The Economics of Financial Markets

THE ECONOMICS OF FINANCIAL MARKETS

MARKET-MAKERS, BIASED INFORMATION, AND FORECASTS

This tutorial will look at financial markets and how they actually function.

There are two general theories about how financial markets work. The first is the Efficient Market Theory, which assumes all decision-makers are rational – they have access to information, can analyze it, and make investment decisions. Strangely, a major conclusion is that investors cannot predict stock price movements, which are random. The second is Behavioral Finance, which assumes that investors are irrational – they have a number of biases and are influenced by the markets’ past behavior.

Both theories based on this supposed distinction that “explain” financial price behavior are irrelevant.

What is left out of theoretical models of financial markets is how financial markets actually operate. Between buyers (investors) and sellers (including issuers of new securities), there are market makers like brokerage firms, managed funds, hedge funds and investment banks. It is in their interest to get investors to invest in financial assets rather than other types of assets, to trade often, to buy riskier securities and derivatives. All these strategies generate more revenue for them. 

It is also in their interest to convince investors to pay large fees for supposedly superior information or analytical skill, that is, to ignore the Efficient Market Hypothesis. And investors, including large pension funds, often do.

The primary selling tools are financial information, analysis of the information, and forecasts. It is the self-interest of all market-makers to present biased information and optimistic forecasts. For market-makers, financial information and forecasts are marketing data.

What is financial information? First, the current earnings of companies. The problem is politely phrased “quality of earnings.” Earnings, and earnings per share (EPS), are routinely managed by corporate accountants to show steady or exponential earnings growth. This increases the share price as EPS goes up and, if earnings rise by a high or increasing percent every quarter, by increasing the price/earnings (P/E) ratio of the stock. 

Since top management now receives much of its compensation from stock options and bonuses based on earnings growth, they have a personal reason to see that earnings are managed.

Even a casual reading of the financial press or case studies on corporate finance reveal the numerous ways reported earnings per share can be increased when there is no increase in operating earnings or even a decrease. The simplest way is to increase financial leverage. The current version is to borrow money to finance share buybacks. This increases earnings per share since there are fewer shares outstanding.  

For a sample of corporate accounting frauds, see http://www.accounting-degree.org/scandals/. Also see the entertaining film The Smartest Guys in the Room.

Then analysts who work for market-makers spin the earnings. They invent plausible reasons for the steady or accelerating increase in EPS and P/E ratios. Assuming these reasons will continue if not accelerate, they make optimistic forecasts of future earnings. Since the stock market is “forward-looking,” these biased forecasts are important pieces of “information” that investors use to make decisions. Analysts (and CNBC) become cheerleaders for the industries and companies they study, especially if they work for a financial institution that competes for investment banking business. Often analysts will hype a company that they privately know is a dog.

Analysts overwhelming issue buy or hold recommendations and very seldom issue sell recommendations.

The worse scenario for an investor is an industry with new technology. Analysts are free to make whatever predictions they want, no matter how improbable. A good story sells. In the decades I’ve followed the stock market, I’ve never heard an analyst tell the simple truth:  Almost all the companies trying to develop a new technology will go bankrupt and it is impossible to tell which few will be big winners (if any). Think about the biotech, dotcom and “clean energy” booms. I think it is fair to include the clever innovations that made the subprime mortgage and derivative boom possible.

Investors are buying the future. Even stock index funds, which are weighted averages of the market value of the underlying stocks, go up mostly because of the more rapid than average increase in the hot stocks and hot industries. 

This game comes to a temporary reversal when there is an “external shock” such as a recession. The phrase indicates important events than cannot be forecasted. Then there are the inevitable losses, “restatement of earnings,” “extraordinary losses” and “write-down of assets.” Hyped new technology companies go bankrupt. Hot stocks and industries driving the market get clobbered and indexes go down. Often a lot. 

Besides reducing past earnings, companies will also take a “big bath” write-off of assets and anticipated future expenses. It is common practice to overestimate future expenses and losses. Then when actual expenses are incurred, they are smaller than announced and earnings are higher. This is one reason for the apparent paradox that a company’s stock price often goes up when the company announces a large loss and a large write-off of assets. 

