The authors use a common trend-following strategy to measure the predictive ability of technical analysis. They apply their method to portfolios arranged by idiosyncratic volatility and variables related to information uncertainty. They document high abnormal returns relative to standard pricing models that are unexplained by investor sentiment, market timing, or liquidity risk. Such findings, according to the authors, establish a new anomaly.
Researchers often apply technical analysis to attempt to predict the evolution of asset prices mainly using prior histories (of prices) and relevant secondary data. The authors provide the first study examining the cross-sectional profitability of technical analysis. They use a standard moving average (MA) of technical analysis and apply it to portfolios sorted by volatility. The abnormal returns of the strategy are extremely high when compared with those of buy-and-hold and momentum strategies.
How Is This Research Useful to Practitioners?
The authors use a set of 10 volatility decile portfolios as the underlying assets for their technical analysis. For each of the portfolios, the authors focus on the cross-sectional profitability of the MA timing strategy relative to that of the buy-and-hold strategy of the volatility decile portfolios. In the main study, the lag length is 10 days. The authors also test the alternative strategy with lag lengths of 20, 50, 100, and 200 days for the MA portfolios (MAPs).
The authors find that the 10 MAP returns are positive and increase with the volatility deciles (excluding the highest decile); returns range from 8.42% a year to 18.70% a year. In addition, the capital asset pricing model (CAPM) risk-adjusted returns, or abnormal returns, increase with the volatility deciles (also excluding the highest decile), ranging from 9.31% a year to 21.76% a year. When the authors apply the Fama–French three-factor model, they find the familiar pattern of monotonically increasing returns across 9 of 10 volatility deciles.
The results are robust when tested with the longer MA lag lengths. The abnormal returns seem to be more short term, and their magnitude decreases as the lag length increases. They are still highly significant, however, over the longer lag lengths. The abnormal returns range from 7.93% to 20.78% a year across the deciles when the lag length is 20 days but are mostly more than 5% a year even when the lag length is 200 days.
The results suggest that the noise-to-signal ratio, or information uncertainty, is what leads to the superior performance of the MA timing strategy. When that ratio is high, the fundamentals are less informative and thus technical analysis is more profitable.
The authors believe that the abnormal returns on the MAPs constitute a new anomaly. The momentum anomaly seems to be the only one supported by empirical evidence and earns roughly 12% annually, substantially less than the abnormal returns earned by the MA timing strategy on the highest-volatility decile portfolio.
How Did the Authors Conduct This Research?
The construction of 10 decile portfolios is based on the NYSE/Amex stocks sorted into deciles according to their annual standard deviations, which are estimated using the daily returns within the prior year. Daily index levels—that is, prices—and returns of all the decile portfolios are available from CRSP. The portfolios are rebalanced at the end of each year. The sample period is 1 July 1963–31 December 2009.
The authors consider 10-, 20-, 50-, 100-, and 200-day MAs. For each MA length (e.g., 10 days), they determine on each trading day whether the last closing price of the corresponding portfolio price (index price) is higher than the average of the last same-length-period (e.g., 10-day) price. When it is higher, they invest in the decile portfolio on that trading day; otherwise, they invest in the 30-day T-bill. The authors focus on how the constructed MAPs outperform the corresponding portfolio.
Next, the authors regress the MAP on the CAPM market factor and the three Fama–French factors. The results show that the positive alphas are significant at both 1% and 5% for all 10 portfolios, and the alphas also increase monotonically from the lowest-volatility decile to higher-volatility deciles, except that the highest-volatility decile yields a slightly lower alpha than the second-highest-volatility decile.
The dependence of the superior performance of the MA timing strategy on information uncertainty is investigated next; the authors consider four alternative decile portfolios. These portfolios are formed by sorting stocks by the distance to default measure, credit rating, analyst forecast dispersion, and income volatility. The results are very similar to those of the main study.
To further understand the abnormal returns on the MAPs, the authors attempt to analyze the source of the profitability. After controlling for timing ability, the trend-following factor, investor sentiment, and the liquidity factor, abnormal returns still exist, which means that they cannot be explained by these factors. The MAPs, however, do seem to have sensitivity to default risk during recessions, but only when returns are explained by a conditional variant of the Fama–French model (Journal of Financial Economics 1993).
Although the authors seem confident in the durability of economic abnormal returns after transaction costs—especially for low-volume strategies—I do have a concern about the trading cost of this strategy: Switching between the index portfolio and the 30-day T-bill frequently could involve large transaction costs that could significantly erode abnormal returns.
According to the random walk model and the efficient market hypothesis, stock returns are unpredictable and technical analysis has no value. The current study provides a solid piece of empirical evidence to support the value of technical analysis.