Financial Thinking

How we make investment decisions is based on our conception of how the investments work and what our expectations are. Before 1900, in the West, investing and financial activities were seen as vulgar. Respectable intellectuals didn’t give themselves to contemplating the vagaries of the market. More recently, much research has been done to try to improve the stability and profitability of stock market investments. It seems incredible that the market crash of 2008 was as severe as the Panic of 1907. Could it be true that our knowledge and expertise relating to financial investments has made little or no progress over the last century of study and thought?

What happened between the Panic of  1907 and the present day? The Roaring 20s drew many people into speculating on the stock market, using increased margin and betting that prices would always rise. That ended abruptly in 1929, with the market crash, the policy mistakes of governments and the Great Depression. An entire generation swore off stock market investments for life, seeing it as too risky. And, indeed, it took about 20 years for the market to reach and exceed its previous highs.

Two things happened in the early 1950s. Alfred Winslow Jones, a journalist at Fortune magazine, set up the first hedge fund, using hedging techniques to produce returns that beat the top mutual fund by 87% over five years. His hedgeing technique focused on using long-short pairs to increase returns while decreasing variability. Separately, Harry Markowitz developed a modern portfolio theory, using a variety of assets to optimise a portfolio for improved returns at a given level of “risk” or reduced “risk” at a given level of returns. Because risk needed to be measured and calculated, this led to the equation of risk with volatility. And since volatility and probability involved mathematical variability, Gaussian statistics were adopted by the interested academics in order to develop a mathematical model for financial returns.

The problem is that the model didn’t match reality. In 1998, Nobel laureates Myron Scholes and Robert Merton came very close to causing catastrophic losses across Wall Street that their hedge fund, Long-Term Capital Management needed to be bailed out in an arrangement by the Federal Reserve. The model that LTCM was using was based on the application of Gaussian statistics, assuming that the fluctuations of the stock market match brownian random motion (movements are unrelated, steps are of equal size). It basically presents a world view where markets rise and fall unpredictably, but unspectacularly. As we all know, markets crash.

This is where time-tested experience can help. Warren Buffet has said that he appreciates modern portfolio theory being taught in business schools because it makes his job easier. He has decades of experience in choosing companies that are both profitable and undervalued. His teacher, Benjamin Graham, was a lifelong student of the markets. And there are many aphorisms that traders use to sum up collective experience. The difficulty, then, is in separating the superstition from reality. Do market crashes always happen in September and October? Should we really sell in May and go away?

An additional benefit to paying attention to commonly accepted aphorisms is that a rational person will treat them with some skepticism. The problem with a theory or a model that has been accepted is that it may be acted upon unquestioningly. Question models, question accepted wisdom, and always plan for the case where everything goes wrong, however unlikely. The principals of LTCM learned these lessons the hard way, but we now know to be skeptical of academic models that work only under certain conditions, that may differ from real life.

As I’ve previously pointed out, leverage is a major source of risk in an investment portfolio. Not only does borrowed money magnify gains and losses, it also introduces obligations and requirements. Without leverage, a company with failing financial health has options available. It can reduce, then stop dividends, it can sell assets and it can take on new loans. A company that is highly leveraged has none of these options, and a failure will arrive much more quickly and with less warning to owners. Let’s look at how leverage puts a portfolio at risk of failure.

First, leverage can be used to purchase investments. Because equity investments are considered assets, it is quite easy to buy them with borrowed money. An investor could borrow against their own credit, or against other assets to arrange a line of credit. An investor could borrow against the investments themselves, either with a margin account, usually in the case of stocks, or with a 1:1 investment loan, usually in the case of a mutual fund. The loan will magnify gains and losses as a percentage of the original investment. Suppose $50,000 is invested but, using a $50,000 loan, $100,000 of stock is purchased. If the stock portfolio rises to $110,000, that’s a 10% increase on the total investment, but a 20% increase on the investor’s capital. Now let’s assume that this return is earned over the course of a year, and the loan is at an interest rate of 5%. The net return, after paying interest, is $10,000 – ($50,000 x 5%) = $7,500 / $50,000 = 15%. In this example, the investor earns 15% on his original investment. But what if the market falls by 10%? A loss of $10,000 and an interest cost of $2,500 mean -$12,500 / $50,000 = -25%.

From the two hypothetical examples above, we see that the return profile has been fundamentally altered. The lender is paid a 5% guaranteed return per year, no matter the performance of the equity investment. The investor, however, loses more from a negative return than they gain with a positive return. Because probabilities are unknown, and unknowable, with stock market returns, the profile of the returns is important. Most investors will prefer a return profile with more possibility to gain than to lose. This is the basic tenet of value investing: buy something that’s already low so that there’s more to gain than to lose.