But memories are short, hope springs eternal and there is always a new story to tell – a new hot industry, new hot companies, especially if in a new technology. And the game moves to a different location.

The main point here is that even if investors “rationally” analyze the biased information and forecasts, their subsequent behavior will not be any different than “irrational” investors who follow trends created by the biases and asymmetries of the financial information.

UNDERPRICING RISK AND FAULTY MARKETS

An important part of the information used to make investment decisions is forecasts. Yet the analytical tools used to forecast, and also price financial instruments, are defective. They are mostly based on a normal distribution of price movements and related linear regression models. Normal distribution models underestimate the probability of a large downward movement in stock prices. The market has much more risk (volatility) than the models indicate. Some of the studies and statistics are summarized in Benoit Mandelbrot, The (Mis)Behavior of Markets. This conclusion has been popularized in the best-seller, Nassim Taleb’s Fooled by Randomness.

A recent example was the credit default swap market. The pricing of credit default swaps in the 2000s was based on a correlation model that assumed away the possibility of defaults! Since AIG thought there was no risk of actually paying off for defaults, it underpriced the credit swaps and could not afford to hedge its positions. The resulting defaults of derivatives and investment companies led to the bankruptcy of the largest insurance company in the world.

There are some markets that cannot be forecasted. The bond market is bigger than the stock market. Yet economists cannot forecast interest rates and thus the movement of bond prices. The mortgage market cannot be forecasted. The future cash flows of variable rate mortgages are highly uncertain and fixed rate mortgages can be refinanced. Thus the future cash flows of mortgage-backed securities cannot be forecasted. Uncertainty is compounded by leverage in buying mortgage-backed securities  (MBSs), the creation of derivatives based on MBSs, and default risk.

INDEX FUNDS VERSUS MANAGED FUNDS AND PICKING STOCKS:  INFORMATION AND IGNORANCE

Financial markets are information rich. According to economic theory, prices should reflect this information. The pricing mechanism should be very efficient, summarizing the analysis of the large amount of data. But most individual investors and many institutional investors such as pension fund trustees are totally ignorant of financial markets and incapable of interpreting and analyzing financial data. What to do?

1) Pay someone else to analyze data and pick stocks. (Managed funds)

2) Buy index funds and index ETFs.

An index fund like the S&P 500 ignores the problem of picking good stocks and buys the entire stock market. Stocks in the index are weighted by their total market value (in the jargon, called “market cap,” short for market capitalization). So the index buys 10 times as much of a stock with a market cap of $100 billion than another stock with a market cap of $10 billion. The index has to adjust the weights as the relative market caps change.

Indexes tend to be dominated by very large companies and rapidly growing technology companies with high and rising P/E ratios. Before the 2008-2009 crash, indexes were dominated by financial companies. Six of the top ten market cap companies in the S&P 500 are tech companies. Currently (2018), the eight companies in the world with the highest market cap are all information technology companies.

“Buying the market” (index funds) rather than individual stocks is a strategy for totally ignorant investors. Much of the increase in money going into the stock market after the 2008-2009 crash has gone into index funds and index ETFs. All that investors have to assume (believe) is that the real economy will grow, total profits and earnings per share (EPS) will thus increase, and that most if not all stock prices will rise as a consequence. Investors can ignore the competitive strategies and financial performance of individual companies, industry analysis, monetary and fiscal policies, and global and macroeconomic trends.

And total ignorance works. A great deal of statistical evidence indicates that index funds outperform over 90% of managed funds over long periods of time. They also have lower costs – no expensive analysis costs. Managed fund managers also tend to take greater risks and create leverage (invest with borrowed funds in additional to investors’ money) to offset higher costs and achieve higher rates of return than index funds. Because of greater leverage, greater risk, and high costs, managed funds tend to do poorly in stock market downturns. Many “blow up” (go out of business).

