The Corporate Life Cycle

1
The Search for a
Unifying Theory

It is the dream of many researchers and practitioners, whatever their field of study, to come up with a construct that explains all observed behavior and a template for forecasting future behavior. In the physical sciences, that search is abetted by nature, which imposes its order on observed phenomena, allowing for cleaner tests of any theory. In the social sciences, the search has been less focused, partly because human behavior does not always follow predictable patterns.

It is easy to understand why we search for universal theories that explain everything, since they offer the promise of restoring order to chaos, but that search comes with risk. The most significant risk is overreach: sensible theories get pushed to their breaking point and beyond in order to explain phenomena that they were never meant to cover. Once a theory becomes prevailing wisdom in a discipline, the temptation to use it to explain everything becomes overwhelming.

The second significant risk is bias, which takes shape as a theory's most ardent supporters become selective in their assessment of evidence, choosing to see only what they want to in the data, focusing on supportive evidence and denying evidence that contradicts their theory. Eventually, if a theory has weak links or is wrong, the weight of data or evidence contradicting it will lead to its modification or abandonment-but not before its pursuit by single-minded supporters creates damage.

The Search in Finance and Investing

Economics is a social science, but what sets it apart from the other social sciences is the easy access that its theorists have to rich economic data and, especially, market data. Researchers and many practitioners have tried, over time, to come up with economic theories or models that explain everything from how businesses make investments to financing and dividend decisions and how investors price companies. In this section, I will lay out some of the attempts over the last seventy years to build an overarching theory of finance-and explain why they have all fallen short.

Economic Theories

To the extent that finance is an offshoot of economics, it stands to reason that many of the early theories in finance came from economics, with economists' work on risk aversion and utility functions animating the search for financial theories that would explain market pricing and investor return. It can be argued that modern finance had its beginnings when Harry Markowitz, with an assist from the field of statistics, put forth his work on modern portfolio theory. In effect, Markowitz drew on the law of large numbers to argue that investing across multiple risky assets that do not move together yields better return payoffs, for any given level of risk, than investing in an individual asset. The Markowitz efficient frontier provided an elegant way of compressing the investment process into a search for higher returns, with risk operating as a constraint.

Figure 1.1 | The Markowitz Efficient Portfolio



The power of Markowitz's theory went well beyond the optimized portfolios that it could be used to generate, since it upended the very notion of risk in markets, supplanting the old idea that investors should assess risk on an investment on a stand-alone basis with the idea that the risk of an individual investment comes from the risk it adds to a portfolio of investments.

By introducing a riskless asset into the Markowitz universe, John Lintner and William F. Sharpe changed and simplified the efficient frontier. They showed that for all investors, no matter their level of risk aversion, a combination of a riskless asset and a supremely diversified portfolio (labeled the market portfolio because it includes every traded asset in the market), held in proportion to each asset's market value, would generate a better risk/return trade-off than any portfolio composed purely of risky investments.

Figure 1.2 illustrates the effect.

Figure 1.2 | The Capital Asset Pricing Model



Lintner and Sharpe's capital asset pricing model ("the CAPM," as finance geeks refer to it) also had a reach that well exceeded the core application, since it allowed for a linear equation that could be used to both explain past returns and predict future ones on risky assets:

E(Return on Investment j) = Risk-free Rate + Betai
(Expected Return on the Market Portfolio − Risk-free Rate)

The reach of this equation, extending from businesses using it to determine their hurdle rates (for accepting or rejecting investments) to investors using it to estimate the expected returns on individual stocks and portfolios, made it one of the most widely used and studied economic models in history. Those studies, though, uncovered a painful truth: the model lacked predictive power with regard to large segments of the market.

The benefit of the grounding in theory that characterizes theory-based models-wherein you start with economic first principles and build up to models-is that the development process constrains you from fitting the data that you observe to preconceptions that you may have about how the world operates. The drawback of these models is that for them to be useful, you have to make simplifying assumptions about human
behavior (ranging from how human beings derive utility to what comprises rationality), and to the extent that these assumptions are erroneous, you will end up with models that are elegant in theory but have little real-world explanatory power.

