Volatility and How To Handle It By
Transcription
Volatility and How To Handle It By
Volatility and How To Handle It By Dr. Robert C. Smithson Anava Capital Management LLC 2 North First St., 4th Floor San Jose, CA 95113 408-918-9333 Please Note: Individual companies shown or discussed in this presentation have been used as examples only and are not intended as recommendations of any kind by Anava Capital Management LLC or any of its representatives. An investment involves buying something with the hope, but not the guarantee, that its future value will be greater than the price you paid for it. Volatility is the risk that the future value will not be what you think it will be. In return for taking that risk, the investor expects to receive a higher average return than would be received from a less risky investment. Conventional wisdom, then, is that investors that make high profits are also highly likely to lose money instead. This idea, however, is vastly over-simplified. Investors who understand volatility and risk can actually achieve better returns and lower risk than those who simply select low-risk investments. Before we can talk about investing and markets, however, we have to be able to recognize when we are doing better or worse. So, we’ll first define what value actually is and what money actually is. Once we know that, we can tell if the value of our investments is actually increasing or decreasing. That’s what we really want to know, and it’s not the same thing as how much money they represent. As we shall see, value is created in the minds of people, and the same thing can be valued very differently by different people. We will also see that money is not a physical thing. Money simply represents promises, promises that can be made and broken with equal ease. Because of the subjective nature of both value and money, it is impossible to define a specific “correct” price for anything. Because there is no single price that is correct for everyone, it is possible in principal for all investors to “beat the market.” For example, people who value the immediate safety of their funds will be glad to trade possibly large long-term profits for lower short-term volatility. The investor that takes the other side of the deal will also be happy because in the long run he makes more money, money for which he has no short-term need. This is a win-win situation because the definition of value is different for the two people. Financial markets have the means in place to allow individual investors, within limits, to optimize their risks and returns over time. It’s no exaggeration to say that what financial markets are really for is to transfer risk from people who don’t want it to people who do. Investors reduce the risk for businesses in return for an uncertain share in profits. How do you optimize your investments to meet your particular requirements? We’ll look at how the stock market has behaved in the past and why. Then we’ll discuss several optimization strategies that have stood the test of time, and are appropriate for different kinds of investors. By the way, the best portfolios are rarely, if ever, found by random selection of stocks on the theory that the market has efficiently found the correct price for all stocks. An “efficient market” is impossible because there is no one correct price for everyone. You can use this to your advantage. Finally we’ll discuss some actual applications of these ideas and the results obtained. 1 A LOOK AT THE MARKET The S&P 500 S&P 500 Price Changes 1871 - 2005 Stock Data from Robert J. Shiller, Yale University (http://www.econ.yale.edu/~shiller/data.htm) 1871 Dollars 1000 100 S&P Real S&P Nom. 10 1 0.1 1850 1900 1950 2000 2050 Year The S&P 500 was started in 1923 when it was introduced as the “Standard and Poor’s Index” containing 233 companies. In 1957, the present index, the S&P 500 began, with 500 companies representing about 80% of the total market value of U.S. stocks. The composition varies, and is intended to represent not only the largest, but the most representative companies in each important market sector. The index is weighted by market capitalization, so it is dominated by largecap stocks. It is probably the most widely-used stand-in for the entire market in studies of market characteristics, and in measuring the effectiveness of investing techniques and managers. We people say they are trying to “beat the market” they usually mean they are trying to get better percentage returns than the percentage change in the S&P 500 over the same period. One thing to notice about this chart is that something dramatic happened to the S&P around 1950. Prior to that time, there were wild swings in the real (inflation adjusted) index that were not duplicated by the nominal (not inflation adjusted) index. This was caused by large changes in the rate of inflation In 1921, inflation drove the real value of the S&P to a level last seen in 1878. The nominal index was only down a little at the same time. At the time of the depression, there was actually deflation, and the market remained long term flat with a slight downward trend and wild shot-term swings until the start of WWII. At the end of the war, the market showed a marked reduction in volatility. The large swings in value prevalent before the war were replaced by an unprecedented bull market with only minor short-term wiggles. This general trend was interrupted once by the “stagflation” of the 70s and 80s, a 10 to 20 year interlude, and has been interrupted again by the tech bubble boom and bust, after which the upward trend seems to be back where it would have been had the tech bubble not occurred. The next few slides will show what happened in some detail. The important thing to see here is that the market since WWII is very different in character than the market was before that time. 2 GROSS DOMESTIC PRODUCT The Gross Domestic Product from 1928 to 2005 GDP Data: U.S. Bureau of Economic Analysis (www.bea.gov) 1000 1928 Dollars 100 GDP Real GDP Nom. 10 1 0.1 1920 1940 1960 1980 2000 2020 Year The gross domestic product (GDP) measures all the goods and services produced within the borders of the United States, regardless of who produced them. It differs from the earlier gross national product (GNP) which measures all the goods and services produced by United States citizens, regardless of where they were produced. Both are commonly used as measures of the entire economic output of the country. This chart shows only the period since 1928. The first thing that strikes the eye is that after about 1947, there is an almost complete absence of large fluctuations compared to what was seen in the S&P graphs. Even the stagflation of the 70s and 80s shows up in the nominal GDP graph as only a gentle increase of the slope, followed by an equally gentle return to the earlier slope seen in the 50s. Looking at these two graphs might well make one wonder if it’s really true that the S&P efficiently reflects the “correct” value of the 80% of the economy it represents. Let’s look a little closer. 3 S&P VS GDP SINCE 1928 S&P Index vs GDP from 1928 to 2005 S&P Data: Robert J. Shiller, Yale University (www.econ.yale.edu/~shiller/data.htm) GDP Data: U.S. Bureau of Economic Analysis (www.bea.gov) 1000 1928 Dollars 100 S&P Real S&P Nom. GDP / Real GDP Nom. Expon. (S&P Real) Expon. (GDP / Real) 10 1 0.1 1920 1940 1960 1980 2000 2020 Year It appears that since 1928, the best that can be said for the S&P is that it is approximately long-term efficient. From 1928 to 2005 the real S&P 500 best-fit exponential trend line earned 3.09% per year, while the trend line for the GDP gained 3.5% annually. The fluctuations away from the line for the S&P are so large that it is apparent that something completely unrelated to the underlying value of the assets is affecting the market’s pricing. However, if you look very closely at the fluctuations around the trend line for the real GDP, you will see very small fluctuations that are similar to the much larger swings in the real S&P. We have found the first rule of market inefficiency. The market can be counted on to over-react to both good and bad events. Let’s check on this. We’ll use the stable post-war period. 4 S&P VS GDP SINCE 1950 S&P Index vs GDP from 1950 to 2005 S&P Data: Robert J. Shiller, Yale University (www.econ.yale.edu/~shiller/data.htm) GDP Data: U.S. Bureau of Economic Analysis (www.bea.gov) 1950 Dollars 100 S&P Real S&P Nom. GDP Real GDP Nom. Expon. (GDP Real) Expon. (S&P Real) 10 1 1940 1960 1980 2000 2020 Year The efficiency of the long-term market trend line is somewhat worse in this time period. The S&P has an geometric mean yearly return of 2.70%% compared to 3.33% for the GDP. However, in this period there are times when the S&P vastly outperforms and periods when it vastly underperforms the growth of the economy. There is no rational reason why this should be true, but there are some compelling irrational ones. Let’s look a little closer. 