Ennustemallin kehittäminen suomalaisten PK-yritysten konkurssiriskin määritykseen

Bankruptcy prediction is a subject of significant interest to both academics and practitioners because of its vast economic and societal impact. Academic research in the field is extensive and diverse; no consensus has formed regarding the superiority of different prediction methods or predictor variables. Most studies focus on large companies; small and medium-sized enterprises (SMEs) have received less attention, mainly due to data unavailability. Despite recent academic advances, simple statistical models are still favored in practical use, largely due to their understandability and interpretability. This study aims to construct a high-performing but user-friendly and interpretable bankruptcy prediction model for Finnish SMEs using financial statement data from 2008–2010. A literature review is conducted to explore the key aspects of bankruptcy prediction; the findings are used for designing an empirical study. Five prediction models are trained on different predictor subsets and training samples, and two models are chosen for detailed examination based on the findings. A prediction model using the random forest method, utilizing all available predictors and the unadjusted training data containing an imbalance of bankrupt and non-bankrupt firms, is found to perform best. Superior performance compared to a benchmark model is observed in terms of both key metrics, and the random forest model is deemed easy to use and interpretable; it is therefore recommended for practical application. Equity ratio and financial expenses to total assets consistently rank as the best two predictors for different models; otherwise the findings on predictor importance are mixed, but mainly in line with the prevalent views in the related literature. This study shows that constructing an accurate but practical bankruptcy prediction model is feasible, and serves as a guideline for future scholars and practitioners seeking to achieve the same. Some further research avenues to follow are recognized based on empirical findings and the extant literature. In particular, this study raises an important question regarding the appropriateness of the most commonly used performance metrics in bankruptcy prediction. Area under the precision-recall curve (PR AUC), which is widely used in other fields of study, is deemed a suitable alternative and is recommended for measuring model performance in future bankruptcy prediction studies.Konkurssien ennustaminen on taloudellisten ja yhteiskunnallisten vaikutustensa vuoksi merkittävä aihe akateemisesta ja käytännöllisestä näkökulmasta. Alan tutkimus on laajaa ja monipuolista, eikä konsensusta parhaiden ennustemallien ja -muuttujien suhteen ole saavutettu. Valtaosa tutkimuksista keskittyy suuryrityksiin; pienten ja keskisuurten (PK)-yritysten konkurssimallinnus on jäänyt vähemmälle huomiolle. Akateemisen tutkimuksen viimeaikaisesta kehityksestä huolimatta käytännön sovellukset perustuvat usein yksinkertaisille tilastollisille malleille johtuen niiden paremmasta ymmärrettävyydestä. Tässä diplomityössä rakennetaan ennustemalli suomalaisten PK-yritysten konkurssiriskin määritykseen käyttäen tilinpäätösdataa vuosilta 2008–2010. Tavoitteena on tarkka, mutta käyttäjäystävällinen ja helposti tulkittava malli. Konkurssimallinnuksen keskeisiin osa-alueisiin perehdytään kirjallisuuskatsauksessa, jonka pohjalta suunnitellaan empiirinen tutkimus. Viiden mallinnusmenetelmän suoriutumista vertaillaan erilaisia opetusaineiston ja ennustemuuttujien osajoukkoja käyttäen, ja löydösten perusteella kaksi parasta menetelmää otetaan lähempään tarkasteluun. Satunnaismetsä (random forest) -koneoppimismenetelmää käyttävä, kaikkia saatavilla olevia ennustemuuttujia ja muokkaamatonta, epäsuhtaisesti konkurssi- ja ei-konkurssitapauksia sisältävää opetusaineistoa hyödyntävä malli toimii parhaiten. Keskeisten suorituskykymittarien valossa satunnaismetsämalli suoriutuu käytettyä verrokkia paremmin, ja todetaan helppokäyttöiseksi ja hyvin tulkittavaksi; sitä suositellaan sovellettavaksi käytäntöön. Omavaraisuusaste ja rahoituskulujen suhde taseen loppusummaan osoittautuvat johdonmukaisesti parhaiksi ennustemuuttujiksi eri mallinnusmetodeilla, mutta muilta osin havainnot muuttujien keskinäisestä paremmuudesta ovat vaihtelevia. Tämä diplomityö osoittaa, että konkurssiennustemalli voi olla sekä tarkka että käytännöllinen, ja tarjoaa suuntaviivoja tuleville tutkimuksille. Empiiristen havaintojen ja kirjallisuuslöydösten pohjalta esitetään jatkotutkimusehdotuksia. Erityisen tärkeä huomio on se, että konkurssiennustamisessa tyypillisesti käytettyjen suorituskykymittarien soveltuvuus on kyseenalaista konkurssitapausten harvinaisuudesta johtuen. Muilla tutkimusaloilla laajasti käytetty tarkkuus-saantikäyrän alle jäävä pinta-ala (PR AUC) todetaan soveliaaksi vaihtoehdoksi, ja sitä suositellaan käytettäväksi konkurssimallien suorituskyvyn mittaukseen. Avainsanat konkurssien ennustaminen, luottoriski, koneoppiminen

