The Art and Science of Investing
Introduction
There are multiple approaches to investing in the financial markets, creating many ways to fail as well as to succeed. Based on different technical skill sets and behavioral biases, investors trade financial assets employing some combination of art and science. Reviewing both professional and amateur investor performance in the literature, it is clear many investors possess minimal awareness of the interaction of art and science and the importance of both. A classic example was the rise and fall of hedge fund Long Term Capital Management (LTCM). Run by some of the best and brightest, including two Nobel Prize winners in Economics and a number of PhDs from Harvard and MIT, LTCM managed to lose over $4 billion in a six week period. The potential damage to the global financial system required a massive bailout orchestrated by the Federal Reserve. The following quote from Michael Lewis’ New York Times article When the Egg Heads Cracked sums up the trading mentality at LTCM. “In October 1987, the markets took power from people who traded with their intuition and bestowed it upon people who traded with their formulas. In August 1998, the markets took power away from people with formulas who hoped to remain detached from the marketplace.” By reducing their trading to a pure science, LTCM totally abandoned the art of investing, resulting in gross mismanagement of the hedge fund.
The science of investing can be thought of as the objective application of models. The art of investing can be thought of as the subjective decisions made in determining appropriate models, model inputs and the interpretation of model outputs. An understanding of the difference between risk and uncertainty is also useful for transitioning across art and science. This article identifies the interaction of art and science in successfully navigating the financial markets.
Models
Models are frequently used in the investment process. An understanding of what a model represents is essential to gaining insight to the art and science of investing. A model can be thought of as a simplified version of reality. A model is typically a graph, an equation, an explanation or some combination or variation of these. A model typically focuses on key issues and omits trivial details.
An example of a model is a road map. The map illustrates routes from one location to the next, but it is certainly a simplified version of reality. Looking at the map reveals no information regarding road work, a car accident, a road closure, etc. The science of the road map model is the illustrated route. The art is anticipating potential delays based on common sense, intuition and experience. The art is also going through some what if scenarios to determine the consequences of not being on time for an appointment, given a delay.
All models have underlying assumptions. Events in the real world can occur unexpectedly and underlying assumptions can be violated. The underlying assumption on the road map model is the road is open and accessible, but there are times when that assumption can be mildly or severely violated. The art is how we anticipate a problem and/or how we respond to a problem.
Some of the most elegant and widely used financial models, considered pillars of modern finance, include the Markowitz Mean Variance model and the Black Scholes Option Pricing model. There are a number of simplifying underlying assumptions in these models that are consistently violated to one degree or another. Model practitioners have developed the judgement to know when and approximately what degree the underlying assumptions have been violated and adjust their model inputs and/or interpretation of the model outputs to be in sync with the real time investment environment. These mathematical models represent science, but the effective application requires common sense, experience and intuition — the art.
A good model achieves the following:
• Explains observed outcomes
• A reliable predictor of future outcomes
Some academics will point out there are now better models than the two referenced above. When there are competing models we choose the simplest model (Occam’s razor) if the more complex model does not yield materially superior results. The more complex substitute financial models for Markowitz and Black Scholes typically require more inputs, are more difficult to use and do not consistently provide materially superior results.
A skilled model practitioner understands the relationship between model inputs and outputs. In some models small changes in input values can result in large changes in output values. A good example is the volatility input value in the Black Scholes option pricing model. Small changes in volatility create significant changes in output values. Using the Black Sholes formula to demonstrate this is the science. The art is determining what that volatility input should be. Is it the historical volatility, implied volatility, some weighting of the two? All of a sudden the art becomes very important, given the sensitivity of output values to small changes in input values.
When using financial models in the investment process always consider the following:
• What models are appropriate and when
• What a model can and can’t do
• What are the underlying assumptions / when and to what degree have those assumptions been violated
• What are appropriate values of the input variables
• When interpreting model outcomes — is action required and if so what action
We can summarize the effective use of quantitative models with the simple model below:
Inputs →Calculations →Output
The inputs and input values heavily rely on art (judgement). The calculations rely on science (formulas). The interpretation of the output heavily relies on art (judgement).