5 REAL S&P VS GDP RESIDUALS S&P Residuals vs 10 X GDP Residuals from 1950 to 2005 S&P Data: Robert J. Shiller, Yale University (www.econ.yale.edu/~shiller/data.htm) GDP Data: U.S. Bureau of Economic Analysis (www.bea.gov) 7 1950 Dollars 6 5 4 S&P 10 X GDP Best Fit GDP 3 2 1 0 -1 -2 -3 1940 1950 1960 1970 1980 1990 2000 2010 Year This chart shows the departure of the real S&P 500 and the real GDP from a best exponential fit. Separate fits were done for the two curves. For the S&P, the best fit was for an annual increase of 2.70%. For the GDP the annual best fit increase was 3.33%.We want to just look at it and see if we can see any correlations between the two. For clarity, the best fit zero line is plotted in heavy black and the GDP residuals are plotted twice, using yellow for the actual values, and magenta to show ten times the actual values. This allows us to easily see the relative magnitude of the volatility of the GDP (yellow) and that of the S&P (dark blue). The ten-times magnified GDP plot (magenta) allows us to easily see if there are any correlations between the two that might be of predictive value. The first thing we see is just how uniform the economic growth has been during the last 55 years. In that time the largest deviation from the 3.33% annual increase was less than 26 cents in 1950 dollars. By comparison the S&P 500 at the peak of the tech boom was nearly $6.00 off the best fit of 2.70% per year. The stable increase in the S&P is no accident. The Federal Reserve began operations in 1914, but its original purpose was to help the Treasury sell bonds at a high price, not to control the economy. However, the Fed was directed to control employment, inflation, and economic growth in 1946 and again in 1978. The depression was a traumatic event, and it is not clear we would have recovered from it even now if it had not been for the massive government spending required during World War II. After the war, steps were taken to reduce or eliminate the possibility of another depression. So far it has worked in this country. Economic growth is so stable because it is carefully controlled to be that way. Please notice that the Fed is not required to control stock prices. Given that economic growth is now deliberately controlled to remain at a sustainable rate, why does the stock market fluctuate so much? Look at the chart and notice the almost one for one correspondence of the year-to-year variations. The difference is that the S&P varies around ten times as much as the GDP. Over a period of about ten years, there are fluctuations in both that are fairly large compared to the yearly changes. They are loosely correlated, but the only consistent correlation is that the two move together. You might make an argument that in the 60s and 70s the S&P led the market. This fits conventional wisdom, but since about 1982 the two have no obvious correlation or move together. I can assure you that on the basis of this chart there is no reason to believe that there is any statistically significant predictive ability shown. A much more significant fact is the similar movements of the two, and the fact that the S&P moves so much more than the GDP. 6 INVESTING IN THE S&P 500 Real S&P Index Best Fit to Real GDP Trend from 1950 to 2005 S&P Data: Robert J. Shiller, Yale University (w ww.econ.yale.edu/~shiller/data.htm) GDP Data: U.S. Bureau of Economic Analysis (www .bea.gov) 9 8 1950 Dollars 7 6 5 S&P Real GDP Real Best Fit 4 3 2 1 0 1940 1960 1980 2000 2020 Year Here’s a plot showing the best fit of the last 50 years of the S&P to the best fit trend line for the GDP. If we define the GDP as “reality” then this plot shows the variations of the S&P that occur for irrational or at least unpredictable reasons. If you like diversifying by buying S&P index funds, it would not be unreasonable in the modern era to simply learn to trust the “invisible hand” of the market to be long-term efficient. It is easier to do this if you trust and love the Fed. If you do, then you know that “efficient” means the 3.33% per year rate the economy is following so closely. Then buy when the real value of a dollar invested in the S&P 500 in 1950 is a dollar or so below it’s value predicted by the annual 3.33% since 1950, there is good news, and the market is is at least 2 years into the recovery. If you trust the Fed to keep the market drop from messing up the economy too much, there will be no particular reason for the market to be down there, and it will eventually go up – but you will probably have to wait a decade or two. You might sell when it goes up to the same amount above the trend line, there’s bad news, and the market has been dropping for a couple of years. During bad times to hold stocks because the market is too high, keep most of your money in something like treasuries. Modern Portfolio Theory provides a systematic way to do this. Will this always work? There are probably other systems that will pay better, and this one assumes that in the future the Fed will still be able to control economic growth almost perfectly, and that the market will still refuse to believe that the Fed will be able to do it this time. It depends on the tendency of the market to exaggerate the economic impact of news, and on the longer term tendency for the market to revert to reality. It also assumes the best definition of reality is what’s been so reliable in the post- WWII era. Any of those things could change, but they probably won’t very soon, so it’s not an unreasonable strategy. By this line of reasoning, it appears that the market at the end of 2005 was a little below the economy’s trend line, but probably not enough to be significant. Over the next decade or so it’s most likely to move along a bit above the 3.33% trend line like it did in the late 50s and early 60s. Hopefully we learned something from the tech bubble and won’t do anything that dumb until a new generation of money managers who have seen only prosperity appears. If we haven’t learned, then the present recovery could end with a drop as everyone bails out of, say, energy stocks. That would be a buying opportunity. I’d keep some money in treasuries if this were my only strategy. 