In this new Dutton release Tyler Cowen, a professor of economics at George Mason University, analyzes today’s economic reality in which Average Is Over. In its place is a world of income disparity, with big earners and big losers. This dichotomy, Cowen argues, is not likely to change in the future. Although the subtitle of the book is Powering America Beyond the Age of the Great Stagnation, future economic growth will not provide more wage equality. Since the source of this power is the use of new technologies, the economic divide will in fact only be exacerbated: “Marriages, families, businesses, countries, cities, and regions all will see a greater split in material outcomes; namely, they will either rise to the top in terms of quality or make do with unimpressive results.” (p. 4)
It’s not the case, writes Cowen, that the middle class has been decimated, but “the middle of the distribution is thinning out and this process appears to have a long ways to run. … The longer-term trend is fewer jobs in middle-skill, white-collar clerical, administrative, and sales occupations. Demand is rising for low-pay, low-skill jobs, and it is rising for high-pay, high-skill jobs, including tech and managerial jobs, but pay is not rising for the jobs in between.” (pp. 38-40) Fret not, finance remains one of those areas that recruits freshly minted college grads with a high ‘g factor’—i.e., high general intelligence (even if not high skill)—and pays them well.
What will the United States look like in twenty to forty years? Extrapolating from the present, Cowen argues that “we will move from a society based on the pretense that everyone is given an okay standard of living to a society in which people are expected to fend for themselves much more than they do now.” He imagines “a world where, say, 10 to 15 percent of the citizenry is extremely wealthy and has fantastically comfortable and stimulating lives…. Much of the rest of the country will have stagnant or maybe even falling wages in dollar terms, but a lot more opportunities for cheap fun and also cheap education. Many of these people will live quite well, and those will be the people who have the discipline to benefit from all the free or near-free services modern technology has made available. Others will fall by the wayside. … It will become increasingly common to invoke ‘meritocracy’ as a response to income inequality” and this “framing of income inequality in meritocratic terms will prove self-reinforcing. Worthy individuals will in fact rise from poverty on a regular basis, and that will make it easier to ignore those who are left behind.” (pp. 228-230)
The future Cowen paints is pretty bleak for the majority of Americans. For instance, “the less wealthy will be pushed out of the nicer living areas.” He contemplates the possibility of building some makeshift structures for the poor, including the elderly poor, “similar to the better dwellings you might find in a Rio de Janeiro favela. The quality of the water and electrical infrastructure might be low by American standards, though we could supplement the neighborhood with free municipal wireless….” (p. 244)
This is not a future I want to see. We can only hope that, as often happens, trends are disrupted.
Wednesday, September 11, 2013
Monday, September 9, 2013
Baker & Kiymaz, Market Microstructure in Emerging and Developed Markets
Not so very long ago investors and traders had little reason to worry about what goes on behind the scenes. They placed an order, got a fill, paid a commission, and that was that. They didn’t have to know “how the sausage was made.” But times have changed. Infrastructure malfunctions and flash crashes have unnerved the investing community—in part because they (and often the regulators as well) didn’t understand how such disruptions could happen.
Market Microstructure in Emerging and Developed Markets: Price Discovery, Information Flows, and Transactions Costs, edited by H. Kent Baker and Halil Kiymaz (Robert W. Kolb Series in Finance, Wiley, 2013) offers a detailed analysis of the market’s “guts.” Or, more accurately, I should make the word “market” plural because the book covers not only the equity market but bond, derivatives, and currency markets, not only exchanges but dark pools, not only developed markets but emerging markets. Both academics and practitioners contributed to the book.
I can’t, of course, give a proper sense of this book as a whole since it is so wide-ranging, but here are a few topics that are analyzed. Do circuit breakers reduce volatility, enhance price discovery, interfere with the trading process, prompt a magnet effect? Why does the bid-ask spread exist? (Yes, of course, we know the easy answer.) What is the impact of high-frequency trading on liquidity and market quality? How does pretrade transparency affect liquidity? How would you design a market from scratch?
I personally have always been intrigued by dark pools. Put most crudely, are they good or bad? The authors of the chapter on dark trading note that “dark pools free ride on the price discovery of other markets…. This may give rise to manipulation strategies that may negatively affect both dark pools and the main market. Dark pools also influence the market quality of other markets. Two opposite forces appear. First, dark pools allow for additional risk-sharing benefits as they may cater to traders who would otherwise not participate in the trading process. This should improve market quality. Second, market quality may deteriorate when dark pools are skimming off part of the uninformed traders. These trade-offs underpin why regulators are concerned that price discovery and market quality may suffer when the market share of dark pools becomes too large.” (p. 227) And you wonder why regulators have such difficulty coming up with clear guidelines.