Index funds work because in the long run the economy does grow, total profits of public companies rise, and most stock prices go up. As more money goes into index funds, the funds must buy more of all of the stocks in the fund. The whole market goes up and the index funds prosper.

All of this also benefits managed funds. Experienced fund managers with access to all past and current financial and economic data, data and trend analysis programs, and proprietary models should be able to outperform the market in such an environment. But they do not. Why?

They are making decisions based on biased and misleading information.

There is a problem of too much information that is hard to analyze. For example, many companies no longer release an annual report. Instead, they send their stockholders (and analysts) their 10-k, which is the annual report they have to file with the government’s Securities and Exchange Commission (the SEC). These are incredibly detailed reports with small type that go on typically for 100-150 pages. Most of the content is unimportant or irrelevant. (This is the mushroom effect. How do you raise mushrooms?  Keep them in the dark and pile manure on them.)

They tend to buy companies with rapidly rising sales and profits. These companies also have high and rising P/E ratios during the innovative, rapid growth phase, increasing the rise in the stock price. As these innovative companies mature, their growth slows down. Profit growth also slows down or stops. P/E ratios fall. The result is a drop in their stock price, often large, followed by mediocre stock performance. Many of these companies are attacked by smaller companies developing or using newer technology.

Companies like IBM, Microsoft, Intel, and Oracle were innovative growth companies, are now large and profitable, but have been lousy stock investments for a long time. The large percentage increase in their stock prices in 2017 and early 2018 looks similar to the large runup in their prices in 1999, just before the dotcom market crash of 2000-2002.

Professional investors cannot predict “phase transitions” in industry technology or organization, or in the underlying economy. Just as it is difficult to predict the winners developing a new technology, it is difficult to predict the losers they will replace. It is the winners, not the losers, that make it into the indexes.

Managed funds do a lot of trading – buying and selling stocks in their portfolio. They try to time their trades with major moves in the stock market. This is difficult to do. Very few investment professionals ever predict market downturns.

Some managed funds buy a subset of large, mature companies. A diversified portfolio of about 30 stocks reduces risk almost as much as a total market index. Their economic performance as a group will be about the same as the large, mature companies in the index funds. With about the same sales and profit growth over time, the subsets in the managed funds should have long-term stock price increases about the same as the market. But at a higher cost.

On average, about 60-70% of an individual stock’s price movement will be correlated to the price movement of the whole market. So, much of the movement of stocks in a managed funds will move with the market, especially a managed fund dominated by large companies with large market capitalization.

Some large cap companies are so diversified that they are a diversified portfolio in themselves. Johnson & Johnson could represent much of the pharmaceutical and health care industry. Parker Hannifin could be a proxy for investing in cyclical industrial companies. Companies like Google and Celgene buy or invest in new and small technology companies in their industries, almost like a venture capital company. Many large companies not only have a diversified business but are also multinational corporations, a proxy for investing outside the United States.

Some managed funds concentrate on innovative companies. The problem is that many new tech and startup companies fail; their stock prices will go to zero. Many did in the 2000-2002 dotcom market downturn. They took a lot of managed funds down with them.

As discussed throughout these tutorials, an important factor for the success of a startup is the drive, determination, focus, and strategy of the founders/entrepreneurs. It is hard for an outsider like an investment manager to evaluate the intelligence, dedication, and personality of the founders.

Outside analysts and investors do not have the key information they need to evaluate a company – the internal detailed proprietary knowledge responsible for the competitive advantage causing sales and profit growth.

Most stocks, including those of the large, mature companies that tend to dominate index funds tend to go up and down (are correlated) with the overall market. There may be individual exceptions because of company-specific events but as a group they heavily influence (account for) overall stock market changes. There is no need to try to pick individual stocks among this group.

Often, one sector drives the market – IT and Internet stocks in the 1990s, finance in the 2000s, and technology in the 2010s. It is hard to pick winners early and the timing of the downturn is also unpredictable. Index funds ride through the downturn and are there for the next upturn fueled by innovative companies in new sectors and industries.

