Risk is the probability of loss. It is best to estimate it and to adjust your purchase and sell strategies to it in order to control loss before the purchase is made. Correct timing of purchases, buying near support, limiting loss potential, and stopping the decline by using volatility stop losses are all ingredients of a good risk control system. Let’s look at a few of these loss control discipline components.
One method of controlling risk is by timing purchases so that they occur at or near support. That way, your stop loss can be a very small distance away from your purchase price. If you buy when the stock is 5% above its trendline, for example, it will mean little if the stock declines 5% to reach its trendline. Since stocks often return to support, why would you sell? You would sell only if it broke to the downside through its rising trendline. Therefore, your loss would be calculated by adding the distance the sell point is below the trendline to the distance the purchase price was above the trendline. Buying at the trendline instead of above it would eliminate that unnecessary 5% loss.
However, stocks often make a small temporary penetration through a support line and then resume their climb. When, precisely do you sell? Let us use the suggestions offered in Technical Analysis of Stock Trends by Edwards and Magee as an example. If you are using stops that are based on closing prices, they suggest a trendline penetration of 3% would warrant selling. If your stop loss is placed with a broker, they recommend that the stop be placed 6% below the trendline because of the possibility of inconsequential intra-day spikes. Therefore, if you buy when the stock is 8% above its rising trendline and place the stop loss 3% below the trendline, you will lose 11% before your stop is triggered. On the other hand, if you wait for the stock to return to its trendline before buying, you will lose only 3% if your stop is triggered. It is important to buy right so that you can sell right.
Risk is also blunted when the downside behavior of stocks is strictly limited to predefined tolerances. For example, a trader might plan his purchases so that the projected profit is about three times the expected loss if the trade goes against him. Thus, in order to try to capture a gain of 6%, the stop loss must be no more than 2% below the purchase price. If he can reasonably expect a gain of 12%, then his stop loss would be no more than about 4% below the purchase price. Long-term investors can use a ratio perhaps as low as two to one because they have a presumptive tolerance for wider price swings and a longer time-horizon. It can take more than one price cycle to reach the targeted profit, and the uncertainty associated with the accomplishment of that is already part of the risk accepted by the long-term investor. Therefore, there is greater tolerance for negative price movement relative to the expected gain. The trader, on the other hand, does not have that luxury. He must put into effect more rigorous profit to loss ratio requirements.
Another approach to blunting the downside behavior of stocks is to reject as a purchase candidate any stock that has a logical stop loss placement greater than a certain amount. Let’s say that our investor or trader finds a stock with a great story and feels he must have it. The stock is climbing rapidly and it looks as though it will never be at the current price level again. If the stock is rising at a steep angle of ascent, an appropriate stop loss may be 16% below support. If his rule is never to risk more than a 1% portfolio loss because of a single position and he has 15 positions, the stock must be rejected. A 15% loss on one position when there are 15 positions would cause the portfolio to lose 1%. A 16% loss would exceed the limit. Though downside behavior would be permitted within the parameters and tolerances of the prevailing growth pattern, the outer limit is set at some specific amount by design. The amount should be determined by the overall risk assumed by the portfolio. For example, limiting the portfolio’s risk to 1% per position would mean that a portfolio of 10 stocks would have to reject any stock that has a logical stop loss more than 10% below the purchase price.
The volatility-adjusted stop loss makes use of probability theory. The idea here is to measure the stock volatility and place the stop loss just beyond the normal price excursion of the stock. The distance of the stop will be determined by the investor’s preference as to the probability that the stop loss will be triggered by the random non-meaningful fluctuations of the stock. Thus, he can set the stop so that it will be triggered once in twenty days, once in 100 days, or once in 200 days because of a random surge. Any probability can be chosen. Let’s assume that our trader wants to minimize the chances that a random spike will trigger the stop. He could set the stop so that a random spike would be likely to trigger the stop no more than once out of 161 days by setting the stop 2.5 standard deviations below the average price. Other probabilities are reported in the twenty-fourth stockdiscipline tutorial. These statistical references may sound complicated, but there is a tool available to traders that makes it possible to do this without any knowledge of statistics. Since random noise in the stock’s behavior would cause a sale only once in 161 days, then the probability is quite high that if the stop loss is triggered, it is because the stock is misbehaving to a significant degree. An unusual decline has occurred, a decline that is well beyond what is probable for that stock. Think about it. Those are precisely the conditions under which an investor would want to sell. The beauty of this approach is that the stock tells on itself. It’s as if the stock were shouting “hey, I’m behaving badly. Sell me before I cause you pain!”
One characteristic that differentiates an expert trader from an amateur, is that all the losses of the expert are small. He has no large losses. The trading pattern of amateurs is strewn with losses and gains of all sizes. Amateurs and experts can both find big winners, but amateurs are likely to lose those gains on subsequent trades. Only experts consistently control their losses so that none of them are large. If you learn to control risk (limit the downside behavior of stocks), accumulated profits should be the consequence. In the market, it is wiser to concentrate on developing a good sell strategy than to concentrate on developing a good way to find winning stocks. Part of controlling risk is buying right. Any stock can be a winner if it is bought right. The bottom line is that it is more a matter of what you can keep than what you can gain. If you want to perform like an expert, develop your stop loss and selling disciplines. Many professional money managers do not have true ownership of this principle. It is imperative that you make loss-control an integral part of your discipline.
The increased volatility of the market caused me to review my data on thousands of different strategies. I saw the elements that all the most profitable strategies had in common. The point was driven home. All these strategies exercised rigorous risk control. Sometimes they even generated more losses than gains, but they were all profitable. There was one characteristic trading pattern all the strategies had in common. They all generated consistently small losses and occasional big gains, but they never had a large gain wiped out by a large loss. There were no large losses.