Eficiência de estratégias de Stop Loss para carteira igualmente ponderada

Detalhes bibliográficos
Ano de defesa: 2018
Autor(a) principal: Moraes, Vinicius Moulin de
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal do Rio de Janeiro
Brasil
Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia
Programa de Pós-Graduação em Engenharia de Produção
UFRJ
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://hdl.handle.net/11422/12256
Resumo: Investment strategies with stop loss are commonly used in the Financial Market. Stop Losses are used in order to avoid large losses in investments. There is little literary discussion about their efficiency in investment strategies. Some theories are against stop loss efficiency, such as the Efficient Market Theory and the Random Walk Theory. However, these theories are also questioned by evidences of market irrationality that indicates excess movements outside the fair price of assets. The excess movements can create an opportunity for investors when using stop loss. This thesis evaluates the use of stop loss strategy in equally weighted portfolios (naive portfolios). It verifies the possibility of short-term gains with the use of stop loss, in order to create long-term value to the portfolio. The Naive Portfolio was chosen because it represents an investor profile more susceptible to errors, behavioural biases and disinformation. The method used was the backtest of stock purchase and sale operations. The purchase criteria is the best Sharpe Index stocks in the in-sample period and the sell criteria is by achieving the rebalance term or when activated by the stop loss strategy. This work evaluates daily simple stop loss, cumulative stop loss and trailing stop loss. Their parameters are calculated by linear optimization and by a list of single parameters varying by 1%.