What's New: Stop Losses - Help or Hindrance?

[Part 1 in a series of 3 articles]

April 9, 2011 1:30 a.m. ET (3:30 p.m. AET)

This newsletter is subject to Incredible Charts Terms of Use.

Background Bruce Vanstone is Assistant Professor at Bond University in Australia. He completed his PhD in Computational Finance in 2006 and is a regular presenter and publisher of academic work on stockmarket trading systems. He teaches stockmarket trading courses, and consults to Porter Capital Management on the design of mechanical, rules-based trading systems. More information on Bruce's research and methods can be found at http://trading.it.bond.edu.au.

Bruce has a controversial view on the effectiveness of stop losses and I have asked him to write a series of articles based on his research.

~ Colin Twiggs

Introduction

Many traders and investors place Stop Loss orders as part of their day-to-day investment activity. Virtually all trading books recommend the use of stops, with many making statements like "Trading without stops is like driving without a seatbelt". The argument for the use of stop-loss rules seems inherently sound, yet there appears to be no real evidence that stops are providing the safety benefits that many traders expect.

With regard to medium to longer term equity trading systems (which appears to cover the majority of investors and traders), it may well be that stops are causing more harm than good!

As traders, we are used to having an initial stop loss on a trade, and congratulating ourselves when the stop saves us money as the trade goes south very quickly. Although a stop-loss rule may save us from damage on specific trades, it seems doubtful whether this beneficial effect actually holds when we measure it at a portfolio level. There are a number of specific reasons why this may be the case, which I will touch on later in this series.

As traders, we shouldn't really focus on the return of each individual trade; rather we should focus on the overall return of our portfolio. A large amount of my empirical testing appears to show a mismatch between stop performance at an individual trade level, and stop performance at a portfolio level.

In this series of articles, I would like to demonstrate the mismatch that stops appear to introduce, and show you a way to be able to test this for yourself. This article is part 1 of a 3-part series. In this article, I will introduce an example system, and demonstrate how to benchmark the system with and without a variety of stops, and statistically analyse the results.

You can then use this same process to benchmark the effect stops are having on your own individual trading system, to determine if you are actually benefiting from using stops.

Measuring the impact of Stops

To measure the impact of stops on a trading system, it is necessary to consider the effect that stops have on both individual trades, and on specific portfolios constructed from those trades.

To assess the effect that stops have on individual trades, we can benchmark and measure changes in:

Trade daily mean return ($) – average return per day
Average number of days trades are open

To benchmark the raw trades signalled by the entry and exit rules, we initially assume unlimited equity, and a nominal investment of $10,000 per trade.

To assess the effect that stops have on specific portfolios, we can benchmark and measure changes in:

APR% (Annual Percentage Return) – a portfolio's return
Max DD% (Maximum % Drawdown) – which shows the worst case drawdown (peak to valley) that the portfolio equity curve has suffered.
Sharpe Ratio - which shows the amount of risk taken per unit of return. Ignoring the risk-free rate adjustment, the Sharpe Ratio is a measure of how volatile portfolio returns have been. (As an example, two different traders may both have achieved a return of 20% over time. The Sharpe Ratio will be highest for the trader who has achieved this result with the least volatility.)

When benchmarking a portfolio, it is important to take account of the amount of equity used. In this case, a relatively simple 'percentage of equity' model is used. We allocate 2% of available equity to each trade, from an initial starting capital of $1,000,000.

By monitoring the variables above, we can benchmark the metrics that are obtained from a set of trading rules. We can then add stops to the trading rules and repeat this process. This will allow us to empirically measure the effects that the stops have on those key metrics. We can then statistically determine whether the portfolio outcome has been improved by the addition of the stop rules.

Case Study

The majority of traders would be best described as medium to longer-term equity investors. In essence, this means that they trade ordinary shares, and aim to hold each share from several months to several years. Typically, this group of investors name themselves 'trend traders', and their aim is to identify and ride a trend for as long as possible. Often one or more simple (or exponential) moving averages provide entry and exit setups. Typically, this group also only trades the long side.