7 MODERN PORTFOLIO THEORY High Risk & Return vs. Low Risk & Return $30 $25 Price $20 High Risk $15 Low Risk $10 $5 97 91 85 79 73 67 61 55 49 43 37 31 25 19 13 7 1 $0 Trading Days We won’t spend too much time on Modern Portfolio Theory. There are many books on how to use it, and most investment advisors use it, especially for conservative, loss-intolerant customers. It’s conservative because you can control your investment risk very easily by creating a portfolio of appropriate amounts of both high-yield investments like stocks and low-risk or “risk-free” securities like treasuries. Of course, the lower risk comes with lower returns. It’s important to do your calculations with after-tax inflation-adjusted returns, because with this method your earnings will sometimes be low enough that you can’t just assume that these things don’t matter. The basic idea is simple enough. If your portfolio contains two assets, one very risky (stocks) but with a high expected return and the other with low risk (treasuries) and a low expected return, then you can choose from a range of possible risks and returns by varying the proportions of the two in your portfolio. Obviously the highest expected return you can achieve is with all stocks, and the lowest would be all treasuries. What is not obvious is that unless the variations in value of the two are exactly correlated, the lowest risk does not happen with an all-treasuries portfolio. Because the values of the two investments move differently, adding a small amount of stocks to a portfolio of treasuries actually reduces the risk and increases the return at the same time. This graph shows two securities, one very volatile, one not. They might be a stock fund and a bond fund. Notice that the high risk security went up more than the low risk one, but that was clearly just luck. During the 100 trading day holding period its price varied more than a factor of two, and if there’s an overall upward trend it’s not clear. The low risk security varied a few percent at most, and has a clear, if small, upward trend. Can you improve your risk/return ratio with an appropriate mixture of the two? 8 RESULTS OF BLENDING Low Risk vs. Blended Portfolio $26 $26 $25 Price $25 $24 Low Risk $24 9% High Risk $23 $23 $22 97 91 85 79 73 67 61 55 49 43 37 31 25 19 13 7 1 $22 Trading Days If you didn’t peak at your handout, this may surprise you. The magenta line is the same low risk security, plotted on a magnified scale so you can see the wiggles better. Notice that they are a mirror image of the high risk security. The two are highly anti-correlated. A 91% low-risk and 9% highrisk portfolio reduces the risk to almost zero, and at the same time, increases the return to 5.07%, a bit better than the 4.74% produced by the low-risk security alone. This was manufactured data. The “securities” shown are a random walk superimposed on a linear up-trend, which is about 24% in 100 trading days for the high risk security, and a little over 4% for the low risk one. The point of using this method was to show how realistic a random-walk model of stock prices can look, and to illustrate in principle how adding a little high-risk stock to a portfolio can reduce risk. This reduction also happens if the two securities are merely uncorrelated, but it’s not as easy to see. Now let’s look at an example using real data. It was done done with the benefit of hindsight, but it is sufficiently obvious that it was probably used by a few people at least. 9 REAL STOCKS This is a portfolio for the aggressive investor. I held Continental during most of this period, but sold it a while ago. I’ve never held Chevron. CAL was a lucrative investment, but as you can see, volatile. I considered at the time balancing it with some petroleum stocks. They were also rising, but not as fast. I didn’t do it, but was therefore running more risk that the high price of oil would hit the stock price very hard. An investor in oil runs the same risk, but in the opposite direction. Then it might be a good idea to add a little volatile CAL to reduce the risk of lower oil prices to a holding in CVX. The next slide shows the results. 