Head and Shoulders above the Rest? The Performance of Institutional Portfolio Managers who Use Technical Analysis Head and Shoulders above the Rest? The Performance of Institutional Portfolio Managers who Use Technical Analysis

Abstract

Abstract This study takes a novel approach to testing the efficacy of technical analysis. Rather than testing specific trading rules as is typically done in the literature, we rely on institutional portfolio managers' statements about whether and how intensely they use technical analysis, irrespective of the form in which they implement it. In our sample of more than 10,000 portfolios, about one-third of actively managed equity and balanced funds use technical analysis. We compare the investment performance of funds that use technical analysis versus those that do not using five metrics. Mean and median (3 and 4-factor) alpha values are generally slightly higher for a cross section of funds using technical analysis, but performance volatility is also higher. Benchmark-adjusted returns are also higher, particularly when market prices are declining. The most remarkable finding is that portfolios with greater reliance on technical analysis have elevated skewness and kurtosis levels relative to portfolios that do not use technical analysis. Funds using technical analysis appear to have provided a meaningful advantage to their investors, albeit in an unexpected way. Keywords: Technical analysis, portfolio management, institutional investment For decades the academic profession has derided and essentially relegated technical analysis to the same status as alchemy (Malkiel, 2003). Yet technical analysis remains a staple among many retail and institutional investors. Park and Irwin (2007) report that 30-40% of surveyed foreign exchange traders believe that technical analysis is an important tool for determining price movement at shorter time horizons up to 6 months. Past studies have tended to show that technical analysis does not outperform simple buy-and-hold strategies after transaction costs are accounted for White's Reality Check (White, 2000) is a popular methodology developed to remedy this issue, but it is not a panacea, especially given that it is based on a universe of "tested" rules that by construction does not include all actual rules. For example, Sullivan, Timmermann, and White (1999) test 7,846 trading rules, drawn from five commonly used classes of rules in financial markets. Although 7,846 is a large number, this collection may not be comprehensive enough. Several well-known classes of trading rules, such as momentum strategies, are not included in the study. Of course, later studies have augmented the size of the trading rules universe Essentially, academic studies have not been able to peer into and reproduce the content of the black box of tools used by professional chartists. In this article, we tackl