For those who want to know where to place their limit orders, here’s a finding from 2004 about quote clustering: “The proportion of quotes that are divisible by $0.05 is around 39 percent for NASDAQ and 40 percent for the NYSE. The proportion of quotes divisible by $0.10 is 22 percent for NASDAQ and 24 percent for the NYSE.” (p. 208)
Market Microstructure doesn’t read like a novel, but it’s an excellent reference for those who would like to delve deeper into how markets do and don’t work.
Market Microstructure in Emerging and Developed Markets: Price Discovery, Information Flows, and Transactions Costs, edited by H. Kent Baker and Halil Kiymaz (Robert W. Kolb Series in Finance, Wiley, 2013) offers a detailed analysis of the market’s “guts.” Or, more accurately, I should make the word “market” plural because the book covers not only the equity market but bond, derivatives, and currency markets, not only exchanges but dark pools, not only developed markets but emerging markets. Both academics and practitioners contributed to the book.
I can’t, of course, give a proper sense of this book as a whole since it is so wide-ranging, but here are a few topics that are analyzed. Do circuit breakers reduce volatility, enhance price discovery, interfere with the trading process, prompt a magnet effect? Why does the bid-ask spread exist? (Yes, of course, we know the easy answer.) What is the impact of high-frequency trading on liquidity and market quality? How does pretrade transparency affect liquidity? How would you design a market from scratch?
I personally have always been intrigued by dark pools. Put most crudely, are they good or bad? The authors of the chapter on dark trading note that “dark pools free ride on the price discovery of other markets…. This may give rise to manipulation strategies that may negatively affect both dark pools and the main market. Dark pools also influence the market quality of other markets. Two opposite forces appear. First, dark pools allow for additional risk-sharing benefits as they may cater to traders who would otherwise not participate in the trading process. This should improve market quality. Second, market quality may deteriorate when dark pools are skimming off part of the uninformed traders. These trade-offs underpin why regulators are concerned that price discovery and market quality may suffer when the market share of dark pools becomes too large.” (p. 227) And you wonder why regulators have such difficulty coming up with clear guidelines.
For those who want to know where to place their limit orders, here’s a finding from 2004 about quote clustering: “The proportion of quotes that are divisible by $0.05 is around 39 percent for NASDAQ and 40 percent for the NYSE. The proportion of quotes divisible by $0.10 is 22 percent for NASDAQ and 24 percent for the NYSE.” (p. 208)
Market Microstructure doesn’t read like a novel, but it’s an excellent reference for those who would like to delve deeper into how markets do and don’t work.
Thursday, September 5, 2013
O’Connell, Stats & Curiosities
The official publication date for Stats & Curiosities from Harvard Business Review, written and edited by Andrew O’Connell (Harvard Business Review Press), is October 15, so consider this a pre-release preview.
Remember the Bangladeshi butter-production theory of asset prices gag? Some twenty years ago David Leinweber and Dave Krider looked for a non-financial series that had the highest correlation with the S&P 500 over a ten-year period. The winner: butter production in Bangladesh. As Leinweber subsequently wrote, “we had a good laugh over it, added a few more dairy products and third world livestock [butter production in the U.S., U.S. cheese production, and sheep population in Bangladesh and U.S.], and lo and behold, found a regression that ‘explained’ 99% of the S&P 500 using this nonsense.” (Forbes, 7/24/2012)
Well, amid the 165 stats and curiosities in O’Connell’s collection, some sound an awful lot like these useless investment signals. And that despite the fact that they stem from serious academic and field studies. Does that mean that the studies were flawed? Or did the researchers unearth genuine connections that occasionally simply seem odd or unnerving? For the most part, I assume the latter is true.
But then, as the author points out, “using legitimate statistical analyses, researchers were able to show in an experiment that participants were nearly 1.5 years younger after listening to the Beatles’ ‘When I’m Sixty-Four’ than after listening to a song that comes with the Windows 7 operating system—an obviously ridiculous finding.” (p. 133)
Moving to less ridiculous (though still odd) findings, “about one-third of drivers of Prius hybrids failed to yield to pedestrians in a series of experiments on crosswalks in the San Francisco Bay area, giving the brand one of the highest rankings for ‘unethical driving.’” (p. 36) Along the same lines, “people who viewed images of food labeled ‘organic’ made harsher moral judgments about others’ behavior and volunteered 6 minutes less of their time to help someone out, compared with people who viewed nonorganic foods.” (p. 44) So much, I guess, for the good earth folks.
