Applications of machine learning in finance: analysis of international portfolio flows using regime-switching models

Recent advances in machine learning are finding commercial applications across many sectors, not least the financial industry. This thesis explores applications of machine learning in quantitative finance through two approaches. The current state of the art is evaluated through an extensive review of recent quantitative finance literature. Themes and technologies are identified and classified, and the key use cases highlighted from the emerging literature. Machine learning is found to enable deeper analysis of financial data and the modelling of complex nonlinear relationships within data. The ability to incorporate alternative data in the investment process is also enabled. Innovations in backtesting and performance metrics are also made possible through the application of machine learning. Demonstrating a practical application of machine learning in quantitative finance, regime-switching models are applied to analyse and extract information from international portfolio flows. Regime-switching models capture properties of international portfolio flows previously found in the literature, such as persistence in flows compared to returns, and a relationship between flows and returns. Structural breaks and persistent regime shifts in investor behaviour are identified by the models. Regime-switching models infer regimes in the data which exhibit unique characteristic flows and returns. To determine whether the information extracted could aid in the investment process, a portfolio of global assets was constructed, with positions determined using a flowbased regime-switching model. The portfolio outperforms two benchmarks, a buy & hold strategy and the MSCI World Index in walk-forward out-of-sample tests using daily and weekly data

Machine learning for financial applications: self-organising maps, hierarchical clustering and dynamic time-warping for portfolio constructive

This study investigates how modern machine learning (ML) techniques can be used to advance the field of quantitative investing. A broad literature review evaluated the common applications for ML in finance, and what ML algorithms are being used. The results show ML is commonly applied to the areas of Return Forecasting, Portfolio Construction, Ethics, Fraud Detection Decision Making Language Processing and Sentiment Analysis. Neural Network technology and support vector machine are identified as popular ML algorithms. A second review was carried out, focusing in the area of ML for quantitative finance in recent years finds three primary areas; Return forecasting, Portfolio construction and Risk management. A practical ML experiment carried out as a proof of concept of ML for financial applications. This experiment was informed by the results of the broad and more focused literature searches. Two forms of ML techniques are used to analyse market return data and equity flow data (provided by State Street Global Markets) and create a portfolio from insights derived from the ML technology. The ML technologies employed are those of Self-Organising Maps and Hierarchical Clustering. The portfolios created were tested in terms of risk, profitability and stability. Stable regimes and profitable portfolios are created. Results show that portfolios obtained by analysing equity flow data consistently outperform those created by analysing return data

Teste de indicadores econômico-financeiros para gestão ativa de portfólio de ações.

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Engenharia de ProduçãoO objetivo deste estudo é testar três indicadores econômico-financeiros (juros, cotação do real e cotação do peso mexicano) e a sua relação com a cotação de ações de empresas listadas em bolsas latino-americanas cada qual com suas características específicas (endividamento e exposição à moeda estrangeira). Confrontando os resultados com hipóteses adotadas por uma gestora de fundos de investimento na construção de uma carteira de ações. O intuito é de apresentar metodologia e sistemática replicáveis para que outros estudos de análise econômico-financeira sejam realizados, contribuindo com o conhecimento científico e empírico do mercado financeiro. Os resultados mostram que decisões de compra ou venda de ações podem ser pautados pelos indicadores analisados neste estudo, pois possuem relevância significativa. Esta Pesquisa-Ação possui caráter explicativo, com uso de dados quantitativos, fornecidos pela empresa e por plataformas de dados financeiros como Economática, Bloomberg e VMQ+.The objective of this study is the evaluation of three financial and economical factors (interest rates, Brazilian real currency and Mexican peso currency) and its relationship to Latin-American listed stocks with specific characteristics (leverage and currency exposure). Confronting the results with hypothesis adopted by an asset management company in its portfolio construction. It is intended to present a replicable methodology so that other studies may be realized, further contributing with the scientific knowledge of the financial markets. The results produced by this study show that the decision of buying or selling a stock may be based on the referred factors as they present significant relevance. This Action-Research possesses explanatory bias, with the usage of quantitative data, supplied by Gestora and financial market platforms such as Economática, Bloomberg and VMQ+

Artificial Intelligence in Asset Management

Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways