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.

A wide variety of machine learning algorithms havebeen used to predict stock prices. The aim of this project hasbeen to implement a machine learning algorithm using supportvector regression to predict the stock price of two well knowncompanies—Apple and Microsoft—one day into the future usingthe current day’s stock price, macroeconomic data and microeconomicdata and to compare the prediction error with the differentdata inputs. The results show that the addition of macroeconomicand microeconomic data did not improve the prediction error.This suggests that the macroeconomic and microeconomic dataused in this project does not contain additional information aboutfuture stock prices. The results also show that support vectorregression performs worse than linear regression, however inthis case no definite conclusion can be drawn since only onekernel and a handful of parameter values were considered whentraining and testing the algorithm. However, these results mightalso suggest that using the current day’s data is not sufficient tobe able to predict the non-linear relationships. / Ett flertal maskininlärnings-algoritmer har använts för att förutspå aktiepriser. Målet med det här projektet har varit att implementera en maskininlärnings-algoritm som använder sig av support vector regression för att förutspå aktiepriset av två välkända företag—Apple och Microsoft—en dag in i framtiden genom att använda dagens aktiepris, makroekonomisk data och mikroekonomisk data samt att jämföra prediktionsfelet med dem olika indata. Resultaten indikerar att additionen av makroekonomisk och mikroekonomisk data inte förbättrade prediktionsfelet. Detta antyder att den makroekonomiska och mikroekonomiska data som användes i projektet inte innehåller någon ytterliggare information om framtida aktiepriser. Resultaten indikerade också att linjär regression presterar bättre än support vector regression, men i detta fallet kan ingen definitiv slutsats dras eftersom endast en kernel och ett par parameter-värden användes för att träna och testa algoritmen. Däremot kan dessa resultat också antyda att a inte är tillräcklig för att kunna förutspå dem icke-linjära förhållandena. / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm

Many machine learning approaches have been usedfor financial forecasting to estimate stock trends in the future. Thefocus of this project is to implement a Support Vector Machinewith price and news analysis for companies within the technologysector as inputs to predict if the price of the stock is going torise or fall in the coming days and to observe the impact on theprediction accuracy by adding news to the technical analysis.The price analysis is compiled of 9 different financial indicatorsused to indicate changes in price, and the news analysis uses thebag-of-words method to rate headlines as positive or negative.There is a slight indication of the news improving the resultsif the validation data is randomly sampled the testing accuracyincreases. When testing on the last fifth of the data of eachcompany, there was only a small difference in the results whenadding news to the calculation and such no clear correlation canbe seen. The resulting program has a mean and median testingaccuracy over 50 % for almost all settings. Complications whenusing SVM for the purpose of price forecasting in the stockmarket is also discussed. / Många metoder för maskininlärning har använts i syfte av finansiell prognos för att uppskatta aktie trender i framtiden. Fokus för detta projekt är att implementera en Support Vector Machine med pris- och nyhetsanalys för företag inom teknologisektorn som inmatning för att förutsäga om priset på aktien kommer att öka eller minska under de kommande dagarna och för att observera påverkan på förutsägelsens noggrannhet av att lägga till nyheter till den tekniska analysen. Prisanalysen består av 9 olika finansiella indikatorer som används för att indikera prisändringar, och nyhetsanalysen använder metoden bag-of-word för att betygsätta rubriker som positiva eller negativa. Det finns en liten indikation på att nyheterna förbättrar resultat där om valideringsdata stickas ur slumpmässigt provningsnoggrannheten ökar. När man testade den sista femte delen av inmatningsdatan från varje företag, fanns det bara en liten skillnad i resultaten när nyheterna beräknades vilket leder till att en tydlig korrelation kan inte ses. Det resulterande programmet har en genomsnittlig och median test nogrannhet över 50 % för nästan alla inställningar. Komplikationer när SVM används för prisprognoser på aktiemarknaden diskuteras också. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

