The Persistence of Finnish Equity Fund Performance

Tutkimuksen tavoitteena on selvittää, esiintyykö suomeen sijoittavilla osakerahastoilla menestyksen pysyvyyttä. Tutkimusaineisto koostuu kaikista suomalaisista osakerahastoista, jotka toimivat ajanjaksolla 15.1.1998-13.1.2005. Aineisto on vapaa selviytymisvinoumasta. Suorituskyvyn mittareina käytetään CAPM-alfaa sekä kolmi- ja nelifaktori-alfaa. Empiirisessä osassa osakerahastojen menestyksen pysyvyyttä testataan Spearmanin järjestyskorrelaatiotestillä. Evidenssi menestyksen pysyvyydestä jäi vähäiseksi, vaikkakin sitä esiintyi satunnaisesti kaikilla menestysmittareilla joillakin ranking- ja sijoitusperiodin yhdistelmillä. CAPM-alfalla tarkasteltuna tilastollisesti merkitsevää menestyksen pysyvyyttä esiintyi selvästi useammin kuin muilla menestysmittareilla. Tulokset tukevat viimeaikaisia kansainvälisiä tutkimuksia, joiden mukaan menestyksen pysyvyys riippuu usein mittaustavasta. Menestysmittareina käytettyjen regressiomallien merkitsevyystestit osoittavat multifaktorimallien selittävän osakerahastojen tuottoja CAPM:a paremmin. Lisätyt muuttujat parantavat merkittävästi CAPM:n selitysvoimaa.

Contact Center Functionality - Collecting and Prioritizing Features of Modern Contact Center.

Tässä diplomityössä pohditaan call centereiden asemaa tämän päivän palveluympäristössä ja myöskin call centereiden tulevaisuutta contact centereinä. Tämä työ tutkii kuinka asiakastarpeita ja uusia toiminnallisuuksia voidaan etsiä olemassaolevaan, mutta vielä keskeneräiseen call center tuotteeseen. Tutkimus on tehty lukemalla artikkeleita ja kirjoja tulevaisuuden contact centereistä, haastattelemalla asiakkaita ja järjestämällä ideointisessio yrityksen asiantuntijoille. Näin saadut tulokset priorisoitiin tätä tarkoitusta varten kehitellyllä matriisilla. Lopullisena tuloksena on lista toiminnallisuuksista tärkeysjärjestyksessä ja tuote roadmap kaikkein tärkeimmistä toiminnallisuuksista. Tämä roadmap antaa tuotekehitykselle ehdotuksen mitä tulisi implementoida nykyiseen tuotteeseen ja mitkä ovat prioriteetit. Tässä työssä pohdiskellaan myös tuotteen modulaarista rakennetta.

Moottorinohjaukseen tarkoitettujen signaaliprosessoreiden vertailu

Työssä tarkastellaan kolmen eri valmistajan signaaliprosessoriperheitä. Työn tavoitteena on tutkia prosessoreiden teknistä soveltuvuutta suunnitteilla olevaan taajuusmuuttajatuoteperheeseen. Työn alkuosassa käydään taajuusmuuttajan rakenne läpi ja selostetaan oikosulkumoottorin yleisimmät ohjausmenetelmät. Työssä selvitetään myös signaaliprosessorin ja integroitujen oheispiirien toimintaa. Työn painopiste prosessoreiden teknisten ominaisuuksien vertailussa. Työssä on vertailtu muun muassa prosessoreiden sisäistä rakennetta, käskykantojen ominaisuuksia, keskeytysten palveluun kuluvaa aikaa ja oheispiirien ominaisuuksia. Oheispiirien, erityisesti analogiadigitaalimuuntimen halutunlainen toiminta on moottorinohjausohjelmiston kannalta tärkeää. Työhön sisällytetyt prosessoriperheet on pisteytetty tarkasteltujen ominaisuuksien osalta. Vertailun tuloksena on esitetty haettuun tarkoitukseen teknisesti soveltuvin prosessoriperhe ja prosessorityyppi. Työssä ei kuitenkaan voida antaa yleistä paremmuusjärjestystä tutkituille prosessoreille.

