A inovação apresenta-se, primordialmente, nas indústrias intensivas em tecnologia que, nos últimos anos, criaram redes globais de parceiros para melhorar o alcance e a gama de seus produtos, serviços e tecnologias. Em virtude de o conceito de inovação estar intimamente ligado à tecnologia, o presente trabalho toma por base o ambiente de negociações de ações na maior bolsa de valores do mundo das empresas de tecnologia, a NASDAQ. Por ser a representação das maiores companhias na bolsa, o Índice NASDAQ-100 constitui o universo da pesquisa que buscou estudar as empresas quanto ao grau de inovação aberta por meio da construção e análise das redes de cooperação. Em vista disso, apresenta-se um estudo sobre essas empresas denominadas de alta tecnologia para o período 1995-2014 analisando suas patentes protegidas em parcerias com o objetivo de classificá-las em uma matriz de inovação aberta, bem como, mapear a configuração e abrangência das suas redes de cooperação. Além disso, propicia discussão sobre a evolução dos investimentos em P&D e as áreas tecnológicas priorizadas pelas empresas. Por abordar extenso horizonte de 20 anos e adotar como paradigma o vínculo dos agentes nos pedidos de patentes, as análises permitiram o diagnóstico dos diversos padrões de comportamento no tocante às redes de cooperação para inovação dentre as empresas. Fundamentalmente, como resultantes das estratégias particulares e específicas adotadas por cada companhia, as redes de cooperação são distintas, entretanto, as principais métricas das redes são convergentes quanto ao grau de inovação aberta. Além disso, os resultados indicam que as áreas tecnológicas de interesse das empresas da amostra pesquisada estão mais concentradas na tecnologia proprietária, o que reforça a postura em manter investimentos em P&D na sua principal área de atuação. Assim, as parcerias são firmadas no sentido de intensificar o domínio tecnológico, na busca das complementaridades necessárias. / Innovation is primarily present in intensive technology industries, which, in recent years, have created global networks of partners to improve the scope and range of products, services and technologies. Because the concept of innovation is closely linked to technology, this study is based on the environment of trading of shares in the largest stock exchange in the world of technology companies, NASDAQ. Being the representation of the largest companies in the stock exchange, the NASDAQ-100 Index represents the universe of this research that focused on studying the companies according to the degree of open innovation by means of constructing and analyzing cooperation networks. In view of this, we present a study of the so called high-tech companies for the 1995-2014 period by analyzing the patents protected under partnerships in order to classify the firms into an open innovation matrix and map the configuration and scope of their cooperation networks. In addition, it presents discussions on the evolution of investments in R&D and the technological areas prioritized by companies. Addressing an extensive horizon of 20 years and adopting the relationship of the agents in patent applications as a paradigm, the analysis brought the diagnosis of the various behavioral patterns with regards to cooperation networks for innovation among these companies. Fundamentally, as a result of particular and specific strategies adopted by each company, cooperation networks are distinct, however, the key metrics of networks are converging towards the degree of open innovation. Moreover, the results indicate that technological areas of interest of the surveyed sample are more focused on proprietary technology, which reinforces the stance in maintaining investments in R&D in the main area of expertise. Thus, partnerships are signed to intensify the technological field and seek the necessary complementarities.

A inovação apresenta-se, primordialmente, nas indústrias intensivas em tecnologia que, nos últimos anos, criaram redes globais de parceiros para melhorar o alcance e a gama de seus produtos, serviços e tecnologias. Em virtude de o conceito de inovação estar intimamente ligado à tecnologia, o presente trabalho toma por base o ambiente de negociações de ações na maior bolsa de valores do mundo das empresas de tecnologia, a NASDAQ. Por ser a representação das maiores companhias na bolsa, o Índice NASDAQ-100 constitui o universo da pesquisa que buscou estudar as empresas quanto ao grau de inovação aberta por meio da construção e análise das redes de cooperação. Em vista disso, apresenta-se um estudo sobre essas empresas denominadas de alta tecnologia para o período 1995-2014 analisando suas patentes protegidas em parcerias com o objetivo de classificá-las em uma matriz de inovação aberta, bem como, mapear a configuração e abrangência das suas redes de cooperação. Além disso, propicia discussão sobre a evolução dos investimentos em P&D e as áreas tecnológicas priorizadas pelas empresas. Por abordar extenso horizonte de 20 anos e adotar como paradigma o vínculo dos agentes nos pedidos de patentes, as análises permitiram o diagnóstico dos diversos padrões de comportamento no tocante às redes de cooperação para inovação dentre as empresas. Fundamentalmente, como resultantes das estratégias particulares e específicas adotadas por cada companhia, as redes de cooperação são distintas, entretanto, as principais métricas das redes são convergentes quanto ao grau de inovação aberta. Além disso, os resultados indicam que as áreas tecnológicas de interesse das empresas da amostra pesquisada estão mais concentradas na tecnologia proprietária, o que reforça a postura em manter investimentos em P&D na sua principal área de atuação. Assim, as parcerias são firmadas no sentido de intensificar o domínio tecnológico, na busca das complementaridades necessárias. / Innovation is primarily present in intensive technology industries, which, in recent years, have created global networks of partners to improve the scope and range of products, services and technologies. Because the concept of innovation is closely linked to technology, this study is based on the environment of trading of shares in the largest stock exchange in the world of technology companies, NASDAQ. Being the representation of the largest companies in the stock exchange, the NASDAQ-100 Index represents the universe of this research that focused on studying the companies according to the degree of open innovation by means of constructing and analyzing cooperation networks. In view of this, we present a study of the so called high-tech companies for the 1995-2014 period by analyzing the patents protected under partnerships in order to classify the firms into an open innovation matrix and map the configuration and scope of their cooperation networks. In addition, it presents discussions on the evolution of investments in R&D and the technological areas prioritized by companies. Addressing an extensive horizon of 20 years and adopting the relationship of the agents in patent applications as a paradigm, the analysis brought the diagnosis of the various behavioral patterns with regards to cooperation networks for innovation among these companies. Fundamentally, as a result of particular and specific strategies adopted by each company, cooperation networks are distinct, however, the key metrics of networks are converging towards the degree of open innovation. Moreover, the results indicate that technological areas of interest of the surveyed sample are more focused on proprietary technology, which reinforces the stance in maintaining investments in R&D in the main area of expertise. Thus, partnerships are signed to intensify the technological field and seek the necessary complementarities.

This work is about trading futures markets. It defines rules of two different intraday strategies with the same basic trading idea on e-mini NASDAQ 100 market. First strategy is more mechanical, second is more discrete. These strategies are tested via backtest and papertrading. The results of tests are evaluated and based on that is chosen one strategy, which is then applied on live trading. Results from live trading are compared with the results of previous tests for a purpose finding a difference between them and identifying the causes of these differences.

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.