Browsing by Author "Kaila, Ruth"
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Item 2000-luvun lopun finanssikriisin kootut selitykset(2010) Viitanen, Maria; Immonen, Stina; Informaatio- ja luonnontieteiden tiedekunta; Kaila, RuthItem Active management, investment style and mutual fund performance - Evidence from the Nordic market(2021-12-14) Vikelä, Janne; Kaila, Ruth; Perustieteiden korkeakoulu; Salo, AhtiThis thesis examines the impact of active management and investment style on mutual fund performance in the Nordic market. Both active management and investment style have been investigated separately in other markets, but in the Nordic context, little previous research exists. The study builds on Cremers and Petäjistö (2009) and Petäjistö (2013) work, expanding the Cremers and Petäjistö work to incorporate the impact of investment style on mutual fund performance. The sample consists of Nordic funds domiciled in Finland, Sweden, Norway and Denmark with their investment area in the Nordic countries. The data is from 2000-2020. The funds are sorted based on the mutual funds’ active share and tracking error, following the methodology by Petäjistö (2013). The five categories used in the sorting are closet indexers, moderately active, factors bets, concentrated and stock pickers. Contrary to the Petäjistö (2013) findings, the best-performing funds in the Nordic sample are the concentrated funds, i.e. the funds that generally have an undiversified holding. In the original Petäjistö (2013) work, the best performing funds are the stock pickers. In the Nordic sample, the stock pickers do not offer superior performance compared to the other fund classes. In addition to examining active management, this thesis investigates the mutual fund performance by sorting the funds based on style. The style is examined for all funds and separately for each Petäjistö (2013) fund class. Small-cap style, proxied by the Small-Minus-Big Carhart factor loading seems to provide an ability to generate positive abnormal returns. However, the value style, proxied by the HML factor loading, does not seem to yield a difference in risk-adjusted performance. Within the Petäjistö classes, shifting weight onto the small-cap style seems to improve mutual fund performance in closet indexer and moderately active fund categories. This is measured proxying the style with the SMB factor loading. Moreover, the factor bets, concentrated and stock picker funds seek to earn returns by shifting weight onto small-cap stocks, based on the SMB factor loading. This thesis contributes to the literature in two ways. First, it examines the active management in the Nordic market. Second, it provides evidence that active managers are able to improve their funds’ performance by shifting weight onto small-cap stocks.Item Alustatalous liiketoiminnan mullistajana(2018-09-10) Pitkänen, Karri; Kaila, Ruth; Perustieteiden korkeakoulu; Mäki, EerikkiItem Älysopimukset finanssisektorilla(2023-12-09) Latvakoski, Olli; Kaila, Ruth; Perustieteiden korkeakoulu; Rajala, RistoItem Arvopaperimarkkinoiden manipulointi(2017-05-05) Vaskikari, Valtteri; Kaila, Ruth; Perustieteiden korkeakoulu; Mäki, EerikkiItem Aurinkoenergian ekosysteemien kehitys – vertailu nykyisten ja nousevien talousjättien välillä(2012-05-07) Räisänen, Paavo; Kaila, Ruth; ; Perustieteiden korkeakoulu; Järvenpää, EilaItem Avoin innovaatio yrityksen toimintatapana(2011) Kokkola, Juho; Kaila, Ruth; Perustieteiden korkeakoulu; Immonen, StinaItem Biopolttoaineiden makroekonomiset, ympäristölliset ja sosiaaliset vaikutukset sekä markkina maailmalla(2016-05-23) Laukia, Lari; Kaila, Ruth; Perustieteiden korkeakoulu; Mäki, EerikkiItem Black-Scholes -yhtälö optioiden hinnoittelussa(2011) Savelainen, Terhi; Kaila, Ruth; Perustieteiden korkeakoulu; Järvenpää, EilaItem BRIC-maiden talouksien tilanteet ja talouskasvuun