La presente tesis tiene como objetivo principal identificar los determinantes que intervienen en el desarrollo del mercado de valores: Perú y una muestra de países (2005-2019). La muestra proviene de las bases de datos del Banco Mundial, World Economic Forum (WEF), la Superintendencia del Mercado de Valores (SMV), la Bolsa de Valores de Lima (BVL), Polity IV, Datamarket y The Economist Intelligence Unit, donde se aplica el modelo del análisis factorial para 19 variables de 25 economías con una frecuencia anual en el periodo 2005-2019. Con la aplicación del análisis factorial la muestra de 19 variables se redujo a solo 5 factores o componentes que son: (1) Desarrollo del mercado de bancario; (2) Institucionalidad; (3) Desarrollo del mercado de valores; (4) Apertura y (5) Protección al inversionista. Estos 5 factores óptimos explican el 77.39% del total de la varianza del modelo de las 19 variables con una adecuación de 0.737 mediante la prueba muestral KMO y esfericidad de Bartlett. Perú mostró un nivel bajo en desarrollo financiero y mercado de valores, en comparación a países latinoamericanos y al resto de la muestra. Además, se confirmó mediante estudios previos y el presente trabajo: países con mejor ambiente institucional y de protección al inversionista, y con mayor apertura al comercio e inversión extranjera, tienen más posibilidades de desarrollo de sus mercados financieros. / The main objective of this thesis is to identify the determinants that intervene in the development of the stock market: Peru and a sample of countries (2005-2019). The sample comes from the databases of the World Bank, World Economic Forum (WEF), the Superintendency of the Securities Market (SMV), the Lima Stock Exchange (BVL), Polity IV, Datamarket and The Economist Intelligence Unit, where the factor analysis model is applied for 19 variables from 25 economies with an annual frequency in the 2005-2019 period. With the application of factor analysis, the sample of 19 variables was reduced to only 5 factors or components, which are: (1) Banking development; (2) Institutionalism; (3) Stock market Development; (4) Free trade and (5) Investor Protection. These 5 optimal factors explain 77.39% of the total variance of the model of the 19 variables with an adequacy of 0.737 using the KMO sample test and Bartlett's sphericity. Peru showed a low level of financial development and stock market, compared to Latin American countries and the rest of the sample. Additionality, it was confirmed by previous studies and the present work: countries with a better institutional environment and investor protection, and with greater openness to trade and foreign investment, have more possibilities for the development of their financial markets.

Purpose The purpose of this study is to investigate whether abnormal return patterns can occur on different share indices, as a result of the outcome in national team matches. The subordinary aim is to investigate whether there are differences between the three share indices, (small-, mid- and large cap) depending on the match category and how it relates regarding the five countries in the study. Method This study has a positivistic and deductive approach, using a modified event study methodology. The event period is one day after the event. For each nation, year and share index, different estimation periods have been created. The study comprises 760 national team football matches and is investigating how each different share index is affected by match outcomes in championship matches, qualifying matches and friendlies. Results Upon compilation of all 760 matches, the result of this study shows a statistically significant impact on two of the three possible match outcomes, regarding small cap index. Furthermore the result also shows a connection between friendly matches and small cap index. The match categories championship matches and qualifying matches demonstrates no connection to the three diffrent kind of share indices. Regarding the different nations, Spain and their small- and large cap index shows the most significant connection between the match outcome and abnormal return. Conclusions The small cap share index is basically the only index that is affected by the all the matches that is involved in this study (after a victory or a loss). The magnitude of a match does not seem to have a greater influence on investor rationality. Over all, the match outcome draw does not lead to negative abnormal return. Of this studys five surveyed countries (England, France, Spain, Sweden and Germany), the english and german share indicies seems to be least likley to be affected by the outcome in national team football matches.