For this reason, I have chosen a 60-day ema crossover system as the example case study system . A 60-day ema crossover system buys when the price crosses above a 60-day ema, and sells when the price crosses below a 60-day ema.

An example trade is shown below in Figure 1. The pink line represents the value of the EMA(60).

Figure 1: Example of a 60-day EMA crossover trade

The data chosen for the case study is the constituents of the ASX200 (since inception April 2000) until the end of 2009. Where possible, I have adjusted this data for delistings and code changes, and trading results include an allowance for transaction costs. To address survivorship bias, buy signals are only issued on stocks which were constituents of the ASX200 on the day the signal was generated.

Remember the objective is not to determine whether these are desirable rules for trading; it is to allow us to select and emulate the basic characteristics of the kind of stocks that the majority of traders and investors in the ASX200 are focused on.

No stops

Initially, we need to benchmark the buy and sell rules without any stops. This gives us a baseline against which to compare the performance of the stops we will introduce.

Raw Trades

The key characteristics of the raw trades generated by buying/selling $10,000 worth of stock every time the buy/sell conditions occur are:

Daily Mean Return = $ 0.61, Average Number of days trades are open = 21.44

Later, when we introduce a variety of stop combinations to the buy/sell rules, we can measure the effects they have using this baseline.

Portfolio

The key characteristics of the portfolio generated by these trades are:

APR = 2.63 %, MAX DD = -34.63 %, Sharpe Ratio = 0.31

Now we know how much potential return there is in the rules (APR%), how risky those rules are (DD%), and a measure of the overall risk for that specific return (Sharpe ratio). Later, when we introduce a variety of stop combinations to the buy/sell rules, we can measure the effects they have using this baseline.

Initial Percentage Stops

Many traders simply use a fixed percentage to determine their stop level price. As an example, a trader might say, "I will set a stop loss 5% below my entry price". Here, we test every initial stop loss percentage threshold from 1% - 10% in steps of 1, for all the trades generated by the ema crossover rules.

The impact that these initial stops have on both return and risk is presented next.

Raw Trades

From the table presented, it is clear that none of the stop methods tested improved the 'NO STOP LOSS' portfolio's daily mean return. This is as expected, given that, by definition, an initial stop loss rule entails selling at a loss. To determine whether this approach has decreased our risk, we next test within a portfolio setting.

Portfolio

From this table, we can see that none of the stop methods have improved the 'NO STOP LOSS' portfolio's APR. Further, none of the stop loss settings was able to improve the Sharpe Ratio. Some of the higher percentage stops achieve similar Maximum Drawdown%, but none of the stop loss settings was able to improve the Sharpe Ratio. In essence, all combinations of stop loss tested achieved less return, and were riskier.

Implications

To statistically compare the portfolio results, we can use the ANOVA procedure, which allows us to simultaneously compare all the trades generated under the 'NO STOP LOSS' condition, with all the sets of trade possibilities from the 10 stop loss combinations. This allows us to determine whether there is any statistical significance in our findings.

The results indicate that no benefit has been obtained from any of the stop combinations. I have purposefully omitted a detailed explanation of using the ANOVA procedure in this article, to allow us to keep focused on the effects of stop losses. Those readers that are interested in pursuing the benchmarking of trading systems using statistical methods can find details of this and many other useful procedures in my book, Designing Stockmarket Trading Systems (with and without soft computing).

Summary

In this article, I have benchmarked the results of a simple EMA crossover strategy. Next, the strategy was tested with a variety of initial percentage based stops to see if adding these stops was able to decrease the risk in the strategy. It was found that all stops tested increased the risk and reduced the return of the original strategy.

In the next article, I will test percentage-based trailing stops and ATR-based trailing stops to see whether these types of stops can decrease the strategy risk.

Forum Discussion

We hope you find this series of articles interesting. I have opened a thread on the forum for readers to contribute their views: Do Stop Losses Really Work?

Regards,

Colin Twiggs

Risk comes from not knowing what you're doing.
~ Warren Buffett.