vaikuttavat tekijät(2016-12-14) Karinen, Laura; Kaila, Ruth; Perustieteiden korkeakoulu; Mäki, EerikkiItem Carbon neutrality investment strategies of Finnish and Swedish institutional investors(2023-01-25) Moustgaard, Theresia; Kaila, Ruth; Perustieteiden korkeakoulu; Maula, MarkkuItem COVID-19-pandemian vaikutukset lääkeyhtiöiden arvostuksiin arvopaperimarkkinoilla(2020-12-08) Honkasalo, Arttu; Kaila, Ruth; Perustieteiden korkeakoulu; Kaipia, RiikkaItem Currency overlays on international equity portfolios(2018-08-22) Purkunen, Aleksi; Kaila, Ruth; Perustieteiden korkeakoulu; Jääskeläinen, MikkoItem Detecting anomalies in microservices using telemetry data: a case study(2023-03-21) Ilomäki, Risto; Kaila, Ruth; Piironen, Janne; Perustieteiden korkeakoulu; Lassenius, CasperMicroservices, small program components specialized for one task, are a common solution in modern software architecture. To keep the software reliable, secure, and fluent, incidents must be detected so that they can be remediated. Incidents are mostly significantly different abnormal activities, i.e., anomalies. Developing a tool for anomaly detection in microservices involves (1) defining the relevant metrics based on which anomalies are detected, (2) creating a model that detects abnormal activities, and (3) evaluating the results and reliability of the model. This thesis approaches the general problem as a case study conducted in Design Science methodology. It aims to develop an anomaly detection tool for a startup operating in SaaS business. To make the detections, the tool applies the unsupervised machine learning models autoencoder and K-means to metrics calculated from telemetry data. The anomaly detection tool is evaluated in qualitative and quantitative methods. The end user of the tool evaluates the accuracy and correctness of the detections. Quantitative approximate accuracy is determined by running models on historical test data. Furthermore, the end user of the tool elaborates on the usability of the tool. The results are promising. K-means was observed to detect more relevant instances and have higher accuracy in detecting past incidents than autoencoder. Most of the detected anomalies are worth remediating, i.e., true positives, and it is understandable why the detection is classified as an anomaly with the help of benchmark values from calculated from the training data. With the help of this tool, the end user can easily monitor, locate and further investigate the anomalies. While the tool is usable only within the specific context, the findings on how to aggregate telemetry data and use it for anomaly detection are scalable to the general level too. The thesis points out relevant metrics and methods with which anomalies can be detected. The anomaly detection tool has its limitations, however. In the unsupervised setting, determining the exact accuracy is infeasible and the fraction of undetected anomalies is not clear. Nonetheless, the tool is a good addition to the company’s software monitoring toolkit.Item Digitaalinen keskuspankkiraha(2023-09-16) Heikkilä, Elias; Kaila, Ruth; Perustieteiden korkeakoulu; Rajala, RistoItem Digitaalinen keskuspankkiraha(2018-09-15) Honkamaa, Emma; Kaila, Ruth; Perustieteiden korkeakoulu; Jaatinen, MiiaItem Dollari reservivaluuttana(2023-05-02) Valtonen, Veikko; Kaila, Ruth; Perustieteiden korkeakoulu; Rajala, RistoItem Efektiivinen korko pääomasijoittajien kannustimien määrittäjänä(2019-01-17) Pietilä, Sakari; Kaila, Ruth; Perustieteiden korkeakoulu; Giesecke, StinaItem Electricity Markets: Projected Development of Spot Price and Volatility under Key Variables(2015-12-14) Myllymäki, Mikael; Kaila, Ruth; Perustieteiden korkeakoulu; Mäki, EerikkiItem Elinkaariarvioinnin käyttö yrityksissä - haasteita ja mahdollisuuksia(2011) Rossi, Marko; Kaila, Ruth; Perustieteiden korkeakoulu; Immonen, Stina
