Cette thèse se compose de trois essais autonomes sur le marché boursier chinois. Le premier essai examine le processus de la découverte du prix des actions A et H pour des sociétés chinoises double cotées à la fois sur les bourses de Shanghai/Shenzhen et de Hong Kong durant les sessions d'échange communes. Nous mettons en évidence une relation de long terme entre les prix des actions A et H. En appliquant la méthode de l'information partagée de Hasbrouck (1995), il apparaît, quand la Chine adoptait un régime de change fixe, le marché domestique contribuait plus d'information à la découverte du prix que le marché étranger; tandis que sous un régime de change flexible, c'est le marché étranger qui dominait dans la découverte du prix.Le deuxième essai prenant les réformes chinoises du régime de Juillet 2005 et de Juillet 2008 comme des événements spéciaux, il étudie si ces changements de régime de change affectent l'arbitrage entre les marchés des actions A et H. En comparant les niveaux des impacts des facteurs idiosyncratiques sur la décote de prix des actions A et H avant et après les changements de régime, les résultats montrent que la relaxation des contrôles des changes ne favorise pas l'arbitrage entre les deux marchés. Par ailleurs, ce changement de régime de change introduit un risque de change important dans la stratégie des arbitragistes.Le troisième essai aborde la transmission d'information en séance et hors séance de cotation en termes de rendements et de volatilités entre la Chine, l'Amérique et l'Europe. Le problème du synchronisme est considéré avec soin dans la modélisation bivariée avec la Chine comme référence avec des données journalières. / This thesis consists of three self-contained essays on the Chinese stock market. The first essay examines the price discovery process of Chinese dual-listed firms on the A-share and H-share markets during overlapping trading hours. We provide evidence that there exists a long-term relationship between the A- and H-share markets. By applying the information share model of Hasbrouck (1995), we find that: under a fixed exchange rate, the A-share market contributes more innovations in price discovery than the H-share market; while under a managed floating exchange rate, it is the H-share market that plays a dominant role in the price discovery process.In the second essay, by using the exchange rate regime changes of July 21, 2005 and July 01, 2008 of as special events, we examine whether changes in exchange-rate regime affect the intensity of inter-market arbitrage between A- and H-share markets. By comparing the significance of the impact of idiosyncratic factors on the H-share discount before and after the changes of exchange rate regime, the results show that the relaxation of exchange controls does not encourage inter-market arbitrage between the Chinese mainland and Hong Kong markets. Further, the switch from a fixed to a floating exchange-rate regime introduces an important exchange rate risk to arbitrageurs.The last essay studies daytime and overnight information transmission in terms of returns and volatility between China, America and Europe. The asynchronicity issue is carefully considered in the bivariate modelling with China as benchmark with daily data.

The thesis deals with design, implementation and optimization of a model based on artificial intelligence and neural networks, which is able to predict future time series prices on a stock market. Main goal is to create an object oriented application for successful future trend prediction of financial derivatives with the use of cooperating methods such as Hurst exponent evaluation and automated market simulation.

The subject of this thesis is to introduce the reader to the issue of possible financial investment in the virtual economy with real funds and design strategies to maximize the initial capital appreciation. The introduction describes the analysis of virtual PED currency, the economy and the system of publicly traded shares. The main part is focused on presenting the results of practical traded investment based on fundamental analysis, speculation about the intrinsic value of the shares and evaluating applied strategies, including the benefits of work.

