The Future of Capitalism (and Commission Sharing Arrangements)

Two weeks ago I attended an all day session, “The Future of Capitalism,” put together by the the World Economic Forum (WEF) and The Forum of Young Global Leaders, with a big attendance by and assist from the World Policy Institute. While this session was under Chatham House Rules so I can’t go into great detail, it is safe to say that the general thrust of a series of wide-ranging discussions was that, in its current form, Capitalism has yet to prove it can function in a sustainable manner on a planet approaching a population of 9 billion.  There was much discussion of what could or should happen–some naive, but most quite pragmatic with some actionable steps. My favorite actionable step  was to require that CEOs, in addition to preparing their annual report to shareholders, should prepare an annual report to their children and grandchildren, describing what they do and don’t do and why, in clear understandable language relevant to these inheritors of what one would hope to be a sustainable planet.

More recently I attended a meeting where some activities in the financial world were described that may have more immediate negative impact on the Future of Capitalism. This is where Commission Sharing Arrangements (CSAs) have relevance.  A CSA is an arrangement in the investment community whereby an institutional investor does a security transaction with a broker and directs that a portion of the commission on the trade be paid out to a third party.  The third party is typically a smaller specialized firm that has provided research services to the institution. The institution has chosen to do the trade with a particular broker, usually one of the ten largest  broad-based broker-dealers (BDs), because of the belief that the BD will provide better execution at a lower commission than if the trade were done with a smaller firm. The larger BD typically has broader reach and visibility to get a larger trade done, including use of electronic trading systems developed by the BD or more accessible to the BD. In addition, the larger BD is more likely to be willing and able to commit capital to complete a trade in the event that the market place doesn’t. CSAs, a variation on soft dollar payments, that now is used with smaller BDs,  have come into vogue over the last several years, with help from the larger BDs, and already account for upwards of 30% of all commission dollars generated by institutional investors.

The above is a long explanation of what may appear to be a minor thing happening to Capitalism, in contrast to the grand ideas that came out of a WEF meeting. In my view it is not minor and has significant unintended consequences. Historically, smaller BDs have been an important part of the capital allocation and capital raising functions of the financial markets. They have provided research on smaller companies, been a part of price discovery in the marketplace through their trading desks and provided investment banking services including IPOs and secondary offerings for these companies that typically could not command the attention of the larger BDs. As institutions have elected to pay for the research by check vs trades, the ability of the smaller BDs to service these small companies as investment bankers has become problematic.  As trading becomes more concentrated among the big guys the economics are forcing many of these companies out of the trading and capital raising function and lessening their ability to hire and retain high quality professionals. The larger BDs do not step into the breach to service smaller private and public companies because the economics just don’t justify it. Thus, the capital markets business becomes more concentrated among a smaller number of bigger players. In a way, it is Capitalism at work within the financial services industry where unfettered Capitalism leads to concentration into fewer entities and, ultimately, monopoly positions.

Unfortunately, it doesn’t stop there. Concentration in the capital markets affects the role of Capitalism in the broader non-financial world as well. The less apparent outcome of this concentration, is a stunting of the capital-raising function for private companies leading to more limited access to the public markets. It produces an approach by early-stage investors of less reliance on the public markets providing liquidity and more focus on the direct sale of a company. I see that working its way into the investment decision process by venture capitalists and others, and the subsequent strategic process around the growth of a small company. If the expected exit or liquidity event for the investor is a sale, that becomes a big part of the way capital is allocated to “grow” the company. It also appears to shorten expected time frames from start-up to potential liquidity. And, it leads to the creation of products as opposed to companies, with an eye on where the product would fit into the business of potential corporate buyers. It changes the nature of the skill sets at the investment banking firms from understanding and supporting functioning capital markets to advisory merger and acquisition talent. Finally, it has the insidious effect of leading to concentration and lack of innovation in the corporate world as well. A large company can survey the universe of start-ups and smaller companies and snap them up before they become a threat or to fill a gap in their skill sets or product offerings. It is no longer a “make or buy” decison. It is just a “buy” decision.  With lower odds of a smaller company ultimately getting liquidity and raising capital through the public markets, the focus internally becomes one of positioning the company for a sale to one of the behemoths in its industry. Once the acquisition takes place, I have seen, in many instances, the innovation pace slowing or in many cases just disappearing. In some instances it can actually add to the portfolio of products offered by the larger entity, but not always. Ultimately, concentration increases, stifling growth and innovation, and creating less-free markets. It then takes big government to “regulate” these entities to prevent the ultimate outcome of true capitalism–a monopoly position.

