Pricing of Liquidity Risks in London Stock Exchange

This thesis aims to investigate pricing of liquidity risks in London Stock Exchange. Liquidity Adjusted Capital Asset Pricing Model i.e. LCAPM developed by Acharya and Pedersen (2005) is being applied to test the influence of various liquidity risks on stock returns in London Stock Exchange. The Liquidity Adjusted Capital Asset Pricing model provides a unified framework for the testing of liquidity risks. All the common stocks listed and delisted for the period of 2000 to 2014 are included in the data sample. The study has incorporated three different measures of liquidity – Percent Quoted Spread, Amihud (2002) and Turnover. The reason behind the application of three different liquidity measures is the multi-dimensional nature of liquidity. Firm fixed effects panel regression is applied for the estimation of LCAPM. However, the results are robust according to Fama-Macbeth regressions. The results of the study indicates that liquidity risks in the form of (i) level of liquidity, (ii) commonality in liquidity (iii) flight to liquidity, (iv) depressed wealth effect and market return as well as aggregate liquidity risk are priced at London Stock Exchange. However, the results are sensitive to the choice of liquidity measures.

Low-Volatility Anomaly in Helsinki Stock Exchange

International research shows that low-volatility stocks have beaten high-volatility stocks in terms of returns for decades on multiple markets. This abbreviation from traditional risk-return framework is known as low-volatility anomaly. This study focuses on explaining the anomaly and finding how strongly it appears in NASDAQ OMX Helsinki stock exchange. Data consists of all listed companies starting from 2001 and ending close to 2015. Methodology follows closely Baker and Haugen (2012) by sorting companies into deciles according to 3-month volatility and then calculating monthly returns for these different volatility groups. Annualized return for the lowest volatility decile is 8.85 %, while highest volatility decile destroys wealth at rate of -19.96 % per annum. Results are parallel also in quintiles that represent larger amount of companies and thus dilute outliers. Observation period captures financial crisis of 2007-2008 and European debt crisis, which embodies as low main index annual return of 1 %, but at the same time proves the success of low-volatility strategy. Low-volatility anomaly is driven by multiple reasons such as leverage constrained trading and managerial incentives which both prompt to invest in risky assets, but behavioral matters also have major weight in maintaining the anomaly.

Sähkön ja lämmön yhteistuotannon optimointi

Työn tavoitteena oli suunnitella ja toteuttaa sähkön ja lämmön yhteistuotantolaitoksen tuotannon optimointi. Optimoinnin kriteerinä on tuotannon kannattavuus. Pyrittiin luomaan optimointimalli, joka ottaa optimoinnissa huomioon erityisesti kaukolämmön kulutusennusteen muutokset sekä sähkön pörssihinnan vaihtelut. Tuotannon kannalta olennaisin kriteeri on kaukolämmön kulutusennusteen pohjalta arvioidun kaukolämpökuorman tyydyttäminen mahdollisimman tehokkaasti ja taloudellisesti. Sähkön tuotannon merkittävimmiksi kriteereiksi muodostuivat sähkön tuotannon ennustettavuus ja tuotannon maksimointi sähkön pörssihinnan asettamissa puitteissa. Optimointiohjelmaa ei ole tarkoitus kytkeä suoraan voimalaitoksen ajojärjestelmään, vaan siitä on tarkoitus tulla erillinen ajosuunnittelijan työkalu. Itse ajosuunnitteluun vaikuttaa usein monipuolisemmat suunnittelukriteerit kuin pelkästään tuotannon tuottavuus. Näiden eri kriteerien painotuksia ei ohjelmassa huomioida, vaan ne päättää ajosuunnittelija. Tuloksena saatiin aikaan optimointiohjelma, joka laskee valittujen tuotantovaihtoehtojen kokonaistuotot eri kaukolämmön kulutusennusteiden ja sähkön pörssihintaennusteiden pohjalta.

Capital structure of Finnish firms – Estimating the effects of firm characteristics on capital structure

One of the most disputable matters in the theory of finance has been the theory of capital structure. The seminal contributions of Modigliani and Miller (1958, 1963) gave rise to a multitude of studies and debates. Since the initial spark, the financial literature has offered two competing theories of financing decision: the trade-off theory and the pecking order theory. The trade-off theory suggests that firms have an optimal capital structure balancing the benefits and costs of debt. The pecking order theory approaches the firm capital structure from information asymmetry perspective and assumes a hierarchy of financing, with firms using first internal funds, followed by debt and as a last resort equity. This thesis analyses the trade-off and pecking order theories and their predictions on a panel data consisting 78 Finnish firms listed on the OMX Helsinki stock exchange. Estimations are performed for the period 2003–2012. The data is collected from Datastream system and consists of financial statement data. A number of capital structure characteristics are identified: firm size, profitability, firm growth opportunities, risk, asset tangibility and taxes, speed of adjustment and financial deficit. A regression analysis is used to examine the effects of the firm characteristics on capitals structure. The regression models were formed based on the relevant theories. The general capital structure model is estimated with fixed effects estimator. Additionally, dynamic models play an important role in several areas of corporate finance, but with the combination of fixed effects and lagged dependent variables the model estimation is more complicated. A dynamic partial adjustment model is estimated using Arellano and Bond (1991) first-differencing generalized method of moments, the ordinary least squares and fixed effects estimators. The results for Finnish listed firms show support for the predictions of profitability, firm size and non-debt tax shields. However, no conclusive support for the pecking-order theory is found. However, the effect of pecking order cannot be fully ignored and it is concluded that instead of being substitutes the trade-off and pecking order theory appear to complement each other. For the partial adjustment model the results show that Finnish listed firms adjust towards their target capital structure with a speed of 29% a year using book debt ratio.