Predicting bankruptcy of Finnish limited liability companies from historical financial statements

This master’s thesis studies the probability of bankruptcy of Finnish limited liability companies as a part of credit risk assessment. The main idea of this thesis is to build and test bankruptcy prediction models for Finnish limited liability companies that can be utilized in credit decision making. The data used in this thesis consists of historical financial statements from 2112 Finnish limited liability companies, half of which have filed for bankruptcy. A total of four models are developed, two with logistic regression and two with multivariate discriminant analysis (MDA). The time horizon of the models varies from 1 to 2 years prior to the bankruptcy, and 14 different financial variables are used in the model formation. The results show that the prediction accuracy of the models ranges between 81.7% and 88.9%, and the best prediction accuracy is achieved with the one year prior the bankruptcy logistic regression model. However the difference between the best logistic model and the best MDA model is minimal. Overall based on the results of this thesis it can be concluded that predicting bankruptcy is possible to some extent, but naturally the results are not perfect.

Value at Risk in Foreign Exchange Risk Management

This thesis examines the suitability of VaR in foreign exchange rate risk management from the perspective of a European investor. The suitability of four different VaR models is evaluated in respect to have insight if VaR is a valuable tool in managing foreign exchange rate risk. The models evaluated are historical method, historical bootstrap method, variance-covariance method and Monte Carlo simulation. The data evaluated are divided into emerging and developed market currencies to have more intriguing analysis. The foreign exchange rate data in this thesis is from 31st January 2000 to 30th April 2014. The results show that the previously mentioned VaR models performance in foreign exchange risk management is not to be considered as a single tool in foreign exchange rate risk management. The variance-covariance method and Monte Carlo simulation performs poorest in both currency portfolios. Both historical methods performed better but should also be considered as an additional tool along with other more sophisticated analysis tools. A comparative study of VaR estimates and forward prices is also included in the thesis. The study reveals that regardless of the expensive hedging cost of emerging market currencies the risk captured by VaR is more expensive and thus FX forward hedging is recommended

Genes outside The Hla Region affecting Susceptibility to Type 1 Diabetes - The Role of Iddm2 and Iddm9 in the Finnish Population

Tyypin 1 diabeteksen perinnöllinen alttius Suomessa - HLA-alueen ulkopuolisten alttiuslokusten IDDM2 ja IDDM9 rooli taudin periytymisessä HLA-alue, joka sijaitsee kromosomissa 6p21.3, vastaa noin puolesta perinnöllisestä alttiudesta sairastua tyypin 1 diabetekseen. Myös HLA-alueen ulkopuolisten lokusten on todettu liittyvän sairausalttiuteen. Näistä kolmen lokuksen on varmistettu olevan todellisia alttiuslokuksia ja lisäksi useiden muiden, vielä varmistamattomien lokusten, on todettu liittyvän sairausalttiuteen. Tässä tutkimuksessa 12:n HLA-alueen ulkopuolisen alttiuslokuksen kytkentä tyypin 1 diabetekseen tutkittiin käyttäen 107:aa suomalaista multiplex-perhettä. Jatkotutkimuksessa analysoitiin IDDM9-alueen kytkentä ja assosiaatio sairauteen laajennetuissa perhemateriaaleissa sekä IDDM2-alueen mahdollinen interaktio HLA-alueen kanssa sairauden muodostumisessa. Lisäksi suoritettiin IDDM2-alueen suojaavien haplotyyppien alatyypitys tarkoituksena tutkia eri haplotyyppien käyttökelpoisuutta sairastumisriskin tarkempaa ennustamista varten. Ensimmäisessä kytkentätutkimuksessa ei löytynyt koko genomin tasolla merkitsevää tai viitteellistä kytkentää tutkituista HLA-alueen ulkopuolisista lokuksista. Voimakkain havaittu nimellisen merkitsevyyden tavoittava kytkentä nähtiin IDDM9-alueen markkerilla D3S3576 (MLS=1.05). Tutkimuksessa ei kyetty varmistamaan tai sulkemaan pois aiempia kytkentähavaintoja tutkituilla lokuksilla, mutta IDDM9-alueen jatkotutkimuksessa havaittu voimakas kytkentä (MLS=3.4) ja merkitsevä assosiaatio (TDT p=0.0002) viittaa vahvasti siihen, että 3q21-alueella sijaitsee todellinen tyypin 1 diabeteksen alttiusgeeni, jolloin alueen kattava assosiaatiotutkimus olisi perusteltu jatkotoimenpide. Sairauteen altistava IDDM2-alueen MspI-2221 genotyyppi CC oli nimellisesti yleisempi matalan tai kohtalaisen HLA-sairastumisriskin diabeetikoilla, verrattuna korkean HLA-riskin potilaisiin (p=0.05). Myös genotyyppijakauman vertailu osoitti merkitsevää eroa ryhmien välillä (p=0.01). VNTR-haplotyyppitutkimus osoitti, että IIIA/IIIA-homotsygootin sairaudelta suojaava vaikutus on merkitsevästi voimakkaampi kuin muiden luokka III:n genotyypeillä. Nämä tulokset viittaavat IDDM2-HLA -vuorovaikutukseen sekä siihen että IDDM2-alueen haplotyyppien välillä esiintyy etiologista heterogeniaa. Tämän johdosta IDDM2-alueen haplotyyppien tarkempi määrittäminen voisi tehostaa tyypin 1 diabeteksen riskiarviointia.

