This study examines how the stock market reacts when a publicly-listed company announces a corporate green bond issuance in the European market. We examine 155 corporate green bond issuance announcements from 2017 to 2021 made by companies listed on the European stock exchange. Our findings can not confirm a stock market reaction to the announcement of a corporate green bond. The result shows no significant positive stock market reaction when a company announces a corporate green bond issuance for the first time. This finding suggests that the signaling argument is not necessarily applicable, as it suggests that companies signal their environmental commitment to the investors by announcing a corporate green bond issuance. Our findings do neither show a stronger stock market reaction due to a company having a low environmental performance at the time of announcement. This means that companies that actively try to transition into climate-friendly financing are not rewarded by the stock market.

Bakgrund: Samtidigt som breda stockholmsbörsen under 2010-talet genomgick en av de längsta tjurmarknaderna i svensk historia blev svenskarnas närvaro på sociala medier såväl utbrett som vardagligt. Det nuvarande kunskapsläget belyser skillnader inom såväl användandet av sociala medier som uppvisandet av relevanta börspsykologiska faktorer mellan kvinnor och män, men också bland olika åldersgrupper. Hur svenska småsparare ställer sig till denna nya informationskanal i sina investeringsbeslut saknas det i mångt och mycket forskning kring. Dessa omständigheter har skapat en kunskapslucka som inte bara är relevant för småspararna, utan även för finansiella aktörer, politiker, och börsbolag. Syfte: Studiens syfte är att kartlägga i vilken utsträckning de börspsykologiska faktorerna flockbeteende, dispositionseffekten, kognitiv förmåga, och Fear-of-Missing-Out indikeras hos svenska småsparare. Vidare ämnar studien undersöka om det finns någon relation mellan uppvisandet av nämnda faktorer, de demografiska faktorerna kön och ålder, och attityden till användandet av sociala medier i investeringsprocessen. Metod: Studien genomfördes med en kvantitativ metod där primärdata inhämtades från en online-enkät. För att undersöka relationen mellan småspararnas attityd till användandet av sociala medier i investeringsprocessen, de demografiska faktorerna, och de börspsykologiska faktorerna genomfördes en linjär multipel regressionsanalys. Dessutom genomfördes t-tester och one way ANOVA för att undersöka skillnader mellan grupperna. Slutsats: Studien fann en signifikant relation mellan attityden till användandet av sociala medier i investeringsprocessen och fyra av sex förklaringsvariabler; kognitiv förmåga, flockbeteende, kön, och ålder. Regressionen fann en negativ relation mellan attityden till sociala medier som investeringsverktyg och att identifiera sig som kvinna. Även ålder uppvisade en negativ relation. För både flockbeteende och kognitiv förmåga uppvisades en positiv samvariation. Vidare antydde studiens deskriptiva statistik att samtliga undersökta börspsykologiska faktorer uppvisades av respondenterna, om än till varierande grad. / Background: Simultaneously as the Stockholm Stock Exchange during the 2010’s had one of the longest bull markets in Swedish history the Swedes presence on social media became widespread and common. Differences between the usage of social media, as well as demonstrating relevant biases within behavioural finance, has been studied for the genders and different age groups. However, how Swedish retail investors interact with social media in their investment process has not been studied sufficiently. These circumstances have created a scientific gap that is not only of relevance to retail investors, but also financial intermediates, politicians, and publicly traded companies. Purpose: The purpose of the thesis is to map out to what degree the behavioural finance biases herding, disposition effect, cognitive ability, and Fear-of-Missing-Out are indicated by Swedish retail investors. Furthermore, the study aims to explore if there is any relationship between the mentioned biases, the demographic factors gender and age, and the attitude to using social media in the investment process. Methodology: The study was conducted through a quantitative research method that gathered the primary data through a survey. To explore the relationship between the retail investors attitude towards using social media in the investment process, the demographic factors, and the behavioural finance biases a multiple linear regression analysis was performed. Furthermore, t-tests and one way ANOVA was performed to examine differences between groups. Conclusion: The study found a significant relationship between the attitude towards using social media in the investment process and four out of six independent variables; cognitive ability, herding, gender, and age. The regression found a negative relationship between the attitude towards using social media in the investment process and identifying as a woman. Lastly, the descriptive statistics implied that all four behavioural finance factors were exhibited by the respondents, though to a varying degree.