	What's New: Stop Losses - Help or Hindrance? [Part 1 in a series of 3 articles] By Dr. Bruce Vanstone April 9, 2011 1:30 a.m. ET (3:30 p.m. AET) This newsletter is subject to Incredible Charts Terms of Use. Background Bruce Vanstone is Assistant Professor at Bond University in Australia. He completed his PhD in Computational Finance in 2006 and is a regular presenter and publisher of academic work on stockmarket trading systems. He teaches stockmarket trading courses, and consults to Porter Capital Management on the design of mechanical, rules-based trading systems. More information on Bruce's research and methods can be found at http://trading.it.bond.edu.au. Bruce has a controversial view on the effectiveness of stop losses and I have asked him to write a series of articles based on his research. ~ Colin Twiggs Introduction Many traders and investors place Stop Loss orders as part of their day-to-day investment activity. Virtually all trading books recommend the use of stops, with many making statements like "Trading without stops is like driving without a seatbelt". The argument for the use of stop-loss rules seems inherently sound, yet there appears to be no real evidence that stops are providing the safety benefits that many traders expect. With regard to medium to longer term equity trading systems (which appears to cover the majority of investors and traders), it may well be that stops are causing more harm than good! As traders, we are used to having an initial stop loss on a trade, and congratulating ourselves when the stop saves us money as the trade goes south very quickly. Although a stop-loss rule may save us from damage on specific trades, it seems doubtful whether this beneficial effect actually holds when we measure it at a portfolio level. There are a number of specific reasons why this may be the case, which I will touch on later in this series. As traders, we shouldn't really focus on the return of each individual trade; rather we should focus on the overall return of our portfolio. A large amount of my empirical testing appears to show a mismatch between stop performance at an individual trade level, and stop performance at a portfolio level. In this series of articles, I would like to demonstrate the mismatch that stops appear to introduce, and show you a way to be able to test this for yourself. This article is part 1 of a 3-part series. In this article, I will introduce an example system, and demonstrate how to benchmark the system with and without a variety of stops, and statistically analyse the results. You can then use this same process to benchmark the effect stops are having on your own individual trading system, to determine if you are actually benefiting from using stops. Measuring the impact of Stops To measure the impact of stops on a trading system, it is necessary to consider the effect that stops have on both individual trades, and on specific portfolios constructed from those trades. To assess the effect that stops have on individual trades, we can benchmark and measure changes in: Trade daily mean return ($) – average return per day Average number of days trades are open To benchmark the raw trades signalled by the entry and exit rules, we initially assume unlimited equity, and a nominal investment of $10,000 per trade. To assess the effect that stops have on specific portfolios, we can benchmark and measure changes in: APR% (Annual Percentage Return) – a portfolio's return Max DD% (Maximum % Drawdown) – which shows the worst case drawdown (peak to valley) that the portfolio equity curve has suffered. Sharpe Ratio - which shows the amount of risk taken per unit of return. Ignoring the risk-free rate adjustment, the Sharpe Ratio is a measure of how volatile portfolio returns have been. (As an example, two different traders may both have achieved a return of 20% over time. The Sharpe Ratio will be highest for the trader who has achieved this result with the least volatility.) When benchmarking a portfolio, it is important to take account of the amount of equity used. In this case, a relatively simple 'percentage of equity' model is used. We allocate 2% of available equity to each trade, from an initial starting capital of $1,000,000. By monitoring the variables above, we can benchmark the metrics that are obtained from a set of trading rules. We can then add stops to the trading rules and repeat this process. This will allow us to empirically measure the effects that the stops have on those key metrics. We can then statistically determine whether the portfolio outcome has been improved by the addition of the stop rules. Case Study The majority of traders would be best described as medium to longer-term equity investors. In essence, this means that they trade ordinary shares, and aim to hold each share from several months to several years. Typically, this group of investors name themselves 'trend traders', and their aim is to identify and ride a trend for as long as possible. Often one or more simple (or exponential) moving averages provide entry and exit setups. Typically, this group also