Pankkien vakavaraisuussäännöstön uudistus

Tutkielman tavoitteena on kuvata pankkien vakavaraisuusuudistuksen eri osa-alueita. Tarkempi analyysi rajautuu uudistuksen tuomiin muutoksiin luotto- ja operatiivisen riskin pääomavaateissa. Tutkielman empiirisen osuuden tavoitteena on perehtyä vakavaraisuussäännöstön uudistusten vaikutuksiin Nordeassa. Tutkimusmetodologiaksi on valittu normatiivinen tutkimusote. Lisäksi tutkielma sisältää deskriptiivisiä ja positivistisia osia. Lähdeaineisto koostuu Baselin pankkivalvontakomitean ja Suomen Pankin julkaisemista tutkimuksista ja dokumenteista sekä alan julkaisuissa ilmestyneistä artikkeleista. Pankkien vakavaraisuussäännöstöuudistuksen tavoitteena on lisätä rahoitusmarkkinoiden vakautta. Sääntelyn kautta pyritään turvaamaan pankkien varojen riittävyys suhteessa niiden riskien ottoon. Vakavaraisuussäännöstön uudistus muodostuu kolmesta pilarista: (1) minimipääomavaatimuksista, (2) pankkivalvonnan vahvistamisesta ja (3) markkinakurin hyödyntämisestä luottolaitosten toiminnan julkistamisvaatimuksia lisäämällä. Pankkivalvonnan harmonisoinnista vallitsee kansainvälinen yhteisymmärrys, mutta ennen kuin Basel II voi astua voimaan on useita ongelmia ratkaisematta. Baselin vakavaraisuuskehikko ei ole ainut lähitulevaisuudessa pankkitoimialaa koetteleva uudistus. Kansainväliset tilinpäätösstandardit; International Accounting Standards ja erityisesti IAS 39 sekä International Financial Reporting Standards, lyhyemmin IFRS tulevat muuttamaan merkittävästi pankkien tilinpäätöskäyttäytymistä. Epäselvää on vielä kuitenkin tukevatko uudistukset toisiaan ja missä määrin pankkien tulosvolatiliteetin odotetaan kasvavan. Tutkielmassa pohditaan vakavaraisuussäännöstön uudistuksen hyötyjä kansainvälisen kilpailuneutraliteetin osalta, sillä Yhdysvalloissa uudistus koskee vain suurimpia pankkeja. Tutkielmassa paneudutaan lisäksi uudistuksen mahdolliseen talouden syklejä voimistavaan vaikutukseen ja tarkastellaan parannusehdotuksia prosyklisyyden hillitsemiseksi. Yksi vakavaraisuusuudistuksen tärkeimmistä tehtävistä on luoda pankeille kannustin kehittää omia riskienhallinta malleja. Kannustin ongelma on pyritty ratkaisemaan vapaampien sisäisten mallien menetelmien avulla. Ongelmaa ei ole pystytty kuitenkaan ratkaisemaan aivan täysin, sillä luottoriskien osalta pankkien lainaportfolioiden rakenne määrittää sen, hyötyvätkö pankit siirtymisestä sisäisten mallien menetelmän käyttöön. Tutkielma sisältää myös Nordean arvion vakavaraisuusuudistuksen vaikutuksista pankkitoimialaan.