This thesis consists of four self-contained papers related to the change of market structure and the quality of equity market. In Paper [I] we found, by using of a Flexible Dynamic Component Correlations (FDCC) model, that the creation of a common cross-border stock trading platform has increased the long-run trends in conditional correlations between foreign and domestic stock market returns. In Paper [II] we study whether the creation of a uniform Nordic and Baltic stock trading platform has affected weak-form information efficiency. The results indicate that the stock market consolidations have had a positive effect on the information efficiency and turnover for an average firm. The merger effects are, however, asymmetrically distributed in the sense that relatively large (small) firms located on relatively large (small) markets experience an improved (reduced) information efficiency and turnover. Although the results indicate that changes in the level of investor attention (measured by turnover) may explain part of the changes in information efficiency, they also lend support to the hypothesis that merger effects may partially be driven by changes in the composition of informed versus uninformed investors following a stock. Paper [III] analyzes whether the measured level of trust in different countries can explain bilateral stock market correlations. One finding is that generalized trust among nations is a robust predictor for stock market correlations. Another is that the trust effect is larger for countries which are close to each other. This indicates that distance mitigates the trust effect. Finally, we confirm the effect of trust upon stock market correlations, by using particular trust data (bilateral trust between country A and country B) as an alternative measurement of trust. In Paper [IV] we present the impact of the stock market mergers that took place in the Nordic countries during 2000 – 2007 on the probabilities for stock price jumps, i.e. for relatively extreme price movements. The main finding is that stock market mergers, on average, reduce the likelihood of observing stock price jumps. The effects are asymmetric in the sense that the probability of sudden price jumps is reduced for large and medium size firms whereas the effect is ambiguous for small size firms. The results also indicate that the market risk has been reduced after the stock market consolidations took place.

With the surge of Internet-based corporate communication, organization, andinformation management, financial markets have undergone radical transformation. Inthe interconnected economy of today, market participants are forced to acceptcyberattacks, data breaches, system failures, or security flaws as any other (varying)cost of doing business. While cyberspace encompasses practically any firm indeveloped economies and a large portion in developing ones, combatting such risks isdeemed a question of firm-specific responsibility: the situation resembles an ‘every manfor himself’ scenario. Consulting standard financial theory, rational utility-maximizinginvestors assume firm-specific (idiosyncratic) risk under expectations of additionalcompensation for shouldering such risk – they are economically incentivized. The omnipresence of cyberattacks challenges fundamental assumptions of the CapitalAsset Pricing Model, Optimal Portfolio Theory, and the concept of diversifiability. Thethesis problematizes underlying rationality notions by investigating the effect of acyberattack on stock volatility. Explicitly, the use of stock volatility as a proxy for riskallows for linking increased volatility to higher risk premiums and increased cost ofcapital. In essence, we investigate the following research question: What is the effect ofa disclosed cyberattack on stock volatility for firms listed in the USA?. Using event study methodology, we compile a cyberattack database for events between2010 and 2015 involving 115 firms listed on US stock exchanges. The specified timeperiod cover prevailing research gaps; due to literature paucity the focus on volatilityfits well. For a finalized sample of 189 events, stock return data is matched to S&P500index return data within a pre-event estimation window and a post-event window tocalculate abnormal returns using the market model. The outputs are used to estimateabnormal return volatility before and after each event; testing pre and post volatilityagainst each other in significance tests then approximates the event-induced volatility.Identical procedures are performed for all subsamples based on time horizon, industrybelonging, attack type, firm size, and perpetrator motivation. The principal hypothesis, that stock volatility is significantly higher after a cyberattack,is found to hold within both event windows. Evidence on firm-specific characteristics ismore inconclusive. In the long run, inaccessibility and attacks on smaller firms seem torender significantly larger increases in volatility compared to intrusion and attacks onlarger firms; supporting preexisting literature. Contrastingly, perpetrator motive appearsirrelevant. Generally, stocks are more volatile immediately after an attack, attributableto information asymmetry. For most subsamples volatility seem to diminish with time,following the Efficient Market Hypothesis. Summing up, disparate results raisequestions of the relative importance of contingency factors, and also about futuredevelopments within and outside academic research.