10 CAL & CVX Portfolio 20% CAL & 80% CVX 3 2.5 Dollars 2 CVX 1.5 Porfolio CAL 1 0.5 0 6 l- 0 Ju 26 -0 ay -M 13 6 -0 ar -M 24 06 bFe 25 -0 ec -D 14 5 -0 ct -O 25 05 pSe 55 -0 ul -J 17 5 -0 ay -M 28 5 r-0 Ap 8- Date These are real stocks. This shows CAL and CVX for the last year. (The previous chart showed two years.) By adding 20% of a risky but anti-correlated stock (CAL) to a safer and less volatile holding (CVX), volatility is reduced and return is nearly doubled. Similar but less dramatic results are possible with stocks that are merely uncorrelated. This means that picking stocks whose price movements are unrelated produces a reduction in volatility. It can be shown that with completely uncorrelated random fluctuations, combining some number, N, of the stocks in equal dollar amounts reduces the volatility of the portfolio by an amount equal to 1/√N. In general, a 4-stock portfolio will have half the volatility of a single stock, a 16-stock portfolio a quarter the volatility of one stock, and so on. In the example shown in the chart, we have achieved a reduction of volatility of more than a factor of two with only two stocks because their short-term price fluctuations are anti-correlated. The opposite is true as well, combining stocks with strongly correlated price movements, typically those in the same or similar industries, produces little or no reduction in volatility. Those who invested heavily in internet stocks before the bubble burst learned that lesson. 11 BUY AND HOLD – GOOD? Percent Return Dow Industrials Per-Year Nominal Returns - Hold Ten Years 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 89 85 81 77 73 69 65 61 57 53 49 45 41 37 33 29 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% -10.00% -20.00% -30.00% -40.00% -50.00% -60.00% -70.00% -80.00% Year People often say that buy and hold strategies work well. Here’s why. This chart shows what your annual rate of return would be if you had bought the Dow Industrials in a given year, and sold after ten years. Impressive isn’t it? You would have made money almost every time, often over ten percent annually. Who could argue with that? It’s easy and it works! Are there any problems? See the next slide. 12 BUY AND HOLD – BAD? Dow Industrials Per-Year Real Returns - Hold Ten Years 30.00% 25.00% 20.00% Percent Return 15.00% 10.00% 5.00% 0.00% -5.00% -10.00% 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 89 85 81 77 73 69 65 61 57 53 49 45 41 37 33 29 -15.00% -20.00% -25.00% Year When you look at real, inflation-adjusted returns, you lose money after ten years, sometimes a lot of money, about a quarter to a third of the time. This is with the Dow, a selection of very high quality and stable companies. Buy and hold is not so easy after all. You still have to watch your investments because market conditions and company fortunes change. There have been decade-long periods in the past when just holding on was not a very good strategy. The stagflation period in the sixties and seventies is the most obvious example of the danger in buyand-hold. The market was flat then and inflation was high. So were interest rates. You would have been much better off putting money in treasuries or bonds as recommended by Modern Portfolio Theory. 13 SECTOR SPDRS 70 70 60 60 50 50 40 Cons. Disc. Cons. Stap. Energy 30 40 20 20 10 10 0 24-Jul-98 Fin. Hlth. Care Ind. 30 0 6-Dec-99 19-Apr-01 1-Sep-02 14-Jan-04 28-May-05 10-Oct-06 24-Jul-98 6-Dec-99 19-Apr-01 1-Sep-02 14-Jan-04 28-May-05 10-Oct-06 70 60 50 40 Mater. Tech. Util. 30 20 10 0 24-Jul-98 6-Dec-99 19-Apr-01 1-Sep-02 14-Jan-04 28-May-05 10-Oct-06 Sectors don’t necessarily move together, but most of the time they tend to cluster closer to each other than individual stocks do. However, when sectors get hot, they can be very dangerous to be heavily invested in. For example, energy looks like the tech boom did just before the bust. Yes, energy is up for good reasons. Booms always have good reasons. Remember that the market always overreacts to events. It’s a good idea not to have everything on the same bandwagon. In the last two years just about every sector has been going up, particularly if you invest in well-run companies. Don’t put everything into a hot sector. Diversify across sectors. 