The thesis examines the comprehensive relationship between all aspects of financial liberalisation and economic growth in three countries from the Economic Community of West African States (ECOWAS). Employing ARDL bounds test approach and real GDP per capita as growth indicator; the thesis finds support in favour of the McKinnon-Shaw hypothesis but also finds that the increases in the subsequent savings and investments have not been transmitted into economic growth in two of the studied countries. Moreover, the thesis also finds that stock market developments have negligible or negative impact on economic growth in two of the selected countries. The thesis concludes that in most cases, it is not financial liberalisation polices that affect economic growth in the selected ECOWAS countries, but rather increase in the productivity of labour, increase in the credit to the private sector, increase in foreign direct investments, increase in the capital stock and increase in government expenditure contrary to expectations. Interestingly, the thesis also finds that export has only negative effect on economic growth in all the selected ECOWAS countries. The thesis therefore, recommends that long-term export diversification programmes be implemented in the ECOWAS regions whilst further investigation is carried on the issue. / Economics / D. Litt et Phil. (Economics)

Proteins are considered the primary actors in living organisms. Proteins mainly perform their functions by interacting with other proteins. Protein-protein interactions underpin various biological activities such as metabolic cycles, signal transduction, and immune response. PPI identification has been addressed by various experimental methods such as the yeast two-hybrid, mass spectrometry, and protein microarrays, to mention a few. However, due to the sheer number of proteins, experimental methods for finding interacting and non-interacting protein pairs are time-consuming and costly. Therefore a sequence-based framework called ProtInteract is developed to predict protein-protein interaction. ProtInteract comprises two components: first, a novel autoencoder architecture that encodes each protein's primary structure to a lower-dimensional vector while preserving its underlying sequential pattern by extracting uncorrelated attributes and more expressive descriptors. This leads to faster training of the second network, a deep convolutional neural network (CNN) that receives encoded proteins and predicts their interaction. Three different scenarios formulate the prediction task. In each scenario, the deep CNN predicts the class of a given encoded protein pair. Each class indicates different ranges of confidence scores corresponding to the probability of whether a predicted interaction occurs or not. The proposed framework features significantly low computational complexity and relatively fast response. The present study makes two significant contributions to the field of protein-protein interaction (PPI) prediction. Firstly, it addresses the computational challenges posed by the high dimensionality of protein datasets through the use of dimensionality reduction techniques, which extract highly informative sequence attributes. Secondly, the proposed framework, ProtInteract, utilises this information to identify the interaction characteristics of a protein based on its amino acid configuration. ProtInteract encodes the protein's primary structure into a lower-dimensional vector space, thereby reducing the computational complexity of PPI prediction. Our results provide evidence of the proposed framework's accuracy and efficiency in predicting protein-protein interactions.

