The main purpose of this paper is to provide evidence of financial leverage, liquidity, profitability, and firm size ability to predict bankruptcy of Swedish small and medium-sized enterprises (SMEs), and to create a bankruptcy prediction model for Swedish SMEs. The sample consists of 1086 Swedish SMEs, among which 543 did go bankrupt between 2015 and 2019. The paper employs logistic regression and Mann-Whitney U-test to test the hypotheses. The independent variables are derived from previous research and further filtered in a selection process, resulting in a final set of six variables. Financial leverage, liquidity, profitability, and firm size is found to have significantly predictive abilities to determine SME bankruptcy. The model has an overall classification accuracy of 77.6% out-of-sample and is able to classify 82.2% of the bankruptcies correctly out-of-sample.

As automation in the financial service industry continues to advance, online investment advice has emerged as an exciting new field. Vital to the accuracy of such service is the determination of the individual investors’ ability to bear financial risk. To do so, the statistical method of logistic regression is used. The aim of this thesis is to identify factors which are significant in determining a financial risk profile of a retail investor. In other words, the study seeks to map out the relationship between several socioeconomic- and psychometric variables to develop a predictive model able to determine the risk profile. The analysis is based on survey data from respondents living in Sweden. The main findings are that variables such as income, consumption rate, experience of a financial bear market, and various psychometric variables are significant in determining a financial risk profile. / I samband med en ökad automatiseringstrend har digital investeringsrådgivning dykt upp som ett nytt fenomen. Av central betydelse är tjänstens förmåga att bedöma en investerares förmåga till att bära finansiell risk. Logistik regression tillämpas för att bedöma en icke- professionell investerares vilja att bära finansiell risk. Målet med uppsatsen är således att identifiera ett antal faktorer med signifikant förmåga till att bedöma en icke-professionell investerares riskprofil. Med andra ord, så syftar denna uppsats till att studera förmågan hos ett antal socioekonomiska- och psykometriska variabler. För att därigenom utveckla en prediktiv modell som kan skatta en individs finansiella riskprofil. Analysen genomförs med hjälp av en enkätstudie hos respondenter bosatta i Sverige. Den huvudsakliga slutsatsen är att en individs inkomst, konsumtionstakt, tidigare erfarenheter av abnorma marknadsförhållanden, och diverse psykometriska komponenter besitter en betydande förmåga till att avgöra en individs finansiella risktolerans

PLAS is a cloud-based software used for planning and scheduling fleets of vehicles for material transport. PLAS consists of two components; the Logistic Flow Solver (LFS) and the Material Transport Scheduler (MTS). Based on transportation requests, the LFS generates a set of logistic flows. The MTS then transforms the logistic flows into tasks that are assigned to the vehicles. The LFS is implemented with Mixed Integer Linear Programming (MILP). Currently, the LFS and the MTS are decoupled from each other and there is information that is not considered in the LFS. Thus, the choice of logistic flows generated with the current formulation may negatively impact the final transport plan. The objective of this thesis is to investigate how the generation of logistic flows can be improved. Two alternative mathematical models for the LFS were developed using MILP formulation. Compared to the current model, more information is taken into account in the two new models. Three different objective functions were considered. Scheduling of the vehicles were modelled as pickup and delivery problems, where pickup and delivery pairs correspond to the generated logistic flows. The models were implemented using Google OR-Tools, an open-source software suite for optimization. The different mathematical formulations were evaluated based on their performance for test problems with different fleet compositions. The results show that problem characteristics influence the performance of the models and that there is no model that gives the best result for every type of problem. Therefore, it is necessary to analyse problem characteristics in order to choose a suitable model for generation of logistic flows. / PLAS är en molnbaserad mjukvara som används för planering och schemaläggning av fordonsflottor för materialtransport. PLAS består av två komponenter; Logistic Flow Solver (LFS) and Material Transport Scheduler (MTS). Baserat på transportbehov genererar LFS ett antal logistikflöden. MTS omvandlar sedan logistikflödena till uppdrag som är tilldelade till fordonen. LFS är implementerad med blandad heltalsprogrammering. För närvarande är LFS och MTS frikopplade från varandra och det finns information som inte tas hänsyn till i LFS. Därför kan valet av logistikflöden genererade med den nuvarande formuleringen negativt påverka den slutliga transportplanen. Målet med detta examensarbete är att undersöka hur genereringen av logistikflöden kan förbättras. Två alternativa matematiska modeller utvecklades med MILP-formulering. Jämfört med den nuvarande modellen, tar de två nya modellerna hänsyn till mer information. Tre olika målfunktioner beaktades. Modellerna implementerades med Google OR-Tools, en öppen programvara för optimering. De matematiska formuleringarna utvärderades baserat på deras prestation på testproblem med olika kompositioner av fordonsflottor. Resultaten visar att problemegenskaper påverkar modellernas prestationer och att det inte finns någon modell som ger bäst resultat för varje problemtyp. Därför är det nödvändigt att analysera problemegenskaper för att kunna välja en lämplig modell för generering av logistikflöden.

”Den efterfrågan som e-handeln driver på idag går stick i stäv med oss fastighetsägare. Då de har ett temporärt behov av något som ska stå väldigt länge.” - Matthias Kettelhoit, NREP I takt med digitaliseringens framfart har detaljhandeln på senare tid genomgått stora förändringar, där framförallt förändrade konsumentbeteenden varit drivkraften till att detaljhandelsaktörer tvingats anpassa sina affärsmodeller. Vidare har nya trender vuxit fram inom detaljhandeln och antalet e-handelsbolag ökat kraftigt. Detta gör att det ställs nya krav på fastighetsägare, inte minst på hyresmarknaden inom lager- och logistiksegmentet, där de traditionellt sett långa hyresavtalen nu utmanas av hyresgästers efterfrågan på flexibla hyresförhållanden. Syftet med denna studie var att utreda hur fastighetsägare kan möta denna efterfrågan. Den metod som låg till grund för genomförandet av studien var en kvalitativ metod, där semistrukturerade intervjuer hölls med fastighetsägare, e-handelsaktörer samt en tredjepartslogistiker. Studien visade på att e-handeln är drivkraften till att moderna lager och flexibla hyresförhållanden eftersöks av hyresgäster. Den slutsats som kunde dras är att det skiljer sig mellan stora och små e-handelsaktörer i vilken typ av flexibilitet som eftersöks. För att fastighetsägare ska kunna möta efterfrågan på flexibilitet från större e-handelsaktörer blir erbjudandet av expansionsmöjligheter ytterst avgörande. Dessutom är, av denna studie att döma, den noterade efterfrågan på korta hyresavtal en reaktion på bristen på ytflexibilitet inom segmentet. Vidare kan det komma att krävas en implementering av nya hyresmodeller för att fastighetsägare bättre ska kunna möta nystartade och små e-handelsaktörers efterfrågan på flexibilitet. I denna studie lyftes en omsättningsbaserad hyresmodell fram, vilken skulle kunna implementeras av fastighetsägare som ett försök i att kunna attrahera denna målgrupp och på så vis kunna erhålla större marknadsandelar av e-handeln. / "The demand that e-commerce is giving rise to today is contradictive to us property owners. Because e-commerce companies have a temporary need of something that will last for a very long time." - Matthias Kettelhoit, NREP The continuous development of digitalization as well as the growing e-commerce have profoundly changed the retail industry, where shifts in consumer behavior in particular has been one of the driving forces for retailers to adapt their business models. The rise of new retail trends and the number of e-commerce companies has increased significantly. This imposes new demands on property owners, not least on the rental market in the warehouse and logistics segment, where the traditionally long leases now are being challenged by tenants' demand for flexible rental conditions. The purpose of this study was to investigate how property owners can meet this demand. The method chosen for the study was a qualitative method, where semi-structured interviews were conducted with property owners, e-commerce companies and a third-party logistics provider. The study showed that the rapidly increasing e-commerce is a driving force for modern warehouses and flexible leasing conditions being sought by tenants. The conclusion that can be drawn from this study is that there is a difference in what large and small e-commerce companies define as valuable flexible leasing conditions. In order for property owners to meet the demand for flexibility from large e-commerce companies, offering of expansion opportunities will be crucial. Moreover, the study shows that the noted demand for short leases is merely a response to the lack of space flexibility within the segment. To further add, it may be necessary to implement new rental models in order for property owners to better meet the demand for flexibility from startups and small e-commerce companies. In an attempt to attract this target group, a revenue-based rental model was brought up for discussion in this paper as a way for property owners to gain greater market share of e-commerce.

