The Anchoring Bias of Investors and the 52-Week High Momentum Strategy in the OMX Helsinki Stock Exchange: Psychological Approach to Market Efficiency

The behavioural finance literature expects systematic and significant deviations from efficiency to persist in securities markets due to behavioural and cognitive biases of investors. These behavioural models attempt to explain the coexistence of intermediate-term momentum and long-term reversals in stock returns based on the systematic violations of rational behaviour of investors. The study investigates the anchoring bias of investors and the profitability of the 52-week momentum strategy (GH henceforward). The relatively highly volatile OMX Helsinki stock exchange is a suitable market for examining the momentum effect, since international investors tend to realise their positions first from the furthest security markets by the time of market turbulence. Empirical data is collected from Thomson Reuters Datastream and the OMX Nordic website. The objective of the study is to provide a throughout research by formulating a self-financing GH momentum portfolio. First, the seasonality of the strategy is examined by taking the January effect into account and researching abnormal returns in long-term. The results indicate that the GH strategy is subject to significantly negative revenues in January, but the strategy is not prone to reversals in long-term. Then the predictive proxies of momentum returns are investigated in terms of acquisition prices and 52-week high statistics as anchors. The results show that the acquisition prices do not have explanatory power over the GH strategy’s abnormal returns. Finally, the efficacy of the GH strategy is examined after taking transaction costs into account, finding that the robust abnormal returns remain statistically significant despite the transaction costs. As a conclusion, the relative distance between a stock’s current price and its 52-week high statistic explains the profits of momentum investing to a high degree. The results indicate that intermediateterm momentum and long-term reversals are separate phenomena. This presents a challenge to current behavioural theories, which model these aspects of stock returns as subsequent components of how securities markets respond to relevant information.

Integration of Torrefaction with Steam Power Plant

Torrefaction is one of the pretreatment technologies to enhance the fuel characteristics of biomass. The efficient and continuous operation of a torrefaction reactor, in the commercial scale, demands a secure biomass supply, in addition to adequate source of heat. Biorefinery plants or biomass-fuelled steam power plants have the potential to integrate with the torrefaction reactor to exchange heat and mass, using available infrastructure and energy sources. The technical feasibility of this integration is examined in this study. A new model for the torrefaction process is introduced and verified by the available experimental data. The torrefaction model is then integrated in different steam power plants to simulate possible mass and energy exchange between the reactor and the plants. The performance of the integrated plant is investigated for different configurations and the results are compared.

Greenhouse gas emissions from peat and biomass-derived fuels, electricity and heat — Estimation of various production chains by using LCA methodology

More discussion is required on how and which types of biomass should be used to achieve a significant reduction in the carbon load released into the atmosphere in the short term. The energy sector is one of the largest greenhouse gas (GHG) emitters and thus its role in climate change mitigation is important. Replacing fossil fuels with biomass has been a simple way to reduce carbon emissions because the carbon bonded to biomass is considered as carbon neutral. With this in mind, this thesis has the following objectives: (1) to study the significance of the different GHG emission sources related to energy production from peat and biomass, (2) to explore opportunities to develop more climate friendly biomass energy options and (3) to discuss the importance of biogenic emissions of biomass systems. The discussion on biogenic carbon and other GHG emissions comprises four case studies of which two consider peat utilization, one forest biomass and one cultivated biomasses. Various different biomass types (peat, pine logs and forest residues, palm oil, rapeseed oil and jatropha oil) are used as examples to demonstrate the importance of biogenic carbon to life cycle GHG emissions. The biogenic carbon emissions of biomass are defined as the difference in the carbon stock between the utilization and the non-utilization scenarios of biomass. Forestry-drained peatlands were studied by using the high emission values of the peatland types in question to discuss the emission reduction potential of the peatlands. The results are presented in terms of global warming potential (GWP) values. Based on the results, the climate impact of the peat production can be reduced by selecting high-emission-level peatlands for peat production. The comparison of the two different types of forest biomass in integrated ethanol production in pulp mill shows that the type of forest biomass impacts the biogenic carbon emissions of biofuel production. The assessment of cultivated biomasses demonstrates that several selections made in the production chain significantly affect the GHG emissions of biofuels. The emissions caused by biofuel can exceed the emissions from fossil-based fuels in the short term if biomass is in part consumed in the process itself and does not end up in the final product. Including biogenic carbon and other land use carbon emissions into the carbon footprint calculations of biofuel reveals the importance of the time frame and of the efficiency of biomass carbon content utilization. As regards the climate impact of biomass energy use, the net impact on carbon stocks (in organic matter of soils and biomass), compared to the impact of the replaced energy source, is the key issue. Promoting renewable biomass regardless of biogenic GHG emissions can increase GHG emissions in the short term and also possibly in the long term.