Konkurssin ennustaminen ja makrotaloudelliset muuttujat: rakennusalan pk-yritykset

Tämän tutkimuksen tavoitteena on selvittää kannattaako makrotaloudellisia muuttujia käyttää tunnuslukujen lisäksi yrityksen konkurssin ennustamisessa. Perinteiset konkurssinennustamismallit hyödyntävät pelkästään tilinpäätöksestä saatavia tunnuslukuja eivätkä huomioi yrityksen toimintaympäristöä ja sen muutoksia. Aiemmissa tutkimuksissa makrotaloudellisilla muuttujilla on pystytty parantamaan perinteisiä ennustamismalleja. Tutkimus toteutetaan luomalla kolme erilaista konkurssinennustamismallia logistista regressioanalyysiä hyödyntäen ja vertailemalla niiden paremmuutta. Tutkimustulokset osoittavat, että rakennusalan pk-yritysten konkursseja pystytään ennustamaan kolmen tunnusluvun mallilla. Taloudellisen ajanjakson, jolta yrityksen tiedot ovat peräisin, huomioiminen ei tuo malliin lisäarvoa eikä paranna ennustamiskykyä merkittävästi.

Konkurssin ennustaminen ja makrotaloudelliset muuttujat: rakennusalan pk-yritykset

Tämän tutkimuksen tavoitteena on selvittää kannattaako makrotaloudellisia muuttujia käyttää tunnuslukujen lisäksi yrityksen konkurssin ennustamisessa. Perinteiset konkurssinennustamismallit hyödyntävät pelkästään tilinpäätöksestä saatavia tunnuslukuja eivätkä huomioi yrityksen toimintaympäristöä ja sen muutoksia. Aiemmissa tutkimuksissa makrotaloudellisilla muuttujilla on pystytty parantamaan perinteisiä ennustamismalleja. Tutkimus toteutetaan luomalla kolme erilaista konkurssinennustamismallia logistista regressioanalyysiä hyödyntäen ja vertailemalla niiden paremmuutta. Tutkimustulokset osoittavat, että rakennusalan pk-yritysten konkursseja pystytään ennustamaan kolmen tunnusluvun mallilla. Taloudellisen ajanjakson, jolta yrityksen tiedot ovat peräisin, huomioiminen ei tuo malliin lisäarvoa eikä paranna ennustamiskykyä merkittävästi.

Lightweight, liquid-cooled, direct-drive generator for highpower wind turbines: motivation, concept, and performance

