Trend-following Effect in Stocks

Fundamental reason

Simple trading strategy

Related Dataset

Out-of-sample strategy's implementation/validation in QuantConnect's framework(chart, statistics & code)

Source paper

Cole Wilcox, Eric Crittenden: Does Trend Following Work on Stocks

Abstract: Over the years many commodity trading advisors, proprietary traders, and global macro hedge funds have successfully applied various trend following methods to profitably trade in global futures markets. Very little research, however, has been published regarding trend following strategies applied to stocks. Is it reasonable to assume that trend following works on futures but not stocks? We decided to put a long only trend following strategy to the test by running it against a comprehensive database of U.S. stocks that have been adjusted for corporate actions1. Delisted2 companies were included to account for survivorship bias3. Realistic transaction cost estimates (slippage & commission) were applied. Liquidity filters were used to limit hypothetical trading to only stocks that would have been liquid enough to trade, at the time of the trade. Coverage included 24,000+ securities spanning 22 years. The empirical results strongly suggest that trend following on stocks does offer a positive mathematical expectancy4, an essential building block of an effective investing or trading system

Other papers

• Wojtow: Theoretical basis and a practical example of trend following

  Abstract: The purpose of this paper is to provide a usable framework for detecting, measuring and exploiting trends in financial markets. Using technical analysis (TA) indicators we challenge Efficient Market Hypothesis (EMH) that says that markets are random and that is not possible to regularly outperform a passive investment strategy.

• Beekhuizen, Hallerbach: Uncovering Trend Rules

  Abstract: Trend rules are widely used to infer whether financial markets show an upward or downward trend. By taking suitable long or short positions, one can profit from a continuation of these trends. Conventionally, trend rules are based on moving averages (MAs) of prices rather than returns, which obscures how much weight is assigned to different historical time periods. In this paper, we show how to uncover the underlying historical weighting schemes of price MAs and combinations of price MAs. This leads to surprising and useful insights about popular trend rules, for example that some trend rules have inverted information decay (i.e., distant returns have more weight than recent ones) or hidden mean-reversion patterns. This opens the possibility for improving the trend rule by analyzing the added value of the mean reversion part. We advocate designing trend rules in terms of returns instead of prices, as they offer more flexibility and allow for adjusting trend rules to autocorrelation patterns in returns.

• Haghani, McBride: Return Chasing and Trend Following: Superficial Similarities Mask Fundamental Differences

  Abstract: Return chasing is often cited as one of the primary behavioral foibles of investors, resulting in sub-par returns. Surprisingly, the literature does not provide a generally accepted and testable description of return chasing. This paper proposes a simple definition. It then describes how return chasing so defined differs from trend following and how return chasing explains the shortfall of the returns of active, market timing investors compared to static asset allocation strategies. Finally, it shows that if the trading flows of return chasers are large enough to impact prices, then return chasing provides a powerful explanation of the positive returns earned by trend following strategies, which alternative descriptions of return chasing, such as it is trend following but with too long of a horizon, do not provide.

• Bouchaud, Challet: Why have asset price properties changed so little in 200 years

  Abstract: We first review empirical evidence that asset prices have had episodes of large fluctuations and been inefficient for at least 200 years. We briefly review recent theoretical results as well as the neurological basis of trend following and finally argue that these asset price properties can be attributed to two fundamental mechanisms that have not changed for many centuries: an innate preference for trend following and the collective tendency to exploit as much as possible detectable price arbitrage, which leads to destabilizing feedback loops.

• Sepp: Trend-Following Strategies for Tail-Risk Hedging and Alpha Generation

  Abstract: Because of the adaptive nature of position sizing, trend-following strategies can generate the positive skewness of their returns, when infrequent large gains compensate overall for frequent small losses. Further, trend-followers can produce the positive convexity of their returns with respect to stock market indices, when large gains are realized during either very bearish or very bullish markets. The positive convexity along with the overall positive performance make trend-following strategies viable diversifiers and alpha generators for both long-only portfolios and alternatives investments. I provide a practical analysis of how the skewness and convexity profiles of trend-followers depend on the trend smoothing parameter differentiating between slow-paced and fast-paced trend-followers. I show how the returns measurement frequency affects the realized convexity of the trend-followers. Finally, I discuss an interesting connection between trend-following and stock momentum strategies and illustrate the benefits of allocation to trend-followers within alternatives portfolio.

• Liu, Zhou, Zhu: Trend Factor in China

  Abstract: We propose a 4-factor model by adding an additional trend factor to Liu, Stambaugh and Yuan’s (2018; LSY-3) 3-factor model: market, size and value. Since individual investors contribute about 80% of the trading volume in China, the trend factor captures well the resulting important price and volume trends, and has a monthly Sharpe ratio of 0.48, much greater than those of the market (0.11), size (0.19) and value (0.28). The proposed 4-factor model explains all reported Chinese anomalies, including turnover and reversal unexplained previously by LSY-3. Moreover, the model explains well mutual fund returns, working as an analogue of Carhart 4-factor model in China.

• Zarattini, Carlo and Pagani, Alberto and Wilcox, Cole: Does Trend-Following Still Work on Stocks?

  Abstract: This paper revisits and extends the results presented in 2005 by Wilcox and Crittenden in a white paper titled Does Trend Following Work on Stocks? Leveraging a survivorship-bias-free dataset of all liquid U.S. stocks from 1950 through November 2024, we examine more than 66,000 simulated long-only trend trades. Our results confirm a highly skewed profit distribution, with less than 7% of trades driving the cumulative profitability. These core statistics hold up out-of-sample (2005–2024), maintaining strong returns despite a modest decline in average trade profitability following the original publication. In the second part of this study, we backtest a long-only trend-following portfolio specifically aimed at capturing outlier returns in individual stocks. While the theoretical portfolio exhibits exceptional gross-of-fees performance from 1991 until 2024 (e.g., a CAGR of 15.19% and an annualized alpha of 6.18%), its extensive daily turnover poses a significant challenge once transaction costs are considered. Examining net-of-fee performance across various asset under management (AUM) levels, we find that the base trend-following approach is not viable for smaller portfolios (AUM less than $1M) due to the dampening effect of trading costs. However, by incorporating a Turnover Control algorithm, we substantially mitigate these transaction cost burdens, rendering the strategy attractive across all tested portfolio sizes even after fees.