In this thesis, extensive research regarding the relationship between macroeconomic variables and the stock market is carried out. For this purpose the two largest stock markets in the world, namely the US and Japan, are chosen. As a proxy for the US stock market we use the S&P500 and for Japan the Nikkei225. Although there are many empirical investigations of the US stock market, Japan has lagged behind. Especially the severe boom and bust sequence in Japan is unique in the developed world in recent economic history and it is important to shed more light on the causes of this development. First, we investigate the long-run relationship between selected macroeconomic variables and the stock market in a cointegration framework. As expected, we can support existing findings in the US, whereas Japan does not follow the same relationships as the US. Further econometric analysis reveals a structural break in Japan in the early 1990s. Before that break, the long-run relationship is comparable to the US, whereas after the break this relationship breaks down. We believe that a liquidity trap in a deflationary environment might have caused the normal relationship to break down. Secondly, we increase the variable set and apply a non-linear estimation technique to investigate non-linear behaviour between macroeconomic variables and the stock market. We find the non-linear models to have better in and out of sample performance than the appropriate linear models. Thirdly, we test a particular non-linear model of noise traders that interact with arbitrage traders in the dividend yield for the US and Japanese stock market. A two-regime switching model is supported with an inner random or momentum regime and an outer mean reversion regime. Overall, we recommend investors and policymakers to be aware that a liquidity trap in a deflationary environment could also cause severe downturn in the US if appropriate measures are not implemented accordingly.

This thesis comprises, firstly, a careful and detailed description of the institutional workings of the Chinese stock market; secondly, a literature review of the Chinese segmented markets and dual-listed shares price premium; and thirdly, three evidence-based contributions designed to cast new light on the Chinese A-shares premium puzzle. Publicly-listed firms in China, under certain criteria, can issue two different types of shares, namely A-shares and B-shares, to local and foreign investors respectively. These shares carry the same rights and obligations, but are however priced differently due to market segmentation. After a review of the literature on determinants of the premium, the first contribution offers a complementary explanation. I propose that the premium reflects the difference in valuation preferences between the local and foreign investors, i.e., local investors pay more attention to stock liquidity, while foreign investors pay more attention to firm’s intrinsic value, and so firms having more favorable fundamentals tend to have lower premia. The second contribution involves the examination of a controversial question that which investor group is better informed about local assets, by testing the direction of information flows between the A- and B-shares markets. Both time series methods, and panel data techniques which are used for the first time in this context, are employed, in order to get a distinct and more insightful picture against the current literature. The third contribution compares and contrasts institutional settings of China, Singapore and Thailand which have similar market segmentation and dual-listing systems; examines whether or not the premia in the three countries are caused by same factors; and tries to answer why foreign investors in China pay less, rather than more, as commonly observed in other segmented markets, for identical assets. It provides the first cross-country comparison evidence after 1999 with updated data.

本論文共一冊，計八萬字，分五章十五節。 本研究主要目的有三：（一）探討國內股票市場是否符合半強強效率市場假設，（二 ）探討股票價格是否正確充分地反映國內油品價格調整之資訊，（三）了解油品價格 調整對各產業股票價格之影響程度。 本研究由台灣石油公司業務處取得（六十六年至七十五年）油品價格調整時期及調整 福度，並由證交資料及經濟日報、工商時報取得各產業（水泥窯製類、食品類、塑膠 化工類、紡織纖維類、機電類、造紙類及營造建材類）的股價指數及股市發行量加權 股價指數。採用市場模式研究，並以D－W檢定,Ｒ2值、t檢定、F檢定、Kolmogorov－ Simirnov D－Statisitic檢定及Tukey之Stem Leaf圖示、Box 圖示來確定模式。再以 殘差分析（包括平均殘差分析及累積平均殘差分析）及異常績效指標分析檢驗證半強 勢效率市場之假設。 本研究結果發現，在油品價格調整日之前有資訊效果，在調整日之後，市場顯現效率 性，總合來說，國內股市符合半強勢效率市場之假設。