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This chapter is from the book

Strategies

Investment management consists of three basic strategies. Two have to do with the management of activity, buying and selling. The third is a strategy not covered in this book: how to manage portfolio risk called money management. In individual investments, a stock must be picked and then bought, the bought stocks must be sold at some time, and the whole process must be managed for risk. The first is called entry strategy; the second is called exit strategy. Exit strategy is also called risk management. The third strategy is money management or portfolio management. No investment program can succeed without all three. Unfortunately, most portfolio managers and investors focus on entry, and relegate exit and money management to the bottom of their priorities. Ironically, the reason most professional managers and investors consistently lose money is that the latter two are the more important.

In my graduate-school classes on technical analysis, I ask students to randomly pick a stock, regardless of its earnings, management, industry, and so forth and rather than analyze those particulars, invest on the flip of a coin. Using the price history of their chosen stock over the previous six months, they are to record the results of each transaction. At a coin flip of heads, the entry signal is to buy, and at a flip of tails to sell short. This is their entry strategy. Using an equal dollar amount of shares for each transaction so as not to be influenced by position size, they are to record holding the position until they exit their position on the exit rules. They are to close the position when either it has a 5% loss or it has retraced from its most profitable gain by 5%. In other words, if the student buys a stock at 100, he then either sells it at 95 (5% loss) or at a retracement of 5% from its most profitable price. Say the stock runs to 130 and then retraces 5% to 123.50. The position is then closed for a gain of 23.5%. In a short sale, the procedure is the same only the risk is 5% above the short-sale price. This is their exit strategy. On the day after a position is closed, they are to start the process over again by flipping a coin and acting according to the original instructions.

Of course, in this simple system, it helps to have a volatile stock. Dull stock behavior won’t produce many transactions and thus little profit. This, in itself, is a lesson on why volatility is a desirable facet of investment and especially trading, provided risk of loss is controlled, as it is in this experiment. Do all of the students make money? No, but a plurality do, enough so that if a portfolio were constructed of all the students’ individual experiments, it would consistently make money. The lesson: Buying or short-selling a stock is not the most important aspect of investment—it was done on the flip of a coin. Selling and controlling risk are the most important and why an explicit exit strategy is necessary.

Entry Strategy

There are many excellent entry strategies, both fundamental and technical. Several recent books have described fundamental strategies using various corporate statistics and have given statistical evidence on their merits. The problem of selecting stocks has been well researched and is available in book form for anyone to learn.

In my earlier investment book, Beat the Market,2 I demonstrated methods I found at the time to be the best for selecting stocks and for timing their purchase and sale. This system included a screening of all U.S. stocks for those that had the best recent history of price rise, called relative strength. I also used two screens of fundamental, company-specific information. These were relative earnings growth and relative price-to-sales. The 2006 study included figures going back to 1998. It was an evaluation of a system that had been operating live for nine years, but it was not an optimization for the best parameters. I only looked at what had happened with predetermined parameters from the 1970s.

Until relatively recently, to prove the validity of a systematic method, analysts had to develop a hypothesis, perhaps backtest it, and calculate it each day and measure the results on paper for a long period to see if it actually worked. The process was similar to the scientific method of hypothesis, trial, and confirmation or rejection. Because my earlier study used weekly data only, the time necessary for conclusive results was long, often many years. I am getting older now and don’t have the time ahead to do such tests again. Thankfully, the computer, easily accessible stock market price data, and new methods of backtesting have been developed and are being used by students of the markets today to investigate systems without waiting years for results.

The primary testing method I now use is called walk-forward optimizing. It optimizes segments of price data in the past; for each segment, it finds the best fit for a proposed system and tests it in data that is not part of the optimization. It duplicates as well as possible the earlier lengthy method of waiting for results. When properly constructed, it covers substantial periods of time in deriving the optimizations to represent all kinds of markets over time; it runs through a number of different sequences to ensure the results are not a “best fit” to existing data; and it uses a substantial amount of data, enough to include real-time anomalies in the tests. When it fails to show a robust system, the failure is useful because it shuts off further inquiry in the direction of that failure and often suggests other approaches that may be better. Once this optimization is completed and the results show a viable system is present, the probability of it working in the future is high. Of course, there is no guarantee that the system will prove to be viable in the future, but the odds are significantly increased by the use of out-of-sample testing within the optimization.

I know from past history, professional and academic literature, and from watching its success in live markets that there is validity to the relative price strength method, but when I began, I did not know the specific parameters that would be optimal. Earlier parameters had been somewhat arbitrarily derived years before the long-term test began. For example, Bob Levy, who originated the method in the late 1960s, suggested a lookback period for the calculation of relative strength of 26 weeks. He had tried periods of 4 weeks through 52 weeks, finding that 52 weeks worked but not as well as 26 weeks and that 4 weeks gave a negative performance. Thus, I used 26 weeks in the original formula and followed its use for more than 30 years. You will see later in this book, when I get to the actual tests of relative strength, that the optimal lookback is similar to what was hypothesized. I test not only the lookback period but also the ideal buy rank and sell rank, the minimum volume and price necessary to profit, and the percent protective stop. I assume that most investors and portfolio managers using relative strength as a stock selection method are interested only in taking investment positions. The emphasis in this book in the investment section is thus on holding long positions only. Short-selling is presented in the second section of this book on trading strategies beginning in Chapter 6, “Trading Strategies.”