So we end up with big financial firms, big corporations and big government—most likely all too big to fail or change, but more dependent on each other for their raison d’etre and most likely less responsive to leaving a better world for their children and grandchildren.  It’s not a pretty picture. It’s not all because of Commission Sharing Arrangements, but the pace at which concentration is happening is accelerated by this small change in the way business is being done in the financial sector.

I was actually encouraged by the discussions at the WEF meeting and walked away with some hope that the upcoming generation of Young Global Leaders might actually find ways to get this right. But, the small changes which have big implications and are taking effect daily will make their job tougher.

Lean In Again: Some Observations on the Comments re Sheryl Sandberg’s Great Book

In Dealbook, Professor Steven Davidoff published an interesting article, “Why So Few Women Reach the Executive Rank,” which provided a pretty good summary of what many have drawn from Sheryl Sandberg’s observations in her book. I still say everyone really does need to read her book. I would add to my admonition in my earlier post–Read the book, carefully. I would also suggest reading Davidoff’s article carefully as well.

I think one quote in Davidoff’s article is very telling– “women have to behave like men to rise to the top.” This seems to be a universal conclusion of what Sandberg is saying in her book. I think a more complete statement,  is “women have to behave like the men who are currently at the top to rise to the top themselves.”

I would posit that in many cases we have the wrong men at the top who are pretty much, across the board, producing sub-optimal performance relative to what it could be. Most corporations are operating below their potential because the culture does not allow the best talent, female or male, to rise within the organizations. Exclusion and prejudices about what constitutes a good worker or a good manager, in my view, often lead to less capable people managing parts of any organization. It is easy to identify women as a class being excluded. And there are a set of prejudices and difficult work environments that exist specifically related to women. There are also less explicit prejudices which end up excluding a set of men as well. Sandberg hints at this in her book. Organizations that minimize both these prejudices–female and male–end up with more successful women and a different set of successful men. And, I believe, a more successful business–certainly relative to their peers. We need to identify more clearly what it is about those organizations (too few in number) where this is happening.

Trying to understand what it is about the culture and the general environment in those few organizations where the presence of more women in the ranks may be an indication of that culture, might provide some clues about what it takes to create the right environment.  I am still trying to figure that out, in spite of having been part of an organization where that happened. I think we need a few professors to take on the challenge of identifying the organizations and truly figuring it out.

In Praise of Sheryl Sandberg (and all women)

While working out in a gym in Abu Dhabi, of all places, I watched Soledad O’Brien’s CNN interview with Sheryl Sandberg re “Lean In: Women, Work and the Will to Lead.” It was a reinforcing interview between two intelligent and focused women with a great supporting cast. It is worth watching as an adjunct to the book. Everyone should, of course, read the book–I mean EVERYONE.

My read of the primary focus of the book is an exhortation for professional women to look in the mirror with a new eye and seek leadership opportunities and not convince themselves (or allow others to do so) that they can’t or shouldn’t. There is an occasional recognition that there are many other women throughout the workforce who don’t get a fair shake. It is also an honest personal appraisal of Sandberg’s career to date, the mistakes she has made and the lessons she has learned. It is a management primer for women–and men–that specifically deals with the prejudices we all bring to our interactions with the other sex (notice I didn’t say “opposite”). It has application to interactions with anyone, since we are all different products of nature and nurture. The facts and data–read the appendix!–that document reasons for the gender gap, as well as the anecdotes throughout the book provide support for Sandberg’s conclusion that things won’t really have changed “until half our institutions are run by women and half our homes are run by men.” They certainly won’t change for the women further down the pyramid until that happens. She often says that men have to do their part to produce this change but she really puts the onus on the women. It is an important message.

