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A mean-variance look at robo-advising
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Resumo(s)
Nos últimos anos, a indústria de gestão de riquezas enfrentou desafios significativos e tendências impactantes, tais como a diminuição da confiança dos clientes nos serviços financeiros tradicionais, novos encargos regulatórios e aumento da concorrência. Neste contexto, a ascensão de gestores de investimento automatizados, conhecidos como "roboadvisors" e a nova combinação de ciência e capital humano tem desafiado a indústria de gestão de capital a encontrar novas formas de criar valor para beneficiar o cliente. Sobre esse assunto, esse projeto contribui para uma análise de risco-retorno e analise das fronteiras eficientes do portfólio recomendado de cinco plataformas online nos Estados Unidos em março de 2017: Charles Schwab, SigFig, Wealthfront, ToleRisk e RiskAlyze. Nessa análise, são realizados "backtesting" para avaliar o desempenho, a volatilidade, o valor em risco e os índices de Sharpe. Esse projeto é baseado na Teoria da Variação Média e é baseado em preços históricos de fechamento semanal de fundos de investimento abertos negociados em bolsa. Os resultados indicam que a prática atual de utilizar questionários para determinar o perfil de risco do investidor é de confiabilidade limitada. Os resultados também mostram que o modelo "robo-advisory" aparentemente beneficia investidores conservadores. Assim, esta dissertação contribui para uma visão sobre os benefícios e limitações das plataformas de investimento online, fornecendo um parâmetro para uma melhor compreensão do seu potencial futuro.
In the last few years the wealth management industry has experienced significant challenges and impactful trends, such as a decrease in customers' trust of traditional financial services, new regulatory burdens and increase of competition. In this context, the rise of automated investment managers, well known as "robo-advisors" and the new combination of science and human capital has been challenging the wealth management industry to find new ways to create value benefiting the client. On this matter, this project contributes to a analysis of risk-return look and efficient frontiers of the recommended portfolio of five online platforms in United States in March 2017: Charles Schwab, SigFig, Wealthfront, ToleRisk and RiskAlyze. In this analysis, back-testing is conducted to assess performance, volatility, value at risk and sharpe ratios. This project is based on the Mean-Variance Theory and uses historical weekly closing prices of exchanged traded-funds. Results indicates that the current practice of using questionnaires to determine investor risk profiles is of limited reliability. It also benefits investing that the robo-advisor model is seemingly benefits investingting conservative investors the most. Thus, this dissertation contribute to a view on Robo-advisors benefits and limitations, providing a parameter for better understanding its future potential.
In the last few years the wealth management industry has experienced significant challenges and impactful trends, such as a decrease in customers' trust of traditional financial services, new regulatory burdens and increase of competition. In this context, the rise of automated investment managers, well known as "robo-advisors" and the new combination of science and human capital has been challenging the wealth management industry to find new ways to create value benefiting the client. On this matter, this project contributes to a analysis of risk-return look and efficient frontiers of the recommended portfolio of five online platforms in United States in March 2017: Charles Schwab, SigFig, Wealthfront, ToleRisk and RiskAlyze. In this analysis, back-testing is conducted to assess performance, volatility, value at risk and sharpe ratios. This project is based on the Mean-Variance Theory and uses historical weekly closing prices of exchanged traded-funds. Results indicates that the current practice of using questionnaires to determine investor risk profiles is of limited reliability. It also benefits investing that the robo-advisor model is seemingly benefits investingting conservative investors the most. Thus, this dissertation contribute to a view on Robo-advisors benefits and limitations, providing a parameter for better understanding its future potential.
Descrição
Mestrado em Finanças
Palavras-chave
fundos de investimento abertos negociados em bolsa fronteira eficiente teoria da variação média plataformas de investimento online robo-advisory gestão de riqueza exchanged-traded-funds efficient frontier mean-variance theory online investment platforms robo-advisor wealth management.
Contexto Educativo
Citação
Rodrigues, Alessandra Alves (2019). "A mean-variance look at robo-advising". Dissertação de Mestrado, Universidade de Lisboa. Instituto Superior de Economia e Gestão.
Editora
Instituto Superior de Economia e Gestão