BOD-päästön vähentäminen hienopaperitehtaalla

Diplomityön tavoitteena oli selvittää hienopaperitehtaan BOD-päästön vähentämismahdollisuudet. Tehtaalla tapahtuvien uudistusten vaikutus BOD-päästön määrään selvitettiin. Lisäksi selvitettiin uudistetun paperivalmistusprosessin BOD-päästön alkuperä ja syntyvien jätevesien tilavuusvirta. BOD:n kuormitusmäärän kehittymistä arvioitiin mallintamalla. Kirjallisuusosassa on käsitelty hienopaperin valmistusprosessin vaiheita sekä tarkasteltu paperitehtaan vedenkäyttöä ja vesikiertoja. Myös jätevesien COD- ja BOD-kuormituslähteet on selvitetty. Kokeellinen osa alkaa hienopaperitehtaan prosessikuvauksella. Paperitehtaan malli rakennettiin VTT:n Tekesin CACTUS-teknologiaohjelmassa kehittämällä Balas-simulointiohjelmalla. Kokeellisessa osassa on käyty läpi mallin rakennusvaiheet. Paperinvalmistusprosessissa toteutettavien uudistusvaiheiden vaikutus päästöihin mallinnettiin. Tuloksena voitiin todeta, että tehtaan tuotantosuunnan ja -määrien muutoksesta huolimatta BOD-päästö ei merkittävästi muutu. Sen sijaan ominaiskuormitus laskee. Ennen uudistuksia BOD-kuorma oli pääasiassa peräisin pintaliimatusta hylystä, kun taas uudistusten jälkeen suurin osa BOD-kuormasta tulee tuoremassojen mukana prosessiin.

Performance persistence of equity funds in the Russian stock market

This thesis investigates performance persistence among the equity funds investing in Russia during 2003-2007. Fund performance is measured using several methods including the Jensen alpha, the Fama-French 3- factor alpha, the Sharpe ratio and two of its variations. Moreover, we apply the Bayesian shrinkage estimation in performance measurement and evaluate its usefulness compared with the OLS 3-factor alphas. The pattern of performance persistence is analyzed using the Spearman rank correlation test, cross-sectional regression analysis and stacked return time series. Empirical results indicate that the Bayesian shrinkage estimates may provide better and more accurate estimates of fund performance compared with the OLS 3-factor alphas. Secondly, based on the results it seems that the degree of performance persistence is strongly related to length of the observation period. For the full sample period the results show strong signs of performance reversal whereas for the subperiod analysis the results indicate performance persistence during the most recent years.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Role of human hydroxysteroid (17beta) dehydrogenase type 1 (HSD17B1) in steroid-dependent diseases in females –novel indications for HSD17B1 inhibitors. Phenotypic analysis of transgenic mice overexpressing human HSD17B1.