Screening models are tools for predicting which stock are the most likely to perform well on a stock market. They do so by examining the financial ratios of the companies behind the stock. The ratios examined by the model are chosen according to the personal preferences of the particular investor. Furthermore, an investor can apply different weights to the different parameters they choose to consider, according to the importance they apply to each included parameter. In this thesis, it is investigated whether a screening model can beat the market average in the long term. It is also explored whether parameter-weight-optimization in the context of equity trading can be used to improve an already existing screening model. More specifically, a starting point is set in a screening model currently in use at a successful asset management firm, through data analysis and an optimization algorithm, it is then examined whether a programmatic approach can identify ways to improve the original screening model by adjusting the parameters it looks at as well as the weights assigned to each parameter. The data set used in the model contains daily price data and annual data on financial ratios for all stocks on the Stockholm Stock Exchange as well as the NASDAQ-100 over the time period 2004-2018. The results indicate that it is possible to beat the market average in the long term. Results further show that a programmatic approach is suitable for optimizing screening models.

Interpreting time varying phenomena is a key challenge in the capital markets. Time series analysis using autoregressive methods has been carried out over the last couple of decades, often with reassuring results. However, such methods sometimes fail to explain trends and cyclical fluctuations, which may be characterized by long-range dependencies or even dependencies between the input features. The purpose of this thesis is to investigate whether recurrent neural networks with LSTM-cells can be used to capture these dependencies, and ultimately be used as a complement for index trading decisions. Experiments are made on different setups of the S&P-500 stock index, and two distinct models are built, each one being an improvement of the previous model. The first model is a multivariate regression model, and the second model is a multivariate binary classifier. The output of each model is used to reason about the future behavior of the index. The experiment shows for the configuration provided that LSTM RNNs are unsuitable for predicting exact values of daily returns, but gives satisfactory results when used to predict the direction of the movement. / Att förstå och kunna förutsäga hur index varierar med tiden och andra parametrar är ett viktigt problem inom kapitalmarknader. Tidsserieanalys med autoregressiva metoder har funnits sedan årtionden tillbaka, och har oftast gett goda resultat. Dessa metoder saknar dock möjligheten att förklara trender och cykliska variationer i tidsserien, något som kan karaktäriseras av tidsvarierande samband, men även samband mellan parametrar som indexet beror utav. Syftet med denna studie är att undersöka om recurrent neural networks (RNN) med long short-term memory-celler (LSTM) kan användas för att fånga dessa samband, för att slutligen användas som en modell för att komplettera indexhandel. Experimenten är gjorda mot en modifierad S&P-500 datamängd, och två distinkta modeller har tagits fram. Den ena är en multivariat regressionsmodell för att förutspå exakta värden, och den andra modellen är en multivariat klassifierare som förutspår riktningen på nästa dags indexrörelse. Experimenten visar för den konfiguration som presenteras i rapporten att LSTM RNN inte passar för att förutspå exakta värden för indexet, men ger tillfredsställande resultat när modellen ska förutsäga indexets framtida riktning.

Структура магистерской диссертации включает в себя введение, три главы, заключение, список использованных источников. В первой части работы рассматривается определение «портфеля ценных бумаг», описываются принципы и этапы его прогнозирования, а также производится классификация портфелей ценных бумаг. Во второй части работы проводится анализ текущего состояния портфеля ценных бумаг компаний агропромышленной отрасли, а также оценивается состояние самих предприятий. В третьей части работы предлагаются меры по составлению наилучшего инвестиционного портфеля для владельца ценных бумаг в агропромышленной отрасли на перспективу. Анализируются факторы, влияющие на доходность такого портфеля, и разрабатываются рекомендации для инвестора по формированию оптимального портфеля. В заключении сформированы основные выводы. / The structure of the master’s thesis includes an introduction, three chapters, a conclusion, and a list of sources used. The first part of the work considers the definition of “securities portfolio”, describes the principles and stages of its forecasting, and also classifies securities portfolios. The second part of the work analyzes the current state of the securities portfolio of agro-industrial companies, as well as assesses the state of the enterprises themselves. The third part of the work proposes measures for creating the best investment portfolio for the holder of securities in the agro-industrial sector for the future. Factors affecting the profitability of the portfolio are analyzed, and recommendations are developed for an investor on the formation of an optimal portfolio. The conclusion summarizes the main findings.

