The mention of technical analysis in the halls of academia can cause serious angst. The disdain for technical analysis likely stems from a firm belief that markets can’t possibly be weak-form inefficient. The other reason researchers find technical analysis hard to swallow is the skepticism related to data-mining and the meta-analysis studies, which suggest that a majority of technical trading rules are simply bunk. Fair enough. And yet, cross-sectional momentum strategies, or strategies that sort securities on relative momentum at a given point in time, are widely published in top-tier academic journals. Why momentum is considered a valid topic to discuss, while other well known technical trading rules such as simple trend-following rules are considered heresy, is a bit odd. We decided it was time to torture the simple moving average trading rule and determine if it is simply a waste of time or something we should consider in our investment programs.
The setup for our studyThe inspiration for this study stemmed from a curiosity we had after reading the following paper on Inter-Temporal Risk Parity:
Inter-temporal risk parity is a strategy which rebalances between a risky asset and cash in order to target a constant level of risk over time. When applied to equities and compared to a buy and hold strategy it is known to improve the Sharpe ratio and reduce drawdowns. We used Monte Carlo simulations based on a number of time series parametric models from the GARCH family in order to analyze the relative importance of a number of effects in explaining those benefits. We found that volatility clustering with constant returns and the fat tails are the two effects with the largest explanatory power. The results are even stronger if there is a negative relationship between return and volatility. On the other hand, if the Sharpe ratio remains constant over time, the only benefit would arise from an inter-temporal risk diversification effect which is small and has a negligible contribution. Using historical data, we also simulated what would have been the performance of the strategy when applied to equities, corporate bonds, government bonds and commodities. We found that the benefits of the strategy are more important for equities and high yield corporate bonds, which show the strongest volatility clustering and fat tails. For government bonds and investment grade bonds, which show little volatility clustering, the benefits of the strategy have been less important.The results from the paper are promising, but we always re-backtest studies because the devil is in the details. In this situation, the paper also inspired a framework for testing the moving average rule in the context of simulations. Strategy 1: Buy and hold of S&P500. Strategy 2: When the 20-day moving average is above the 252-day moving average, risk-on; otherwise, risk-off. We tested the strategies in four different simulation environments:
- A normal distribution
- GARCH with T-student noise
- Historical data bootstrapping