The purpose of this study is to identify what company specific parameters prior to an IPO have significant impact on share price performance one year after listing. This is done by analysing listings on the Stockholm Stock Exchange in the period 2014-2019.  The method which has been used is a multiple linear regression with adjusted share price as response variable and 7 specific company data points as independent variables. The share price development of companies is adjusted to the SIX Return Index and the 7 company variables cover size, growth, profitability and ownership. The results from the study imply that the independent variables covering size and profitability have the highest impact on share price performance after listing and that ownership had the least impact. The final model with the independent variables that had the highest relevance still only display a small significant correlation with an adjusted R2 = 0.09, which is understandable due to the nature of share prices not being able to be predicted one year into the future. Furthermore, the stock market is a large and complex system of many unknowns, which aggravates the process of simplifying and quantifying data of only one source into a regression model with high predictability. / Syftet med denna studie är att identifiera vilka företagsspecifika parametrar före en börsintroduktion som har en betydande inverkan på aktiekursutvecklingen ett år efter notering. Detta görs genom att analysera noteringar på Stockholmsbörsen från 2014 till 2019.  Metoden som har använts är en multipel linjär regression med justerad \\ aktiekursutveckling som responsvariabel och 7 specifika företagsdatapunkter som regressorer. Aktiekursutvecklingen i företag anpassas till SIX Return Index och de sju företagsvariablerna täcker storlek, tillväxt, lönsamhet och ägande. Resultaten från studien antyder att regressorer som täcker storlek och lönsamhet har störst inverkan på aktiekursutvecklingen efter notering och att ägandet hade minst påverkan. Den slutliga modellen med de regressorer som hade störst relevans visar fortfarande endast en liten signifikant korrelation med en justerad R2 = 0,09, vilket är förståeligt på grund av att aktiekursernas karaktär inte kan förutses ett år in i framtiden. Dessutom är aktiemarknaden ett stort och komplext system med många okända faktorer, vilket förvärrar processen att förenkla och kvantifiera data från endast en källa till en regressionsmodell med hög förutsägbarhet.

This thesis examines the impact of FTSE 100 index revisions on the various aspects of stock market quality and the cost of equity capital. Our study spans over the period 1986¿2009. Our analyses indicate that the index membership enhances all aspects of liquidity, including trading continuity, trading cost and price impact. We also show that the liquidity premium and the cost of equity capital decrease significantly after additions, but do not exhibit any significant change following deletions. The finding that investment opportunities increases after additions, but do not decline following deletions suggests that the benefits of joining an index are likely to be permanent. This evidence is consistent with the investor awareness hypothesis view of Chen et al. (2004, 2006), which suggests that investors¿ awareness improve when a stock becomes a member of an index, but do not diminish after it is removal from the index. Finally, we report significant changes in the comovement of stock returns with the FTSE 100 index around the revision events. These changes are driven mainly by noise-related factors and partly by fundamental-related factors. / International Fellows Program, USA, (IFP) and American-Mideast-Educational and Training Services, Inc. (AMIDEAST).

This study examines the relationship between banks, stock market and economic development in Botswana using quarterly data from 1995 to 2016. To find out if there is a link between financial development and economic growth, the three measures of stock market development used are stock market capitalization, total value of shares traded and turnover. For bank-based financial development, the proxy is bank credit to private sector and the measure of economic growth is real gross domestic product (GDP) per capita. To analyse the long run and short run relationships among the variables of interest, this study implements the Autoregressive Distributed Lag (ARDL) cointegration technique and the Granger causality technique to find the direction of causality. The findings indicate that there is a positive short and long run relationship between stock market variables and economic growth when turnover and market capitalization are used as proxies and value traded is significant and negatively related to economic growth. The study found that bank credit to private sector is negatively related to economic growth both in the short and the long run. There is bidirectional causality between stock market financial development and economic growth and no causal relationship between banking financial development and economic growth in Botswana. This study recommends that there should be appropriate reforms to develop the financial sector in Botswana to help promote economic growth. Botswana should also have reforms to promote economic growth to foster stock market financial development. This study also offers a comprehensive and detailed overview of the state of the economy, banking system and the financial markets system of Botswana which can help foreign investors as well as individual and institutional investors in making sound investment decisions.