My earlier method of stock selection worked in historical testing for a few years. The relative earnings growth never worked, and the relative price-to-sales ratio never came close as a separate selection criteria to the success from relative price strength alone, but it did prove useful at market peaks. Since then, I’ve found that a relative price-to-sales screen is not fruitful, detracting from the performance of relatively strong stocks. I also found that the relative strength itself was not providing the spectacular results it had in the past. I knew from having watched these figures in real time for more than 30 years that there were periods when they didn’t work, but I had to admit that I never investigated the best all-round parameters. I notice in O’Shaughnessy’s fourth edition of his book What Works on Wall Street (2012)3 that he also has found that price-to-sales is no longer a highly rated selection means, yet he still maintains that relative price strength is the best selection method of all, though his calculation and lookback are quite different from mine. So I have eliminated price-to-sales and continue with just relative price strength, back to my roots as a technical analyst, a place where I feel more comfortable.

Limiting Capital Loss—Drawdown, Volatility, and Diversification

Most investment managers and portfolio managers don’t have an explicit exit strategy and thus have no idea of what their potential capital risk may be. The next time you sit down with your investment manager or read literature on your mutual fund, look for evidence of an explicit exit strategy. You’ll be surprised how vague is their coverage, if at all, of closing positions and limiting capital risk. As demonstrated in the student experiment, it is the limiting of the losses that provides the profit, not the stock picking. This is the same principle that the hardware store owner faces: Limit loss and hold profit.

There are many ways to limit loss, most of them technical, having to do with the price action of the security itself. It is very difficult to have an exit strategy based on earnings, sales, or any of the scores of fundamental factors that accompany a stock. O’Shaughnessy has experimented with fundamental factors that consistently cause losses or underperformance, but even his study avoids the question of when to sell a stock either to protect against loss or to protect against loss of accumulated gain. So technical analysis, the study of price action, is the primary method of protecting against loss and even more importantly is the primary method of determining risk of loss.

Drawdown, the amount by which a portfolio can decline from its highest value to its lowest value, is the best measure of risk. It quantifies the percentage and dollars at risk. Drawdown is the ultimate risk of capital. A 100% drawdown means you have been wiped out. A lesser drawdown may be impossible for you to mentally and emotionally withstand. You should understand what you will accept as the largest loss and approach your investments with that in mind. Most investors are willing to accept a loss of as much as 20% in a drawdown, provided the upside performance potential is two to three times better. The relationship between acceptable gains versus drawdown is personal. Some commodity speculators are willing to take 60% to 80% drawdowns, but they know their system will eventually double or triple their investment. It is a standard rule that gains and drawdowns are related and that you cannot have substantial gains without being able to accept sizable drawdowns.

Volatility is a measure of how much a price oscillates back and forth. It is never a constant, as volatility changes with market conditions and the stock’s trend. It also doesn’t tell you what your dollar or percentage capital risk might be. Volatility should never be the primary gauge of risk. Whereas drawdown is forever, volatility as a risk measure is limited. No volatility value can tell you that you will be wiped out. Unbelievably, recent academic and professional literature focuses on volatility as something to avoid. Yet, volatility is where profits are made and is thus something to seek. Loss comes from drawdown, not volatility. In my student exercise, it was the curtailing of loss through the 5% protective exit, limiting my drawdown to 5%, that allowed the upward volatility to profit. The drawdown was controlled with the 5% limit. Measures like the Sharpe ratio, a common measure of risk, that contain a divisor based on volatility are deceiving because they include both up and down volatility. An investment should always have a large upward volatility—you can’t profit from a dull stock—and have a limit on its downside volatility.

Sharpe ratio figures are incorrectly presented as measures of risk. Because of the universality of this misconception, portfolio managers in most firms have to include their Sharpe ratio in reports of their performance to show risk. This convention is also seen in most mutual fund monitoring services and most literature from mutual fund companies. This public display of volatility as a measure of risk forces portfolio managers, who must compete in the performance world, to buy stocks with lower-than-average volatility so as to keep their risk low. They thus are forced to buy stocks that are not trending, yet what is necessary for substantial profit with limited capital risk is a strong, steeply rising, volatile stock price, a price-related method of selling to protect against initial loss in case the investment turns out to be a poor one, and a price-related method of selling to avoid losing a majority of the gain from a successful one.

Volatility calculated as the standard deviation of prices is also a false volatility yet it is included in the Black-Scholes option pricing model. When a security is in an upward trend, where the price is rapidly advancing, standard deviation includes the strength of the trend as well as variability around it. In other words, a rising stock will have a high standard deviation because by being in a trend, the price deviates significantly from its mean. If such a calculation of volatility is used as a measure of risk, it thus excludes a strong stock from investment consideration because a strong stock is by this definition very risky. This is nonsense. The many professional and academic studies of the relative strength concept prove that price strength is profitable. Indeed, upside volatility is desirable, not something to fear. It is the risk of capital loss, something totally different, that is worrisome. I don’t know if the prejudice about technical analysis is the cause of this misapplication of volatility as a concept or not, but it is unrealistic and has led to many portfolio disasters.