Consumer shopping preferences has changed in today’s society. E-commerce is rapidlygrowing and the turnover for physical stores is effected negatively. A segment that hashad an upswing in recent years is the warehouse and logistic property market. Thechange in consumer behavior requires property owners to adapt to the development indemand from their tenants. Furthermore, e-retailers must adapt to how they managethe warehouse and logistic operations. The thesis investigates the development of thewarehouse and logistic property market and if e-commerce has an effect on theproperty segment.Results show a growth in rent levels, negative yield development and an increase intransaction price per square meter. This is further confirmed by interviews held duringthe thesis process. Interviews has been executed with three different segments;warehouse and logistic property owners, retail property owners as well as e-retailers.The development of yield and rent levels show an increase in demand within thewarehouse and logistic property segment and the new developments illustrates thesame for the supply. Furthermore, the transaction price suggests an increase in theinterest for this property segment, compared to 10 years ago. Simultaneously, the ecommerceis rapidly increasing and will expand even more in the future.The effect of e-commerce on the warehouse and logistic property market means newrequired solutions within the warehouse and logistic properties. Focus for the future ison flexibility, automation and other creative solutions. Furthermore, tenant fitting-outand new constructions within the segment is required. / Konsumenters preferenser och köpvanor har förändrats vilket har genererat en ökade-handel. Detta har i sin tur påverkat omsättningen för den fysiska handeln negativt.Samtidigt har fastighetssegmentet lager- och logistik haft en positiv utveckling desenaste åren. Förändringen i konsumentbeteende kräver att fastighetsägare anpassarsig till det ändrade behovet från deras hyresgäster. Dessutom, måste ehandelsföretagenanpassa och ändra sin lager- och logistikhantering. Detta arbeteundersöker utvecklingen på lager- och logistikfastighetsmarknaden samt om ehandelnpåverkat fastighetssegmentet.Resultat visar en tillväxt i hyresnivåer, negativ utveckling av avkastningskravet samten ökning i pris per kvadratmeter. Vidare bekräftas detta även av de intervjuer somhölls under studien. Intervjuerna genomfördes inom tre olika segment; lager- ochlogistikfastighetsägare, fastighetsägare inom detaljhandel och slutligen ehandelsföretag.Utvecklingen av hyresnivåer och avkastningskrav uppvisar en ökadefterfrågan inom lager- och logistikfastighetssegmentet och antalet nybyggnationervisar det samma för utbudet. Vidare demonstrerar de ökade transaktionspriserna perkvadratmeter ett ökat intresse från investerare. Samtidigt ökar e-handeln snabbt samtär förväntad att fortsätta expandera de kommande åren.Den växande e-handeln har en påverkan på lager- och logistikfastighetsmarknaden iform av nya lösningar inom lager och logistikfastigheter. Fokus för framtiden ärflexibilitet, automatisering och andra kreativa lösningar. Dessutom krävs merhyresgästanpassningar och nybyggnationer inom segmentet för att bemöta denstigande efterfrågan.

Forecasts of short term volcanic activity are generated using an event tree process that is driven by a set of empirical statistical models derived through logistic regression. Each of the logistic models are constructed from a sparse and geographically diverse dataset that was assembled from a collection of historic volcanic unrest episodes. The dataset consists of monitoring measurements (e.g. seismic), source modeling results, and historic eruption information. Incorporating this data into a single set of models provides a simple mechanism for simultaneously accounting for the geophysical changes occurring within the volcano and the historic behavior of analog volcanoes. A bootstrapping analysis of the training dataset allowed for the estimation of robust logistic model coefficients. Probabilities generated from the logistic models increase with positive modeling results, escalating seismicity, and high eruption frequency. The cross validation process produced a series of receiver operating characteristic (ROC) curves with areas ranging between 0.78 - 0.81, which indicate the algorithm has good predictive capabilities. In addition, ROC curves also allowed for the determination of a false positive rate and optimum detection threshold for each stage of the algorithm. The results demonstrate the logistic models are highly transportable and can compete with, and in some cases outperform, non-transportable empirical models trained with site specific information. The incorporation of source modeling results into the event tree’s decision making process has begun the transition of volcano monitoring applications from simple mechanized pattern recognition algorithms to a physical model based forecasting system.

