In January, I posted How to Create a New Technology Category with Gartner. It described a three-step process used to create a new online-security category:
I've developed a short eDoc that can be downloaded at SlideShare.
“For decades, teachers, managers and parents have assumed that the performance of students and employees fits what's known as the bell curve—in most activities, we expect a few people to be very good, a few people to be very bad and most people to be average. New research suggests, however, that rather than describe how humans perform, the bell curve may actually be constraining how people perform. Minus such constraints, a new paper argues, lots of people are actually outliers.” – Put Away The Bell Curve: Most Of Us Aren’t ‘Average’ by Shankar Vedantam
In a November post, Risky Business (Part 2), I described how quantitative financial analysts, so-called “quants,” had applied Browian motion from physical sciences—visually described as a bell curve—to market prices. I further described how Benoit Mandelbrot observed that the behavior of prices in financial markets frequently demonstrated huge leaps to the outer edges of the bell curve. When plotted on a chart, the curve produced bubbled out on both ends, and became known as “fat tails.” According to Mandelbrot, the standard bell curve was not applicable to model financial markets because, “Large price changes are much more frequent than predicted.”
Now comes research from Ernest O'Boyle Jr., of Longwood University's College of Business and Economics, and Herman Aguinis at Indiana University's Kelley School of Business that challenges the long-held assumption that the bell curve describes how humans perform. An abstract for the research titled, “The Best and the Rest: Revisiting the Norm of Normality of Individual Performance,” published in the Spring 2012 issue of Personnel Psychology, states, in part, that:
“We revisit a long-held assumption in human resource management, organizational behavior, and industrial and organizational psychology that individual performance follows a Gaussian (normal) distribution. We conducted 5 studies involving 198 samples including 633,263 researchers, entertainers, politicians, and amateur and professional athletes. Results are remarkably consistent across industries, types of jobs, types of performance measures, and time frames and indicate that individual performance is not normally distributed—instead, it follows a Paretian (power law) distribution. Assuming normality of individual performance can lead to misspecified theories and misleading practices. Thus, our results have implications for all theories and applications that directly or indirectly address the performance of individual workers including performance measurement and management, utility analysis in preemployment testing and training and development, personnel selection, leadership, and the prediction of performance, among others.”
An excerpt from an NPR report titled, “Put Away The Bell Curve: Most Of Us Aren’t ‘Average’,” states, "We looked at researchers, we looked at entertainers, we looked at politicians, and we looked at collegiate as well as professional athletes," Aguinis said in an interview. "In each of these kinds of industries, we found that a small minority of superstar performers contribute a disproportionate amount of the output."
If the research is correct, it has huge implications for organizational behavior and human resource management (OBHRM), industrial and organizational (I-O) psychology, and other related fields. Again, quoting from the research:
“Quite simply, if performance is not normally distributed, theories that directly or indirectly build upon individual job performance and its prediction may need to be revisited. In addition, popular practices (e.g., utility analysis of preemployment tests and training and development interventions), which also rely on the assumption of individual performance normality, would also need to be revisited.”
The above graph on the left shows a Gaussian (normal or bell curve) distribution, and the graph on the right shows a Paretian (power law) distribution. The Gaussian distribution indicates that the majority of people in a random sample will be average performers, while a relatively equal minority of people will underperform and overperform the average. The Paretian distribution indicates that a small minority of people, in the random sample, will far outperform the majority.
In my experience, a self-aware CEO is very likely to increase enterprise value, while a CEO that lacks self-awareness will almost certainly decrease it.
Recently, I came across an interesting discussion on an executive forum. The topic was, “What is the most important quality for a CEO?” The question seemed rather vague to me, but it appeared that most people had interpreted it as, “What quality is most likely to help a CEO successfully lead a company?” And the most frequent answer, by a wide margin, was “competence.”
To me, this answer seemed about as vague as the initial question. The most prevalent definition of competence is the condition of being capable. This implied, to me, that a competent CEO is successful—and vice versa—but didn’t really explain why. So I began to consider: What makes a CEO competent and, therefore, successful?
One caveat: While I’ve worked closely with over a dozen CEOs and division GMs, in corporate and consulting roles, my perspective is limited, primarily, to companies that develop, produce, and deliver technology-based, business-to-business (B2B) products and services.
Measuring Business Success
My personal experience includes 1) working with a startup on the verge of bankruptcy, where success was simply a turnaround and acquisition that recovered the VCs’ investments, 2) working with several public and private companies that achieved liquidity events (merger or acquisition) at desirable valuations (well above book value), and 3) growing revenue and profit at publicly traded companies where the market caps increased. Conversely, I’ve worked with a few small companies (public and private) where profitability was either stagnant or never achieved. This led to layoffs, restructurings, high employee turnover, and in one case an eventual sell for the value of the company’s assets.