Computational studies for the design of process equipment with complex geometries

This study combines several projects related to the flows in vessels with complex shapes representing different chemical apparata. Three major cases were studied. The first one is a two-phase plate reactor with a complex structure of intersecting micro channels engraved on one plate which is covered by another plain plate. The second case is a tubular microreactor, consisting of two subcases. The first subcase is a multi-channel two-component commercial micromixer (slit interdigital) used to mix two liquid reagents before they enter the reactor. The second subcase is a micro-tube, where the distribution of the heat generated by the reaction was studied. The third case is a conventionally packed column. However, flow, reactions or mass transfer were not modeled. Instead, the research focused on how to describe mathematically the realistic geometry of the column packing, which is rather random and can not be created using conventional computeraided design or engineering (CAD/CAE) methods. Several modeling approaches were used to describe the performance of the processes in the considered vessels. Computational fluid dynamics (CFD) was used to describe the details of the flow in the plate microreactor and micromixer. A space-averaged mass transfer model based on Fick’s law was used to describe the exchange of the species through the gas-liquid interface in the microreactor. This model utilized data, namely the values of the interfacial area, obtained by the corresponding CFD model. A common heat transfer model was used to find the heat distribution in the micro-tube. To generate the column packing, an additional multibody dynamic model was implemented. Auxiliary simulation was carried out to determine the position and orientation of every packing element in the column. This data was then exported into a CAD system to generate desirable geometry, which could further be used for CFD simulations. The results demonstrated that the CFD model of the microreactor could predict the flow pattern well enough and agreed with experiments. The mass transfer model allowed to estimate the mass transfer coefficient. Modeling for the second case showed that the flow in the micromixer and the heat transfer in the tube could be excluded from the larger model which describes the chemical kinetics in the reactor. Results of the third case demonstrated that the auxiliary simulation could successfully generate complex random packing not only for the column but also for other similar cases.

Prediction of market switching and delisting events from OMX First North Nordic multilateral stock exchange

This thesis studies the predictability of market switching and delisting events from OMX First North Nordic multilateral stock exchange by using financial statement information and market information from 2007 to 2012. This study was conducted by using a three stage process. In first stage relevant theoretical framework and initial variable pool were constructed. Then, explanatory analysis of the initial variable pool was done in order to further limit and identify relevant variables. The explanatory analysis was conducted by using self-organizing map methodology. In the third stage, the predictive modeling was carried out with random forests and support vector machine methodologies. It was found that the explanatory analysis was able to identify relevant variables. The results indicate that the market switching and delisting events can be predicted in some extent. The empirical results also support the usability of financial statement and market information in the prediction of market switching and delisting events.

Choosing the target loci : heat shock factors HSF1 and HSF2 as regulators of cell stress and development

Heat shock factors (HSFs) are an evolutionarily well conserved family of transcription factors that coordinate stress-induced gene expression and direct versatile physiological processes in eukaryote organisms. The essentiality of HSFs for cellular homeostasis has been well demonstrated, mainly through HSF1-induced transcription of heat shock protein (HSP) genes. HSFs are important regulators of many fundamental processes such as gametogenesis, metabolic control and aging, and are involved in pathological conditions including cancer progression and neurodegenerative diseases. In each of the HSF-mediated processes, however, the detailed mechanisms of HSF family members and their complete set of target genes have remained unknown. Recently, rapid advances in chromatin studies have enabled genome-wide characterization of protein binding sites in a high resolution and in an unbiased manner. In this PhD thesis, these novel methods that base on chromatin immunoprecipitation (ChIP) are utilized and the genome-wide target loci for HSF1 and HSF2 are identified in cellular stress responses and in developmental processes. The thesis and its original publications characterize the individual and shared target genes of HSF1 and HSF2, describe HSF1 as a potent transactivator, and discover HSF2 as an epigenetic regulator that coordinates gene expression throughout the cell cycle progression. In male gametogenesis, novel physiological functions for HSF1 and HSF2 are revealed and HSFs are demonstrated to control the expression of X- and Y-chromosomal multicopy genes in a silenced chromatin environment. In stressed human cells, HSF1 and HSF2 are shown to coordinate the expression of a wide variety of genes including genes for chaperone machinery, ubiquitin, regulators of cell cycle progression and signaling. These results highlight the importance of cell type and cell cycle phase in transcriptional responses, reveal the myriad of processes that are adjusted in a stressed cell and describe novel mechanisms that maintain transcriptional memory in mitotic cell division.