There are internal dynamics of the stock market. Companies increase dividends, buy back their stock, and do mergers and acquisitions. All these moves can increase the price of an individual stock; collectively, they increase the value of the entire stock market. Index funds automatically benefit. Managed funds often do not.

Picking stocks to outperform the market critically depends on predicting growth rates in expected EPS for years into the future. Any forecast will be highly uncertain and subject to large errors.

In conclusion, the stock market is not a random walk (impossible to forecast) or the result of irrational, emotional behavior by investors and money managers. Professional money managers seldom “beat the market” because of uncertain forecasts, the domination of mature companies, the difficulty of outsiders to pick innovative winners, and incomplete and misleading data.

I recommend reading Burton Malkiel, A Random Walk Down Wall Street. Revised and Updated Edition, 2007. Professor Malkiel was associated with Vanguard for a long time. Earlier editions of this book were an argument for the index fund approach to investing made popular by Vanguard. This edition gives a more balanced approach than earlier editions.

INSIDER INFORMATION:  PROFITING FROM ASYMMETRIC INFORMATION

Financial markets are rife with insider information. Inside information is a classic example of asymmetrical information, where insiders can profit at the benefit of investors not yet knowing the information. Sometimes insiders use their information and position to manipulate prices, such as the massive LIBOR price-fixing scandal.

Many foreign markets are insider markets, where locals can conspire to manipulate and fix prices, especially at the expense of foreign investors. This is similar to what U.S. markets were like before the reforms of the 1930s.

FINANCIAL MARKETS AND MORAL HAZARD

Conservatives argue that deregulation of financial markets leads to innovation and more efficient markets. The first part is true; many new financial instruments and new types of financial companies have been created. The implication of the second part is that because of more competition prices in financial markets quickly adjust to something approaching “fundamental value” or, in economic jargon, equilibrium. The basic problem is that the first effect works against the second effect.

The problem is moral hazard, an idea that says that individuals like managers and owners of financial institutions will take more risk if someone else (the U.S. government and taxpayers) pays the price of failure.

This happened in the savings and loan crisis of the late 1980s. The industry was deregulated so that S&L managers could make riskier loans at higher interest rates but deposits were still federally insured. So the more aggressive banks offered higher interest rates on deposits, took in a lot of money, and made a lot of high-risk bets, including illegal loans to insiders. They lost. Half of the S&Ls went bankrupt and it cost U.S. taxpayers over $130 billion in losses on bad loans.

Deposit insurance is one source of moral hazard. Another is that the two largest players in the mortgage market, Fannie Mae and Freddie Mac, had the implicit guarantee of the government. A third source is the “too big to fail” doctrine, already invoked in a big bank rescue in the 1980s. Combined with that is the idea of “systemic risk,” which implies that if a financial institution failed, even if it wasn’t a bank or “too big to fail,” it might set off a chain reaction that would threaten the collapse of the entire financial system. This is what happened with the failure of Long-Term Capital Management in 1998.

EXTREME RISK AND TOO BIG TO FAIL:  LONG-TERM CAPITAL MANAGEMENT (LTCM)

In the 1990s, the company with the most sophisticated models and trading strategies was Long-Term Capital Management. Its partners included the former head of bond trading at Solomon and two Nobel Prize winners for their work in financial models (Myron Scholes and Robert Merton). Its strategy was based on reversion to the mean of the difference in the prices of a large number of supposedly unrelated financial instruments, another correlation model. But in the global financial crisis environment of 1998, the difference in prices moved in the opposite direction of historical behavior partly because of a “run to safety” in buying U.S. Treasuries. Spreads between Treasuries and other instruments increased instead of the expected decrease. This, plus enormous leverage based on underestimating risk, led to a massive bankruptcy. Only a huge infusion of capital from other firms, made under pressure from the Fed, averted a financial crisis.