The common metric in each situation was an increase or decrease in enterprise value, measured as a multiple of the company’s book value. In the short-term, a company’s valuation can be influenced by many factors (e.g., market, technology, regulation, irrational exuberance), but over the long-term (several years to decades), the success of a company is best measured by its increase in enterprise value.
Domain Expertise
I’ve read that a successful general manager must have deep domain expertise in at least one of a company’s major functions. Given my experience, the major functions are 1) research and development, 2) production operations, 3) sales and marketing, and 4) finance and administration. Using this framework, I can make two definite statements about the CEOs and GMs I’ve worked with: 1) everyone was capable in at least one of the major functions, and 2) none was highly capable in all four. Furthermore, when I force-ranked the CEOs and GMs by how well they increased enterprise value, there was no strong correlation between success and functional expertise: every domain was represented above and below the median.
Self-Awareness
So, if all CEOs were capable (i.e., competent) in at least one functional area, and there was no strong correlation between any single functional area and success, what made some more successful than others? In my experience, the CEOs and GMs who honestly and accurately assessed their strengths and weaknesses, hired people with complementary strengths, decentralized decision authority, and built well-rounded executive teams achieved the greatest success. Conversely, the CEOs and GMs that lacked self-awareness were more inclined to closely control decision-making across all functions, and the companies were unsuccessful (did not increase enterprise value).
The Limits of Self-Awareness
Self-awareness, alone, does not ensure that a CEO will lead a company to its maximum enterprise value. For example, one CEO was self-aware, but avoided conflict. As a result, he delayed difficult personnel decisions during a period of transition. While the company was successful, it’s unclear whether it achieved a maximum valuation. Another self-aware CEO made several bad executive-hiring decisions. While he was able to increase enterprise value, the company might have achieved an even higher market cap with better hiring decisions.
Self-Awareness in Action
One of the most successful CEOs I’ve work with, whose domain expertise was in production operations, frequently said, “I don’t know much about selling, but I know a lot about buying.” During dozens of meetings over several years, he asked me numerous questions about various aspects of marketing plans, always from a buyer’s perspective. Many of his questions caused me to rethink and retune an initial approach, and the strategies were better and more effective as a result. But during all the meetings over all the years, he never once told me what to do or how to do it.
Another successful GM (who became a successful CEO), with domain expertise in finance, had previously led a product line to over 50% market share by emphasizing product performance. Shortly after I took leadership of the product line, the agency and I presented a new advertising campaign that was more edgy and image oriented. The GM asked several probing questions as to why we were shifting the message. We explained that we were now a dominant market leader (over twice the share of number two) with good brand awareness, and we wanted to build a strong brand image consistent with our target market: rock and country musicians, producers, and studio engineers. When I mentioned that he did not seem overly enthusiastic about the campaign, he said, “It’s not what I would have done, but you have sound logic for doing it. Just make sure you keep growing market share.” He was uncompromising about the goal, but very open has to how it was achieved.
By contrast, the least successful CEOs where more likely to describe exactly how they wanted something done, while often being rather vague as to the goals they wanted to achieve.
Observations and Conclusion
My experience is that:
Assuming that the CEO is competent in at least one of the company’s major functions, self-awareness is the primary characteristic that is most likely to correlate with his or her ability to increase enterprise value. A CEO’s self-awareness, alone, does not guarantee that he or she will maximize enterprise value; good personnel decisions are critical to adding people whose strengths are complementary to the CEO. However, a CEO who lacks self-awareness will not perceive his or her own weaknesses, the company’s strengths will never the greater than the CEO’s, and the company is unlikely to increase its enterprise value.
Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets
“This book is about luck—or, more precisely, about how we perceive and deal with luck in life and business. Set against the backdrop of the most conspicuous form in which luck is mistaken for skill—the world of [financial] trading.”
Nassim Nicholas Taleb is extremely smart, extremely well-read, and has studied deeply and broadly how we humans fool ourselves into believing that being in the right place at the right time is, in fact, proof that we, or others, possess above-average knowledge and skill. He has been described as an “erudite raconteur,” and this characteristic serves him well in his writing, where he interweaves interesting, entertaining stories of how people fool themselves (or are fooled by others) with rigorous explanations of why this occurs—over and over again.