Data-Driven Models/Theories

Just as the Markowitz portfolio theory and the capital asset pricing model were being advanced as offering answers to all finance-related questions, a group of researchers centered at the University of Chicago were advancing a different approach, grounded in the belief that markets were efficient and that market prices were therefore the ultimate signals of truth. In the efficient-market world, the market response provides the tiebreaking answer to the question of whether a business decision was good or bad, with good decisions provoking positive market reactions and bad decisions resulting in negative ones. The efficient-market view of active investing, wherein investors attempt to time markets and pick the best stocks, was that it was pointless, since market prices already reflected all available information.

With an abundance of data (at both market- and company-level) that has been easy to access for decades, you could argue that finance discovered big data well before the rest of the world caught on to its allure. In fact, the first model to seriously challenge the capital asset pricing model was the arbitrage pricing model, in which researchers using observed data on asset prices and related expected returns to statistical (and unnamed) factors. In effect, in the arbitrage pricing model, you assume that if risky assets are priced in the market to prevent riskless profits (arbitrage), you can back out the risk factors from the pricing. These data-driven pricing attempts, which began in the late 1970s, picked up steam in the following years as access to macro- and microeconomic data widened and deepened, resulting in factor pricing models.

In 1992, Eugene Fama and Kenneth French looked at returns on all US stocks between 1962 and 1990 and noted that a significant portion of the variation in annual returns across stocks during this period could be explained by two characteristics: their market capitalizations and their book-to-market equity ratios. Specifically, they found that small-market-cap and high book-to-market (low price-to-book) stocks earned higher annual returns than large-market-cap and low book-to-market (high price-to-book stocks. They attributed the higher returns to the risks in small-market-cap and low price-to-book stocks.

In the years since, with access to more and richer data, researchers have added to the list of characteristics that explain differences in market returns, in what can broadly be categorized as factor pricing models. By 2019, there were more than 400 factors that had been identified as explaining price movements and differences in returns in major finance journals, leading some researchers to talk of a "factor zoo" and argue that most of these "market-explaining" factors are more attributable to data mining than to market behavior.

While academics were attracted to data-driven pricing models because of their capacity to explain investor and market behavior, practitioners were drawn to these models for a much more prosaic reason: to the extent that these models can uncover market mispricing, they offer the potential for profits to those who can find those market mistakes and benefit from their correction. Jim Simons was an early adopter, and his mathematical and statistical skills allowed him to earn market-beating returns for decades.

In more recent years, quantitative investing has drawn more players into this game and, with powerful computing added to the mix, driven down the returns available from using data to find investment opportunities. Put simply, using powerful computers to find moneymaking opportunities, as high-frequency traders did in the early part of the last decade, comes with a countdown clock for that profit making as new investors enter the market with their own computing power.

Data-driven pricing models do have an advantage over theoretical models in their capacity to explain observed behavior, but you can argue that this is an unfair test, since a data-driven model preserves the ability to add more or different variables to improve explanatory power, unconstrained by theory or even the need to provide an economic rationale for the presence of a factor. Having worked with data, and speaking as a believer in data, I understand how easy it is to manipulate data to yield the results that you would like to see, especially if you have strong priors. Put simply, access to data has proven to be a mixed blessing in finance and investing, yielding powerful results that emerge from some data analysis mixed with a great deal of sophistry that merely claims to be data driven.