A team of researchers from the Federal Reserve Bank of San Francisco studied the link between happiness levels and suicide rates and found that “countries and US states with higher happiness levels tend to have higher suicide rates. … Unhappy people may become suicidally distressed by others’ contentment, the researchers suggest.” (p. 50) Somehow I doubt this interpretation of the data. Findings from two states may prompt readers to formulate an alternative hypothesis: “Utah is ranked number 1 in life satisfaction but has the ninth-highest suicide rate, whereas New York, ranked 45th in satisfaction, has the lowest suicide rate in the United States.”
Here’s another finding that may help to account for the high rate of obesity among the poor. “People who were observed choosing large-size coffees, pizzas, and smoothies were rated by others as having higher status—an average of 4.98 on a 1-to-7 scale—than people who chose small sizes (3.03). … The research shows, moreover, that people who feel powerless tend to choose larger options than people who feel powerful, regardless of the items’ price.” (p. 53)
Some stats that apply directly to the world of finance:
Only 24% of workers in finance and banking would recommend their job to their children as opposed to 67% in agriculture and ranching (presumably they want to keep the family business going), 44% in professional services, 43% in IT, and 42% in health care.
Firms provide more stock options in areas with fewer Protestants.
Negative online chatter volume about company products predicts lower stock prices; positive chatter and five-star ratings have no predictive value.
“Research participants who strongly trusted their feelings were able to predict future levels of the Dow Jones average with 25% greater accuracy than people who didn’t.” (p. 185)
And here’s a truly weird one: “Wide-faced CEOs’ companies perform better.”
So, all of you who are looking for an edge, cast your net wide, be imaginative, but beware the Bangladeshi-butter syndrome or the “fountain of youth” effect of listening to “When I’m Sixty-Four.”
Remember the Bangladeshi butter-production theory of asset prices gag? Some twenty years ago David Leinweber and Dave Krider looked for a non-financial series that had the highest correlation with the S&P 500 over a ten-year period. The winner: butter production in Bangladesh. As Leinweber subsequently wrote, “we had a good laugh over it, added a few more dairy products and third world livestock [butter production in the U.S., U.S. cheese production, and sheep population in Bangladesh and U.S.], and lo and behold, found a regression that ‘explained’ 99% of the S&P 500 using this nonsense.” (Forbes, 7/24/2012)
Well, amid the 165 stats and curiosities in O’Connell’s collection, some sound an awful lot like these useless investment signals. And that despite the fact that they stem from serious academic and field studies. Does that mean that the studies were flawed? Or did the researchers unearth genuine connections that occasionally simply seem odd or unnerving? For the most part, I assume the latter is true.
But then, as the author points out, “using legitimate statistical analyses, researchers were able to show in an experiment that participants were nearly 1.5 years younger after listening to the Beatles’ ‘When I’m Sixty-Four’ than after listening to a song that comes with the Windows 7 operating system—an obviously ridiculous finding.” (p. 133)
Moving to less ridiculous (though still odd) findings, “about one-third of drivers of Prius hybrids failed to yield to pedestrians in a series of experiments on crosswalks in the San Francisco Bay area, giving the brand one of the highest rankings for ‘unethical driving.’” (p. 36) Along the same lines, “people who viewed images of food labeled ‘organic’ made harsher moral judgments about others’ behavior and volunteered 6 minutes less of their time to help someone out, compared with people who viewed nonorganic foods.” (p. 44) So much, I guess, for the good earth folks.
A team of researchers from the Federal Reserve Bank of San Francisco studied the link between happiness levels and suicide rates and found that “countries and US states with higher happiness levels tend to have higher suicide rates. … Unhappy people may become suicidally distressed by others’ contentment, the researchers suggest.” (p. 50) Somehow I doubt this interpretation of the data. Findings from two states may prompt readers to formulate an alternative hypothesis: “Utah is ranked number 1 in life satisfaction but has the ninth-highest suicide rate, whereas New York, ranked 45th in satisfaction, has the lowest suicide rate in the United States.”
Here’s another finding that may help to account for the high rate of obesity among the poor. “People who were observed choosing large-size coffees, pizzas, and smoothies were rated by others as having higher status—an average of 4.98 on a 1-to-7 scale—than people who chose small sizes (3.03). … The research shows, moreover, that people who feel powerless tend to choose larger options than people who feel powerful, regardless of the items’ price.” (p. 53)
Some stats that apply directly to the world of finance:
Only 24% of workers in finance and banking would recommend their job to their children as opposed to 67% in agriculture and ranching (presumably they want to keep the family business going), 44% in professional services, 43% in IT, and 42% in health care.
Firms provide more stock options in areas with fewer Protestants.