Hormone-dependent diseases, e.g. cancers, rank high in mortality in the modern world, and thus, there is an urgent need for new drugs to treat these diseases. Although the diseases are clearly hormone-dependent, changes in circulating hormone concentrations do not explain all the pathological processes observed in the diseased tissues. A more inclusive explanation is provided by intracrinology – a regulation of hormone concentrations at the target tissue level. This is mediated by the expression of a pattern of steroid-activating and -inactivating enzymes in steroid target tissues, thus enabling a concentration gradient between the blood circulation and the tissue. Hydroxysteroid (17beta) dehydrogenases (HSD17Bs) form a family of enzymes that catalyze the conversion between low active 17-ketosteroids and highly active 17beta-hydroxysteroids. HSD17B1 converts low active estrogen (E1) to highly active estradiol (E2) with high catalytic efficiency, and altered HSD17B1 expression has been associated with several hormone-dependent diseases, including breast cancer, endometriosis, endometrial hyperplasia and cancer, and ovarian epithelial cancer. Because of its putative role in E2 biosynthesis in ovaries and peripheral target tissues, HSD17B1 is considered to be a promising drug target for estrogen-dependent diseases. A few studies have indicated that the enzyme also has androgenic activity, but they have been ignored. In the present study, transgenic mice overexpressing human HSD17B1 (HSD17B1TG mice) were used to study the effects of the enzyme in vivo. Firstly, the substrate specificity of human HSD17B1 was determined in vivo. The results indicated that human HSD17B1 has significant androgenic activity in female mice in vivo, which resulted in increased fetal testosterone concentration and female disorder of sexual development appearing as masculinized phenotype (increased anogenital distance, lack of nipples, lack of vaginal opening, combination of vagina with urethra, enlarged Wolffian duct remnants in the mesovarium and enlarged female prostate). Fetal androgen exposure has been linked to polycystic ovary syndrome (PCOS) and metabolic syndrome during adulthood in experimental animals and humans, but the genes involved in PCOS are largely unknown. A putative mechanism to accumulate androgens during fetal life by HSD17B1 overexpression was shown in the present study. Furthermore, as a result of prenatal androgen exposure locally in the ovaries, HSD17B1TG females developed ovarian benign serous cystadenomas in adulthood. These benign lesions are precursors of low-grade ovarian serous tumors. Ovarian cancer ranks fifth in mortality of all female cancers in Finland, and most of the ovarian cancers arise from the surface epithelium. The formation of the lesions was prevented by prenatal antiandrogen treatment and by transplanting wild type (WT) ovaries prepubertally into HSD17B1TG females. The results obtained in our non-clinical TG mouse model, together with a literature analysis, suggest that HSD17B1 has a role in ovarian epithelial carcinogenesis, and especially in the development of serous tumors. The role of androgens in ovarian carcinogenesis is considered controversial, but the present study provides further evidence for the androgen hypothesis. Moreover, it directly links HSD17B1-induced prenatal androgen exposure to ovarian epithelial carcinogenesis in mice. As expected, significant estrogenic activity was also detected for human HSD17B1. HSD17B1TG mice had enhanced peripheral conversion of E1 to E2 in a variety of target tissues, including the uterus. Furthermore, this activity was significantly decreased by treatments with specific HSD17B1 inhibitors. As a result, several estrogen-dependent disorders were found in HSD17B1TG females. Here we report that HSD17B1TG mice invariably developed endometrial hyperplasia and failed to ovulate in adulthood. As in humans, endometrial hyperplasia in HSD17B1TG females was reversible upon ovulation induction, triggering a rise in circulating progesterone levels, and in response to exogenous progestins. Remarkably, treatment with a HSD17B1 inhibitor failed to restore ovulation, yet completely reversed the hyperplastic morphology of epithelial cells in the glandular compartment. We also demonstrate that HSD17B1 is expressed in normal human endometrium, hyperplasia, and cancer. Collectively, our non-clinical data and literature analysis suggest that HSD17B1 inhibition could be one of several possible approaches to decrease endometrial estrogen production in endometrial hyperplasia and cancer. HSD17B1 expression has been found in bones of humans and rats. The non-clinical data in the present study suggest that human HSD17B1 is likely to have an important role in the regulation of bone formation, strength and length during reproductive years in female mice. Bone density in HSD17B1TG females was highly increased in femurs, but in lesser amounts also in tibias. Especially the tibia growth plate, but not other regions of bone, was susceptible to respond to HSD17B1 inhibition by increasing bone length, whereas the inhibitors did not affect bone density. Therefore, HSD17B1 inhibitors could be safer than aromatase inhibitors in regard to bone in the treatment of breast cancer and endometriosis. Furthermore, diseases related to improper growth, are a promising new indication for HSD17B1 inhibitors.

Choosing the optimal energy system for buildings and districts

The purpose of this master’s thesis was to develop a method to be used in the selection of an optimal energy system for buildings and districts. The term optimal energy system was defined as the energy system which best fulfils the requirements of the stakeholder on whose preferences the energy systems are evaluated. The most influential stakeholder in the process of selecting an energy system was considered to be the district developer. The selection method consisted of several steps: Definition of the district, calculating the energy consumption of the district and buildings within the district, defining suitable energy system alternatives for the district, definition of the comparing criteria, calculating the parameters of the comparing criteria for each energy system alternative and finally using a multi-criteria decision method to rank the alternatives. For the purposes of the selection method, the factors affecting the energy consumption of buildings and districts and technologies enabling the use of renewable energy were reviewed. The key element of the selection method was a multi-criteria decision making method, PROMETHEE II. In order to compare the energy system alternatives with the developed method, the comparing criteria were defined in the study. The criteria included costs, environmental impacts and technological and technical characteristics of the energy systems. Each criterion was given an importance, based on a questionnaire which was sent for the steering groups of two district development projects. The selection method was applied in two case study analyses. The results indicate that the selection method provides a viable and easy way to provide the decision makers alternatives and recommendations regarding the selection of an energy system. Since the comparison is carried out by changing the alternatives into numeric form, the presented selection method was found to exclude any unjustified preferences over certain energy systems alternatives which would affect the selection.