An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

Credit Scoring Using Machine Learning

For financial institutions and the economy at large, the role of credit scoring in lending decisions cannot be overemphasised. An accurate and well-performing credit scorecard allows lenders to control their risk exposure through the selective allocation of credit based on the statistical analysis of historical customer data. This thesis identifies and investigates a number of specific challenges that occur during the development of credit scorecards. Four main contributions are made in this thesis. First, we examine the performance of a number supervised classification techniques on a collection of imbalanced credit scoring datasets. Class imbalance occurs when there are significantly fewer examples in one or more classes in a dataset compared to the remaining classes. We demonstrate that oversampling the minority class leads to no overall improvement to the best performing classifiers. We find that, in contrast, adjusting the threshold on classifier output yields, in many cases, an improvement in classification performance. Our second contribution investigates a particularly severe form of class imbalance, which, in credit scoring, is referred to as the low-default portfolio problem. To address this issue, we compare the performance of a number of semi-supervised classification algorithms with that of logistic regression. Based on the detailed comparison of classifier performance, we conclude that both approaches merit consideration when dealing with low-default portfolios. Third, we quantify the differences in classifier performance arising from various implementations of a real-world behavioural scoring dataset. Due to commercial sensitivities surrounding the use of behavioural scoring data, very few empirical studies which directly address this topic are published. This thesis describes the quantitative comparison of a range of dataset parameters impacting classification performance, including: (i) varying durations of historical customer behaviour for model training; (ii) different lengths of time from which a borrower’s class label is defined; and (iii) using alternative approaches to define a customer’s default status in behavioural scoring. Finally, this thesis demonstrates how artificial data may be used to overcome the difficulties associated with obtaining and using real-world data. The limitations of artificial data, in terms of its usefulness in evaluating classification performance, are also highlighted. In this work, we are interested in generating artificial data, for credit scoring, in the absence of any available real-world data

Data mining in computational finance

Computational finance is a relatively new discipline whose birth can be traced back to early 1950s. Its major objective is to develop and study practical models focusing on techniques that apply directly to financial analyses. The large number of decisions and computationally intensive problems involved in this discipline make data mining and machine learning models an integral part to improve, automate, and expand the current processes. One of the objectives of this research is to present a state-of-the-art of the data mining and machine learning techniques applied in the core areas of computational finance. Next, detailed analysis of public and private finance datasets is performed in an attempt to find interesting facts from data and draw conclusions regarding the usefulness of features within the datasets. Credit risk evaluation is one of the crucial modern concerns in this field. Credit scoring is essentially a classification problem where models are built using the information about past applicants to categorise new applicants as ‘creditworthy’ or ‘non-creditworthy’. We appraise the performance of a few classical machine learning algorithms for the problem of credit scoring. Typically, credit scoring databases are large and characterised by redundant and irrelevant features, making the classification task more computationally-demanding. Feature selection is the process of selecting an optimal subset of relevant features. We propose an improved information-gain directed wrapper feature selection method using genetic algorithms and successfully evaluate its effectiveness against baseline and generic wrapper methods using three benchmark datasets. One of the tasks of financial analysts is to estimate a company’s worth. In the last piece of work, this study predicts the growth rate for earnings of companies using three machine learning techniques. We employed the technique of lagged features, which allowed varying amounts of recent history to be brought into the prediction task, and transformed the time series forecasting problem into a supervised learning problem. This work was applied on a private time series dataset

Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems

EFFECTIVE METHODS AND TOOLS FOR MINING APP STORE REVIEWS

Research on mining user reviews in mobile application (app) stores has noticeably advanced in the past few years. The main objective is to extract useful information that app developers can use to build more sustainable apps. In general, existing research on app store mining can be classified into three genres: classification of user feedback into different types of software maintenance requests (e.g., bug reports and feature requests), building practical tools that are readily available for developers to use, and proposing visions for enhanced mobile app stores that integrate multiple sources of user feedback to ensure app survivability. Despite these major advances, existing tools and techniques still suffer from several drawbacks. Specifically, the majority of techniques rely on the textual content of user reviews for classification. However, due to the inherently diverse and unstructured nature of user-generated online textual reviews, text-based review mining techniques often produce excessively complicated models that are prone to over-fitting. Furthermore, the majority of proposed techniques focus on extracting and classifying the functional requirements in mobile app reviews, providing a little or no support for extracting and synthesizing the non-functional requirements (NFRs) raised in user feedback (e.g., security, reliability, and usability). In terms of tool support, existing tools are still far from being adequate for practical applications. In general, there is a lack of off-the-shelf tools that can be used by researchers and practitioners to accurately mine user reviews. Motivated by these observations, in this dissertation, we explore several research directions aimed at addressing the current issues and shortcomings in app store review mining research. In particular, we introduce a novel semantically aware approach for mining and classifying functional requirements from app store reviews. This approach reduces the dimensionality of the data and enhances the predictive capabilities of the classifier. We then present a two-phase study aimed at automatically capturing the NFRs in user reviews. We also introduce MARC, a tool that enables developers to extract, classify, and summarize user reviews

8th Annual Research Week- Event Proceedings

8th Annual Research Wee