In my mind, though, there is a bigger message in the book and an unstated opportunity for companies who get it now–and some have–to set themselves up to take advantage of “gender arbitrage.” This is not the classic definition of gender arbitrage, which is hiring smart women for less, but the “gender arbitrage” that was defined back in the late ’80’s when we took the Lehman Research effort from 15th to 1st in the rankings in three years. Boris Groysberg and Ashish Nanda, who did much of the work creating a series of Harvard Business School cases about what happened there, pointed out that we had more women and more successful women in the department–statistically off the charts–than any other firm in the business. Somehow, we created an environment where the best women on Wall Street were attracted to the firm and thrived and new female  analysts became successful quickly. It got defined as gender arbitrage after the cases were written. We also had many very successful men. It was a hard-working but supportive place to work. It was really IQ/Acumen/Attitude arbitrage. The women were not paid less than the men and in many cases made more, tied to their success as analysts and, ultimately, managers. There were many little and some big things we did that helped produce the success. I would suggest that you go to the HBS Publications website and spring for a copy of the Jack Rivkin Lehman A case if you really want to get into the nuances. Boris is now teaching a course at HBS, “How Star Women Succeed,” and the case is a part of it.  Why the environment initially existed is hard to explain. It started with just wanting to create something special that had nothing to do with gender–just capabilities and a big “no-jerk” policy.  There was one thing, though, that made a difference. From the very beginning there were many people involved in the interviewing processes, but our lead interviewers were two of our first outside hires–two very capable women. An interesting thing happened as a result. Here were two people who were clearly part of the decision-making process and were serious about talent and attitude. Other women they interviewed were attracted by their attitudes, their openness, their empathy and the clear understanding that they were decision-makers. Interestingly, the men they interviewed knew that they were a big part of deciding whether the men would be hired and that they (the men) might end up working for them (the women). Some men just opted out because of that, which was fine with us. The ones who didn’t, understood how the organization was going to work, recognized the talent and the opportunity and clearly didn’t have a problem with the working relationships. In fact they saw the working relationships as a big plus. It all fed on itself and created a supportive environment where the gender balance really worked for us. We didn’t just have a good gender balance. We got the absolute best where there were no impediments and much support in the work place and in their lives in general–for both the women and the men of like minds.

I don’t think organizational success has to wait until talented women lean in and work their way up the jungle gym to the top. If the organization truly creates the environment that doesn’t tolerate the jerks and provides continual support for both the women and men making decisions about their lives, they will end up with teams that really work. And the best managerial talent, which should be equally balanced among the sexes, will rise through the ranks. Who knows if the real organizational structure, if this truly happens, will have “ranks” as opposed to something more like the jungle gyms Sandberg refers to. Sandberg points out that “Research already suggests that companies with more women in leadership roles have better work life policies, smaller gender gaps in executive compensation, and more women in mid-level management.” I would submit that there are companies with all or some of the above because they started down this path many years ago. We need many more. What a waste of having impediments that prevent the best to rise. It is important to read Sandberg’s book, though. If male and female employees  and executives cannot empathize and learn from her story, the gender arbitrage, as I define it, won’t happen. There is more to say on this subject, but everyone please read the book, and then we’ll talk.

What is the Big Deal about Big Data?

I have always been fascinated by data and how it could be used to run a business, create investment opportunities and understand and affect behavior. As I was getting my engineering degree in the ’60’s, I was exposed to the value of historical data and the use of algorithms to reach conclusions under uncertainty. My first job out of the Colorado School of Mines was, strangely, with Procter & Gamble. P&G was a big user of data in its product development, marketing and manufacturing operations. After business school, I was fortunate enough to go to work for a research boutique, Mitchell Hutchins, that was prepared to take full advantage of the early big data manipulation capabilities coming from dumb terminals connected through time sharing to large computers. Mitchell Hutchins was involved in the creation of Data Resources, an economic forecasting company co-founded by the late Otto Eckstein. Data Resources created very sophisticated forecasting models that could either be accepted or manipulated by its clients over these early networks. We also used the network to create individual company models and valuation models which became a regular part of reports to clients. The reports themselves were printed and distributed through the US mail or some early private delivery services. I must say the models that were developed through all this data manipulation were seductive and created an air of certainty in the conclusions that we reached. The higher the correlation coefficients and the R-squared, the more we believed. I am not sure if our forecasts, or those of Data Resources were that much better than others created through less sophisticated means. To Otto’s credit, while publishing his models, he remained a skeptic of the end results. “Add factors” were always a part of his forecasts in discussions with clients. I think we all ultimately learned to respect the power of data, but, at the same time, recognized that the models were only as good as the inputs we were using, and what we didn’t know or measure was as important or more important than what we knew. I think that applies even more so today as the available data expand. We will get some answers that we didn’t have before, but let’s all remain skeptics and avoid the seduction of the certainty associated with the size of input, the sophistication of the models, and the speed with which we get the answers.