Thesis: A liquid-cooled, direct-drive, permanent-magnet, synchronous generator with helical, double-layer, non-overlapping windings formed from a copper conductor with a coaxial internal coolant conduit offers an excellent combination of attributes to reliably provide economic wind power for the coming generation of wind turbines with power ratings between 5 and 20MW. A generator based on the liquid-cooled architecture proposed here will be reliable and cost effective. Its smaller size and mass will reduce build, transport, and installation costs. Summary: Converting wind energy into electricity and transmitting it to an electrical power grid to supply consumers is a relatively new and rapidly developing method of electricity generation. In the most recent decade, the increase in wind energy’s share of overall energy production has been remarkable. Thousands of land-based and offshore wind turbines have been commissioned around the globe, and thousands more are being planned. The technologies have evolved rapidly and are continuing to evolve, and wind turbine sizes and power ratings are continually increasing. Many of the newer wind turbine designs feature drivetrains based on Direct-Drive, Permanent-Magnet, Synchronous Generators (DD-PMSGs). Being low-speed high-torque machines, the diameters of air-cooled DD-PMSGs become very large to generate higher levels of power. The largest direct-drive wind turbine generator in operation today, rated just below 8MW, is 12m in diameter and approximately 220 tonne. To generate higher powers, traditional DD-PMSGs would need to become extraordinarily large. A 15MW air-cooled direct-drive generator would be of colossal size and tremendous mass and no longer economically viable. One alternative to increasing diameter is instead to increase torque density. In a permanent magnet machine, this is best done by increasing the linear current density of the stator windings. However, greater linear current density results in more Joule heating, and the additional heat cannot be removed practically using a traditional air-cooling approach. Direct liquid cooling is more effective, and when applied directly to the stator windings, higher linear current densities can be sustained leading to substantial increases in torque density. The higher torque density, in turn, makes possible significant reductions in DD-PMSG size. Over the past five years, a multidisciplinary team of researchers has applied a holistic approach to explore the application of liquid cooling to permanent-magnet wind turbine generator design. The approach has considered wind energy markets and the economics of wind power, system reliability, electromagnetic behaviors and design, thermal design and performance, mechanical architecture and behaviors, and the performance modeling of installed wind turbines. This dissertation is based on seven publications that chronicle the work. The primary outcomes are the proposal of a novel generator architecture, a multidisciplinary set of analyses to predict the behaviors, and experimentation to demonstrate some of the key principles and validate the analyses. The proposed generator concept is a direct-drive, surface-magnet, synchronous generator with fractional-slot, duplex-helical, double-layer, non-overlapping windings formed from a copper conductor with a coaxial internal coolant conduit to accommodate liquid coolant flow. The novel liquid-cooling architecture is referred to as LC DD-PMSG. The first of the seven publications summarized in this dissertation discusses the technological and economic benefits and limitations of DD-PMSGs as applied to wind energy. The second publication addresses the long-term reliability of the proposed LC DD-PMSG design. Publication 3 examines the machine’s electromagnetic design, and Publication 4 introduces an optimization tool developed to quickly define basic machine parameters. The static and harmonic behaviors of the stator and rotor wheel structures are the subject of Publication 5. And finally, Publications 6 and 7 examine steady-state and transient thermal behaviors. There have been a number of ancillary concrete outcomes associated with the work including the following. X Intellectual Property (IP) for direct liquid cooling of stator windings via an embedded coaxial coolant conduit, IP for a lightweight wheel structure for lowspeed, high-torque electrical machinery, and IP for numerous other details of the LC DD-PMSG design X Analytical demonstrations of the equivalent reliability of the LC DD-PMSG; validated electromagnetic, thermal, structural, and dynamic prediction models; and an analytical demonstration of the superior partial load efficiency and annual energy output of an LC DD-PMSG design X A set of LC DD-PMSG design guidelines and an analytical tool to establish optimal geometries quickly and early on X Proposed 8 MW LC DD-PMSG concepts for both inner and outer rotor configurations Furthermore, three technologies introduced could be relevant across a broader spectrum of applications. 1) The cost optimization methodology developed as part of this work could be further improved to produce a simple tool to establish base geometries for various electromagnetic machine types. 2) The layered sheet-steel element construction technology used for the LC DD-PMSG stator and rotor wheel structures has potential for a wide range of applications. And finally, 3) the direct liquid-cooling technology could be beneficial in higher speed electromotive applications such as vehicular electric drives.

Influence of multi-phase phenomena on semibatch crystallization processes of aqueous solutions

Crystal properties, product quality and particle size are determined by the operating conditions in the crystallization process. Thus, in order to obtain desired end-products, the crystallization process should be effectively controlled based on reliable kinetic information, which can be provided by powerful analytical tools such as Raman spectrometry and thermal analysis. The present research work studied various crystallization processes such as reactive crystallization, precipitation with anti-solvent and evaporation crystallization. The goal of the work was to understand more comprehensively the fundamentals, phenomena and utilizations of crystallization, and establish proper methods to control particle size distribution, especially for three phase gas-liquid-solid crystallization systems. As a part of the solid-liquid equilibrium studies in this work, prediction of KCl solubility in a MgCl2-KCl-H2O system was studied theoretically. Additionally, a solubility prediction model by Pitzer thermodynamic model was investigated based on solubility measurements of potassium dihydrogen phosphate with the presence of non-electronic organic substances in aqueous solutions. The prediction model helps to extend literature data and offers an easy and economical way to choose solvent for anti-solvent precipitation. Using experimental and modern analytical methods, precipitation kinetics and mass transfer in reactive crystallization of magnesium carbonate hydrates with magnesium hydroxide slurry and CO2 gas were systematically investigated. The obtained results gave deeper insight into gas-liquid-solid interactions and the mechanisms of this heterogeneous crystallization process. The research approach developed can provide theoretical guidance and act as a useful reference to promote development of gas-liquid reactive crystallization. Gas-liquid mass transfer of absorption in the presence of solid particles in a stirred tank was investigated in order to gain understanding of how different-sized particles interact with gas bubbles. Based on obtained volumetric mass transfer coefficient values, it was found that the influence of the presence of small particles on gas-liquid mass transfer cannot be ignored since there are interactions between bubbles and particles. Raman spectrometry was successfully applied for liquid and solids analysis in semi-batch anti-solvent precipitation and evaporation crystallization. Real-time information such as supersaturation, formation of precipitates and identification of crystal polymorphs could be obtained by Raman spectrometry. The solubility prediction models, monitoring methods for precipitation and empirical model for absorption developed in this study together with the methodologies used gives valuable information for aspects of industrial crystallization. Furthermore, Raman analysis was seen to be a potential controlling method for various crystallization processes.