Vaccination Competence - The Concept and Evaluation

The purpose of this two-phase study was to define the concept of vaccination competence and assess the vaccination competence of graduating public health nurse students (PHN students) and public health nurses (PHNs) in Finland, with the goal of promoting and maintaining vaccination competence and developing vaccination education. The first phase of the study included semi-structured interviews with vaccination professionals, graduating PHN students and clients (a total of n=40), asking them to describe vaccination competence as well as the factors strengthening and weakening it. The data were analyzed through content analysis. In the second phase of the study, structured instruments were developed, and vaccination competence of PHN students (n=129) in Finland and PHNs (n=405) was assessed using a self-assessment scale (VAS) and taking a knowledge test. PHNs were used as a reference group, enabling us to determine whether a satisfactory level of vaccination competence was achieved by the end of studies, or whether it was gained through work experience vaccinating clients. The data were collected from five polytechnic institutions and seven health centers located in various parts of the country. The data were collected using instruments developed for this study, and were analyzed statistically. In the first phase, based on the results of the interviews, vaccination competence was defined as a large multi-faceted entity, including the concepts of competent vaccinator, competent implementation of the vaccination, and the outcome of the implementation. Semi-structured interviews revealed that factors strengthening and weakening vaccination competence were connected to the vaccinator, the client being vaccinated, the vaccination environment and vaccinator education. On the whole, factors strengthening and weakening vaccination were the opposite of each other. In the second phase, on the self-assessment of vaccination competence, students rated themselves as significantly lower than working professionals. On the knowledge test, the percentage of correct answers was lower for students than PHNs. When all background variables were taken into account in multivariate analysis, there was no longer a significant difference between the students and PHNs on the self-assessment. However, in multivariate analysis, the PHNs still performed better than students on the knowledge test. For this study, a satisfactory level of vaccination competence was defined as a mean of 8.0 on the self-assessment and 80% correct answers on the knowledge test. Based on these criteria, students almost reached the level of satisfactory in their overall self-assessment, and PHNs did. Both groups, however, did rank themselves as satisfactory in some sum variables. On the knowledge test the students did not achieve a level of satisfactory (80%) in their total score, though PHNs did. As before, both groups did achieve a level of satisfactory in several sum variables. Further research and development should focus on vaccination education, the testing of vaccination competence and vaccination practices in clinical practice, as well as on developing the measurement tools.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

Commitment to military service among Finnish conscripts

This research establishes the primary components, predictors, and consequences of organizational commitment in the military context. Specifically, the research examines commitment to the military service among Finnish conscripts and whether initial affective commitment prior to service predicts later commitment, attitudes, behavior, and performance, and, furthermore, analyzes the changes in commitment and its possible outcomes. The data were collected from records as well as by surveys from 1,387 rank and file soldiers, immediately after they reported for duty, near the end of basic training, and near the end of 6 to 12 months of service. The data covered a wide array of predictor variables, including background items, attitudes toward conscription, mental and physical health, sociability, training quality, and leadership. Moreover, the archival data included such items as rank, criminal record, performance ratings, and the number of medical examines and exemptions. The measures were further refined based on the results of factor analysis and reliability tests. The results indicated that initial commitment significantly corresponded with expected adjustment, intentions to stay in the military, and acceptance of authority. Moreover, initial commitment moderately related to personal growth, perceived performance, and the number of effective service days at the end of service. During basic training, affective commitment was mostly influenced by challenging training, adjustment experiences, regimentation, and unit climate. At the end of service, committed soldiers demonstrated more personal growth and development in service, had higher-level expected performance, and less malingering during their service. Additionally, they had significantly more positive attitudes toward national defense. The results suggest that affective commitment requires adequate personal adjustment, experiences of personal growth and development, and satisfaction with unit dynamics and training. This research contributes to the theoretical discussion on organizational commitment and the will to defend the nation and advances developing models to support and manage conscript training, education, leadership, and personnel policy. This is achieved by determining the main factors and variables, including their relative strength, that affect commitment to the military service. These findings may also facilitate in designing programs aimed at reducing unwanted discharges and inadequate performance. In particular, these results provide tools for improving conscripts’ overall attachment to and identification with the military service.

Chasing performance persistence of hedge funds

The thesis examines the performance persistence of hedge funds using complement methodologies (namely cross-sectional regressions, quantile portfolio analysis and Spearman rank correlation test). In addition, six performance ranking metrics and six different combinations of selection and holding periods are compared. The data is gathered from HFI and Tremont databases covering over 14,000 hedge funds and time horizon is set from January 1996 to December 2007. The results suggest that there definitely exists performance persistence among hedge funds and the strength and existence of persistence vary among fund styles. The persistence depends on the metrics and combination of selection and prediction period applied. According to the results, the combination of 36-month selection and holding period outperforms other five period combinations in capturing performance persistence within the sample. Furthermore, model-free performance metrics capture persistence more sensitively than model-specific metrics. The study is the first one ever to use MVR as a performance ranking metric, and surprisingly MVR is more sensitive to detect persistence than other performance metrics employed.