This paper aims to analyze the phenomenon of adverse selection of its presence and potential short-term impact on the Swedish stock market. Adverse selection refers to a situation where information asymmetry among market participants might lead to potential imbalances in information and unfairness among all market participants. The primary objective of this paper is to determine and analyze the potential existence of adverse selection and to explore its effects on the short-term trading volume before announcements.  This study's research design and approach are through data collection, to analyze the relationship between traded volume and disclosures. Five highly traded stocks, Atlas Copco AB, Evolution AB, Swedbank AB, Hexagon AB and AB Volvo are selected for the analysis, representing different sectors. A historical data analysis method and event studies are being used to identify abnormal fluctuations in trading volume before announcements. Data on volume and stock prices are collected over one year, between 11 May 2022 - 11 May 2023. By utilizing various statistical methods and econometric techniques, abnormal volume fluctuations before announcements could be measured and analyzed.  This paper concludes the existence of short-term adverse selection on the Swedish stock market cannot confidently be determined considering this analysis only, as indicated by nonsignificant abnormal fluctuations in the short-term trading volume before announcements. However, the results of the data collection in the period between 11 May 2022 - 11 May 2023, on five high-market capitalization companies, still emphasize and illuminate the importance of ensuring and maintaining efficient and fair markets.

Clean energy and machine learning are subjects that play significant roles in shaping our future. The current climate crisis has forced the world to take action towards more sustainable solutions. Arrangements such as the UN’s Sustainable Development Goals and the Paris Agreement are causing an increased interest in renewable energy solutions. Further, the EU Taxonomy Regulation, applied in 2020, aims to scale up sustainable investments and to direct cash flows toward sustainable projects and activities. These measures create interest in investing in renewable energy alternatives and predicting future movements of stocks related to these businesses. Machine learning models have previously been used to predict time series with promising results. However, predicting time series in the form of stock price indices has, throughout previous attempts, proved to be a difficult task due to the complexity of the variables that play a role in the indices’ movements. This paper uses the machine learning algorithm long short-term memory (LSTM) to predict the S&P Global Clean Energy Index. The research question revolves around how well the LSTM model performs on this specific index and how the result is affected when past returns from correlating variables are added to the model. The researched variables are crude oil price, gold price, and interest. A model for each correlating variable was created, as well as one with all three, and one standard model which used only historical data from the index. The study found that while the model with the variable which had the strongest correlation performed best among the multivariate models, the standard model using only the target variable gave the most accurate result of any of the LSTM models. / Den pågående klimatkrisen har tvingat allt fler länder till att vidta åtgärder, och FN:s globala hållbarhetsmål och Parisavtalet ökar intresset för förnyelsebar energi. Vidare lanserade EU-kommissionen den 21 april 2021 ett omfattande åtgärdspaket, med syftet att öka investeringar i hållbara verksamheter. Detta skapar i sin tur ett ökat intresse för investeringar i förnyelsebar energi och metoder för att förutspå aktiepriser för dessa bolag. Maskininlärningsmodeller har tidigare använts för tidsserieanalyser med goda resultat, men att förutspå aktieindex har visat sig svårt till stor del på grund av uppgiftens komplexitet och antalet variabler som påverkar börsen. Den här uppsatsen använder sig av maskininlärningsmodellen long short-term memory (LSTM) för att förutspå S&P:s Global Clean Energy Index. Syftet är att ta reda på hur träffsäkert en LSTM-modell kan förutspå detta index, och hur resultatet påverkas då modellen används med ytterligare variabler som korrelerar med indexet. De variabler som undersöks är priset på råolja, priset på guld, och ränta. Modeller för var variabel skapades, samt en modell med samtliga variabler och en med endast historisk data från indexet. Resultatet visar att den modell med den variabel som korrelerar starkast med indexet presterade bäst bland flervariabelmodellerna, men den modell som endast användes med historisk data från indexet gav det mest träffsäkra resultatet.

