Asset Clusters and Asset Networks in Financial Risk Management and Portfolio Optimization

In this work we use explorative statistical and data mining methods for financial applications like risk management, portfolio optimization and market analysis. The outcomes are visualized and the relations are quantified by mathematical measures. Researchers, analysts and decision makers can visually explore the structures and can carry out management initiatives based on automatic measures provided by the system. There are example applications to equity and loan portfolios

Higher-order Graph Attention Network for Stock Selection with Joint Analysis

Stock selection is important for investors to construct profitable portfolios. Graph neural networks (GNNs) are increasingly attracting researchers for stock prediction due to their strong ability of relation modelling and generalisation. However, the existing GNN methods only focus on simple pairwise stock relation and do not capture complex higher-order structures modelling relations more than two nodes. In addition, they only consider factors of technical analysis and overlook factors of fundamental analysis that can affect the stock trend significantly. Motivated by them, we propose higher-order graph attention network with joint analysis (H-GAT). H-GAT is able to capture higher-order structures and jointly incorporate factors of fundamental analysis with factors of technical analysis. Specifically, the sequential layer of H-GAT take both types of factors as the input of a long-short term memory model. The relation embedding layer of H-GAT constructs a higher-order graph and learn node embedding with GAT. We then predict the ranks of stock return. Extensive experiments demonstrate the superiority of our H-GAT method on the profitability test and Sharp ratio over both NSDAQ and NYSE datasetsComment: 12 pages, 6 figures

Mining Text and Time Series Data with Applications in Finance

Finance is a field extremely rich in data, and has great need of methods for summarizing and understanding these data. Existing methods of multivariate analysis allow the discovery of structure in time series data but can be difficult to interpret. Often there exists a wealth of text data directly related to the time series. In this thesis it is shown that this text can be exploited to aid interpretation of, and even to improve, the structure uncovered. To this end, two approaches are described and tested. Both serve to uncover structure in the relationship between text and time series data, but do so in very different ways. The first model comes from the field of topic modelling. A novel topic model is developed, closely related to an existing topic model for mixed data. Improved held-out likelihood is demonstrated for this model on a corpus of UK equity market data and the discovered structure is qualitatively examined. To the authors’ knowledge this is the first attempt to combine text and time series data in a single generative topic model. The second method is a simpler, discriminative method based on a low-rank decomposition of time series data with constraints determined by word frequencies in the text data. This is compared to topic modelling using both the equity data and a second corpus comprising foreign exchange rates time series and text describing global macroeconomic sentiments, showing further improvements in held-out likelihood. One example of an application for the inferred structure is also demonstrated: construction of carry trade portfolios. The superior results using this second method serve as a reminder that methodological complexity does not guarantee performance gains

Antecipação na tomada de decisão com múltiplos critérios sob incerteza

Orientador: Fernando José Von ZubenTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: A presença de incerteza em resultados futuros pode levar a indecisões em processos de escolha, especialmente ao elicitar as importâncias relativas de múltiplos critérios de decisão e de desempenhos de curto vs. longo prazo. Algumas decisões, no entanto, devem ser tomadas sob informação incompleta, o que pode resultar em ações precipitadas com consequências imprevisíveis. Quando uma solução deve ser selecionada sob vários pontos de vista conflitantes para operar em ambientes ruidosos e variantes no tempo, implementar alternativas provisórias flexíveis pode ser fundamental para contornar a falta de informação completa, mantendo opções futuras em aberto. A engenharia antecipatória pode então ser considerada como a estratégia de conceber soluções flexíveis as quais permitem aos tomadores de decisão responder de forma robusta a cenários imprevisíveis. Essa estratégia pode, assim, mitigar os riscos de, sem intenção, se comprometer fortemente a alternativas incertas, ao mesmo tempo em que aumenta a adaptabilidade às mudanças futuras. Nesta tese, os papéis da antecipação e da flexibilidade na automação de processos de tomada de decisão sequencial com múltiplos critérios sob incerteza é investigado. O dilema de atribuir importâncias relativas aos critérios de decisão e a recompensas imediatas sob informação incompleta é então tratado pela antecipação autônoma de decisões flexíveis capazes de preservar ao máximo a diversidade de escolhas futuras. Uma metodologia de aprendizagem antecipatória on-line é então proposta para melhorar a variedade e qualidade dos conjuntos futuros de soluções de trade-off. Esse objetivo é alcançado por meio da previsão de conjuntos de máximo hipervolume esperado, para a qual as capacidades de antecipação de metaheurísticas multi-objetivo são incrementadas com rastreamento bayesiano em ambos os espaços de busca e dos objetivos. A metodologia foi aplicada para a obtenção de decisões de investimento, as quais levaram a melhoras significativas do hipervolume futuro de conjuntos de carteiras financeiras de trade-off avaliadas com dados de ações fora da amostra de treino, quando comparada a uma estratégia míope. Além disso, a tomada de decisões flexíveis para o rebalanceamento de carteiras foi confirmada como uma estratégia significativamente melhor do que a de escolher aleatoriamente uma decisão de investimento a partir da fronteira estocástica eficiente evoluída, em todos os mercados artificiais e reais testados. Finalmente, os resultados sugerem que a antecipação de opções flexíveis levou a composições de carteiras que se mostraram significativamente correlacionadas com as melhorias observadas no hipervolume futuro esperado, avaliado com dados fora das amostras de treinoAbstract: The presence of uncertainty in future outcomes can lead to indecision in choice processes, especially when eliciting the relative importances of multiple decision criteria and of long-term vs. near-term performance. Some decisions, however, must be taken under incomplete information, what may result in precipitated actions with unforeseen consequences. When a solution must be selected under multiple conflicting views for operating in time-varying and noisy environments, implementing flexible provisional alternatives can be critical to circumvent the lack of complete information by keeping future options open. Anticipatory engineering can be then regarded as the strategy of designing flexible solutions that enable decision makers to respond robustly to unpredictable scenarios. This strategy can thus mitigate the risks of strong unintended commitments to uncertain alternatives, while increasing adaptability to future changes. In this thesis, the roles of anticipation and of flexibility on automating sequential multiple criteria decision-making processes under uncertainty are investigated. The dilemma of assigning relative importances to decision criteria and to immediate rewards under incomplete information is then handled by autonomously anticipating flexible decisions predicted to maximally preserve diversity of future choices. An online anticipatory learning methodology is then proposed for improving the range and quality of future trade-off solution sets. This goal is achieved by predicting maximal expected hypervolume sets, for which the anticipation capabilities of multi-objective metaheuristics are augmented with Bayesian tracking in both the objective and search spaces. The methodology has been applied for obtaining investment decisions that are shown to significantly improve the future hypervolume of trade-off financial portfolios for out-of-sample stock data, when compared to a myopic strategy. Moreover, implementing flexible portfolio rebalancing decisions was confirmed as a significantly better strategy than to randomly choosing an investment decision from the evolved stochastic efficient frontier in all tested artificial and real-world markets. Finally, the results suggest that anticipating flexible choices has lead to portfolio compositions that are significantly correlated with the observed improvements in out-of-sample future expected hypervolumeDoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétric

An empirical study on the various stock market prediction methods

Investment in the stock market is one of the much-admired investment actions. However, prediction of the stock market has remained a hard task because of the non-linearity exhibited. The non-linearity is due to multiple affecting factors such as global economy, political situations, sector performance, economic numbers, foreign institution investment, domestic institution investment, and so on. A proper set of such representative factors must be analyzed to make an efficient prediction model. Marginal improvement of prediction accuracy can be gainful for investors. This review provides a detailed analysis of research papers presenting stock market prediction techniques. These techniques are assessed in the time series analysis and sentiment analysis section. A detailed discussion on research gaps and issues is presented. The reviewed articles are analyzed based on the use of prediction techniques, optimization algorithms, feature selection methods, datasets, toolset, evaluation matrices, and input parameters. The techniques are further investigated to analyze relations of prediction methods with feature selection algorithm, datasets, feature selection methods, and input parameters. In addition, major problems raised in the present techniques are also discussed. This survey will provide researchers with deeper insight into various aspects of current stock market prediction methods

Examining exchange rate exposure, hedging and executive compensation in US manufacturing Industry

In essay one, my primary objective is to see the sensitivity of foreign exchange rate risk on firm performance in US manufacturing industry and examine if the hedging help reduce the foreign exchange rate risk. I am particularly interested in manufacturing industry because of the nature of business operation of manufacturing firms. Manufacturing firms in US are not only exposed to foreign exchange fluctuation from sales and revenue but also are exposed to foreign exchange rate risk for procurement, placement and investment. I find that the firms with extreme foreign exchange rate risk exposure exhibit lower daily return and firms with very low foreign exchange rate risk exhibit higher daily return using the portfolio approach. I also find that the firms that hedge has lower foreign exchange rate exposure compared to firms that don’t hedge. The coefficient for hedge is negative and statistically significant. In essay two, I investigate the effect of executive compensation on exchange rate risk in US manufacturing industry. There is a large theoretical and empirical interest on executive compensation using agency framework that investigates the conflict of interest between shareholders and corporate executives. That interest has been largely aligned with the use of managerial performance dependent on observable measures of firm performance. Since US manufacturing firm is largely exposed to foreign exchange transactions by design, I investigate if the value of in-the-money unexercised vested executive stock option has any impact on foreign exchange rate exposure. I investigate if the value of in-the-money unexercised unvested executive stock option has any impact on executive stock option. Using pooled OLS, fixed effect panel data and random effect panel data, I find that in all 3 model value of in-the-money unexercised vested executive stock option has negative coefficient and is statistically significant. At the same time in all 3 models the value of in-the-money unexercised unvested executive stock option is positive and is statistically significant

Examining exchange rate exposure, hedging and executive compensation in US manufacturing Industry

In essay one, my primary objective is to see the sensitivity of foreign exchange rate risk on firm performance in US manufacturing industry and examine if the hedging help reduce the foreign exchange rate risk. I am particularly interested in manufacturing industry because of the nature of business operation of manufacturing firms. Manufacturing firms in US are not only exposed to foreign exchange fluctuation from sales and revenue but also are exposed to foreign exchange rate risk for procurement, placement and investment. I find that the firms with extreme foreign exchange rate risk exposure exhibit lower daily return and firms with very low foreign exchange rate risk exhibit higher daily return using the portfolio approach. I also find that the firms that hedge has lower foreign exchange rate exposure compared to firms that don’t hedge. The coefficient for hedge is negative and statistically significant. In essay two, I investigate the effect of executive compensation on exchange rate risk in US manufacturing industry. There is a large theoretical and empirical interest on executive compensation using agency framework that investigates the conflict of interest between shareholders and corporate executives. That interest has been largely aligned with the use of managerial performance dependent on observable measures of firm performance. Since US manufacturing firm is largely exposed to foreign exchange transactions by design, I investigate if the value of in-the-money unexercised vested executive stock option has any impact on foreign exchange rate exposure. I investigate if the value of in-the-money unexercised unvested executive stock option has any impact on executive stock option. Using pooled OLS, fixed effect panel data and random effect panel data, I find that in all 3 model value of in-the-money unexercised vested executive stock option has negative coefficient and is statistically significant. At the same time in all 3 models the value of in-the-money unexercised unvested executive stock option is positive and is statistically significant