So let’s explore Big Data.

According to E-Bay the volume of business data is doubling every 1.2 years. The amount of data Big Science is accumulating dwarfs the business community. Several “laws” have come into play producing these enormous amounts of data and getting everyone excited about what can be done with Big Data if analyzed and manipulated properly.
The Harvard Business Review devoted much of its October, 2012, issue to Big Data. McKinsey and others have published numerous reports on the topic. This is all with the belief that applying the proper analytics to all this data can lead to better business decisions replacing or reinforcing intuition with hard facts coming from more complete and precise information. It all starts with the latest version of Moore’s Law: Processing speeds double every 18 months. This is without question the most important law related to Big Data. In my view, particularly these days, the utility of data is inversely proportional the amount of time it takes to process the data. Wirth’s law comes into play here: Software is getting slower more rapidly than hardware becomes faster. Other more assertive variations have been put forth: May’s law (sometimes facetiously called Gates’ Law): Software efficiency halves every 18 months offsetting Moore’s Law. The impact and perceived importance of processing the Big Data coming at us will very likely put even more of a premium on efficient software. For most applications, developers have had it easy. Processing speeds have allowed for the development of lazy code. One would hope that the exigencies of data growth change that. Otherwise value creation will lag and negate some of the other laws at work here. Metcalfe’s Law: The value of a network is proportional to the square of the number of connected users to the network (~n squared)–or probably more appropriate in today’s social media world, Reed’s Law: The utility of a large network can scale exponentially with the size of the network (~2 to the nth power). The real value or utility becomes, in most instances–certainly in the social media world–the near instantaneous analysis producing an economic action.

It has become a given that the proper use of data, i.e., metrics, can actually allow one to make better judgements and business decisions. Proper and selective use of the data becomes the key. Within a business what one measures can also affect how those generating the data behave. It should be apparent that it becomes important what one measures and how whatever data are collected are used. There is a variation of another law at work here, Parkinson’s Law. In its original: Work expands to fill the time available or in its computer corollary: Data expands to fill the space available for storage. With networks expanding, processing speeds increasing and the cloud and more powerful servers ultimately providing infinite storage, consultants and business school professors have discovered Big Data. In my view, this is creating another variant of Parkinson’s Law: The number of conclusions one can reach expands proportionately with the quantity of data available and inversely with the time it takes to analyze the data. All of those conclusions may be actionable. That doesn’t mean they will have a positive effect. It also doesn’t mean we shouldn’t seek these answers. It is not even a question of “should.” These answers will be sought.

There will be an advantage to those who are the early users of Big Data. This certainly proved to be the case in the investment and trading community. Every day an enormous amount of data are generated on stock price movements, trading volume, business results, economic results and, of course, the opinions of the pundits in the media and in the research departments of a wide variety of financial services entities. Models have been built and continue to be built and modified that attempt to show correlations among securities and deviations from those correlations. The low cost of trading combined with the speed at which a transaction can occur has allowed traders to take advantage of minute variations in highly correlated securities. Those who have created the better models and/or can react more quickly to a variation have done quite well. The importance of speed of reaction has been such that some traders moved their processing closer to the source of the information and the trading shortening the time it takes for electrons to activate and produce a transaction. It is a business where minute fractions of a second can make the difference. The models, though, have to keep morphing in terms of inputs and speed to stay ahead of the competition. Otherwise they all converge eliminating the disparities that produce profits. The Fallacy of Composition comes into play: When everyone stands up to see, no one can see. I think this is already happening in the trading community.

In the long run this will likely happen in other communities as well. We see aspects of this in consumer product marketing. This community has always been good at analyzing the data available to it to discern what customers want or can be made to want. This has led to a wide array of similar products from various companies with little distinction among them. The first movers always had an advantage for a brief period, but ultimately, others developed competitive products. Youngme Moon describes these phenomena well in her wonderful book “Different: Escaping the Competitive Herd.”  What she describes has broad application beyond the marketing community she uses as her examples.