A primary reason activities and events that involve human behavior cannot be modeled on physical science (see Mandelbrot’s The Variation of Certain Speculative Prices in an earlier post) is that we humans have a built-in bias to attribute our successes to skill, and our failures to bad luck. Paul E. Meehl demonstrated the “attribution bias” in his 1954 study of experts comparing their perceived abilities to their statistical ones. “It shows a substantial discrepancy between the objective record of people’s success in prediction tasks and the sincere beliefs of these people about the quality of their performance.”
Taleb is characteristically blunt in his assessment of risk managers in financial institutions.
“Corporations and financial institutions have recently created the strange position of risk manager, someone who is supposed to monitor the institution and verify that it is not to deeply involved in the business of playing Russian roulette.
“The risk managers’ job feels strange: As we said, the generator of reality is not observable. They are limited in their power to stop profitable traders from taking risks…On the other hand, the occurrence of a blowup [severe trading losses] would cause them to be responsible for it. What to do in such circumstances?
“Their focus becomes to play politics, cover themselves by issuing vaguely phrased internal memoranda that warn against risk-taking activities yet stop short of completely condemning it, lest they lose their job. (Personal note: this description could also be applied to the public pronouncements of a certain former fed chairman.)
“From the standpoint of an institution, the existence of a risk manager has less to do with actual risk reduction than it has to do with the impression of risk reduction.”
Anyone who has read a mutual fund prospectus is likely familiar with the phrase, “Past performance is no guarantee of future performance.” However, financial trading institutions routinely make decisions on the basis of past performance. One routinely used risk management tool is value at risk (VaR), defined as, “a technique used to estimate the probability of portfolio losses based on the statistical analysis of historical price trends and volatilities.” Taleb is adamant in his opposition to the use of VaR for managing risk. “An interesting problem is the ‘value at risk’ issue where people imagine that they have a way to understand the risk using ‘complicated mathematics’ and running predictions on rare events—thinking that they were able from past data to observe the probability distributions. The most interesting behavioral aspect is that those who advocate it do not seem to have tested their past predicting record, another Meehl type of problem.”
So, if the deck is so stacked against 1) accurately assessing the probability of a rare event, 2) including the consequence of a rare event in trading decisions, and 3) avoiding the madness of crowds, how did a small group of traders foresee the financial market collapse and make millions placing contrarian bets? That’s the subject of the next book.
Paul Solman interviews Nassim Taleb and Benoit Mandelbrot
Nassim Taleb urges the U.S. Congress to stop the use of Value at Risk (VaR)
The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It
The book jacket states that, “The Quants is at once a timeless masterpiece of explanatory journalism, a gripping tale of ambition and hubris…and an ominous warning about Wall Street’s future.” Unfortunately, the book doesn’t quite live up to this characterization, but 50 pages are essential to an understanding of financial risk management.
Scott Patterson, the author and a staff reporter at The Wall Street Journal, opens the book in March 8, 2006 at the Wall Street Poker Night Tournament (now the Math for America Poker Night Tournament). Gambling is a common interest with many of the quants, and has obvious parallels to the men who developed the science of probability hundreds of years earlier. The main plot centers on four quants: Peter Muller, head of Morgan Stanley’s PDT hedge fund; Ken Griffin, Manager of Chicago hedge fund Citadel Investment Group; Cliff Asness, founder of AQR Capital Management; and Boaz Weinstein, derivatives trader at Deutsche Bank and head of its Saba internal hedge fund.
The non-linear narrative shifts between events central to the financial collapse in 2007 and 2008, the back-stories of how the main characters rose from math whizzes to high-powered hedge fund managers, and the role complex mathematics (differential calculus, quantum physics, and advanced geometry), high-speed computing, and the use of immoderate leverage (trading on margin with over 97% borrowed money) played in enabling people who knew little about finance to control billions of dollars in global trading positions. The story reaches its climax as it describes events in August of 2007, when the billions the hedge funds had gained over a decade of using quantitative techniques were wiped out over the course of a few days. While this approach makes for a lively story, it is too superficial in its explanation of the role quantitative analysts played in developing the complex structured asset-backed securities (ABS), such as collateralized debt obligations (CDO), that spread risk and uncertainty about the value of the underlying assets more widely, rather than reducing risk through diversification, as promoted.
50 Pages Well Worth Reading
The fifty pages that comprise chapters two, three, and four are a concise explanation of how quantitative analysis used models from the physical sciences to create a statistical trading advantage, and how strict adherence to these models eventually lead to disaster. The author uses Ed Thorp, the “Godfather” of the quants, to make what could be a dry, academic subject interesting and engaging.