Characteristics of urban heat island (UHI) in a high latitude coastal city - a case study of Turku, SW Finland

The term urban heat island (UHI) refers to the common situation in which the city is warmer than its rural surroundings. In this dissertation, the local climate, and especially the UHI, of the coastal city of Turku (182,000 inh.), SW Finland, was studied in different spatial and temporal scales. The crucial aim was to sort out the urban, topographical and water body impact on temperatures at different seasons and times of the day. In addition, the impact of weather on spatiotemporal temperature differences was studied. The relative importance of environmental factors was estimated with different modelling approaches and a large number of explanatory variables with various spatial scales. The city centre is the warmest place in the Turku area. Temperature excess relative to the coldest sites, i.e. rural areas about 10 kilometers to the NE from the centre, is on average 2 °C. Occasionally, the UHI intensity can be even 10 °C. The UHI does not prevail continuously in the Turku area, but occasionally the city centre can be colder than its surroundings. Then the term urban cool island or urban cold island (UCI) is used. The UCI is most common in daytime in spring and in summer, whereas during winter the UHI prevails throughout the day. On average, the spatial temperature differences are largest in summer, whereas the single extreme values are often observed in winter. The seasonally varying sea temperature causes the shift of relatively warm areas towards the coast in autumn and inland in spring. In the long term, urban land use was concluded to be the most important factor causing spatial temperature differences in the Turku area. The impact was mainly a warming one. The impact of water bodies was emphasised in spring and autumn, when the water temperature was relatively cold and warm, respectively. The impact of topography was on average the weakest, and was seen mainly in proneness of relatively low-lying places for cold air drainage during night-time. During inversions, however, the impact of topography was emphasised, occasionally outperforming those of urban land use and water bodies.

Working fluid selection and design of small-scale waste heat recovery systems based on organic Rankine cycles

Demand for the use of energy systems, entailing high efficiency as well as availability to harness renewable energy sources, is a key issue in order to tackling the threat of global warming and saving natural resources. Organic Rankine cycle (ORC) technology has been identified as one of the most promising technologies in recovering low-grade heat sources and in harnessing renewable energy sources that cannot be efficiently utilized by means of more conventional power systems. The ORC is based on the working principle of Rankine process, but an organic working fluid is adopted in the cycle instead of steam. This thesis presents numerical and experimental results of the study on the design of small-scale ORCs. Two main applications were selected for the thesis: waste heat re- covery from small-scale diesel engines concentrating on the utilization of the exhaust gas heat and waste heat recovery in large industrial-scale engine power plants considering the utilization of both the high and low temperature heat sources. The main objective of this work was to identify suitable working fluid candidates and to study the process and turbine design methods that can be applied when power plants based on the use of non-conventional working fluids are considered. The computational work included the use of thermodynamic analysis methods and turbine design methods that were based on the use of highly accurate fluid properties. In addition, the design and loss mechanisms in supersonic ORC turbines were studied by means of computational fluid dynamics. The results indicated that the design of ORC is highly influenced by the selection of the working fluid and cycle operational conditions. The results for the turbine designs in- dicated that the working fluid selection should not be based only on the thermodynamic analysis, but requires also considerations on the turbine design. The turbines tend to be fast rotating, entailing small blade heights at the turbine rotor inlet and highly supersonic flow in the turbine flow passages, especially when power systems with low power outputs are designed. The results indicated that the ORC is a potential solution in utilizing waste heat streams both at high and low temperatures and both in micro and larger scale appli- cations.