Arterial roads constitute the majority of the centerline miles of the Florida State Highway System. Severe injury involvements on these roads account for a quarter of the total severe injuries reported statewide. This research focuses on driver injury severity analysis of statewide multilane high speed arterials using crash data for the years 2002 to 2004. The first goal is to test different ways of analyzing crash data (by road entity and crash types) and find the best method of driver injury severity analysis. A second goal is to find driver, vehicle, road and environment related factors that contribute to severe involvements on multilane arterials. Exploratory analysis using one year of crash data (2004) using binary logit regression was used to measure the risk of driver severe injury given that a crash occurs. A preliminary list of significant factors was obtained. A massive data preparation effort was undertaken and a random sample of multivehicle crashes was selected for final analysis. The final injury severity analysis consisted of six road entity models and twenty crash type models. The data preparation and sampling was successful in allowing a robust dataset. The overall model was a good candidate for the analysis of driver injury severity on multilane high speed roads. Driver injury severity resulting from angle and left turn crashes were best modeled by separate non-signalized intersection crash analysis. Injury severity from rear end and fixed object crashes was best modeled by combined analysis of pure segment and non-signalized intersection crashes. The most important contributing factors found in the overall analysis included driver related variables such as age, gender, seat belt use, at-fault driver, physical defects and speeding. Crash and vehicle related contributing factors included driver ejection, collision type (harmful event), contributing cause, type of vehicle and off roadway crash. Multivehicle crashes and interactions with intersection and off road crashes were also significant. The most significant roadway related variables included speed limit, ADT per lane, access class, lane width, roadway curve, sidewalk width, non-high mast lighting density, type of friction course and skid resistance. The overall model had a very good fit but some misspecification symptoms appeared due to major differences in road entities and crash types by land use. Two additional models of crashes for urban and rural areas were successfully developed. The land use models' goodness of fit was substantially better than any other combination by road entity or the overall model. Their coefficients were substantially robust and their values agreed with scientific or empirical principles. Additional research is needed to prove these results for crash type models found most reliable by this investigation. A framework for injury severity analysis and safety improvement guidelines based on the results is presented. Additional integration of road characteristics (especially intersection) data is recommended for future research. Also, the use of statistical methods that account for correlation among crashes and locations are suggested for use in future research.

Population living along the coast are at risk of losing land, households, and economic resources due to the hazards of coastal erosion. Scientific research has indicated that 70% of the planet's sandy coastal environment is being impacted by coastal erosion. Due to the different characteristics of the lands in the coastal zone versus other areas, it is important to understand how the hazard of shoreline erosion contributes to subsequent land use change in affected coastal regions. This study analyzes how the level of erosion, land loss, and developed land loss performs when added with the default land use change parameters such as existing developed land proximity, proximity to forested areas, population, transportation, etc. Sample points of 1020 from 10 years and 15 years of shoreline erosion data for the lower Meghna River estuary of Southeast Bangladesh have been obtained and from there, different erosion statistics have been developed. Developed land use data has been collected from ESA's World Settlement Footprint dataset and other datasets are also collected from secondary data sources. Logistic regression modeling shows that there are verified contributions of proximity to erosion and amount of land loss with the probability of developed land use conversion in the study area. Adding the variables of environmental hazards increases the prediction accuracy by 2-3% and overall, the models are at least 85% accurate. / Master of Science / Population living along the coast are at risk of losing land, households, and economic resources due to the hazards of coastal erosion. The coast of the Lower Meghna estuary in Bangladesh is a region experiencing chronic and severe shoreline erosion that causes the land to be lost to estuarine waters. This research quantifies the amount of land lost to erosion with a special focus on the amount of developed land that is lost. Developed land in this study is defined as a built-up area typically composed of buildings and roads. The research also evaluates the effects of lost land on the subsequent conversion of interior land from a non-developed to developed status. The main contribution is to quantitatively identify the association between the erosion-induced land loss to future land use conversion. Using statistical modeling and digital mapping methods, results show that loss of land is associated with the subsequent conversion of non-developed land to developed land use. In particular, conversion has a higher probability at sites that are located more distant from the eroding shoreline that also are proximal to shoreline sites with higher rates of erosion-induced land loss. These results are suggestive of a relocation process where previously lost developed land is reestablished at interior sites within five kilometers of the eroding shoreline.