14 CAPITALIZATION Russell Indices 900 800 700 600 500 Total Market Large Cap 400 Small Cap 300 200 100 0 6 -0 ct 9 l -0 Ju 6- -O 10 4 -0 an -J 14 8 -9 ul 5 -9 ct 90 1 -0 pr -A 19 -J 24 -O 28 ay 3 -9 an -J 31 M 7- Large caps (market cap above $5B) move with the market because they are the market. Small caps (market cap below $1B) are different. They are more variable in business quality than large caps, which means that it’s necessary to do more study before investing in a particular company. However, a good small cap has room to grow, and makes an excellent investment. Small caps can also be quite safe when portfolios are properly diversified. Mid caps (market cap between $1B and $5B) can also grow and are generally more stable, but they are harder to find bargains in because they are more closely watched. Micro caps (market cap below $500M) can be excellent investments, but there is often little information available concerning them. If you invest in micro caps, expect to work harder to find out about the company. It’s best to confine yourself to businesses you understand, and this is particularly true with micro caps. 15 HOW TO CONTROL VOLATILITY Buy good companies with established value as businesses. Buy companies which are undervalued as businesses by the market, but which are beginning to be noticed. The combination of an undervalued company and an upward price trend is a mark of a good investment Don’t wait until a company is widely publicized. Do your own screening and find the companies the experts will be talking about next month. Diversify both among companies and sectors. Try to find growing companies that respond differently to market forces. Never buy a bad company just to diversify. 16 WHAT YOU ACTUALLY DO Use a good stock screener like Stock Investor Pro to find companies that meet your criteria. (Hard screening) Check news, recent prices, and opinions of experts and other investors. Decide if the company is as good as it looks. Many won’t be. Try to have at least ten, and preferably twenty companies in your portfolio spread over at least five sectors. If necessary buy fewer shares to accomplish this. If your screens aren’t finding enough candidates, loosen your criteria a bit. Would you really refuse to buy a company because its price-to-earnings is 10.1 rather than 9.9? Stock Investor Pro will refuse if you set the threshold at 10. 17 SOFT SCREENING Screening with hard thresholds will cause companies near a threshold to jump on and off your list as normal variations in price, etc. occur. You can buy something one month, sell the next, then buy the next, and so on. This is not good for the investor, but brokers love it. To avoid this, use less strict criteria for selling than for buying. For example, you might buy with the P/E less than 10, but hold the stock unless it goes above 12. We have developed software that works with the Stock Investor Pro database that doesn’t use thresholds at all. Instead, it tells you which companies in the database are closest to the companies that passed a screen. It’s also a great help with diversification. 18 WHY NOT FOLLOW THE EXPERTS? After all, they know more than you do, don’t they? They probably do, so don’t try to outsmart them. But don’t take their advice either, at least not if you want better than average results. Recommendations given on TV are instantly known to millions of people, and have probably already been acted on by thousands of professionals, newsletter subscribers and such. It’s better to do what they do and find the same companies before they recommend them. Do things they can’t do. In addition to any special knowledge they may have about a particular business or a local company, small investors have major advantages. They are described on the next slide. 19 SMALL INVESTOR ADVANTAGES Small investors can wait. In the normal course of business, good companies go through periods of decreased earnings in order to expand, or to meet changing business conditions. Investment managers need to make money every quarter. If you are able to tell the difference between normal business fluctuations and a serious problem, you can buy when earnings and the stock price are low. Then wait until both recover. Small investors can take advantage of excellent opportunities that are too small to interest institutions. A large fund has to invest in big companies because it has big money to invest. 20 HOW WELL CAN YOU DO? The portfolios shown on this slide are maintained by Anava Capital Management LLC as a pool of companies from which we can draw when forming client portfolios. Client results can vary widely from these portfolios depending on their personal investment objectives. Past performance does not guarantee future success, and does not protect against the possibility of loss. Using the techniques described in this talk, we have been successful in outperforming the market substantially over the past year. We believe that individual investors can do as well. However, success in investing, like success in any other field, requires an understanding of the principles involved, and diligence in applying them. There are no guarantees than stocks in general, or any given portfolio, will produce profits for investors, or that losses, including loss of capital, will not occur. There are no quick and easy ways to wealth, but it is quite reasonable to expect that individual effort will produce better returns and greater safety than is available otherwise. 21