LTCM was a hedge fund so the government had no legal obligation to intervene. It also wasn’t that large in terms of capital invested. But it was very highly leveraged, meaning it had borrowed a huge amount of money (about 30 times its invested capital) and had over $100 billion of assets and liabilities on its balance sheet. If it failed, its lenders and counterparties to financial contracts would take a huge hit; it was believed that some credit markets might even freeze up (become illiquid). The Fed decided that this was too big a risk to take and forced nine major banks to chip in over $3 billion to carry the assets. LTCM was liquidated and its positions were eventually sold. But it established a precedent that a threat to financial markets, not necessarily the size of the company or the legal obligation of the government, might be a reason for the government to bailout a company. And the threat to financial markets was rapidly increasing as all large financial institutions increased their leverage in the 1990s and 2000s, many to the 30-1 ratio of LTCM. They were using borrowed funds to buy and trade inherently risky mortgage-based bonds and derivatives.

BIASED INFORMATION, FRAUD, EXTREME RISK, AND TOO BIG TO FAIL:  THE SUBPRIME MORTGAGE MARKET CRASH OF 2007-2009

After the Dotcom market bust of 2000-2002, the market continued upward, fueled by tremendous gains in the financial markets. Financial firms had found a great new business – securitizing mortgages and other debt instruments and selling bonds and other derivatives based on the cash flow of the underlying assets. At the foundation of this was a huge increase in subprime mortgages and mortgage refinancing. Many of the subprime mortgages were blatantly fraudulent or certain to go into default. But banks and other financial institutions were able to sell pyramids of derivatives many times greater, and more profitable, than the original issuance of mortgages. These markets were totally unregulated. (To understand how all this happened, see the movie The Big Short and read Explaining Derivatives – An Analogy after this essay.)

The subprime mortgage business was a con game from the start. Mortgage brokers and loan officers at sketchy banks used deceptive and often fraudulent methods to originate subprime loans. Mortgage and mortgage-baked securities (MBSs) risk analysts at some banks and investment banks, the credit rating agencies, and Fannie Mae knew that there would be a high rate of default after the low “teaser” rates ran out. In loftier language, Alan Greenspan warned in 1994 that there was a good possibility of a housing bubble and massive defaults of mortgages.

The problem was how to sell these “junk” mortgages. In a rational market, investors in subprime mortgages and their MBSs should have received high rates of return to balance the high risks of default. Not to be. If banks kept the mortgages, they could be financed by low-cost short-term borrowing. Why low cost? Because throughout most of the 2000s, the Fed kept short-term interest rates low. The prime rate was below 2% for three years.

But banks sold most of subprime mortgages to other financial institutions that would securitize the mortgages into bonds backed by the monthly payments of the mortgage holders. The bonds should have paid a high rate of return. But they didn’t. The reason was that these mortgages and their derivatives were laundered. The financial industry, with the connivance of credit rating agencies that were paid by the banks, turned bundles of high-risk mortgages into bundles of investment-grade (low-risk) bonds. Then the riskier parts of these bundles were turned into new derivatives that were also rated as investment grade. By labeling these securities as investment grade, this greatly increased the pool of potential institutional buyers such as pension funds. Mortgage origination fees, underwriting fees, selling fees and trading commissions were enormous.

But who bought these instruments? At the height of the subprime boom, large purchasers were Fannie Mae and Freddie Mac. In the past, both companies would have automatically rejected subprime mortgages. They didn’t even have models to evaluate these types of mortgages. As companies with de facto government guarantees, they were obligated to only buy and securitize high quality, low-risk mortgages. But under political and industry pressure, and loss of market share, they became major buyers of subprimes and sold mortgage-backed bonds at rates slightly higher than U.S. government bonds. Massive defaults led to the bankruptcy of both companies, which were taken over by the federal government. For political reasons, most of the losses were not borne by the bondholders such as the Chinese government but by U.S. taxpayers.