Negative online chatter volume about company products predicts lower stock prices; positive chatter and five-star ratings have no predictive value.
“Research participants who strongly trusted their feelings were able to predict future levels of the Dow Jones average with 25% greater accuracy than people who didn’t.” (p. 185)
And here’s a truly weird one: “Wide-faced CEOs’ companies perform better.”
So, all of you who are looking for an edge, cast your net wide, be imaginative, but beware the Bangladeshi-butter syndrome or the “fountain of youth” effect of listening to “When I’m Sixty-Four.”
Tuesday, September 3, 2013
Spitznagel, The Dao of Capital
The Dao of Capital: Austrian Investing in a Distorted World by Mark Spitznagel (Wiley, 2013) is a beautifully crafted book, one I can recommend to readers of all political/economic persuasions. Yes, Ron Paul wrote the foreword and the book will undoubtedly appeal to Fed bashers, but its main line of reasoning transcends ideological divides.
Spitznagel advocates a roundabout (shi, Umweg) approach to investing, which involves “waiting and preparing now in order to gain a greater advantage later.” (p. 3) Admittedly, waiting and preparing can be, and usually is, a painful process. Think of Robinson Crusoe who sacrificed time that could have been spent catching fish by hand or with a crude spear in order to build a simple boat and a fishing net. He went hungry for weeks in order to position himself for a larger catch later on. “This is Umweg: Crusoe ultimately catches more fish by first catching fewer fish, by focusing his efforts in the immediate toward indirect means, not ends.” (p. 110)
Spitznagel develops his thesis by drawing on the wisdom of the Laozi (“the advantage comes not from applying force but from circular yielding”), the paradox of his mentor at the Chicago Board of Trade (“You’ve got to love to lose money, hate to make money, love to lose money, hate to make money…. But we are human beings, we love to make money, hate to lose money. So we must overcome that humanness about us.”), the military strategies of Sunzi and Clausewitz, and—most fully—the writings of the Austrian School.
Spitznagel’s strategy reveals “a clear, systematic source of investment mispricing, ripe for intertemporal arbitrage (a term synonymous with Austrian Investing itself). But this bias of intertemporal inconsistency is not, and for the most part cannot, be arbitraged away, because of the simple reason that the arbitrageurs are the ones most inflicted by the bias.” (p. 158) The problem is that “on Wall Street, roundabout investing—acquiring later stage advantage through an earlier stage disadvantage—is irrational, and acting as if there is no future is perfectly rational” … “No matter how large the ‘later’ might be, the ‘sooner’ is all there is.” (pp. 162-63)
The author devotes two chapters to applied roundabout investing, which in the first instance profits from distortions in the economy and the markets through tail hedging (or at least tips off the retail investor to stay out of the market). He explains how to gauge such distortion using a measure he calls the Misesian Stationarity index. In the second instance he looks for companies that have a high ROIC “where these superior efficiencies at turning invested and reinvested capital into future earnings are apparently not priced in.” (p. 259) The latter is not value investing. “Between Austrian Investing and value investing is again the difference between depth of field and the telescopic long term. … Both require discipline and patience, but Austrian Investing is concerned with the intertemporal process at work, rather than just the endpoint.” (p. 272)
The Dao of Capital is a consistently thought-provoking book. Well, perhaps that’s the wrong adjective since it seems to entail a use of force—and this book aims to twist neither arms nor minds. But it is impossible not to be shaped by its carefully presented history and logic.
Spitznagel advocates a roundabout (shi, Umweg) approach to investing, which involves “waiting and preparing now in order to gain a greater advantage later.” (p. 3) Admittedly, waiting and preparing can be, and usually is, a painful process. Think of Robinson Crusoe who sacrificed time that could have been spent catching fish by hand or with a crude spear in order to build a simple boat and a fishing net. He went hungry for weeks in order to position himself for a larger catch later on. “This is Umweg: Crusoe ultimately catches more fish by first catching fewer fish, by focusing his efforts in the immediate toward indirect means, not ends.” (p. 110)
Spitznagel develops his thesis by drawing on the wisdom of the Laozi (“the advantage comes not from applying force but from circular yielding”), the paradox of his mentor at the Chicago Board of Trade (“You’ve got to love to lose money, hate to make money, love to lose money, hate to make money…. But we are human beings, we love to make money, hate to lose money. So we must overcome that humanness about us.”), the military strategies of Sunzi and Clausewitz, and—most fully—the writings of the Austrian School.
