’da işlem gören Teknoloji Ulaştırma Haberleşme sektörü, Gıda İçki Tütün sektörü ve Toptan Perakende Otel Lokanta sektörlerinde yer alan şirketlerin 31.12.2008- 31.12.2017 arası 10 yıllık dönemdeki bilanço ve gelir tabloları kullanılarak bir yıl önceki verileri ile mali başarısızlık tahminlemeleri yapılmıştır. Elde edilen bulgulara göre; Teknoloji, Ulaştırma, Haberleşme sektöründe stok devir hızı, Gıda, İçki, Tütün sektöründe cari oran ve Toptan Perakende Otel Lokanta sektöründe Vergi Öncesi Kar / Özsermaye oranları mali başarısızlığın habercisi olan oranlar olarak tespit edilmiştir. It is important to know the factors that cause financial distress in companies for being able to take preventive measures and reduce losses. Financial distress estimation provides important information about firms and sectors to stakeholders of the economy that can be listed as managers, lenders, investors, independent auditors and governments. Within the scope of this study, financial distress of companies operating in IT, manufacturing and service sectors, which are traded in Istanbul Stock Exchange, have been estimated and it has been aimed to reveal sectoral differences in estimation. Financial distress estimations of the companies in the Technology Transportation Communication sector, Food Drink Tobacco sector and Wholesale Retail Hotel Restaurant sector traded in Borsa Istanbul were made.This study uses logistic regression as the analysis technique by using the balance sheet and income statements data between the dates of 31.12.2008 and 31.12.2017.The likelihood of companies’ financial distress were calculated one year in advance the existence of the distress. According to the findings; the stock turnover rate in the Technology Transportation Communication sector, the current ratio in Food Drink Tobacco sector and the ratio of Profit Before Tax / Equity in Wholesale Retail Hotel Restaurant sector are the main indicators of the financial distress

Rethinking SME default prediction: a systematic literature review and future perspectives

Over the last dozen years, the topic of small and medium enterprise (SME) default prediction has developed into a relevant research domain that has grown for important reasons exponentially across multiple disciplines, including finance, management, accounting, and statistics. Motivated by the enormous toll on SMEs caused by the 2007-2009 global financial crisis as well as the recent COVID-19 crisis and the consequent need to develop new SME default predictors, this paper provides a systematic literature review, based on a statistical, bibliometric analysis, of over 100 peer-reviewed articles published on SME default prediction modelling over a 34-year period, 1986 to 2019. We identified, analysed and reviewed five streams of research and suggest a set of future research avenues to help scholars and practitioners address the new challenges and emerging issues in a changing economic environment. The research agenda proposes some new innovative approaches to capture and exploit new data sources using modern analytical techniques, like artificial intelligence, machine learning, and macro-data inputs, with the aim of providing enhanced predictive results

Developing a Machine Learning based Systematic Investment Startegy: A case study for the Construction Industry

In this research work, an end-to-end systematic investment strategy based on machine learning models and leveraging the construction industry operational and management practices knowledge, is implemented. First, a literature research in the field of behavioral finance is done, presenting the current state of the knowledge and trends in the industry. A suitable investment opportunity exploiting prevailing market inefficiencies around earnings announcements is identified. Second, an extensive literature research is performed identifying the most relevant characteristics of construction companies’ operations and major risk factors they are exposed to. These insights are used to engineer a set of relevant variables. Third, advanced statistical techniques are used to select the most relevant subset of features, which includes market and analysts’ expectation data, macroeconomic indicators, the delay in reporting earnings, and the most important financial dimensions for construction firms. Fourth, the earnings’ surprise classification problem is characterized by a class imbalance and asymmetric misclassification costs. These issues are a consequence of the desired business application, and are addressed by selecting an appropriate evaluation metric. Additionally, considerations on the temporal dimension and generative process of the data are made to select an appropriate validation scheme. Five different state-of-the-art machine learning algorithms are considered: a multinomial logistic regression, a bagging classifier, a random forest, an XGBoost and a linear Support Vector Machine. The multinomial logistic regression is found to be the most suitable model, exhibiting a bias towards predicting positive earnings’ surprises over the rest of classes. The firm size, and the profitability and valuation measures, portrayed by the Return on Assets and Enterprise Value multiples, are found to be the most important variables when predicting earnings surprises. To conclude, the systematic investment strategy based on the investment signals produced by the selected machine learning model is back-tested, being the performance of the long-short portfolio driven by the positive surprise one as a consequence of the selected model bias. Keywords: Quantitative Investing, Machine Learning, Behavioral Financ