As with others who contributed to the science of probability, Ed Thorp initially applied his research to gambling, specifically blackjack. After reading a ten-page article on blackjack strategy in the Journal of the American Statistical Association by U.S. Army mathematician Roger Baldwin and three colleagues, Thorp contacted Baldwin and obtained the data from the study in 1959. Thorp was in the process of moving from UCLA, where he had completed his Ph.D. in physics, to an instructor position at MIT. Once there, he used a university computer (IBM 704) to evolve the blackjack strategy into what would become the basis for his success in casinos and his 1962 bestselling book, Beat the Dealer. What separated Thorp from those who only focus on probability was his incorporation of a wager strategy that would mitigate his risk of a prolonged losing streak (a statistically wide deviation from the norm) busting him. He derived his betting strategy from a 1956 paper, A New Interpretation of Information Rate, by John Kelly Jr., a physics researcher at Bell Laboratories. Using Kelly criterion, he could determine how much to wager, depending on the situation—larger bets when the odds were in his favor and smaller bets when they were not—that maximized return during advantageous periods of play while minimizing the risk of losing everything during periods of disadvantage.
In 1964, Thorp shifted his sights from blackjack to Wall Street—“the biggest casino of all”—and devised a precise, quantitative method for pricing stock warrants (basically long-term contracts, much like a call option, that investors can convert into common stock). He combined his pricing formula with a strategy of shorting warrants he judged to be overpriced while going long (buying) an appropriate amount of stock to hedge his bet. Theoretically, the inverse correlation of the warrant and stock would offset each other, providing the necessary hedge if the warrant price did not fall within the contract period. In the best-case scenario, the price of the (overpriced) warrant would decline and the stock would rise, closing the inefficiency gap and providing a gain on each side of the trade.
This strategy, commonly called convertible bond arbitrage (a specific application of delta hedging) became one of the most successful and lucrative trading strategies ever devised. The only risk was the unlikely event that the price of the warrant would increase while the price of the underlying stock continued to decline. Thorp understood this risk, and continued to apply Kelly criterion to determine how much he should wager on each trade in order to mitigate the risk of such an unlikely event. Unlike many hedge funds that focused only on outcome probability, Thorp understood and accounted for the other critical variable in risk management—the potentially devastating consequence of a low probability outcome.
Another person who figures prominently in financial risk management is Benoit Mandelbrot, who showed that unlikely events in financial markets are not that unlikely. He starts with the dissertation of Louis Bachelier at the University of Paris in 1900 called, “The Theory of Speculation,” that applied Brownian motion from physical sciences to market prices. Bachelier’s formula showed that the future course of the market is essentially a coin flip. This discovery came to be called the random walk, and is visually described by a bell curve. But Mandelbrot observed that the behavior of prices in financial markets (using data from cotton, wheat, railroad stocks, and interest rates that he ran on the supercomputers at IBM, where he worked at the time) frequently demonstrated huge leaps to the outer edges of the bell curve. In a paper detailing his findings. “The Variation of Certain Speculative Prices,” he asserted that the standard bell curve was not applicable to model financial markets because, “Large price changes are much more frequent than predicted.” Mandelbrot borrowed a mathematical technique devised by the French mathematician Paul Levy, whom he had studied under in Paris, to measure the erratic behavior of prices. When plotted on a chart, the curve produced bubbled out on both ends, and became known as “fat tails.”
Paul Cootner, an MIT finance professor viciously attacked Mandelbrot’s paper in his 1963 book, The Random Character of Stock Market Prices. He, and other critics, claimed that while Mandelbrot’s model might be accurate for brief time periods, over longer time periods, prices appear to move in a more orderly Brownian fashion. However, this misses Mandelbrot’s main point that “prices can gyrate wildly over short periods of time—wildly enough to cause massive, potentially crippling losses to investors who’ve made large leveraged bets.” Mandelbrot’s theories were shelved by the financial engineers who preferred the orderliness of the bell curve and a random walk to the messy, chaotic world of a fat-tail distribution. But from time to time events such as Black Monday (October 19, 1987), the Long Term Capital Management (LTCM) bailout in September 1998, and the Emergency Economic Stabilization Act of 2008, the mother of all bailouts (at least until several countries in the European Union default on sovereign debt), remind us of the consequences of ignoring unlikely events.
Nassim Nicholas Taleb, a critic of quant models, argues that investors who believe the market moves according to a random walk are “fooled by randomness,” and that models based on historical trends and expectations of a random walk, are bound to lead their users to destruction. That’s the subject of the next book.
The hallmarks of a buyer in the awareness phase are research and education. – Digital Body Language: Deciphering Customer Intentions in an Online World
A study conducted by Atrium Research in 2008 showed that despite a decade of market exposure 47% of prospective users knew “very little” to “nothing” about electronic laboratory notebooks (ELN). Although ELN began to cross the chasm from early adoption to mainstream acceptance in 2009 and 2010, there was still a need to provide a general ELN introduction to nearly half of the $1.3B total available market.