There is a Big Deal about Big Data. The advances that can be made in science, business and, in particular, in the social media world are very exciting–a little scary, but most exciting things are. The early users will have an advantage–maybe a sustainable one as they learn what they still don’t know and adjust accordingly. It is important to understand that the outcomes will only be as good as the inputs and the analytics applied to them. To the extent one comes to rely on these outcomes without understanding what remains unknown, it increases the risks of larger and larger unintended consequences through error or just faulty or incomplete models. The models will always be incomplete. The more we accept that premise the more value our use of Big Data will have. It is hard to imagine the outcomes every time processing speeds and data accumulation double. We are on our way to a more superlative adjective replacing Big. Hang on!

The Latest Jobs Report. Lots of Negativity Which I Don’t Buy

I recently joined Patrick O’Keefe, director of economic research at JH Cohn LLP to discuss the April 2012 jobs report. Listen to my talk with Bloomberg’s Kathleen Hays and Vonnie Quinn on “The Hays Advantage” on Bloomberg Radio on May 4, 2012.

Download the podcast

Labor Market, Stock Market, The Economy, and Why the JOLTS Report is Both Good and Problematic

The labor market is fine, but there are some concerns (and opportunities). Listen to the podcast of a conversation with Bloomberg’s Kathleen Hays and Vonnie Quinn of “The Hays Advantage” on Bloomberg Radio from April 10, 2012. These two posts and a link will also provide some background: , , .

Download the podcast

The Employment Situation is Quite Dynamic–2 Million quit their jobs in February

The February employment numbers are showing an encouraging trend that began last year. I expect this to continue with some ups and downs. It is supporting one of the surprises in “What Could Happen in 2012 (and beyond).”  Net, net, 227,000 jobs were added in February, and with a half million increase in new job seekers, the unemployment rate stayed at 8.3%. I am not sure everyone understands the components that go into that net number which reflect a very dynamic labor situation in the United States. The net number of new jobs is a result of about 4 million people being hired every month while roughly the same number leave their jobs. What is interesting is the make-up of those numbers. Using the latest available data (December 2011) here are some interesting facts that, if nothing else, will provide some cocktail conversation at your next party (don’t invite me, please):

In December 2011, 4.0+ million people were hired. 3.9 million were separated. Only 1.9- million were actually laid off. 1.9+ million quit, typically to take other jobs, and 330 thousand left for retirement or other personal reasons. At the end of the month there were 3.4 million job openings remaining to be filled.  This is up from 2.9 million in December 2010.

This kind of dynamic goes on every month in the US. If we look at some of the peak numbers prior to the recession, in 2006, average monthly hires were 5.4 million; layoffs were only 1.8 million; other separations were 0.4 million; Quits were a very large 3.0 million. The average number of unfilled jobs at the end of each month was 4.5+ million. Construction employment also peaked in that year averaging 7.7 million. In December 2011, it was 5.5 million.

December 2011 2006 monthly average
Hires 4.0 million 5.4 million
Total Separations 3.9 5.2
Layoffs 1.9 1.8
Other Separations 0.3 0.4
Quits 1.9 3.0
Net Jobs Added 0.227 0.155
Job Openings 3.4 4.5
Construction Employment 5.5 million 7.7 million
Unemployment Rate 8.5% 4.5%

There are many interesting statistics that tell a story of a fairly dynamic labor picture in the US. One of the most worrisome numbers, in my view, is Job Openings. In such a dynamic labor force there will always be substantial unfilled jobs. While geography, timing and Quits play a role, it is an indication that the skill sets don’t match up with the requirements.  Companies find much of their labor requirements from those who already have jobs and skills. It is great for those with the acquired skills who are improving themselves, but, on balance, it raises labor costs and does nothing about those who want jobs who don’t have the appropriate skills. I think corporations will have to fill the training role–and some are. Clearly, our educational system isn’t doing it, although the unemployment rate for those with a college degree is only 4.2%. The military can also fill this role as an important plus for those who do choose to serve. By the way, the unemployment rate for all veterans is 7% while non-veterans are at 8.6%.  Among male veterans/non-veterans it is 7.2% and 9.3% respectively.  I could go on with these little tidbits. For those who are interested just visit I find it much more interesting than browsing Facebook. It is tougher working it in to a cocktail conversation, though. Seems to have less impact than talking horoscopes or The Voice.