Выпускная квалификационная работа (магистерская диссертация) посвящена исследованию дивидендной политики. Предметом исследования выступает механизм разработки дивидендной политики, со всеми его особенностями формирования, методиками и формулами расчета, а также механизм взаимодействия данной системы со смежными и взаимозависимыми областями. Основной целью магистерской диссертации является разработка ключевых направлений и конкретных мероприятий по развитию системы дивидендной политики для обеспечения развития предприятия и увеличению стоимости компании. В заключении обозначены рекомендации по совершенствованию дивидендной политики и ее применению. / The final qualifying work (master's thesis) is devoted to the study of dividend policy. The subject of the research is the mechanism for developing a dividend policy, with all its peculiarities of formation, methods and calculation formulas, as well as the mechanism of interaction of this system with adjacent and interdependent areas. The main purpose of the master's thesis is to develop key areas and specific measures for the development of the dividend policy system to ensure the development of the enterprise and increase the value of the company. In conclusion, recommendations for improving the dividend policy and its application are indicated.

Исследование посвящено исследованию развития работы брокеров на фондовом рынке в современных условиях. Предметом исследования являются экономические отношения, связанные с исследованием издержек и рисков, возникающих между инвестором и рыночной структурой в момент совершения инвестиционных операций. Основной целью магистерской диссертации является разработка комплексной концепции развития доступной для инвестора ликвидности в кризисные ситуации на рынках и в экономике. В заключении подводятся итоги проведенного исследования, даются практические рекомендации для сокращения торговых издержек в существующих реалиях нашего рынка. / The research is devoted to the study of the development of brokers in the stock market in modern conditions. The subject of the study is the economic relations associated with the study of the costs and risks that arise between the investor and the market structure at the time of investment transactions. The main goal of the master's thesis is to develop a comprehensive concept for the development of liquidity available to investors in crisis situations in the markets and in the economy. In conclusion, the results of the study are summarized, and practical recommendations are given to reduce trading costs in the current realities of our market.

Thesis advisor: Peter Ireland / It is not unreasonable to surmise that public sentiment views stock market behavior as an indicator of economic health. Historically, movements in the the stock market indeed correspond to business cycles, but this is not always the case, and the COVID-19 pandemic serves as a distinct case to highlight such an irregularity. The contrast between the behavior of the stock market and that of the economy during the pandemic compels an analysis of the pandemic's actual impact on the stock market, and this paper finds a negative and significant relationship between the interpolated daily closing prices of the S&P 500 and the daily number of COVID-19 cases. / Thesis (BA) — Boston College, 2023. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Departmental Honors. / Discipline: Economics.