Spitznagel’s strategy reveals “a clear, systematic source of investment mispricing, ripe for intertemporal arbitrage (a term synonymous with Austrian Investing itself). But this bias of intertemporal inconsistency is not, and for the most part cannot, be arbitraged away, because of the simple reason that the arbitrageurs are the ones most inflicted by the bias.” (p. 158) The problem is that “on Wall Street, roundabout investing—acquiring later stage advantage through an earlier stage disadvantage—is irrational, and acting as if there is no future is perfectly rational” … “No matter how large the ‘later’ might be, the ‘sooner’ is all there is.” (pp. 162-63)
The author devotes two chapters to applied roundabout investing, which in the first instance profits from distortions in the economy and the markets through tail hedging (or at least tips off the retail investor to stay out of the market). He explains how to gauge such distortion using a measure he calls the Misesian Stationarity index. In the second instance he looks for companies that have a high ROIC “where these superior efficiencies at turning invested and reinvested capital into future earnings are apparently not priced in.” (p. 259) The latter is not value investing. “Between Austrian Investing and value investing is again the difference between depth of field and the telescopic long term. … Both require discipline and patience, but Austrian Investing is concerned with the intertemporal process at work, rather than just the endpoint.” (p. 272)
The Dao of Capital is a consistently thought-provoking book. Well, perhaps that’s the wrong adjective since it seems to entail a use of force—and this book aims to twist neither arms nor minds. But it is impossible not to be shaped by its carefully presented history and logic.
Monday, August 26, 2013
Yu, Way of the Trade
High frequency traders are a fact of life in the markets. They justify their activity by claiming that they provide liquidity. But, Jea Yu argues in Way of the Trade: Tactical Applications of Underground Trading Methods for Traders and Investors (Bloomberg/Wiley, 2013), the algos/HFTs generate volume and magnify momentum “by luring in and trapping the greatest number of participants on the WRONG side of the trade so they can kidnap all the liquidity and ransom it out to the highest bidders. … They don’t steal liquidity, just as kidnappers don’t steal their victims. They just borrow long enough to extort the highest prices for the return.” (p. 15)
Whatever we might think of high frequency traders (and the debate rages on), retail traders simply don’t have the firepower to compete directly with them. They need new tools to navigate a treacherous landscape.
Jea Yu, cofounder of UnderGround Trader.com and the author of three earlier books, introduces the reader to the ideal trader for these conditions: the hybrid market predator who has “the precision timing of execution, risk averse scaling, and technical analysis of the daytrader, the premeditative assertiveness tethered by patience and risk management of the swing trader, and the relentless investigative fundamental prowess of the investor. Whereas all three roles have butted heads in the past, now they are components that converge to manifest into a more efficient market predator that can seamlessly shift between skillsets to adapt to changing landscapes, climate, and terrain.” (p. 43) Of course, as we know, easier said than done.
The ideal trader seeks to exploit pockets (think football). “The caliber of a trader … can be gauged on how efficiently he can spot, time, and work the pockets in the market. The pockets that pertain to execution are transparency, liquidity, and momentum. The pockets that pertain to conditions are divergence, reversion, and convergence. These pockets construct the elusive window of opportunity.” (p. 48)
In this book Yu offers both general guidelines and specific trading strategies, complete with color charts. Among the general guidelines, he describes the eight-step process required to turn an idea into profit: (1) constantly monitor the macro market conditions, (2) filter/qualify to determine if the idea presents a viable opportunity, (3) analyze the risk/reward, support/resistance, (4) devise a trading/strategy plan with triggers/scaling points/stops/set-ups, (5) factor in the macro market context—convergence/divergence/fades, (6) execute the trade and manage risk (size + set-up + duration), (7) manage the trade by monitoring the technical with macro market, and finally (8) exit the trade—scale out position/down exposure into liquidity pockets.
He describes in detail “the strongest price pattern” he has ever played—what he calls the perfect storm pattern trade. “Simply put, this powerful pattern forms when three or more pups/mini pups (or inverse pups/mini inverse pups) form and converge simultaneously on three or more separate time-frame charts (of the seven total time frames).” (p. 157) I’m not going to explain this pattern—or for that matter the meaning of “pups”—here; Yu himself spends almost fifty pages on it.
Anyone who opens this book thinking that trading is easy and that the market simply hands over profits for the asking (the “15 minutes a day for 50% annual returns” crowd) will be in for a rude awakening. For those who are serious about making money in the markets, however, Yu opens windows himself. There’s something for traders at every level in this book. It even takes a stab at options trading. And for those who are happier with streaming video, each copy of the book comes with its unique access code to a 70- or 90-minute (depending on which description you believe) video course.
Whatever we might think of high frequency traders (and the debate rages on), retail traders simply don’t have the firepower to compete directly with them. They need new tools to navigate a treacherous landscape.