Områdesbeskrivning: IPO-marknaden kan undersökas på olika sätt. Varför underprissättning är så förhärskande, om den går i cykler eller vad det är som påverkar den. Vi vill undersöka den svenska IPO-marknaden under perioden 2006-2020 och om den påverkas av makroekonomiska faktorer såsom inflation eller styrränta. Denna studie är inspirerad av tidigare forskning utförd av Tran och Jeon (2011) som undersöker om det finns samband mellan makroekonomiska faktorer och IPO-marknadens aktivitet på den amerikanska marknaden. Är det så att olika IPO-marknader påverkas av olika faktorer på unika sätt eller är IPO-marknader världen över homogena? Vi försöker dessutom framställa en modell som beskriver det mest gynnsamma förhållandet att genomföra en IPO under om målet är att anskaffa mer kapital. Syfte: Uppsatsen syfte är att undersöka den svenska IPO-marknadens aktivitet under perioden 2006-2020. Samt undersöka i vilken utsträckning den svenska IPO-marknadens aktiviteten påverkas av makroekonomiska faktorer. Med vår undersökning av de makroekonomiska faktorerna som grund kan vi således undersöka vilka makroekonomiska förhållanden som är mest gynnsamma för företag i Sverige att genomföra en IPO under om målet är att anskaffa mer kapital. Metod: En kvantitativ metod appliceras i denna uppsats för att besvara våra forskningsfrågor och datan vi samlar in analyseras med hjälp utav en regressionsanalys. Vi samlar in vårt datamaterial genom att läsa igenom årsredovisningar från de företag som genomfört en IPO under den tidsperioden vi undersöker. Hypoteserna formuleras utifrån tidigare forskning och ämnar att undersöka om de makroekonomiska faktorerna har ett positivt eller negativt samband med IPO-marknadsaktivitet. Resultat: Resultaten som vi finner är att det finns signifikanta samband mellan den svenska IPO-marknadens aktivitet och makroekonomiska faktorer. Vi identifierar ett förhållande som kan beskrivas som det mest gynnsamma makroekonomiska förhållandet utifrån vår modell. Begränsningar: Vår uppsats är begränsad till tidsperiod 2006-2020 samt den svenska IPO-marknaden. På grund av att viss information kring hur mycket kapital ett företag anskaffar vid sin IPO saknas så begränsas vårt urval. / Area description: IPO markets can be studied in different ways. Why underpricing is so prevalent, if the market moves in cycles or what influences the market. We want to study the Swedish IPO market during the period of 2006-2020 and if it is influenced by macroeconomic factors like inflation or the policy rate. This study is influenced by the work done by Tran and Jeon (2011) who examines if there are any relationships between macroeconomic factors and IPO market activity on the American PO market. Is it that different IPO markets are influenced by different factors in unique ways or are the IPO markets around the globe homogeneous. We try to produce a model that describes the most favourable environment to implement an IPO in if the goal is to acquire more capital. Purpose: The purpose of the thesis is to examine the activity of the Swedish IPO market during the period 2006-2020 and examine the extent to which the activity of the Swedish IPO market is affected by macroeconomic factors. Based on our study of the macroeconomic factors, we can therefore examine which macroeconomic conditions are most favourable for companies in Sweden to carry out an IPO under the goal of raising more capital.  Method: A quantitative method is applied in this thesis to answer our research questions and the data we collect is analysed with the help of a regression analysis. We collect our data by reading through annual reports from the companies that conducted an IPO during the period we are investigating. The hypotheses are formulated based on previous research and intend to investigate whether the macroeconomic factors have a positive or negative relationship with IPO market activity.  Results: The results we find is that there are significant relationships between the activity of the Swedish IPO market and macroeconomic factors. We identify a ratio that can be described as the most favourable macroeconomic ratio based on our model.  Limitations: Our thesis is limited to the period 2006-2020 and the Swedish IPO market. Due to the lack of certain information about how much capital a company raises at its IPO, our selection is limited.

Final qualifying work (master's thesis) is devoted to the reserching of the methods of identifying the manipulations that undermine the effectiveness of the stock market. The subject of the research is the way of identifying manipulative transactions in the stock market of Russia. The main purpose of the research is the development of specific proposals and the selection of statistical methods relevant for the Russian stock market to improve the existing system of state control aimed at identifying various types and methods of manipulative trading in the stock market. In conclusion, practical steps for the strengthen of the stock market of the Russian Federation are formulated. / Выпускная квалификационная работа (магистерская диссертация) посвящена изучению методов выявления манипуляций, подрывающих эффективность фондового рынка. Предметом исследования выступают методы выявления манипулятивных сделок на российском фондовом рынке. Основной целью исследования выступает разработка конкретных предложений и выбор статистических методов, релевантных для российского фондового рынка, для совершенствования существующей системы государственного контроля, направленной на выявление различных видов и способов манипулятивной торговли на фондовом рынке. В заключении сформулированы практические шаги по укреплению фондового рынка Российской Федерации.