So the consequence of deregulation was not diversifying risk and self-equilibrating financial markets but accelerating systemic risk underwritten by moral hazard. How could it be otherwise? Selling greater volumes of increasingly riskier assets meant huge increases in salaries and bonuses. What did mortgage originators and managers of banks, investment banks and hedge funds care if they were creating higher levels of risk that could bring down their companies or the entire financial system? Increased leverage meant increased profits and increased bonuses. Fraud was rampant. Regulators were either clueless (SEC) or ignored their feelings that a crash was coming (Greenspan). Most deals were private so that even the hope of “free market discipline” was missing. Best of all, there were huge pools of funds run by unsophisticated trustees (asymmetric information) to finance the whole thing. Wall Street’s attitude was nicely summarized in a line from the movie The Magnificent Seven, “If God didn’t want them sheared, He wouldn’t have made them sheep.”

Will it happen again? Of course. The financial reform bill is a joke, nothing more than a political CYA crafted by the same politicians that helped create the mess. But the Congressional hearings were good theater as every member of Congress repeated a variation of the cynical line from the movie Casablanca, “I am shocked, shocked, to find out that gambling is going on in here!”

As part of its attempt to save the financial industry from imploding, the government brokered a number of “shotgun” mergers between large financial institutions. A small number of banks are now much larger than before the bailouts. They really are “too big to fail.” They are more dominant, gaining market share. They are also closely tied to the large hedge funds and private equity firms, which gives these private, unregulated companies some government protection. And, in a delicious irony, Goldman Sachs, a major private derivatives and trading investment bank, has applied to become a commercial bank so that FDIC can protect some of their creditors. The idea that American taxpayers are providing insurance to Goldman Sachs’ creditors, which include hedge funds, is moral hazard with a vengeance.

Government bailouts went way beyond the usual targets, to include insurance companies, General Motors’ and Chrysler’s financial arms, and GE Capital. There is delicious irony in the bailout of GE Capital. GE Capital is part of General Electric (GE), one of the largest corporations in the world. For many years, GE has paid no U.S. corporate income tax.

A last, major example of moral hazard. Public and private pension funds have made risky investments and lost. So what? The public pension funds must have a certain level of assets in the future. So future taxpayers will pay more in taxes and receive fewer services. And $60 billion of unfunded liabilities in private pension funds are guaranteed by the government.

Large financial firms can expect public bailouts and subsidies when they “blow-up” but investors cannot. So financial firms can take excessive risks with investors’ money to earn large fees. It is only when they start to believe their own propaganda that the financial instruments they sell are really not as risky as they are, and begin holding the securities in their own portfolios, that financial institutions risk bankruptcy.

What this means is that in the future just about any company remotely related to finance can expect a bailout. There are no market restraints on risk left. The U.S. government is now underwriting the entire financial industry, no matter how reckless. And every risk-taking gunslinger in the future knows it.

CONCLUSIONS

Analytical tools and analysts are biased producing biased information and forecasts.

Statistical models underestimate risk. Risk is underpriced and uncertainty cannot be modeled. Combined with the upward bias in public information, this creates higher percent growth of financial prices in “normal” times followed by periodic “blow-ups” in financial markets.

Moral hazard allows investment managers to take great risks since they know that the government or taxpayers will underwrite large losses.

The information and knowledge that most professionals possess does not give them an advantage over the total ignorance of investing in passive index funds. They cannot "beat the market."

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EXPLAINING DERIVATIVES – AN ANALOGY

You go around to farmers with cows. You buy all the cows and pay the farmers a small fee to milk the cows and sell the milk. You pay for the cows with ass(et)-backed securities called MBSs (Milked Bovine Securities) that you tell investors are udderly safe. But some of the cows don't give enough milk (cow flow problem) or give no milk at all. You take some of the asset-backed securities, say they're backed by the subprime cows, and use them as collateral to sell another set of securities called CMOs (Cow Milk Obligations). Then you buy CDSs (Cow Dried-up Swaps) from AIG (Angus Insurance Company) to insure the CMOs when the cows stopped giving milk. If you work it right, you collect more on the CDSs than you pay out to retire the CMOs. The money you get from selling the dead cows go to pay the CLOs (cow leather obligations).

You could also sell CDOs (cow dung obligations) that depend on how much cow dung is produced. This is a typical Wall Street product - turning shit into gold.