Jea Yu, cofounder of UnderGround Trader.com and the author of three earlier books, introduces the reader to the ideal trader for these conditions: the hybrid market predator who has “the precision timing of execution, risk averse scaling, and technical analysis of the daytrader, the premeditative assertiveness tethered by patience and risk management of the swing trader, and the relentless investigative fundamental prowess of the investor. Whereas all three roles have butted heads in the past, now they are components that converge to manifest into a more efficient market predator that can seamlessly shift between skillsets to adapt to changing landscapes, climate, and terrain.” (p. 43) Of course, as we know, easier said than done.
The ideal trader seeks to exploit pockets (think football). “The caliber of a trader … can be gauged on how efficiently he can spot, time, and work the pockets in the market. The pockets that pertain to execution are transparency, liquidity, and momentum. The pockets that pertain to conditions are divergence, reversion, and convergence. These pockets construct the elusive window of opportunity.” (p. 48)
In this book Yu offers both general guidelines and specific trading strategies, complete with color charts. Among the general guidelines, he describes the eight-step process required to turn an idea into profit: (1) constantly monitor the macro market conditions, (2) filter/qualify to determine if the idea presents a viable opportunity, (3) analyze the risk/reward, support/resistance, (4) devise a trading/strategy plan with triggers/scaling points/stops/set-ups, (5) factor in the macro market context—convergence/divergence/fades, (6) execute the trade and manage risk (size + set-up + duration), (7) manage the trade by monitoring the technical with macro market, and finally (8) exit the trade—scale out position/down exposure into liquidity pockets.
He describes in detail “the strongest price pattern” he has ever played—what he calls the perfect storm pattern trade. “Simply put, this powerful pattern forms when three or more pups/mini pups (or inverse pups/mini inverse pups) form and converge simultaneously on three or more separate time-frame charts (of the seven total time frames).” (p. 157) I’m not going to explain this pattern—or for that matter the meaning of “pups”—here; Yu himself spends almost fifty pages on it.
Anyone who opens this book thinking that trading is easy and that the market simply hands over profits for the asking (the “15 minutes a day for 50% annual returns” crowd) will be in for a rude awakening. For those who are serious about making money in the markets, however, Yu opens windows himself. There’s something for traders at every level in this book. It even takes a stab at options trading. And for those who are happier with streaming video, each copy of the book comes with its unique access code to a 70- or 90-minute (depending on which description you believe) video course.
Wednesday, August 21, 2013
Clark and Mills, Masterminding the Deal
After a lengthy hiatus M& A seems to be back in fashion. So it’s time to take another look at the often rocky road that companies face when they contemplate acquiring or merging with another company. (Historical data show that “two-thirds or more of takeovers reduce the value of the acquiring company.”) Masterminding the Deal: Breakthroughs in M&A Strategy and Analysis by Peter J. Clark and Roger W. Mills (Kogan Page, 2013) is both a guidebook for corporate boards and executives and a research tool for investors.
The first step to M&A success is to get the merger valuation methodology right. The authors describe four methods: event studies, total shareholder return, value gap, and incremental value effect. None of these is a standalone guarantor of success; in fact, the authors recommend combining the last two discounted cash flow methods.
Value gap “reflects the commonsense notion that for a merger to be successful, post-merger improvements in the combined company—synergies—must exceed the [acquisition purchase premium] paid by the acquirer to secure control of that target.” (p. 92) As the poster child for what not to do, Hewlett-Packard paid stratospheric premiums for each of its three major acquisitions (3Par, Palm, and Autonomy) when Leo Apotheker was CEO.
Incremental value effect looks to the discounted cash flow-based “valuation of the two principals (acquirer and acquiree) on a standalone basis and combined, including consideration of both realizable synergies and a purchase premium adjustment in the latter.” (p. 108)
In their attempt to assess why some mergers succeed while most fail, the authors offer a ranking scheme by merger type. The most successful deals are made by bottom trawlers (87-92%). Then, in decreasing order of success, come bolt-ons, line extension equivalents, consolidation mature, multiple core related complementary, consolidation-emerging, single core related complementary, lynchpin strategic, and speculative strategic (15-20%). Speculative strategic deals, which prompt “a collective financial market response of ‘Is this a joke?’ have included the NatWest/Gleacher deal, Coca Cola’s purchase of film producer Columbia Pictures, AOL/Time Warner, eBay/Skype, and nearly every deal attempted by former Vivendi Universal chief executive officer Jean-Marie Messier.” (pp. 159-60)
More simply put, acquisitions fail for three key reasons. The acquirer could have selected the wrong target (Conseco/Green Tree, Quaker Oats/Snapple), paid too much for it (RBS Fortis/ABN Amro, AOL/Huffington Press), or poorly integrated it (AT&T/NCR, Terra Firma/EMI, Unum/Provident).