Second, leverage can be embedded within investments that are held within a portfolio. Businesses borrow money to fund their operations. The result is that the return profile for the business is altered. A corporation that has no debt outstanding and no bonds or preferred shares, has full control of how to use their revenues from operations. They can choose to pay bonuses, pay out dividends, invest in productive assets or expand their operations. On the other hand, a corporation that has a significant amount of debt has less flexibility in the use of their revenues. They must pay their short-term liabilities, pay interest and probably principal on long term liabilities and bonds outstanding; if not, they will be forced into bankruptcy. Only then do they have the option of paying bonuses or dividends on preferred shares. Last, they can choose whether or not to pay dividends to common shareholders. Similar to the example of the leveraged investment portfolio above, the return profile favours the lenders, at the potential expense of the owners.

Third, leverage can easily be magnified. This skews potential returns even further to the negative side. Shares of a company that has a 200% debt-to-equity ratio can be purchased by an investor in a margin account using money borrowed from a bank. At the extreme, the money can come from an unsecured line of credit (eg. $10,000), deposited to a margin account and used to buy shares that qualify for 2:1 margin, for a $30,000 investment. That equity, in the company, is accompanied by 200% long-term debt, for an enterprise value of $90,000. The investor has, in effect, used no capital of his own to control $90,000 of enterprise value.

In this example, let’s also review the impact the obligations to the various lenders. We will assume a 6% rate of interest in each cash, which is probably at the low end of a realistic range. Remember that if an interest payment is missed on any of these loans, the investment will fail and capital will be lost. The line of credit at 6% + the margin account at 6% + the company’s long-term debt at 6% mean that the first 18% of profits are paid to banks. If corporate profits exceed 18%, the investor begins to make a positive return. Although many companies may be able to achieve this, especially during the growth phase of the economic cycle, this is not a low hurdle. The return profile is heavily skewed, and the risk of a loss of capital is substantial.

The effect of borrowing money is to skew the potential returns in favour of lenders at the expense of the owner or equity investor. Magnifying leverage has the effect of magnifying risk. It is very easy to increase the leverage of a portfolio, and it will probably improve returns when the investment environment is positive. But when times are lean, a portfolio with significant leverage, external or embedded, has a much higher chance of failure, causing permanent loss of capital. Using minimal to no leverage greatly improves the robustness of the investment portfolio and improves the chance that a financial plan will be successful.

One way to manage the type of uncertainty found in financial markets is through redundancy. Since the 1950s and the introduction of modern portfolio theory, investment managers have worked to optimise their portfolios. What they inadvertently produce is fragility. Portfolios that are supposed to be insulated from fluctuations are actually at greater risk of failure.

Portfolios are optimised for either return or volatility. Portfolio managers focus on adjusting the portfolio mix between stocks, bonds and cash in order to maximise the return for a given level of volatility or minimise the volatility for a given level of return. If you imagine a curve rising toward the right and leveling off as it moves right, this is called the efficient portfolio frontier. In theory, t is not possible to move above the frontier, as it relates return to volatility. Portfolios below the frontier are unoptimised. The suggestion is made that moving a portfolio onto the frontier will increase the return for the same volatility, which is seen as gaining return at no “cost”.

It is easy to see that this view of optimisation is not always appropriate. Mutual funds, for example, need to maintain a cash balance in order to fund redemptions. Cash has no volatility, but also almost no return. It cannot be used to optimise a portfolio, because it doesn’t have a low or negative correlation with other asset classes. From the perspective of optimisation, it is seen as having no benefit, but mutual funds (as well as any opportunistic investor) has a need to hold cash. Further, optimisation has not worked. Correlations are not static, proving that the statistics that underly the modern portfolio theory are an inappropriate model of reality. In practice, all portfolios, optimised or not, present similar performance during market crashes: they all fall in value.

While optimisation focuses on reducing the fluctuations of a portfolio, robustness focuses on protecting value. Using redundancy, a portfolio has several parts, each of which has a different function and can profit in a different environment. The environment remains unpredictable, but the portfolio is prepared to profit. Investments can be classified into as many asset classes as you like. Let’s take stocks, bonds, cash and real estate as the most common examples. During some multi-year periods, stocks perform very well, far better than other asset classes. This was the case during the period from 1982 to 2000, with falling interest rates, low inflation and increasing leverage. During the same period, bonds performed quite well. But bonds shone during the period of 1999 to 2002, while stocks crashed and interest rates fell to rock bottom. Following that, real estate soared, especially in the United States, boosted by low long-term financing rates and increasing leverage. When the market fell apart, all types of equity and debt, except some forms of government debt, crashed at the same time and cash provided the best safe haven, outperforming almost all other investments.