Leadership in the mobile smartphone market

The mobile telecommunications industry has been going through an enormous revolution, especially after mid-1990 when smartphones were introduced to the market. As a consequence, the smartphone market’s dynamism is requesting companies to operate differently in the way they do business. After a long period occupying the leader position in the smartphones manufacturers’ rank, Nokia was outperformed by Apple and Samsung during 2011 and since then has been on the third place. Nevertheless, Nokia is battling for regaining the leadership in such a competitive and high-velocity growing market and that is what this research is about. This research covers the competitive and strategic forces that shape dynamic industries whereas the main purpose is to elucidate the main factors that contribute to a company’s above-average performance and ultimately determine its leadership in the mobile smartphone market. Therefore, this exploratory qualitative research was conducted as a desk research, which utilized various secondary sources of data in the knowledge area of strategic management such as theories about competitive advantages and dynamic capabilities of firms, innovation, and strategy. This research is enriched with a case study about Nokia: how the company has been organizing its corporate structure to support the strategies and hence how it has been competing in the smartphone market is analyzed, taking into account many contemporary data sources, including market analysts’ and business experts’ opinions. As a result of the classic literature exploration and the case study assay, a framework for deeper analysis of the competitiveness of firms in dynamic markets was developed. The conclusion that emerged from this research is that the success of a firm results from the interplay of various factors. To regain the leader position in the mobile smartphone market is a challenging task that requires Nokia to reinvent its core strategy for taking charge of the smartphones’ industry transformation through for example the adoption of the open innovation concept. It is imperative that Nokia designs and implement a breakthrough strategy as well as embraces the uncertainty of the smartphone market competition as an opportunity for discontinuous innovation development with the ultimate goal of recovering the leadership.

Taxonomy, species richness and biogeography of Finnish crane flies (Diptera, Tipuloidea).

Biodiversity is unequally spread throughout terrestrial ecosystems. The highest species richness of animals and plants is encountered around the Equator, and naturalists observe a decrease in the number of creatures with increasing latitude. Some animal groups, however, display an anomalous species richness pattern, but these are exceptions to the general rule. Crane flies (Diptera, Tipuloidea) are small to large sized, non-biting nematoceran insects, being mainly associated with moist environments. The species richness of crane flies is highest in the tropics, but these insects are species rich and abundant in all biogeographic realms, boreal and arctic biomes included. The phylogeny and systematics of crane flies are still at an early stage and somewhat controversial. New species are constantly discovered even from temperate Europe, faunistically the best known continent. Crane flies have been rather neglected group of insects in Finland. The history of Finnish crane fly taxonomy and faunistics started in 1907, the year when Carl Lundström published his two first articles on tipuloids. Within roughly 100 years there have been only a handful of entomologists studying the Finnish fauna, and the species richness and natural history of these flies have remained poorly understood and mapped. The aim of this thesis is to clarify the taxonomy of Finnish crane flies, present an updated and annotated list of species and seek patterns in regional species richness and assemblage composition. Tipula stackelbergi Alexander has been revised (I). This species was elevated to a species rank from a subspecific rank under T. pruinosa Wiedemann and T. stackelbergi was also deleted from the list of European crane flies. Two new synonyms were found: T. subpruinosa Mannheims is a junior synonym of T. freyana Lackschewitz and T. usuriensis Alexander is a junior synonym of T. pruinosa. A new species Tipula recondita Pilipenko & Salmela has been described (II). Both morphology and COI (mtDNA) sequences were used in the assessment of the status of the species. The new species is highly disjunct, known from Finland and Russian Far East. A list of Finnish crane flies was presented, including the presence of species in the Finnish biogeographical provinces (III). A total of twenty-four species were formally reported for the first time from Finland and twenty-two previously reported species were deleted from the list. A short historical review on the studies of Finnish crane flies has been provided. The current list of Finnish species consists of 338 crane flies (IV, Appendix I). Species richness of all species and saproxylic/fungivorous species is negatively correlated with latitude, but mire-dwelling species show a reversed species richness gradient (i.e. an increase in the number of species toward north). Provincial assemblages displayed a strong latitudinal gradient and faunistic distance increased with increasing geographical distance apart of the provinces. Nearly half (48 %) of the Finnish crane flies are Trans-Palaearctic, roughly one-third (34 %) are West Palaearctic and only 16 and 2 % are Holarctic and Fennoscandian, respectively. Due to the legacy of Pleistocene glaciations, endemic Fennoscandian species are problematic and it is thus concluded that there are probably no true endemic crane flies in this region. Finally, there are probably species living within Finnish borders that have hitherto remained unnoticed. Based on subjective assessment, the number of “true” (i.e. recorded + unknown species) species count of Finnish crane flies is at minimum 350.