Background: Asset prices, investment choices, and market mood can all be greatly impacted by macroeconomic factors and risk perception. Therefore, for investors, portfolio managers, policymakers, and regulators looking to negotiate the complexity of financial markets, knowing how macroeconomic factors affect risk is crucial. Objective: This study delves into the intricate relationship between macroeconomic indicators and market risk propensity, offering a comprehensive analysis of both cross-sectional and panel data. Focusing on key factors such as inflation, interest rates, exchange rates, and the Index of Industrial Production (IPP), this study explore their multifaceted impacts on market risk dynamics. Methods: To reveal the complex linkages that determine risk-taking behaviors and affect business outcomes, the study uses sophisticated econometric methodologies. Results: According to our research, inflation has a significant impact on investor sentiment and corporate profitability. Reduced profit margins and increased market risk are the results of higher inflation. Similar to this, interest rates become an important variable that affects borrowing costs, investment options, and the level of competition on the stock market. Exchange rate fluctuations, which are a key component of the global financial landscape, have been shown to have an effect on investor returns, corporate operations, and dynamics of international trade, which in turn shapes market risk. Additionally, this research reveals the complex relationship between the IPP and stock market performance, wherein good growth in industrial output signifies an expansion of the economy and investor confidence, which in turn affects demand for and the price of stocks. In contrast, a drop in the IPP denotes an economic slowdown and increased market risk. The paper also discusses the unusual impact of the COVID-19 pandemic on international financial markets, emphasizing the interaction between pandemic-induced uncertainty, exchange rate changes, and monetary policy reactions to produce novel market risk dynamics. Conclusion: In conclusion, this study offers a thorough grasp of the interactions between macroeconomic data and market risk inclination. For investors, companies, and politicians looking to comprehend the complexity of the global economic landscape, make educated decisions, and successfully manage financial risks, these insights are essential. Keyword: Propensity Risk, Stock Market, Inflation, Exchange rate and IPP.

ABSTRACT MAJID MEMARI, for the Masters of Science degree in Computer Science, presented on November 3rd, 2017 at Southern Illinois University, Carbondale, IL. Title: PREDICTING THE STOCK MARKET USING NEWS SENTIMENT ANALYSIS Major Professor: Dr. Norman Carver Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. GDELT is the largest, most comprehensive, and highest resolution open database ever created. It is a platform that monitors the world's news media from nearly every corner of every country in print, broadcast, and web formats, in over 100 languages, every moment of every day that stretches all the way back to January 1st, 1979, and updates daily [1]. Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. The successful prediction of a stock's future price could yield significant profit. The efficient-market hypothesis suggests that stock prices reflect all currently available information and any price changes that are not based on newly revealed information thus are inherently unpredictable [2]. On the other hand, other studies show that it is predictable. The stock market prediction has been a long-time attractive topic and is extensively studied by researchers in different fields with numerous studies of the correlation between stock market fluctuations and different data sources derived from the historical data of world major stock indices or external information from social media and news [6]. The main objective of this research is to investigate the accuracy of predicting the unseen prices of the Dow Jones Industrial Average using information derived from GDELT database. Dow Jones Industrial Average (DJIA) is a stock market index, and one of several indices created by Wall Street Journal editor and Dow Jones & Company co-founder Charles Dow. This research is based on data sets of events from GDELT database and daily prices of the DJI from Yahoo Finance, all from March 2015 to October 2017. First, multiple different classification machine learning models are applied to the generated datasets and then also applied to multiple different Ensemble methods. In statistics and machine learning, Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Afterwards, performances are evaluated for each model using the optimized parameters. Finally, experimental results show that using Ensemble methods has a significant (positive) impact on improving the prediction accuracy. Keywords: Big Data, GDELT, Stock Market, Prediction, Dow Jones Index, Machine Learning, Ensemble Methods

This thesis describes a method of modeling financial markets by utilizing concepts from mechanical vibration. The models developed represent multi-degree of freedom, mass-spring systems. The economic principles that drive the design are supply and demand, which act as springs, and shareholders, which act as masses. The primary assumption of this research is that events cannot be predicted but the responses to those events can be. In other words, economic stimuli create responses to a stock’s price that is predictable, repeatable and scientific. The approach to determining the behavior of various financial markets encompassed techniques such as Fast Fourier Transform and discretized wavelet analysis. The researched developed in three stages; first an appropriate model of causation in the stock market was established. Second, a model of steady state properties was determined. Third, experiments were conducted to determine the most effective model and to test its predictive capabilities on ten stocks. The experiments were evaluated based on the model’s hypothetical return on investment. The results showed a positive gain on capital for nine out of the ten stocks and supported the claim that stocks behave in accordance to the natural laws of vibration. As scientific approaches to modeling the stock market are beginning to develop, engineering principles are proving to be the most relevant and reliable means of financial market prediction.

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