Although this book abounds in acronyms (fortunately there is a list of what they stand for at the back of the book), it also has some good turns of phrase, original and borrowed. For instance, banker-dealmakers, whose models depend on revenues from merger activity rather than merger success, are, in the words of Reggie Jackson, “the straw that stirs the drink.”
Masterminding the Deal may subscribe to the view that you can’t manage what you can’t measure, but it does not subject the reader to the nitty-gritty of measurement. It’s a book of principles, not an exercise in number-crunching. A book that more deal-chasers should read.
The first step to M&A success is to get the merger valuation methodology right. The authors describe four methods: event studies, total shareholder return, value gap, and incremental value effect. None of these is a standalone guarantor of success; in fact, the authors recommend combining the last two discounted cash flow methods.
Value gap “reflects the commonsense notion that for a merger to be successful, post-merger improvements in the combined company—synergies—must exceed the [acquisition purchase premium] paid by the acquirer to secure control of that target.” (p. 92) As the poster child for what not to do, Hewlett-Packard paid stratospheric premiums for each of its three major acquisitions (3Par, Palm, and Autonomy) when Leo Apotheker was CEO.
Incremental value effect looks to the discounted cash flow-based “valuation of the two principals (acquirer and acquiree) on a standalone basis and combined, including consideration of both realizable synergies and a purchase premium adjustment in the latter.” (p. 108)
In their attempt to assess why some mergers succeed while most fail, the authors offer a ranking scheme by merger type. The most successful deals are made by bottom trawlers (87-92%). Then, in decreasing order of success, come bolt-ons, line extension equivalents, consolidation mature, multiple core related complementary, consolidation-emerging, single core related complementary, lynchpin strategic, and speculative strategic (15-20%). Speculative strategic deals, which prompt “a collective financial market response of ‘Is this a joke?’ have included the NatWest/Gleacher deal, Coca Cola’s purchase of film producer Columbia Pictures, AOL/Time Warner, eBay/Skype, and nearly every deal attempted by former Vivendi Universal chief executive officer Jean-Marie Messier.” (pp. 159-60)
More simply put, acquisitions fail for three key reasons. The acquirer could have selected the wrong target (Conseco/Green Tree, Quaker Oats/Snapple), paid too much for it (RBS Fortis/ABN Amro, AOL/Huffington Press), or poorly integrated it (AT&T/NCR, Terra Firma/EMI, Unum/Provident).
Although this book abounds in acronyms (fortunately there is a list of what they stand for at the back of the book), it also has some good turns of phrase, original and borrowed. For instance, banker-dealmakers, whose models depend on revenues from merger activity rather than merger success, are, in the words of Reggie Jackson, “the straw that stirs the drink.”
Masterminding the Deal may subscribe to the view that you can’t manage what you can’t measure, but it does not subject the reader to the nitty-gritty of measurement. It’s a book of principles, not an exercise in number-crunching. A book that more deal-chasers should read.
Monday, August 19, 2013
Rabins, The Why of Things
Aristotle suggested that “men are never satisfied until they know the ‘why’ of a thing,” where to know the “why of a thing” is to know its cause(s). Centuries later, causation remains an intractable philosophical problem even as we’ve loosened and redefined the bonds between cause and effect in an attempt to deal with it.
Peter V. Rabins, a psychiatrist at the Johns Hopkins School of Medicine, offers a many-model account in The Why of Things: Causality in Science, Medicine, and Life (Columbia University Press, 2013). According to his three-facet schema, there are three conceptual models of causal logic, four levels of analysis, and three logics by which to gain causal knowledge.
Here I will restrict myself to a brief summary of the conceptual facet of causality—that is, to the categorical, probabilistic, and emergent models.
Categorical logic as applied to causality is binary—something either is or is not the cause of something else, and if it is, it acts directly to bring about an event. This model represents the most common view of causation despite the fact that it is beset by a host of philosophical problems.
In the probabilistic model, common in financial valuation and forecasting, “causes are conceptualized as events that affect the likelihood that another event will occur. In this model, causes act as influences, risk factors, predispositions, modifiers, and buffers.” (p. 45) Here the binary is replaced with the continuous, the categorical with gradations of probability.
There is some support for collapsing these two models. For instance, the “dramatic success of the computer and the digital camera … illustrate that complex, graded phenomena can ultimately be coded digitally. Perhaps nature is constructed digitally (categorically), while humans are constructed to perceive it continuously. Or, conversely, perhaps nature functions continuously, but humans have constructed categorical concepts to simplify it.”