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Få studier är genomförda på området om hur kapitalförvaltare skapar och mäter överavkastning i praktiken, och ännu färre avseende svenska kapitalförvaltare. Det finns främst kvantitativa studier inom detta område vilket har lett till ett forskningsgap avseende kvalitativt inriktad forskning. Vidare är oroligt börsklimat en stor faktor för kapitalförvaltare att hantera, vilket innebär att de fortfarande behöver prestera under dessa perioder för att behålla sina kunder. Detta har lett till forskningsfrågan “Hur arbetar svenska kapitalförvaltare i praktiken för att skapa överavkastning och skiljer sig detta vid oroligt börsklimat?”. Kapitalförvaltare behöver också mäta prestationen för att kunna analysera den, men även för att visa befintliga och potentiella kunder tidigare resultat. Detta har resulterat i studiens andra forskningsfråga “Vilka prestationsmått används för att mäta överavkastning av svenska kapitalförvaltare och varför har de valt att använda dem?”. Denna studie är på grund av de två forskningsfrågorna både kvalitativ och kvantitativ. Detta åstadkoms genom att intervjua kapitalförvaltare men även genom en enkätundersökning för att besvara båda forskningsfrågorna. Sex kapitalförvaltare har deltagit i intervjuundersökningen och 37 kapitalförvaltare har svarat på enkätundersökningen. För enkätundersökningen motsvarar detta en svarsfrekvens om 38,5 % av de företag som kontaktats. Respondenterna i studien är anonyma och har tilldelats fiktiva namn, deras deltagande har dessutom varit frivilligt. Resultatet av denna studie visar att svenska kapitalförvaltare använder olika investeringsstrategier för att skapa överavkastning. Både fundamental och kvantitativ analys används, även om de används i kombination med varandra i olika utsträckning. Gällande ett oroligt börsklimat fokuserar respondenterna på att minska den tagna risken och att se till att vara väl diversifierad. För att mäta överavkastning är Sharpekvot och Informationskvot de mest frekvent använda prestationsmåtten i praktiken, i absoluta respektive relativa termer. Dessa har valts på grund av de är enkla och generellt sätt lätta att förstå även för mindre kunniga kunder. / Few studies have been made on the subject of how asset managers in practice create and measure performance and even fewer regarding Swedish asset managers. There are mainly quantitative studies made in this area, which has left a qualitative gap to research. Furthermore, a troubled stock market climate is a big factor for asset managers to deal with, which means that they still need to perform during these periods of time to keep their customers. This has led to the research question “How do Swedish asset managers work in practice to create excess return and does it differ during a troubled stock market climate?”. Asset managers also need to measure their performance to be able to analyze it, but also show existing and potential customers past results. This resulted in the study's second research question “Which performance measures are used to measure excess return by Swedish asset managers and why are they chosen?”.   This study is as a result of the two research questions both qualitative and quantitative. This is accomplished by having interviews with asset managers and also by performing a survey to answer both research questions. Six asset managers have participated in the interview survey and 37 asset managers have answered the survey. The survey has had a participation rate of 38,5 % of the companies that were contacted. The respondents in this study are anonymous and have been given fictional names, their participation have in addition been voluntarily.   The result of this study shows that Swedish asset managers are using different investment strategies to create excess return. Both fundamental and quantitative analysis are used, though these strategies are commonly used combined to varying extent. Regarding a troubled stock market climate, the respondents focus on lowering the risk taken and to make sure they are well diversified. To measure excess return Sharpe ratio and Information ratio are the most commonly used performance measures in practice, in terms of absolute and relative performance. These are chosen due to their simplicity and are overall easy to grasp even for less knowledgeable customers.

Background: With the tremendous growth of the Green Bond (GB) market, understanding the relationship of the GB market with other financial markets gains importance. The Covid19 pandemic causing a recession in most major economies creates an opportunity to see the co-movements of the GB market with other financial markets under a period of economic crisis. Purpose: This study aims to use the economic contraction catalyzed by the 2020’s Covid-19 pandemic as a means to investigate the co-movements between the GB and the treasury bond, corporate bond, stock, and carbon markets during an economic recession. Through this, we intend to find if co-movements of the GB market have changed, and if so, how. Method: As the collected data is time-series data, Augmented Dickey-Fuller and Ljung-Box tests are utilized for preliminary testing. Thereafter, a univariate-GARCH model is used for volatility modeling. Moreover, the DCC-GARCH model has been conducted to determine the co-movements between the markets. Conclusion: The results of the study show that in the case of GB, treasury, and corporate bond markets, no considerable changes were observed in the co-movement among the two different sample periods. Moving to the stock and GB markets, it was found that the co-movement increased at the beginning of the crisis. However, for the whole crisis period, no substantial changes can be seen in comparison to the pre-crisis period. Furthermore, the co-movement between the two markets was found to be weak in general. Moving on to the results obtained for GB and carbon markets, at the start of the crisis, a sharp fall can be observed. When compared to the pre-crisis period, the co-movement showed a slight increase, yet very weak. Furthermore, it was observed that the co-movement between the two markets has been weak during the whole sample period.