Trend-following is one of the oldest trading techniques, and trend-following systems belong to the standard repertoire of investors and traders. Many times it has been successfully used in futures markets. But does it work on stocks?
Research shows it does, and it performs well for such a simple strategy. There are many trend-following systems with different rules, but all systems have a common attribute. They try to buy growing assets, although “grow/trend” could be defined in many ways. We present a research paper documenting a simple trend-following system on stocks which uses new all-time high as entry condition and ATR as a trailing stop.
Behavioral biases (investors herding, under- and over-reaction, etc.) create a non-normal return distribution on financial markets. Trend-following systems cut left tail of the long-tail distribution. This characteristic creates improved risk/return characteristics of trend-following systems when compared to a diversified buy&hold approach.
Confidence in anomaly's validity
Backtest period from source paper
Notes to Confidence in Anomaly's Validity
Period of Rebalancing
Notes to Indicative Performance
per annum, data from table on page 12
Notes to Period of Rebalancing
Number of Traded Instruments
Notes to Estimated Volatility
data from table on page 12
Notes to Number of Traded Instruments
more or less, it depends on investor’s need for diversification and number of stocks which satisfy system’s entry conditions
Notes to Maximum drawdown
data from table on page 12
Notes to Complexity Evaluation
Simple trading strategy
The investment universe consists of US-listed companies. A minimum stock price filter is used to avoid penny stocks, and a minimum daily liquidity filter is used to avoid stocks that are not liquid enough. The entry signal occurs if today’s close is greater than or equal to the highest close during the stock’s entire history. A 10-period average true range trailing stop is used as an exit signal. The investor holds all stocks which satisfy entry criterion and are not stopped out. The portfolio is equally weighted and rebalanced daily. Transaction costs of 0.5% round-turn are deducted from each trade to account for estimated commission and slippage.
Hedge for stocks during bear markets
No - The selected strategy is designed as a long-only; therefore, it can’t be used as a hedge against market drops as a lot of strategy’s performance comes from equity market premium (as the investor holds equities, therefore, his correlation to the broad equity market is very very high).
Cole Wilcox, Eric Crittenden: Does Trend Following Work on Stocks?
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
Strategy's implementation in QuantConnect's framework
Wojtow: Theoretical basis and a practical example of trend following
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
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
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
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
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
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.