It’s easy to look at a narrative like the one above and think: “If only we had known which asset class would perform well at which time. Aren’t there clues that make it obvious to the initiated?” The answer is no, because if there were, those people would have struck it rich, cashed in, and would either be well-known or no longer giving advice. A more realistic approach is to own each asset class. Own some stocks, some bonds, some real estate and keep some cash, just in case. We don’t do this to optimise the portfolio, but to be ready for any environment.

Further, a single investment may have multiple purposes in a portfolio. As an example, let’s look at a hypothetical portfolio for a retiree. Stocks provide dividends, which provide necessary income, as well as growth, which addresses  inflation and longevity. Bonds also play multiple roles in the portfolio, providing guarantees for the value of the principal, providing some income, and moving opposite to stocks during a market correction. Cash provides a source to draw from when there is a need for a lump sum, as well as “dry powder” with which to buy during a market correction. Real estate is also useful to provide some income, while providing an effective hedge against inflation. As the economic environment shifts, the portfolio is able to provide for different needs.

This raises the question of proportions. The proportion is related to the purpose of the investor. In the example above, the retiree may be able to produce most of the income he needs from dividends. In this case, there is only a small need for bonds. The bonds should provide enough guaranteed capital to cover the likely length of a market correction, probably 3 to 5 years. Assuming a withdrawal rate of 5% (or less), having 15% to 25% of the portfolio in bonds would be adequate. This is less than the traditional 40% – 50% recommended at retirement, because the purpose is conceived differently.

This way of building a robust portfolio using redundancy isn’t very different from traditional portfolios. A robust portfolio holds a variety of assets, but the purpose is different from modern portfolio theory. Instead of seeking to optimise return and minimise volatility, the assets are chosen and proportions determined based on the purpose of the investor. A retiree will have various sources of income, where a growth-oriented investor will have various sources of growth. The portfolio will always maintain a portion in cash to take advantage of opportunities. The portfolio will never be optimal and may leave some potential returns untapped, but it should provide the most effective protection against total failure.

If our normal statistical tools can’t describe the type of uncertainty experienced in financial markets, how can we compensate, or at least manage our exposure? Hints can be found in nature. Our financial systems have much in common with systems found in nature. Markets are not emotionless interactions of data, but rather they are the meeting place of action driven by human emotion. The interactions of investors is similar to the interactions of living things in other natural systems.

One example is an ecosystem. It includes resources such as water and food. It includes various types of animals, predators and prey. It includes the external elements such as weather. All of these things combine into a system that finds a point of equilibrium. This stability is, however, in a state of constant flux,including both cycles and directional change.

The human brain is ill-equipped to cope with this type of system. Our brain searches for patterns, and prefers order. We try to simplify and optimise. However, this type of thinking isn’t well-suited to natural systems. Trying to order nature has led to crises relating to biodiversity. Steps taken to optimise a system have led to its failure.

If ordinary human thinking cannot adequately manage natural systems, including financial systems, what principles can we adopt from natural systems that we could apply to our thinking about investment markets? The main purpose of a natural system is survival. Growth is not necessarily a goal. This may also be true in financial systems. Small companies (that don’t fail) tend to grow, but large companies reach a certain point where they can no longer expand profitably and they tend to decline. This “business lifecycle” is not unlike the lifecycle of a living organism. Within a group of living things, various organisms will achieve various degrees of dominance, but none will remain forever. Despite this, the species can continue independent of each individual.

Continuing with that analogy, no one organism is so large that it can individually affect the entire species. The largest land animal is the elephant. The loss of a single elephant doesn’t impact the future of the species or the balance of the ecosystem. Another example is mosquitoes. On a summer day, I may kill a dozen. Even spraying pesticide in breeding ponds by the city doesn’t seem to have much effect on next year’s population of mosquitoes.

An ecological system is not optimised. There are not “just enough” elephants and “just enough” mosquitoes. A robust system contains duplication and redundancy. Take, for example, the human body. I have two ears, two two eyes, two arms, two legs. If I lose the use of a single limb, the other can compensate. Ideally, a single organism will have multiple purposes. You know those birds that live near the hippopotamus? They eat bugs, they produce eggs, they keep the hippos clean and I bet they even have a pleasing song.

These same ideas apply to finance. The system should not be optimised. It should, instead, contain duplication and redundancies. No single element should grow out of proportion. Experience should be prized over models. In this way, instead of fruitlessly trying to reduce volatility, we can be prepared for real risk and survive to invest another day.