only trades the long side. For this reason, I have chosen a 60-day ema crossover system as the example case study system . A 60-day ema crossover system buys when the price crosses above a 60-day ema, and sells when the price crosses below a 60-day ema. An example trade is shown below in Figure 1. The pink line represents the value of the EMA(60). Figure 1: Example of a 60-day EMA crossover trade The data chosen for the case study is the constituents of the ASX200 (since inception April 2000) until the end of 2009. Where possible, I have adjusted this data for delistings and code changes, and trading results include an allowance for transaction costs. To address survivorship bias, buy signals are only issued on stocks which were constituents of the ASX200 on the day the signal was generated. Remember the objective is not to determine whether these are desirable rules for trading; it is to allow us to select and emulate the basic characteristics of the kind of stocks that the majority of traders and investors in the ASX200 are focused on. No stops Initially, we need to benchmark the buy and sell rules without any stops. This gives us a baseline against which to compare the performance of the stops we will introduce. Raw Trades The key characteristics of the raw trades generated by buying/selling $10,000 worth of stock every time the buy/sell conditions occur are: Daily Mean Return = $ 0.61, Average Number of days trades are open = 21.44 Later, when we introduce a variety of stop combinations to the buy/sell rules, we can measure the effects they have using this baseline. Portfolio The key characteristics of the portfolio generated by these trades are: APR = 2.63 %, MAX DD = -34.63 %, Sharpe Ratio = 0.31 Now we know how much potential return there is in the rules (APR%), how risky those rules are (DD%), and a measure of the overall risk for that specific return (Sharpe ratio). Later, when we introduce a variety of stop combinations to the buy/sell rules, we can measure the effects they have using this baseline. Initial Percentage Stops Many traders simply use a fixed percentage to determine their stop level price. As an example, a trader might say, "I will set a stop loss 5% below my entry price". Here, we test every initial stop loss percentage threshold from 1% - 10% in steps of 1, for all the trades generated by the ema crossover rules. The impact that these initial stops have on both return and risk is presented next. Raw Trades From the table presented, it is clear that none of the stop methods tested improved the 'NO STOP LOSS' portfolio's daily mean return. This is as expected, given that, by definition, an initial stop loss rule entails selling at a loss. To determine whether this approach has decreased our risk, we next test within a portfolio setting. Portfolio From this table, we can see that none of the stop methods have improved the 'NO STOP LOSS' portfolio's APR. Further, none of the stop loss settings was able to improve the Sharpe Ratio. Some of the higher percentage stops achieve similar Maximum Drawdown%, but none of the stop loss settings was able to improve the Sharpe Ratio. In essence, all combinations of stop loss tested achieved less return, and were riskier. Implications To statistically compare the portfolio results, we can use the ANOVA procedure, which allows us to simultaneously compare all the trades generated under the 'NO STOP LOSS' condition, with all the sets of trade possibilities from the 10 stop loss combinations. This allows us to determine whether there is any statistical significance in our findings. The results indicate that no benefit has been obtained from any of the stop combinations. I have purposefully omitted a detailed explanation of using the ANOVA procedure in this article, to allow us to keep focused on the effects of stop losses. Those readers that are interested in pursuing the benchmarking of trading systems using statistical methods can find details of this and many other useful procedures in my book, Designing Stockmarket Trading Systems (with and without soft computing). Summary In this article, I have benchmarked the results of a simple EMA crossover strategy. Next, the strategy was tested with a variety of initial percentage based stops to see if adding these stops was able to decrease the risk in the strategy. It was found that all stops tested increased the risk and reduced the return of the original strategy. In the next article, I will test percentage-based trailing stops and ATR-based trailing stops to see whether these types of stops can decrease the strategy risk. Forum Discussion We hope you find this series of articles interesting. I have opened a thread on the forum for readers to contribute their views: Do Stop Losses Really Work? Regards, Colin Twiggs Risk comes from not knowing what you're doing. ~ Warren Buffett.	Share: A+ A− FREE 12-month subscription to The Patient Investor Get the insights you need to stay ahead in financial markets when you sign up to Incredible Charts premium service by 31 December. 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