This thesis deals with design, optimization and testing of an automated trading system intended for trading CFD contracts. The strategy is based on a combination of a moving average and a custom indicator, which gives signals based on convergence of signals of other monitored indicators. The designed automated trading system also contains a simple, but efficient money management. It is responsible for risking a constant portion of current account balance on each trade. The thesis continues with comparison of three ways to optimize chosen input parameters and comparison of performance of the strategy for ten tested market symbols.

This thesis focuses on design, implementation and optimalization of automated trading system based on breakout strategy and public fundamental data wich trades on FOREX. It descripes theoretical backgroud of financial markets and especially focuces on FOREX. This automated trade system is implemented in object oriented programing paradigm for MetaTrader 5 platform. Last part of thesis is aimed at testing implemented system on historical data in order to evaluate the correctness of system and optimalizations.

This thesis is focused on the design and optimization of automated trading system, which will be traded in FOREX. The aim is to create a business strategy that is relatively safe, stable and profitable. Optimization and testing on historical data are a prerequisite for the deployment into real trading.

This master thesis evaluates about investing in the currency market, commonly known as Forex. The master thesis is primarily deal with proposal of automated trading system for trading in major currency pairs using breakout strategies. These strategies creation is based on market analysis, volatility, correlation and analysis revealing patterns of time during the trading day. In practical part is formed diversified investment portfolio composed of five investment profitable strategies, which were used during four-month testing period on unknown market data.

The main focus of the thesis are approaches to technical analysis, trading systems and it summarizes interesting findings, according to which a FOREX automated trading system was designed and implemented. Optimization and testing were a prerequisite for a real-world deployment, so the automated trading system was tested on historical data and some of its input parameters were optimized for maximum stability and profit.

Cette thèse étudie l'impact des marchés financiers sur la croissance économique dans la région MENA. Le premier chapitre présente la situation économique, démographique et financière de la région. Le deuxième chapitre présente un modèle d'équilibre général à générations imbriquées, qui relie la croissance économique, les marchés financiers et l'évolution démographique. Le modèle est calibré et simulé pour trois pays de la région, présentant des tendances démographiques diverses. Les résultats montrent qu'un secteur financier plus performant conduit à une meilleure performance économique et à des taux d'emploi plus élevés. Par ailleurs, les jeunes sont les premiers bénéficiaires de la réforme du secteur financier. Le troisième chapitre teste empiriquement l'impact du secteur financier sur le secteur réel et la croissance dans 15 pays de la région MENA et constate un impact négatif du développement du secteur financier sur la croissance. Ces résultats soulignent l'inefficacité de secteur financier dans la région et le besoin urgent de cibler des politiques qui améliorent l'efficience du secteur et pas seulement sa taille. Dans le quatrième chapitre, un indicateur de développement des marchés financiers est construit pour les pays de la région. Cet indicateur est basé sur les 3 piliers que sont l'environnement macroéconomique, les institutions financières et les marchés financiers. Il prend en compte les spécificités des pays de la région MENA et permet de classer les pays de la région en fonction de leur performance dans le secteur financier / This thesis studies the impact of the financial markets on economic growth for MENA region. The first chapter presents a general overview of the region, with a focus on economic, demographic and financial market outlook of the region. In the second chapter an overlapping generation model is presented, that links economic development, financial markets and demographic shift. The model is simulated for three countries in the region with different speeds in demographic shift. The results show that a more efficient financial sector leads to better economic performance and higher employment rates, furthermore, youth are the primary beneficiaries of the reform in the financial sector. The third chapter tests empirically the link between the financial sector and the real sector in 15 MENA countries and finds a negative impact of financial sector development on growth. These results underline the expansion of an inefficient financial sector in the region, and the urgent need to focus on policies that target the efficiency of the sector and not solely its size. A comprehensive composite index for the financial sector development is developed in the fourth chapter. This index is based on three pillars that are, macroeconomic environment, financial institutions and financial markets. The index takes into account the specificities of MENA countries and allows us to rank the countries in the region according to their performance in the financial sector.