Behavioral Models

The failures and limitations of theory-based models, which gave rise to the data-driven models that I described in the last section, also initiated a very different movement, rooted in psychology, that has now become rich and deep enough to occupy that space called behavioral finance, a melding of psychology with finance. In the 1970s, Daniel Kahneman and Amos Tversky initiated this trend by incorporating well-established patterns in human behavior into the study of markets to provide explanations for phenomena and behavior that previously had been either deemed unexplainable or treated as anomalous. Using their psychological insights, they proposed a new theory to explain decision-making in business and investing, called prospect theory, which conjectures that people underweigh outcomes that are probable and overweigh outcomes that are certain, leading to risk aversion in choices involving sure gains and risk seeking in choices involving sure losses.

In the decades since, behavioral finance has found its way to the heart of finance thinking, with Richard Thaler, Robert Shiller, and a host of others extending its reach to explain business and investor decision-making. Thaler adopted the idea of "bounded rationality" and extended Kahneman and Tversky's work into the pricing of assets; he also developed the theory of mental accounting, wherein people categorize money into groupings based upon the source and intended use of the money and then use different decision-making criteria for how to spend the money in each grouping. Shiller's initial work showed that stock price volatility over time could not be explained solely by fundamentals and formed the basis for his belief that there are market bubbles that can be explained by "animal spirits."

Behavioral finance, in its weaving together of psychology and finance, is a lot more fun and more easily accessible to many investors entering the financial markets for the first time than other theories. Because it starts with a basic recognition of how humans behave and misbehave, it is grounded in reality. However, for much of its existence-albeit with exceptions-behavioral finance has had two problems. The first is that it has expended more resources in explaining past investor and business behavior than in providing prescriptive solutions for either group. The second is that as with data-driven approaches, in which data mining has led to a proliferation of conjectured market-explaining factors, the number of behavioral quirks that have been "identified" in investing and decision-making has expanded to a point where almost every action has a behavioral explanation, no matter how outlandish.

Summing Up

In sum, none of the three broad approaches (theory, data, or behavioral) that have heretofore been used in finance provides a comprehensive pathway to explaining market behavior, but each offers promise with regard to some aspect of it. One solution is to use an amalgam of these approaches where you start with theory, while being open about its limits, then test and retest with the data, and finally overlay this research with the behavioral quirks that we have learned investors exhibit to explain deviations in the results. That being said, the search for a unifying theory will not stop as a new generation of researchers enters the discipline, armed with ever more data and more powerful tools than previous generations.

The Corporate Life Cycle

I am not well-versed enough in the theory, or savvy enough with data science and psychology, to come up with a universal theory of my own to explain everything that happens in business and markets. Instead, I will borrow the notion of a corporate life cycle-a construct that has been researched and used extensively, albeit more in management/strategy than in finance-and argue that while it is neither new nor the answer to all questions in finance, it has a surprisingly comprehensive explanatory power.

The Life Cycle

The corporate life cycle is a concept that has been talked and written about for decades in management and strategy circles. Ichak Adizes, a management expert, developed a ten-stage model, depicted in figure 1.3, to describe the corporate life cycle and used it as the basis for an institute founded to advance his ideas, as well as a book on the concept.

Figure 1.3 | The Adizes Corporate Life Cycle



The focus in the Adizes life cycle was more on management and the strategic choices that companies must make at each stage. The financial questions were posed with the view that aging was not easily reversible but that, with superior management, it could be done. Even within management research, there seems to be no consensus on the number of stages in the corporate life cycle and the process by which companies age. In a 1984 paper, Danny Miller and Peter H. Friesen present the corporate life cycle as comprising five common stages: birth, growth, maturity, revival, and decline. This assessment is based upon a small sample population of 36 firms that they studied through 161 time periods. They concluded that the path and timing of the life cycle vary widely across companies.

In finance, the corporate life cycle has been used more sparingly, often to explain an aspect of corporate or investor decision-making. Accounting researchers, for instance, have used the corporate life cycle to explain how accounting ratios measuring leverage and profitability change over time. They have used that evidence to provide metrics that determine where a company is in its life cycle. Corporate governance researchers have found evidence that corporate governance challenges are greater for young firms and that governance practices improve as they mature.