L’objectif de cette recherche était d’évaluer l’efficacité du paramètre de classification pour prédire suivre les tendances boursières. Les méthodes traditionnelles basées sur la prévision, qui ciblent l’immédiat pas de temps suivant, rencontrent souvent des défis dus à des données non stationnaires, compromettant le modèle précision et stabilité. En revanche, notre approche de classification prédit une évolution plus large du cours des actions avec des mouvements sur plusieurs pas de temps, visant à réduire la non-stationnarité des données. Notre ensemble de données, dérivé de diverses actions du NASDAQ-100 et éclairé par plusieurs indicateurs techniques, a utilisé un mélange d'experts composé d'un mécanisme de déclenchement souple et d'une architecture basée sur les transformateurs. Bien que la méthode principale de cette expérience ne se soit pas révélée être aussi réussie que nous l'avions espéré et vu initialement, la méthodologie avait la capacité de dépasser toutes les lignes de base en termes de performance dans certains cas à quelques époques, en démontrant le niveau le plus bas taux de fausses découvertes tout en ayant un taux de rappel acceptable qui n'est pas zéro. Compte tenu de ces résultats, notre approche encourage non seulement la poursuite des recherches dans cette direction, dans lesquelles un ajustement plus précis du modèle peut être mis en œuvre, mais offre également aux personnes qui investissent avec l'aide de l'apprenstissage automatique un outil différent pour prédire les tendances boursières, en utilisant un cadre de classification et un problème défini différemment de la norme. Il est toutefois important de noter que notre étude est basée sur les données du NASDAQ-100, ce qui limite notre l’applicabilité immédiate du modèle à d’autres marchés boursiers ou à des conditions économiques variables. Les recherches futures pourraient améliorer la performance en intégrant les fondamentaux des entreprises et effectuer une analyse du sentiment sur l'actualité liée aux actions, car notre travail actuel considère uniquement indicateurs techniques et caractéristiques numériques spécifiques aux actions. / The objective of this research was to evaluate the classification setting's efficacy in predicting stock market trends. Traditional forecasting-based methods, which target the immediate next time step, often encounter challenges due to non-stationary data, compromising model accuracy and stability. In contrast, our classification approach predicts broader stock price movements over multiple time steps, aiming to reduce data non-stationarity. Our dataset, derived from various NASDAQ-100 stocks and informed by multiple technical indicators, utilized a Mixture of Experts composed of a soft gating mechanism and a transformer-based architecture. Although the main method of this experiment did not prove to be as successful as we had hoped and seen initially, the methodology had the capability in surpassing all baselines in certain instances at a few epochs, demonstrating the lowest false discovery rate while still having an acceptable recall rate. Given these results, our approach not only encourages further research in this direction, in which further fine-tuning of the model can be implemented, but also offers traders a different tool for predicting stock market trends, using a classification setting and a differently defined problem. It's important to note, however, that our study is based on NASDAQ-100 data, limiting our model's immediate applicability to other stock markets or varying economic conditions. Future research could enhance performance by integrating company fundamentals and conducting sentiment analysis on stock-related news, as our current work solely considers technical indicators and stock-specific numerical features.

Portfolio managers have a great interest in detecting high-performing stocks early on. Detecting outperforming stocks has for long been of interest from a research as well as financial point of view. Quantitative methods to predict stock movements have been widely studied in diverse contexts, where some present promising results. The quantitative algorithms for such prediction models can be, to name a few, support vector machines, tree-based methods, and regression models, where each one can carry different predictive power. Most previous research focuses on indices such as S&P 500 or large-cap stocks, while small- and micro-cap stocks have been examined to a lesser extent. These types of stocks also commonly share the characteristic of high volatility, with prospects that can be difficult to assess. This study examines to which extent widely studied quantitative methods such as random forest, support vector machine, and logistic regression can produce accurate predictions of stock price directions on a quarterly and yearly basis. The problem is modeled as a binary classification task, where the aim is to predict whether a stock achieves a return above or below a benchmark index. The focus lies on Asian small- and micro-cap stocks. The study concludes that the random forest method for a binary yearly prediction produces the highest accuracy of 69.64%, where all three models produced higher accuracy than a binary quarterly prediction. Although the statistical power of the models can be ruled adequate, more extensive studies are desirable to examine whether other models or variables can increase the prediction accuracy for small- and micro-cap stocks. / Portföljförvaltare har ett stort intresse av att upptäcka högpresterande aktier tidigt. Detektering av högavkastande aktier har länge varit av stort intresse dels i forskningssyfte men också ur ett finansiellt perspektiv. Kvantitativa metoder för att förutsäga riktning av aktiepriset har studerats i stor utsträckning där vissa presenterar lovande resultat. De kvantitativa algoritmerna för sådana prediktionsmodeller kan vara, för att nämna ett fåtal, support vector machines, trädbaserade metoder och regressionsmodeller, där var och en kan bära olika prediktiv kraft. Majoriteten av tidigare studier fokuserar på index såsom S&P 500 eller storbolagsaktier, medan små- och mikrobolagsaktier har undersökts i mindre utsträckning. Dessa sistnämnda typer av aktier innehar ofta en hög volatilitet med framtidsutsikter som kan vara svåra att bedöma. Denna studie undersöker i vilken utsträckning väletablerade kvantitativa modeller såsom random forest, support vector machine och logistisk regression, kan ge korrekta förutsägelser av små- och mikrobolags aktiekursriktningar på kvartals- och årsbasis. I avhandlingen modelleras detta som ett binärt klassificeringsproblem, där avkastningen för varje aktie antingen är över eller under jämförelseindex. Fokuset ligger på asiatiska små-och mikrobolag. Studien drar slutsatsen att random forest för en binär årlig prediktion ger den högsta noggrannheten på 69,64 %, där samtliga tre modeller ger högre noggrannhet än en binär kvartalsprediktion. Även om modellerna bedöms vara statistiskt säkerställda, är det önskvärt med fler omfattande studier för att undersöka om andra modeller eller variabler kan öka noggrannheten i prediktionen för små- och mikrobolags aktiekursriktning.