Bayesian methods for estimation, optimization and experimental design

Mathematical models often contain parameters that need to be calibrated from measured data. The emergence of efficient Markov Chain Monte Carlo (MCMC) methods has made the Bayesian approach a standard tool in quantifying the uncertainty in the parameters. With MCMC, the parameter estimation problem can be solved in a fully statistical manner, and the whole distribution of the parameters can be explored, instead of obtaining point estimates and using, e.g., Gaussian approximations. In this thesis, MCMC methods are applied to parameter estimation problems in chemical reaction engineering, population ecology, and climate modeling. Motivated by the climate model experiments, the methods are developed further to make them more suitable for problems where the model is computationally intensive. After the parameters are estimated, one can start to use the model for various tasks. Two such tasks are studied in this thesis: optimal design of experiments, where the task is to design the next measurements so that the parameter uncertainty is minimized, and model-based optimization, where a model-based quantity, such as the product yield in a chemical reaction model, is optimized. In this thesis, novel ways to perform these tasks are developed, based on the output of MCMC parameter estimation. A separate topic is dynamical state estimation, where the task is to estimate the dynamically changing model state, instead of static parameters. For example, in numerical weather prediction, an estimate of the state of the atmosphere must constantly be updated based on the recently obtained measurements. In this thesis, a novel hybrid state estimation method is developed, which combines elements from deterministic and random sampling methods.

Communicative and Anticipatory Decision-Making Supported by Bayesian Networks

The purpose of this research is to draw up a clear construction of an anticipatory communicative decision-making process and a successful implementation of a Bayesian application that can be used as an anticipatory communicative decision-making support system. This study is a decision-oriented and constructive research project, and it includes examples of simulated situations. As a basis for further methodological discussion about different approaches to management research, in this research, a decision-oriented approach is used, which is based on mathematics and logic, and it is intended to develop problem solving methods. The approach is theoretical and characteristic of normative management science research. Also, the approach of this study is constructive. An essential part of the constructive approach is to tie the problem to its solution with theoretical knowledge. Firstly, the basic definitions and behaviours of an anticipatory management and managerial communication are provided. These descriptions include discussions of the research environment and formed management processes. These issues define and explain the background to further research. Secondly, it is processed to managerial communication and anticipatory decision-making based on preparation, problem solution, and solution search, which are also related to risk management analysis. After that, a solution to the decision-making support application is formed, using four different Bayesian methods, as follows: the Bayesian network, the influence diagram, the qualitative probabilistic network, and the time critical dynamic network. The purpose of the discussion is not to discuss different theories but to explain the theories which are being implemented. Finally, an application of Bayesian networks to the research problem is presented. The usefulness of the prepared model in examining a problem and the represented results of research is shown. The theoretical contribution includes definitions and a model of anticipatory decision-making. The main theoretical contribution of this study has been to develop a process for anticipatory decision-making that includes management with communication, problem-solving, and the improvement of knowledge. The practical contribution includes a Bayesian Decision Support Model, which is based on Bayesian influenced diagrams. The main contributions of this research are two developed processes, one for anticipatory decision-making, and the other to produce a model of a Bayesian network for anticipatory decision-making. In summary, this research contributes to decision-making support by being one of the few publicly available academic descriptions of the anticipatory decision support system, by representing a Bayesian model that is grounded on firm theoretical discussion, by publishing algorithms suitable for decision-making support, and by defining the idea of anticipatory decision-making for a parallel version. Finally, according to the results of research, an analysis of anticipatory management for planned decision-making is presented, which is based on observation of environment, analysis of weak signals, and alternatives to creative problem solving and communication.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.