Videos are best for punching through the noise—the awareness problem. – Groundswell: Winning in a World Transformed by Social Technologies
Just remember that (if you succeed), you’re about to enter into a conversation with people who read the Web address at the end of the video. You need to be ready to convert all of that interest into action and consideration. – Groundswell
A
microsite allows those who view the video to learn more about an ELN by attending webinars and reading thought-leadership articles. And when ready, they can take the next step to the
discovery phase, where they begin exploration of potential solutions through customer case studies.
The point of greatest peril in the
development of an emerging technology-based market lies in making the
transition from an early market dominated by a few visionary customers to a
mainstream market dominated by a large block of customers who are predominantly
pragmatists. – Crossing the Chasm
The difficulty
many high-tech companies have in moving from early adoption of their product to
the accelerated acceptance of the mainstream market is that the sales approach
is counterintuitive. Why should
you abandon the technology sale that has been a key to your early success? Here’s why.
The
figure above is the revised technology adoption life cycle developed by
Geoffrey Moore in his book Crossing the
Chasm: Marketing and Selling Disruptive Products to Mainstream Customers.[i] Crossing the chasm from early adopters to early majority is important to a company’s continued early growth
and absolutely critical to accelerating growth, because the early majority
represents about 34% of the total market, versus about 13% for early adopters.
Early adopters are visionaries who find it easy to imagine, understand and appreciate the benefits of a new technology, and to relate these benefits to a strategic opportunity.[ii] Visionaries drive the early adoption phase of an emerging, disruptive technology because they see the potential for an “order-of-magnitude” return on investment and willingly take risks to pursue that goal. Because they see such vast potential, they are the least price-sensitive of any technology adoption segment.[iii]
The early majority shares some of the early adopter’s ability to relate to technology, but ultimately they are driven by a strong sense of practicality.[iv] While the goal of the visionary is to make a quantum leap, the goal of the pragmatist is to make a percentage improvement—incremental, measurable, predictable progress. They are risk adverse, and want to know how other people have fared with the product.[v] They want to see well-established references before investing substantially.[vi]
Continuing
with the technology sale that was so effective with early adopters will not
work, and may actually be detrimental, with the early majority. In order to successfully cross the
chasm, the basis of the sale must fundamentally change from superior technology
to one that emphasizes:
A compelling business case with a value proposition that demonstrates low risk, incremental process improvement, and predictable results
Case studies that show how customers, ideally other pragmatists, have fared with the product, and that supports the value proposition in the business case
[i] Geoffrey
A. Moore, Crossing the Chasm: Marketing
and Selling Disruptive Products to Mainstream Customers (Collins Business
Essentials, 2006), 17.
[ii] Ibid,
12.
[iii] Ibid,
35.
[iv] Ibid, 5.
[v] Ibid,
42.
[vi] Ibid,
13.
I was familiar with several of the frameworks for strategic analysis--including the BGC growth-share matrix, Ken Ohmae's 3C model (The Mind of the Strategist), and Michael Porter's value chain (Competitive Advantage)--from prior coursework and reading, and from applications of all three in marketing strategy development projects. While this book does not provide enough detail for the reader to effectively utilize any of the frameworks, I did find the context as to how the strategy concepts and frameworks were initially developed and used enlightening. Walter Kiechel III, the author and former editorial director of Harvard Business Publishing and managing editor at Fortune magazine, also describes some of the more faddish and less useful concepts such as core competencies, strategic capabilities and reengineering.
The book opens by describing the 3 phases of business strategy history (the 3 Ps): positioning (from the 1960s through the 1980s), process (since the 1980s), and people (a recent and more nebulous phase that is still being defined). Chapters 2 through 15 tell the story of strategy consulting from the 1960s to the present. Chapter 16 poses the question, "Where was strategy when the global financial system collapsed?" It reads as if it the author felt obliged to bolt it on, and doesn't really fit with the rest of the book. The author also includes a coda that speculates on the future of strategy and suggests that four issues in particular--risk, boundaries, corporate purpose and adaptiveness--"will press on the strategic consciousness of companies."
I would recommend this book to someone who has little
experience with business strategy and wants an overview of the field before
drilling into specific topics, or, in my case, someone with a good knowledge of
strategic frameworks who is interested in knowing more about who developed
them, when and why. I fear that
anyone looking for an in depth examination of strategic frameworks or an
evaluation of the value provided by strategic consulting firms will be
disappointed.