Rabins argues, however, that both models should be kept because they have different functions and strengths (as well as limitations) and because “the choice of a specific model is determined or at least strongly influenced by the circumstances or events being considered.” (p. 57)
The third model, the one most applicable to understanding financial markets as complex adaptive systems, takes an emergent, nonlinear approach. The idea here is that systems are interrelated wholes that require models such as chaos theory, complexity theory, self-organizing systems, and network theory.
What are some of the characteristics of nonlinearity that provide a springboard for both defining and characterizing nonlinear causality?
“First, nonlinear change occurs in systems that have a large number of elements. One or two water molecules would not form ice, nor would a system made up of only two tectonic plates generate an earthquake. The presence of a large number of elements increases the number of potential interactions and increases the probability that an uncommon or unanticipated outcome will occur.” (p. 67)
Following from this first characteristic is a second: limited predictability.
A third, all too familiar characteristic is that outliers are more likely to occur.
Fourth, “some changes that precipitate an event appear to be quite small.” For instance, “the formation of ice and the development of superconductor status … seem to occur after small changes in temperature.” (p. 69) We know, of course, that most “sudden” events occur “after a period of gradual and often unrecognized change or accumulation.” (p. 70)
Finally, nonlinear causality combines “top-down” and “bottom-up” approaches. “The top-down approach begins with a systemwide, big-picture view and identifies interactions at that macro level. The bottom-up approach, on the other hand, starts with the smallest elements and builds a causal explanation based on the interactions at the micro level.” (p. 71)
No single model can capture the activity that occurs in financial markets, no single model can describe the nature of financial markets as a whole. We live in a many-model world where, as the saying goes, all models are wrong but some are useful.
Peter V. Rabins, a psychiatrist at the Johns Hopkins School of Medicine, offers a many-model account in The Why of Things: Causality in Science, Medicine, and Life (Columbia University Press, 2013). According to his three-facet schema, there are three conceptual models of causal logic, four levels of analysis, and three logics by which to gain causal knowledge.
Here I will restrict myself to a brief summary of the conceptual facet of causality—that is, to the categorical, probabilistic, and emergent models.
Categorical logic as applied to causality is binary—something either is or is not the cause of something else, and if it is, it acts directly to bring about an event. This model represents the most common view of causation despite the fact that it is beset by a host of philosophical problems.
In the probabilistic model, common in financial valuation and forecasting, “causes are conceptualized as events that affect the likelihood that another event will occur. In this model, causes act as influences, risk factors, predispositions, modifiers, and buffers.” (p. 45) Here the binary is replaced with the continuous, the categorical with gradations of probability.
There is some support for collapsing these two models. For instance, the “dramatic success of the computer and the digital camera … illustrate that complex, graded phenomena can ultimately be coded digitally. Perhaps nature is constructed digitally (categorically), while humans are constructed to perceive it continuously. Or, conversely, perhaps nature functions continuously, but humans have constructed categorical concepts to simplify it.”
Rabins argues, however, that both models should be kept because they have different functions and strengths (as well as limitations) and because “the choice of a specific model is determined or at least strongly influenced by the circumstances or events being considered.” (p. 57)
The third model, the one most applicable to understanding financial markets as complex adaptive systems, takes an emergent, nonlinear approach. The idea here is that systems are interrelated wholes that require models such as chaos theory, complexity theory, self-organizing systems, and network theory.
What are some of the characteristics of nonlinearity that provide a springboard for both defining and characterizing nonlinear causality?
“First, nonlinear change occurs in systems that have a large number of elements. One or two water molecules would not form ice, nor would a system made up of only two tectonic plates generate an earthquake. The presence of a large number of elements increases the number of potential interactions and increases the probability that an uncommon or unanticipated outcome will occur.” (p. 67)
Following from this first characteristic is a second: limited predictability.
A third, all too familiar characteristic is that outliers are more likely to occur.
Fourth, “some changes that precipitate an event appear to be quite small.” For instance, “the formation of ice and the development of superconductor status … seem to occur after small changes in temperature.” (p. 69) We know, of course, that most “sudden” events occur “after a period of gradual and often unrecognized change or accumulation.” (p. 70)
Finally, nonlinear causality combines “top-down” and “bottom-up” approaches. “The top-down approach begins with a systemwide, big-picture view and identifies interactions at that macro level. The bottom-up approach, on the other hand, starts with the smallest elements and builds a causal explanation based on the interactions at the micro level.” (p. 71)
No single model can capture the activity that occurs in financial markets, no single model can describe the nature of financial markets as a whole. We live in a many-model world where, as the saying goes, all models are wrong but some are useful.
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