Risk vs. Uncertainty
The two terms, risk and uncertainty, are frequently used interchangeably, but knowing how they are different and what they represent are key to a rational investment approach. Risk is present when the following two criteria are met:
• A complete outcome set can be identified
• Probabilities can be calculated for each outcome
Examples include games of chance like flipping a coin, tossing a die and spinning a roulette wheel. In each case all possible outcomes are known and the probability of any given outcome can be calculated.
Uncertainty is present in scenarios where outcomes are unknown and therefore no measures of probability are possible. The world of financial markets inhabits the realm of both risk and uncertainty. Contrary to the daily explanations and forecasts of the random and the unknowable by market “pundits”, below is a list of what we typically don’t know and can’t know before new information enters the financial markets:
• The timing of new information, whether company, industry, geopolitical, random Trump tweet, etc. (an exception is the timing of a corporate earnings release)
• The content of new information (positive or negative from a market perspective)
• The market reaction to new information(sometimes as anticipated, sometimes surprising) including
- The direction (up or down) of a market move
- The magnitude (how far up or down) of a market move
• The sequencing of information (do a number of positive or negative events occur one after another or are they spread out)
Managing uncertainty is an art. It requires thinking out of the box by not only reviewing what has occurred in the past, but thinking about what has not occurred and could occur going forward. It requires developing strategies to deal with unlikely scenarios and challenging current assumptions by searching for contradictory evidence. It may be instructive to develop subjective probabilities given that data based probabilities are not known.
Managing uncertainty effectively also requires an awareness of cognitive biases in how we process information. Numerous studies have demonstrated an overconfidence bias resulting in suboptimal investment outcomes when investors consistently overestimate their skill at market timing or stock picking and underweight the role of luck (randomness) in the financial markets. For more on this and other cognitive biases, GOOGLE research done by Professors Daniel Kahneman, Amos Taversky, Paul Slovic, Terrance Odean, Brad Barber, Sarah Lichtenstein and Baruch Fischhoff.
With risk we have perfect knowledge of possible future event outcomes and with uncertainty we have imperfect knowledge of future event outcomes. Dealing with risk is more of a science as formulas can be applied to all possible known outcomes and probabilities derived. Dealing with uncertainty is an art that requires an honest assessment of what we can and cannot know and developing a process to deal with that reality.
Passive vs. Active Investing
Passive investing relies primarily on science. The objective is to match the risk and return of an asset index. Indices typically represent a basket of stocks, bonds or commodities. The index may employ any one of a diverse set of weighting approaches. Equity weighting schemes include traditional capital weights or any number of rules based weighting approaches, including but not limited to equal weight, fundamentals based weights and volatility based weights.
The successful application of the science of passive investing is demonstrated when index tracking is achieved with minimum cost and minimum tracking error. Tracking error is the risk and return performance difference between a fund and the index it tracks. Tracking error can be minimized through purchasing all the stocks in an index, but this can be costly when purchasing a large number of stocks, as in the Russell 2000 and also when purchasing lightly traded stocks with large bid ask spreads. To minimize costs index fund managers frequently limit the number of stocks purchased through taking a sample. The smaller the sample, the lower the cost, but the higher likelihood of increased tracking error. There is an inverse relationship between tracking error and cost when sampling is employed. The art of passive investing is in finding the right balance in the cost vs. tracking error relationship and that balance will be somewhat subjective and dependent on judgement. There is also some judgement required when periodically buying and selling securities to rebalance an index.
The three major active investing approaches include technical analysis, fundamental analysis and quantitative analysis designed to benefit from price or yield relationships between assets. The common denominator to these approaches is they employ models to make trading decisions. Successful active investing requires the effective application of both art and science. The science is typically the models we learn in class lectures or read in a textbook. The art of effectively using the models is acquired over time through experience, critical observation and common sense.
Conclusion
Knowing the limitations of our models and understanding the difference between risk and uncertainty can help us find the optimal mix of art and science when making investment decisions. In the last paragraph in his text Option Volatility & Pricing, Sheldon Natenberg speaks an absolute truth to traders of all financial assets (not only options). “Successful option trading is at least as much art as science, and a trader must know where science levels off, and other intangible assets, whether intuition or market feel or experience, begin. The most important principle of option trading is there is no substitute for common sense. A trader who slavishly uses a model to make every trading decision is heading for disaster.”