Este trabajo se plantea la cuestión que millones de inversores se han planteado en algún momento: ¿cuál es la mejor opción para sus ahorros, fondos de inversión, inversión aleatoria o estrategias de análisis técnico? Para este propósito se describen en primer lugar las normas que regulan a las instituciones de inversión colectiva (IIC) en España, distinguiendo entre los diferentes tipos de fondos en cuanto a su forma legal. A continuación se repasan las teorías sobre eficiencia en los mercados financieros. Estas teorías se enlazan con los estilos de gestión; gestión pasiva para aquellos ortodoxos que defienden la eficiencia fuerte y gestión activa para los gestores que no toman la eficiencia como un dogma. Estos últimos creen en las anomalías de mercado y recurren a estrategias basadas en fundamentos contables (estimación de beneficios, ventas, etc.). Esta primera parte concluye con una evaluación del rendimiento de los fondos españoles según su estilo de inversión. Puesto que esta no es del todo favorable para las gestoras se intentan aportar motivos por los cuales los fondos siguen disfrutando de amplia aceptación. La segunda parte del trabajo describe la metodología empleada para estudiar el comportamiento de una cartera de inversión gestionada mediante estrategias de análisis técnico. Con este fin ha sido necesario desarrollar un software capaz de realizar la gestión de carteras y que se alimenta de cotizaciones históricas desde 1/2003 hasta 1/2012. Los datos se separan en dos estudios paralelos, uno para Europa y el otro para EE.UU con el objetivo de analizar diferencias y semejanzas. El programa permite el control completo sobre la cartera, gestión de liquidez, stop-loss, etc.; y nos abastece al mismo tiempo de una gran cantidad de información estadística. La particularidad del software es la capacidad de poder variar los parámetros de las estrategias mediante barrido, obteniendo así no solamente una única simulación sino una población de simulaciones referidas a una estrategia. En la tercera parte se recurre a este conjunto de simulaciones a las que denominaremos estudios y están compuestas por varios millones de operaciones de compra y venta. Estos estudios se aproximan a funciones normales que describen la esperanza de rentabilidades que tendría un inversor que decidiera participar en el mercado siguiendo alguna de las estrategias descritas. Para poder comparar el comportamiento de las estrategias técnicas se utilizan diferentes métodos aleatorios que pretenden simular una operativa al azar. Por último se confrontan los tres métodos de inversión: fondos, análisis técnico y aleatorio; comparados con los índices de referencia correspondientes. / Plá María, M. (2014). Evaluación del comportamiento de carteras con gestión automatizada comparada con los rendimientos de carteras aleatorias y fondos de inversión [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/38987