Managing Expected Returns and Downside Risk with Information from Technical Analysis

Resumo

With the growing amount of data and information, one of the biggest challenges of information science is to transform information into useful knowledge. This paper presents an example of how we can use the scientific method to transform information from stock market data in useful knowledge, using the bootstrap methodology. We measure downside risk with a Value-at-Risk (VaR) model and take into account the joint performance in terms of returns and risks, with Sharpe type ratios. Empirical evidence presented here confirms results from previous studies that show consistently higher returns during an uptrend and the opposite during a downward trend. Additionally, provides very strong statistical evidence that downside risk is much lower during a primary uptrend than during a downward trend and performance is better in terms of Sharpe type ratios. Empirical results show that information from the primary trend obtained with a 200-days moving average is useful.