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Showing results 1 - 10 of 21 for the search term: "stop loss".

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Using Trailing Stop Losses to Reduce Risk

Do stop-loss orders (automated position exits based on a cumulative loss threshold) enhance returns and reduce risk? In their 2008 paper entitled “The Value of Stop Loss Strategies” Adam Lei and Huihua Li investigate whether traders using stop-loss strategies to exit losing positions in individual stocks outperform a comparable buy-and-hold strategy. They test the following strategy alternatives: holding periods of three months, six months or one year; stop-loss thresholds of 5, 10 or 20 daily return standard deviations; reinvestment of stopped out positions in either the S&P 500 index or the one-month Treasury bill; and, a fixed stop price or a trailing stop price that follows stock price upward (but not downward). Using historical and simulated daily return data for a broad sample of NYSE/AMEX-listed stocks and random buy dates over the period 1970-2005, they conclude that: Keep Reading

Do Stop Losses Work?

Does systematic use of stop-loss orders (automated position exits based on a cumulative loss threshold) improve net returns? Both the April 2008 paper entitled “Re-examining the Hidden Costs of the Stop-Loss” by Kira Detko, Wilson Ma and Guy Morita and the May 2008 draft paper entitled “When Do Stop-Loss Rules Stop Losses?” by Kathryn Kaminski and Andrew Lo address this question with theory and empirical tests. They conclude that: Keep Reading

Stop-losses on Stock Positions in Depth

Do stop-losses usefully mitigate downside risk in realistic scenarios? In their November 2015 paper entitled “Stop-Loss Strategies with Serial Correlation, Regime Switching, and Transactions Costs”, Andrew Lo and Alexander Remorov analyze the value of stop-losses when asset returns are autocorrelated (trending), regime switching (bull and bear) and subject to trading costs. They consider daily and 10-day measurement intervals, with respective stop-loss ranges of 0% to -6% and 0% to -14%. If at any daily close the cumulative return on the risky asset over the measurement interval falls below a specified threshold, they immediately switch to the risk-free asset (U.S. Treasury bills). They consider two ways to execute stop-loss signals: (1) assume it is possible to estimate signals just before the close and sell at the same close; or, (2) use a signal from the prior close to trigger a market-on-close sell order the next day (delayed execution). They re-enter the risky asset when its cumulative return over a specified interval exceeds a specified threshold. They employ both simulations and empirical tests. For simulations, they estimate trading cost as 0.2%, the average half bid-ask spread of all sampled stocks during 2013-2014. For empirical tests, they use actual half bid-ask spreads as available and estimates otherwise. Empirical findings are most relevant to short-term traders who employ tight stop-losses. Using daily returns and bid-ask spreads as available for a broad sample of U.S. common stocks during 1964 through 2014, they find that: Keep Reading

A Few Notes on Systematic Trading

Robert Carver introduces his 2015 book, Systematic Trading: A Unique New Method for Designing Trading and Investing Systems, by stating that: “I don’t believe there is any magic system that will automatically make you huge profits, and you should be wary of anyone who says otherwise, especially if they want to sell it to you. Instead, success in systematic trading is mostly down to avoiding common mistakes such as over complicating your system, being too optimistic about likely returns, taking excessive risks, and trading too often. I will help you avoid these errors. This won’t guarantee returns, but it will make failure less likely. My framework…can be adapted to meet your needs. …Each element of the framework has been carefully designed… I’ll explain the available options, which I prefer, and why.” Based on his experience as a trader/portfolio manager and specific research, he concludes that: Keep Reading

Snooping for Fun and No Profit

How much distortion can data snooping inject into expected investment strategy performance? In their October 2014 paper entitled “Statistical Overfitting and Backtest Performance”, David Bailey, Stephanie Ger, Marcos Lopez de Prado, Alexander Sim and Kesheng Wu note that powerful computers let researchers test an extremely large number of model variations on a given set of data, thereby inducing extreme overfitting. In finance, this snooping often takes the form of refining a trading strategy to optimize its performance within a set of historical market data. The authors introduce a way to explore snooping effects via an online simulator that finds the optimal (maximum Sharpe ratio) variant of a simple trading strategy by testing all possible integer values for strategy parameters as applied to a set of randomly generated daily “returns.” The simple trading strategy each month trades a single asset by (1) choosing a day of the month to enter either a long or a short position and (2) exiting after a specified number of days or a stop-loss condition. The randomly generated “returns” come from a source Gaussian (normal) distribution with zero mean. The simulator allows a user to specify a maximum holding period, a maximum percentage stop loss, sample length (number of days), sample volatility (number of standard deviations) and sample starting point (random number generator seed). After identifying optimal parameter values on “backtest” data, the simulator runs the optimal strategy variant on a second set of randomly generated returns to show the effect of backtest overfitting. Using this simulator, they conclude that: Keep Reading

A Few Notes on Investing with the Trend

In the preface to his 2014 book entitled Investing with the Trend: A Rules-Based Approach to Money Management, author Greg Morris, Chairman of the Investment Committee and Chief Technical Analyst for Stadion Money Management LLC, states: “This book is a collection of almost 40 years of being involved in the markets, sharing some things I have learned and truly believe… You will discover early that sometimes I might seem overly passionate about what I’m saying, but hopefully you will realize that is because I have well-formed opinions and just want to ensure that the message is straightforward and easily understood. It is not only a book on trend following but a source of technical analysis information… If I had to nail down a single goal for the book, it would be to provide substantial evidence that there are ways to be successful at investing that are outside the mainstream of Wall Street. Although it will appear my concern is about modern finance, it is actually directed toward the investment management world and its misuse of the tools of modern finance.” Based on his 40 years of experience and supporting analyses, he concludes that: Keep Reading

Higher Measurement Frequency and Stop-losses for Trend Followers?