Do retail investors use technical analysis? Evidence from Stuttgart stock exchange.

Tutkielma käyttää automaattista kuviontunnistusalgoritmia ja yleisiä kahden liukuvan keskiarvon leikkauspiste –sääntöjä selittääkseen Stuttgartin pörssissä toimivien yksityissijoittajien myynti-osto –epätasapainoa ja siten vastatakseen kysymykseen ”käyttävätkö yksityissijoittajat teknisen analyysin menetelmiä kaupankäyntipäätöstensä perustana?” Perusolettama sijoittajien käyttäytymisestä ja teknisen analyysin tuottavuudesta tehtyjen tutkimusten perusteella oli, että yksityissijoittajat käyttäisivät teknisen analyysin metodeja. Empiirinen tutkimus, jonka aineistona on DAX30 yhtiöiden data vuosilta 2009 – 2013, ei tuottanut riittävän selkeää vastausta tutkimuskysymykseen. Heikko todistusaineisto näyttää kuitenkin osoittavan, että yksityissijoittajat muuttavat kaupankäyntikäyttäytymistänsä eräiden kuvioiden ja leikkauspistesääntöjen ohjastamaan suuntaan.

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

Modeling a Network of Heat Exchangers

This research work addresses the problem of building a mathematical model for the given system of heat exchangers and to determine the temperatures, pressures and velocities at the intermediate positions. Such model could be used in nding an optimal design for such a superstructure. To limit the size and computing time a reduced network model was used. The method can be generalized to larger network structures. A mathematical model which includes a system of non-linear equations has been built and solved according to the Newton-Raphson algorithm. The results obtained by the proposed mathematical model were compared with the results obtained by the Paterson approximation and Chen's Approximation. Results of this research work in collaboration with a current ongoing research at the department will optimize the valve positions and hence, minimize the pumping cost and maximize the heat transfer of the system of heat exchangers.

Effect of heat input on the mechanical properties of welded joints in high strength steels

Experiments were carried out to determine the properties of the welded joints in 8mm thick high-strength steels produced by quenching and tempering and thermomechanical rolling with accelerated cooling (tensile strength 821–835 MPa). The dependence of the strength, elongation, hardness, impact energy and crack opening displacement on the heat input in the range 1.0–0.7 kJ mm21 was determined. The results show that the dependence of the strength of the welded joints decreases and that of the elongation increases. The heat input has only a slight effect on the impact energy and crack opening displacement in the heat-affected zone.

A Bayesian Approach for Forecasting Heat Load in a District Heating System

The growing population in cities increases the energy demand and affects the environment by increasing carbon emissions. Information and communications technology solutions which enable energy optimization are needed to address this growing energy demand in cities and to reduce carbon emissions. District heating systems optimize the energy production by reusing waste energy with combined heat and power plants. Forecasting the heat load demand in residential buildings assists in optimizing energy production and consumption in a district heating system. However, the presence of a large number of factors such as weather forecast, district heating operational parameters and user behavioural parameters, make heat load forecasting a challenging task. This thesis proposes a probabilistic machine learning model using a Naive Bayes classifier, to forecast the hourly heat load demand for three residential buildings in the city of Skellefteå, Sweden over a period of winter and spring seasons. The district heating data collected from the sensors equipped at the residential buildings in Skellefteå, is utilized to build the Bayesian network to forecast the heat load demand for horizons of 1, 2, 3, 6 and 24 hours. The proposed model is validated by using four cases to study the influence of various parameters on the heat load forecast by carrying out trace driven analysis in Weka and GeNIe. Results show that current heat load consumption and outdoor temperature forecast are the two parameters with most influence on the heat load forecast. The proposed model achieves average accuracies of 81.23 % and 76.74 % for a forecast horizon of 1 hour in the three buildings for winter and spring seasons respectively. The model also achieves an average accuracy of 77.97 % for three buildings across both seasons for the forecast horizon of 1 hour by utilizing only 10 % of the training data. The results indicate that even a simple model like Naive Bayes classifier can forecast the heat load demand by utilizing less training data.