Stocks belonging to publicly traded companies is a topic which in society is mystified and by some considered to be an unpredictable phenomenon where you either make an economic loss or gain seemingly by chance. Despite this, there are numerous fields of work where the sole purpose is to predict the movement of stocks in order to maximize economic gain. The purpose of this report was to research whether or not these movements actually can be predicted by the usage of regression analysis. A regression model was constructed where the response variable used was the rate of change of a certain stock over 30 days and numerous different qualitative and quantitative parameters were used as regressors. This full model was then evaluated and improved in order to refine its construction and results yielded in order to present the best possible model. When researching and optimizing the model, it was found that several parameters turned out to be statistically significant for the model. The model itself did however come with some uncertainties in the form of a low R-squared value, meaning that despite the significance of said parameters, it contained a high amount of unrepresented variance. / Aktier tillhörande publikt handlade bolag är i samhället ett mystifierat ämne varvid vissa ser det som ett oförutsägbart fenomen, som kan genera antingen vinst eller förlust, till synes av slumpen. Trots detta finns det flertalet områden vars främsta syfte är att förutspå aktiers prisrörelser med ändamålet att maximera ekonomisk vinning. Syftet med denna rapporten var att studera huruvida dessa prisrörelser faktiskt kan förutspås med hjälp av regressionsanalys. En regressionsmodell skapades där rate of change för flertalet aktier under en period på 30 dagar användes som responsvariabel. Flertalet olika kvantitativa och kvalitativa parametrar för respektive aktie användes som regressorer. Den fullständiga modellen som byggde på all data utvärderades för att sedan förbättras, i syfte att förfina dess uppbyggnad och de resultat den genererade, för att skapa en så bra modell som möjligt.  När modellen studerades och optimerades kunde det konstateras att flertalet parametrar var statistiskt signifikanta för modellen. Modellen hade dock osäkerheter i form av bland annat lågt R-kvadratvärde, vilket innebar att trots statistiskt signifikans i flertalet parametrar, kunde modellen inte förklara en stor del av förekommen varians.

Machine Learning (ML) is the current trend that most industries opt for to improve their business and operations. ML has also been adopted in the financial markets, where well-funded financial institutions employ the latest ML algorithms to gain an advantage on the market. The darker side of ML is the potential emergence of complex algorithmic trading schemes that are abusive and manipulative. Because of this, it is inevitable that ML will be applied to financial market surveillance in order to detect these abusive and manipulative trading strategies. Ideally, an accurate ML detection model would be developed with data from many financial institutions or trading venues. However, such ML models require vast quantities of data, which poses a problem in market surveillance where data is sensitive or limited. Data sharing between companies or countries is typically accompanied by legal and privacy concerns. By training ML models on distributed datasets, Federated Learning (FL) overcomes these issues by eliminating the need to centralise sensitive data. This thesis aimed to address these ML related issues in market surveillance by implementing and evaluating a FL model. FL enables a group of independent data-holding clients with the same intention to build a shared ML model collaboratively without compromising private data. In this work, a ML model is initially deployed in a centralised data setting and trained to detect the manipulative trading scheme known as spoofing. The LSTM-Autoencoder was the model chosen method for this task. The same model is also implemented in a federated setting but with decentralised data, using the FL framework FEDn. Another FL framework, Flower, is also employed to evaluate the performance of FEDn. Experiments were conducted comparing the FL models to the conventional centralised learning model, as well as comparing the two frameworks to each other. The results showed that under certain circumstances, the FL models performed better than the centralised model in detecting spoofing. FEDn was equivalent to Flower in terms of detection performance. In addition, the results indicated that Flower was marginally faster than FEDn. It is assumed that variations in the experimental setup and stochasticity account for the performance disparity.