Recent inspections conducted on levee safety in the U.S. that participate in the United States Army Corps of Engineers (USACE) Levee Safety Program under Public-Law 84-99 have shown that the overwhelming majority (>95%) of these levees have at least some deficiency associated with them, and many being identified as having an unacceptable safety rating (≈30%). In the U.S., many levees were constructed using funding from the federal government, but the responsibility of operation and maintenance of the levees were turned over to local government bodies. Given the local funding of levee maintenance, the socioeconomic characteristics of these levee-protected communities may be useful in identifying which communities may not have the economic, social, and / or political capital to maintain their levees to an acceptable safety standard. Using the lens of socio-hydrology, this study examines the socioeconomic and demographic characteristics of levee-protected communities and their relationship to the safety rating of their levee system. Using GIS, socioeconomic data were compiled for each of the evaluated Illinois levee systems from the US Census and the USACE’s National Levee Database (NLD). In addition to socioeconomic data, the NLD contained information on a levee’s age, protection level, estimated value of structures within the levee-protected area, ownership, inspection status, safety rating, and other structural details. The value of levee-protected agricultural lands was also assessed using a soil productivity index used by the state of Illinois to assess taxes on farmland. This information was compiled to investigate the potential differences of socioeconomic characteristics of communities with unacceptable to those with an acceptable levee rating. To assess the differences between the compiled socioeconomic information, the Independent Samples U-Test was implemented to quantify differences between communities with an acceptable verse unacceptable safety rating. In this study, 71 levee systems were identified with a levee safety rating and socioeconomic data from which to perform the statistical comparison between levee systems with an acceptable versus unacceptable safety rating. Of these 71 levees systems, 28 had an unacceptable and 43 had an at least minimally acceptable safety rating. The results from the Independent Samples U-Test showed that five variables with substantial variance (α ≤ 0.2, 80% CI) between the levee safety ratings were, the age of the levee, property value per structure, the average soil productivity index, per capita income, and the percentage of population being black. Using these substantial variables, a binary logistic regression model was created to see if they could be used to realistically predict the levee system’s safety rating. The regression model was able to accurately predict 84% of the ‘acceptable’ group while only correctly predicting 25% of the ‘unacceptable’ group resulting in an overall accuracy of 61%. The inability of this model to predict a levee system’s safety rating underscores the complexities in trying to determine which socioeconomic factors are important for identifying a given levee system’s safety rating. This finding also suggests there are potentially other variables which may be more robust predictors of a community’s ability to adequately maintain their levee. Future research should investigate these complexities in identifying which communities can adequately maintain their levee system.

Lexical inferencing is considered a listening strategy that is commonly employed by advanced EFL (English as a Foreign Language) listeners and a factor that contributes to successful listening comprehension. However, investigations of the factors that influence inferencing success in listening as well as how much each factor contributes to success are scant, as more studies have been conducted exploring lexical inferencing in reading. In addition, even though idiomatic expressions such as smell a rat, jump the gun, and go cold turkey are ubiquitous in the English language, especially in oral communication, and they are considered crucial in both first language (L1) and second language (L2) acquisition, little is known about the effectiveness of inferencing strategies where idiomatic expressions are concerned.Three goals motivated the current study. The first goal was to investigate whether inferencing is an effective strategy in the case where the target item is an idiomatic expression. The second goal was to investigate how four person-level factors, familiarity, listening proficiency, listening vocabulary size and working memory, two sentence-level factors, lexical density and sentence length, and two lexical-level factors, L1–L2 congruency and semantic transparency, influence the inferencing success of English idiomatic expressions in listening. The third goal, related to the second goal, was to determine which of the two lexical component factors, L1–L2 congruency and semantic transparency, is more important to inferencing success. A mixed methods design, the explanatory sequential design (Creswell & Plano Clark, 2018), was employed in this study. Quantitative data were collected from 89 EFL Japanese university students using a Listening Vocabulary Levels Test, a Listening Span Test, and an Idiom Inferencing Elicitation Task. The collected data were examined using mixed-effects logistic regression. Twelve participants were invited to participate in follow-up interviews based on their response patterns on the Idiom Inferencing Elicitation Task. The quantitative results indicated that familiarity, listening comprehension skills, working memory, and L1–L2 congruency were significant factors influencing inferencing success and the qualitative results supported these findings. In addition, the qualitative analyses suggested that depth of vocabulary is another potentially important factor. Furthermore, listening comprehension moderated the L1–L2 congruency effect. The finding that semantic transparency is not an influential factor in successful inferencing of unfamiliar idiomatic expressions provides evidence that the semantic transparency of known idiomatic expressions formed after learners acquire the meaning of the expression is a different construct from the perceived semantic transparency of unfamiliar idiomatic expressions. In addition, even though the sentence-level factors were not statistically significant in successful idiom inferencing in this study, further studies are required in order to see if this result holds true when the characteristics of the listening tasks differ from those of the task used in this study. It is hoped that the findings provide insights into how to help Japanese university EFL learners improve their listening skills, especially in tasks that include unfamiliar idiomatic expressions. / Teaching & Learning