Motivation to avoid being “burned by the turn” tempts trend followers to increase measurement frequency and/or use stop-losses. Do these approaches help momentum players jump the turn? In their October 2013 paper entitled “The Significance of Trading Frequency and Stop Loss in Trend Following Strategies”, Farzine Hachemian, Sebastien Tavernier and Anne-Sophie Van Royen assess whether increasing measurement frequency from weekly to daily and imposing stop-loss rules enhance the performance of trend-following strategies based on simple moving averages (SMA). They consider a set of 117 timing strategies that go long (short) when a fast SMA is higher (lower) than a slow SMA, with SMAs measured either weekly or daily. For the weekly (daily) signals, the fast SMA measurement interval ranges from 4 to 52 weeks (20 to 260 days) in increments of 4 weeks (20 days). Slow SMA measurement intervals range from 8 to 64 weeks (40 to 320 days) with the same increments. To avoid whipsaws, they insert a buffer equal to the 13-week (65-day) standard deviation of the fast SMA. They apply these strategies to 39 rolling series of the most liquid futures covering all asset classes and most geographies. They apply a round-trip trading friction of $30 and assume zero return on any cash above the required margin. They then add two kinds of stop-losses to the strategies, reset every six months: (1) a loss of five times the standard deviation of weekly or daily returns; or, (2) a loss of 1% of portfolio value. After a stop loss, they re-enter a similar position when the trading strategy generates a new signal or price recovers its previous high watermark. Using futures return data as specified during January 2000 through December 2012, they find that: Keep Reading

Intrinsic Momentum Framed as Stop-loss/Re-entry Rules

Do asset classes generally exhibit enough price momentum to make stop-loss and re-entry rules effective for timing them? In his June 2013 paper entitled “Assessing Stop-loss and Re-entry Strategies”, Joachim Klement analyzes four stop-loss and re-entry rule pairs for six regional stock market indexes, a U.S. real estate investment trust (REIT) index, a commodity index and spot gold. Specifically, he tests:

  1. Fast out-fast in (most effective when there are multiple brief corrections): Exit (re-enter) when the cumulative loss (gain) over the past 3 (3) months exceeds some specified threshold. 
  2. Fast out-slow in (most effective during a downward or sideways trend): Exit (re-enter) when the cumulative loss (gain) over the past 3 (12) months exceeds some specified threshold.
  3. Slow out-fast in (most effective during an upward trend with intermittent crashes): Exit (re-enter) when the cumulative loss (gain) over the past 12 (3) months exceeds some specified threshold.
  4. Slow out-slow in (most effective when momentum is weak and transaction costs are high): Exit (re-enter) when the cumulative loss (gain) over the past 12 (12) months exceeds some specified threshold.

He tests ranges of stop-loss and re-entry decision thresholds. Because asset class return volatilities differ, he scales these thresholds to the annual standard deviation of returns for each asset class. He assumes a constant exit/re-entry trading friction of 0.25% and zero return on cash. For relevant tests, he defines a secular bull (bear) market as an extended subperiod of positive returns significantly above long-term average (negative or zero real returns). Using monthly asset class index returns as available during January 1970 through April 2013 in local currencies when applicable, he finds that: Keep Reading

A Few Notes on The Little Book of Market Myths

In his 2013 book The Little Book of Market Myths: How to Profit by Avoiding the Investing Mistakes Everyone Else Makes, author Ken Fisher, chairman and CEO of Fisher Investments, “covers some of the most widely believed market and economic myths–ones that routinely cause folks to see the world wrongly, leading to investment errors.” His hope is that “the book helps you improve your investing results by helping you see the world a bit clearer. And I hope the examples included here inspire you to do some sleuthing on your own so that you can uncover still more market mythology.” Some notable points from the book are: Keep Reading

Following S&P 500 Index Trends

How well do trend-following rules work when applied to the S&P 500 Index? In the March 2012 version of their paper entitled “Breaking into the Blackbox: Trend Following, Stop Losses, and the Frequency of Trading: The Case of the S&P 500”, Steve Thomas, James Seaton, Andrew Clare and Peter Smith evaluate a variety of simple daily moving average (SMA, 10 to 450 days), moving average crossover (25/50 to 150/350 days) and channel breakout (10-day to 450-day highs) trading rules as applied to the S&P 500 Index. They further investigate: (1) how measurement frequency affects rule performance; (3) effectiveness of combining the rules with stop-losses; and, (3) whether fundamental valuation metrics outperform the rules. They assume an index-cash switching cost of 0.2%. Using daily S&P 500 Index levels and monthly total returns from January 1952 through June 2011, daily S&P 500 Index total returns from July 1988 through June 2011 and contemporaneous Treasury bill yields as the return on cash, they find that: Keep Reading