Another misconception is that diversification is the solution to avoid poor stock selection. It assumes that the investment decision will be wrong sometimes, but that if enough stocks are owned, the total loss will be dampened by the success of the profitable stocks. This philosophy is a misplaced result of the Capital Asset Pricing Model principally because it relies on what is called “beta” as a risk measure. Beta is a measure of how closely a stock’s price changes mimic the changes in a market average such as the Standard & Poor’s 500. When the linear relationship between a stock price change and a market average change is plotted on a graph, the point at which the line crosses the vertical axis is called the alpha, and the slope of the line is called the beta. A steep slope, and thus a high beta, is considered to be a sign of high volatility. However, while being represented as a measure of risk, it tells nothing about the chances of capital loss in the stock. All beta does is measure the oscillations of the stock relative to a market average. It tells me nothing about whether I will lose money. In fact, the alpha is a better measure of a risky stock; a negative alpha, regardless of its volatility, suggests that the stock is performing worse than the market average. I will lose money on a poor-performing stock with a low alpha regardless of what level exists for its beta.

Diversification is also less than optimal for substantial profit. Diversification may lessen the effect of a losing position, but it also lessens the effect of a winning position. It’s the “chicken” way of avoiding investment mistakes and avoiding the use of a tested, robust investment system. In a portfolio, because it is a business, meritocracy must rule. A portfolio should be filled with the strongest, most volatile stocks possible. If the oil stocks are the strongest, the portfolio should own nothing but oil stocks. If the oil and ditch-digging stocks are the strongest and most volatile, the only diversification should be in those two sectors. The purpose in portfolio management is to make money, not be average or equal to other portfolios. Risk should be controlled by risk management, not by diversification. In the market decline between 2007 and 2009, all stocks declined. Diversification was a useless method of controlling capital loss.

Exit Strategy

Exit strategies are risk-avoidance strategies. They are compiled to protect against capital loss or to lock in profits. Protecting against loss can be difficult. The standard method is to implement a protective stop underneath the entry price to protect against a large loss in case the entry decision was incorrect. This may protect a position against a single loss, but it doesn’t protect against a string of losses, nor does it protect against a large drawdown due to poor portfolio management. A string of losses may result from a poor selection method or from a general market decline. Poor selection can be reduced through proper position sizing (never putting too much money at risk in any specific position) or by market timing when a broad signal on the market suggests stepping completely away from the market for a while.

Market timing is a problem that investment managers do not like to face. When market timing signals that it is time to sell stocks, because the majority of investors are optimistic about the markets, and their customers are members of the public, the pressure is heavy on the manager not to act on the market timing signal. These customers often pull their money out of a fund that is selling when the market is high and reduce the management fee on the assets being managed. At that time, the fund’s sales department exerts pressure on the manager to keep the customers happy by remaining in the market even when professionally it is obvious the market is in trouble. Most funds don’t allow market timing at all in their charter, thus forcing the manager into taking his lumps when the market declines. This conflict between the portfolio managers and sales department and customers also occurs at market bottoms as well when customers don’t want to own stock at all, yet the opportunity exists for a large market rise. The relationship between bullish and bearish sentiment in the market and the market’s future direction is an entire study of its own, but the rule of thumb is that when the public is overly optimistic, the market is at a top, and, conversely, when the public is overly pessimistic, the market is at a bottom. Public pressure from news, investor comments, TV, advisors, and other outside sources to conform to existing public opinion make a market timing signal very difficult to follow. It is one reason why superior results come from a disciplined algorithmic system that has proven to work in both types of markets and needs no emotional input.

Although the use of price targets is an exit strategy, I’ve not found any means of accurately projecting a price target. What I have found is that a price target can be deceiving. Invariably, either the price will fall short of the target, leaving the dilemma of when to sell, or it will exceed the target price by such a substantial amount that I feel foolish in having sold it so early. I don’t believe in using price targets except as a technique for gauging the strength of a trend. This method is explained in Chapter 8, “Cycles and the Forward Line.”

Money Management Strategy

This book is about proper investment and trading involving an entry strategy and an exit strategy. I highlight the best strategy that I have found and show how it has worked in the past and will likely work in the future. Money management strategy—how to organize a portfolio to reduce portfolio drawdown, as opposed to individual stock drawdown—is another subject, one I don’t touch. I avoid it because it is complicated and deeply personal. What should the initial capital be, what should the trade size in shares or dollars be, should the strategy be combined with others and to what degree, what should the risk strategies and execution style be, what should the number of positions be, should leverage be used, and so on are all factors that should be addressed, though this is rarely done by portfolio managers and investors. I suggest that when you reach this stage in your investments study, you consult one of the excellent books on the subject. Don’t be fooled by the constant use of the term trader in these books. The principles apply to any size portfolio, and most managers of large portfolios, being unaware of them, have difficulty in understanding why they consistently underperform the markets.

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