We’re in for a bumpy ride in 2011. As the year slowly draws to a close, predictions for 2011 are gaining pace:
The stock market is going to make a 1990s-ish run next year, with the Dow Jones well within reach of 13,500 level, according to Esquire columnist Ken Kurson. Arthur Laffer, on the other hand, forecasts that current corporate profits reflect an income shift into 2010, because taxes will be raised in 2011. These profits will tumble next year, preceded most likely by the stock market. Another prediction, raised from time to time by institutional investors, is that investing in active management strategies will prosper in 2011. If the trend persists that investors switch towards passive management, there will be less competition to hunt for “alpha”, leaving more earnings potential for the remaining investors.
The interesting question is not whether the prediction is right on the mark (public and policy makers pragmatically expect they’re not), but rather why diverging views persist in economics and finance. A solid theory, broad dataset and sound research methods should be able to resolve ongoing debates and lead to accurate predictions. Economists and researchers surely put an enormous effort in research, but resolving debates tends to move slowly. Economics and finance are tough subjects to investigate. Why is this?
A historic perspective comes in handy. Investing theory and practice have developed dramatically over the past five decades, yet there still is no objective framework around as to how we view capital markets and how to apply these insights for investment purposes (Lo, 2005). In the 1950s, the investment philosophy boiled down to a simple approach: stock selection determined which securities were included in the portfolio, beginning with a careful analysis of a company’s income statement and balance sheet. To founding fathers Graham and Dodd, developing financial ratios from the accounting record of companies was a key element for investment decisions.
A paradigm shift took place from the 1960s onwards with the work of Harry Markowitz, which focused on assembling stocks into portfolios to minimize risk at an acceptable level of return: the “don’t put all your eggs in one basket” principle. His main conclusion: portfolio construction is more important than picking individual stocks or timing markets.
The 1970s marked the concept of systematic risk. Active management – earning excess returns relative to benchmarks – met its counterpart in the 1980s with passive management – cleverly combining exposure to different markets to give investors the systematic risk and return they needed. In the 1990s, new investment strategies were developed at an astronomical pace, based on derivates markets that were just 25 years in existence. Views then evolved further; active management since the turn of the century has increasingly meant earning absolute returns, leaving benchmarks out of the equation, with investment managers proclaiming they have forged a felicitous union between exploiting inefficiencies in the financial markets (active management), and clever financial engineering to achieve the right degree of systematic risk. All are different views of capital markets that still co-exist, sometimes in harmony and sometimes at odds with one another.
Yet none can be pinpointed as the right one. Don Raymond argues that theories in investments and finance simply do not have the same degree of confidence as theories in physical sciences for three reasons. First, finance is a relatively young discipline. Modern finance is roughly 50 years old, while other disciplines have been shaped over several hundreds of years. The main theories have not been road tested; basic premises are not conclusive. For example, for more than 30 years economists have hotly debated whether financial market pricing is efficient or not.
Such a debate has some interesting consequences. Those who believe that markets are efficient advocate indexing and other passive strategies such as buy-and-hold, weathering the peaks and troughs of price cycles. Believers in inefficient markets usually invest in what they perceive as undervalued stocks, sectors or assets, and do appreciate market-timing. Additionally, financial data is very ”noisy”. It requires a lot of effort to extract relevant information from price signals, and the predictive power of models for future returns is generally low. New mathematical techniques, the field of econometrics, were developed to cope with this, but only succeed to a limited extent.
In other words, investment management is not a hard science like physics or chemistry; it is above all a social science. A truly scientific investment theory would be based on an equation derived from proven laws of nature that specify how we get from point A to point B in the future (Sherden, 1998). Based on these laws, we can accurately predict how long it takes a car to come to a complete standstill after hitting the brakes at 120 kilometers per hour. Models are based on characteristics such as mass, gravity, velocity, that can be clearly defined and precisely measured, and this enables precise predictions to be made (Gray, 1997). Scientific theories and forecasts for economies and financial markets are impossible for this very reason: there are no proven natural laws underlying the behavior of social systems. Economists therefore opt for constructing yardsticks like utility or risk tolerance to emulate hard science. Analysts might make predictions based on theories, but such theories are not laws of nature. Their models have limited applicability because the yardsticks are inherently unstable as well as difficult to establish objectively: risk tolerance varies immensely per person, or before and after a financial crisis.
The second factor setting investment management and economics apart from the hard sciences is that while for example physics can test hypotheses through controlled experiments, this is very difficult in the case of economics and investments (Gray, 1997). Economists are creative in relaxing this hurdle by gathering as much data as possible and looking for common denominators: when equities go up, on average, bonds do not increase as much in value.
Alternatively, we focus on the actor who sets it all in motion – hence the surge in the study of behavioral finance. While general theories are nearly impossible to construct, modeling structural regularities and irregularities in human behavior is a promising avenue, since human behavior has a tendency to endure for longer periods. However, this allows partial insights at best. With human behavior, we have a pretty clear picture of why markets overreact. This brings us however little relief in the answer to the question of how much or for how long stock markets overreact, insights investment managers and pension funds would pay dearly to know.
The bottom line remains: we still cannot conduct experiments in a controlled environment and draw general conclusions. At best, these general theories result in forecasts that are little more reliable than simple naïve guesses (Sherden, 1998). That is why so many debates never really reach a firm conclusion and keep haunting investors and trustees: proponents of active management – with the ultimate aim of earning more than a benchmark – have just as much ammunition in the form of anecdotal evidence or research to prove their case to sympathizers of passive management as the other way round – especially when active management does not pay off. There is no single objective truth in the financial markets, just an accumulation of learning by doing and adapting to new realities.
So, chances are that the prognostications will miss the mark. This shouldn’t worry investors, and certainly not prevent us from filling out the sweepstakes for 2011. The process of arriving at a prediction might well be more important than the prediction itself.