Diese Dissertation beantwortet verschiedene politikrelevante ökonomische Fragen in den Bereichen Handelspolitik, Geldpolitik, sowie Rohstoffmärkte und Energieökonomik mit Hilfe von strukturellen Vektorautoregressionsmodellen (SVAR). SVARs stellen eine effektive Möglichkeit dar, die Beziehungen zwischen verschiedenen makroökonomischen und/oder Finanzmarkt-Variablen zu modellieren und werden verwendet, um die dynamischen kausalen Effekte von ökonomischen Schocks zu schätzen. Für jede ökonomische Fragestellung wird eine Identifikationsstrategie angewandt, die auf die betrachteten Daten und ihre statistischen Eigenschaften sowie die zugrundeliegenden Annahmen über ökonomische Mechanismen zwischen den betrachteten Zeitreihen zugeschnitten ist. Im Einzelnen besteht diese Dissertation aus vier Kapiteln. In den ersten beiden Kapiteln werden die Auswirkungen von Handelspolitik auf Finanzmärkte und auf die Makroökonomie geschätzt. Das dritte Kapitel liefert einen methodischen Beitrag zur SVAR-Literatur, der in einer Anwendung zu den Effekten von Geldpolitik dargestellt wird. Das letzte Kapitel verlässt die Felder der Handels- und Geldpolitik und wendet sich Rohstoffmärkten und der Energiewirtschaft zu, stützt sich dabei aber ebenfalls auf Zeitreihenmethoden. Es analysiert die Rolle von Metallen in der Energiewende. / This dissertation answers various policy relevant economic questions in the fields of trade policy, monetary policy, and commodity markets and energy economics using structural vector autoregression (SVAR) models. SVARs constitute a parsimonious way to model the relations between different macroeconomic and/or financial variables and they are used to estimate the dynamic causal effects of economic shocks. For each economic question, this dissertation applies an identification strategy that is tailored to the relevant data and its statistical properties as well as the underlying assumptions about economic mechanisms among the regarded time series. Specifically, this dissertation consists of four chapters. The first two chapters estimate the effects of trade policy on financial markets and on the macroeconomy. The third chapter makes a methodological contribution to the SVAR literature in an application to monetary policy shocks. The final chapter moves away from trade and monetary policy to commodity markets and energy economics but also relies on time series methods. It analyzes the role of metals for the clean energy transition.

För att upprätthålla en blomstrande ekonomi i samhället är det viktigt att marknaderna och de finansiella systemen är välfungerande och rationella, eftersom stabil ekonomisk tillväxt ökar välfärden i samhället. En grundläggande förutsättning för detta antagande är att företag värderas med förnuft och rationalitet för att undvika övervärderingar, vilket i sin tur kan ledatill volatilitet och minskad likviditet som inte är fördelaktigt för att upprätthålla förtroende föraktievärdering. Den digitala eran vi lever i har gjort det möjligt för nästan vem som helst att agera investerare på olika börser. Börserna är och har under de senaste åren varit högre värderade än någonsin tidigare. Att investera är att fatta ekonomiska beslut genom att allokera medel i tillgångar, där du förväntar dig en avkastning på investeringen. När en investerare förvärvar sådana tillgångar baserar de vanligtvis sitt beslut på tillgängliga ekonomiska data. Denna ekonomiska data kan komma i olika former, det kan vara årsredovisningar, kvartalsrapporter, värderingar, nyheter om företaget, globala externa faktorer och i vissa fall rekommendationer från andraköpare. Alla investerare har inte den djupgående kunskap som krävs för att sätta ett fundamentaltvärde på en aktie i ett företag, detta medför en risk för övervärdering för företag. Det finns många modeller och metoder för att värdera ett företag och en typisk metod som används är substansvärdering. Kortfattat betyder det att du drar av ett företags totala skulder från de totalatillgångarna, vilket ger dig ett substansvärde på aktierna. Man skulle snabbt inse att detta inte speglar det verkliga värdet av alla företag och därmed inte ger den fullständiga bilden av marknaden och tillväxten. / To sustain a thriving economy in society it's important that the markets and financial systems are well functioning and rational, since stable economic growth increases welfare in society. A fundamental condition for this assumption is that companies are valued with reason and rationality to avoid overevaluations, which in turn may lead to volatility and decreased liquidity that are non-beneficial for maintaining a trust in stock valuation. The digital era we live in has made it possible for almost anyone to act as an investor indifferent stock exchanges. The stock markets are and have for the past years been higher value than ever before. To invest is to conduct economic decisions by allocating means in assets, in which you expect a return on the investment. When an investor acquires such assets they usually base their decision on the economic data available. This economic data may come in different shapes, it could be annual reports, quarterly reports, valuations, news about the company, global external factors and in some cases recommendations from other buyers. All investors don't possess the profound knowledge required to put a fundamental value on a stock in a company, this brings a risk of overvaluation for companies. There are numerous models and methods to value a company and a typical method that is applied is substance valuation. In short, that means that you subtract a company's total liabilities from the total assets, that gives you a substance value of the stocks. One would quickly realize that this doesn't reflect the true value of all companies and thus not giving the full picture of the market and growth. This study is written in Swedish language.