Why Returning $1 Trillion to Shareholders is a Bad Idea

According to the Financial Times, companies are on record pace to return over one trillion dollars to shareholders this year via share buybacks and dividends.  With creaking IT infrastructures and under-investment in other areas such as plants, equipment, employee training and more, this isn’t just a flawed strategy; it’s a dangerous one for the future health of companies across the globe.

Courtesy of Flickr. Creative Commons. By Jeremy Yerse
Courtesy of Flickr. Creative Commons. By Jeremy Yerse

Investors are tired of companies hoarding cash. While much of these dollars are often locked away on international balance sheets, there is clamor to return a significant chunk of this cash back to investors via dividends and share-buybacks.  And just about every company of significant size is either boarding or already on the buyback gravy train according to an FT article; “dividends have climbed on average 14 per cent annually over the past four years” and “buybacks to rise at a double-digit rate this year.”

While $1T is expected to go back to shareholders, the strategy is not without critics. There is concern that returning such a large amount of cash to shareholders simply inflates stock prices and earnings per share growth, consequently leaving companies starved for investment.

“We haven’t seen much allocation of resources to capital,” says Bruce Kasman, head of economic research for JPMorgan. This is concerning, as such business investment can arguably help companies meet the needs of customers today and tomorrow. For example, this article shows the consequences of under-investment in keeping legacy systems performing in the banking industry. Even more troubling is the lack of investment in modern and cutting-edge technologies that might enable killer customer facing applications and improve customer satisfaction scores.

While burgeoning balance sheets may require some share buybacks—or at least enough to offset stock based compensation for directors and above—$1T does an seem excessive sum, especially while companies are spending 80% of their ever-so-flat IT budgets keeping existing and antiquated legacy systems functional.

If not share buybacks and dividends, where else could those monies be spent? For starters, while there’s an under-investment in IT on the whole, there are promising new technologies in play that could really make a difference for companies such as: mobile payments, embedded sensors for manufacturing systems, robotics, and even Hadoop and its related YARN applications.

Moreover, with practically a data breach and a destructive IT attack in the news each day, there’s awoeful under-investment in security (physical and software) to safeguard customer data. Another idea:investing in skills and technical training so that employees can serve customers better; this of course ultimately helps increase customer satisfaction and may improve company revenues.

Too many share repurchases may end up hurting the future performance of companies, especially when there are so many functional company departments, divisions and systems withering on the vine from lack of investment. Not to mention the dearth of innovation and creativity that’s kept at bay while investors and activist shareholders gorge themselves on the redistribution of cash flooding into their accounts.

There’s no way to avoid returning some cash to appease activist investors. But for the majority of it, there must be investment opportunities for innovation that aren’t getting a fair shake.  VC icon, Peter Theil, famously said; “We wanted flying cars, instead we got 140 characters.” Let’s not sell ourselves short on innovation. We can do better than returning $1T to shareholders via buyback binges. Indeed, we must for our companies to have a fighting chance in the future.

Crisis, Technology Adoption and Lizard Brains

lizard brain

Think of technological progress for a minute. Super computers, cloud computing, high speed networks, and advanced algorithms probably come to mind. But when crisis hits, it may surprise you how leading-edge machines, applications and networks are cast off for good old fashioned technologies like the telephone. If people revert “back to basics” in times of crisis—and assuming volatility is the “new normal”—what are the implications for technology adoption and specifically analytics?

Since the year 2000, global markets have dealt with Y2K, tech bubble (2001), subprime loan crisis (2008), and the global debt crisis (2010). That’s four crises in ten years, and it’s doubtful the world economy is out of the woods quite yet. However, something interesting happens in crisis when fear, turmoil and panic reign. All this new fangled technology gets dropped like a hot potato in favor of old and trusted communication mechanisms.

For example, in the August 12, 2011 edition of the Financial Times, writer Gillian Tett relays a story of stock traders going back to basics during the bankruptcy of Lehman Brothers. On that fateful day September 15, 2008, Tett writes; “Suddenly traders started placing orders by telephone rather than computer, (and) dealing only with people they knew personally. They were also refusing to take long term decisions…mostly the reaction was instinctive.”

In another example of leading edge technologies abandoned when panic hits, see what happened in markets dominated by high frequency traders.  During “normal” times, high frequency traders use super computers to trade equities in narrow bands, sometimes making a mere penny per trade. To make money then, high frequency traders need markets to function rationally and efficiently. However, during times of panic, there is evidence that high frequency traders desert the market in droves, causing stock prices to drop precipitously.  In essence, these companies withdraw their algorithms and super computers to live and fight another day.

In “Mean Markets and Lizard Brains” author Terry Burnham says that in chaotic environments, humans rely less on our prefrontal cortex (which provides analytic power) and more on our basal ganglia (lizard brain) which focuses on instinctual behaviors (flight, fight, protection etc). This may explain Gillian Tett’s observations that during the financial crisis of 2008, brokers discarded high powered trading terminals and super computers for phones and traded only with people they knew.

Today’s blogs, articles, whitepapers and more are dominated with topics such as “Big Data”, “Cloud Computing”, and “Business Analytics”, just to name a few. And to be sure, under normal and rational business conditions, these technologies can provide significant benefit.

But what happens when markets spin out of control and everyone activates their impetuous lizard brain? Will individuals have enough composure, courage and prescience to understand these are the times to press for advantage? Or will the lizard brain dominate, leaving advanced projects by the wayside in favor of a back to basics approach?

Don’t Follow Rules Based Decision Making Blindly!


A rules based, structured decision making approach works for many occasions, especially when choices and outcomes are relatively well documented and repetitive. But an exclusive focus on following pre-determined business rules (even when business conditions change) is a recipe for financial disaster.

Author Michael Lewis of Moneyball and The Big Short fame, has long critiqued decisions made by government officials and bankers in just about every country connected to the Great Recession.  In a recent Vanity Fair article titled, “It’s the Economy Dummkopf!” Lewis stays on the attack with his description of how some banks continued to invest in structured products such as collateralized debt obligations (CDOs), long after investors fled the market.

As the US housing market declined in 2006 and CDOs based on souring loans lost significant value, many investment bankers sold their vast CDO portfolios. However, one banker interviewed by Michael Lewis says that even as the market for CDOs took a turn for the worst, his firm loaded up on CDOs. Adding insult to injury, the banker says; “(The bank’s portfolio) would have gotten bigger if they had more time to buy. They were still buying when the market crashed.”

Picture this scenario: Every other company is fleeing the market and only a few are buying. Did these banks know something others did not? Was this calculated “big bet” that the market would turn and they’d make tons of profit?  Michael Lewis explains the opposite was true; “This was a mindless, rule based investment strategy”, he says. “As long as the bonds offered up by Wall Street firms abided by the rules (as designed by the banks, the bonds were purchased.)”

In search of yield, all that mattered for some banks was whether investments met a few significant criteria. Check box one, two and three, then buy.  Never mind that the market for such bonds was tumbling. In fact, Michael Lewis asserts the only thing that stopped these banks from losing more money was that the CDO market ceased to exist. “Nothing that happened—no fact, no piece of data—was going to alter their approach to investing money,” Lewis says.

Rules based decision making codifies decisions into “if –then” trees to arrive at optimum outcomes. And rules are often straightforward and inflexible because in most “normal” environments when business conditions fluctuate within prescribed volatility, the right decision can be counted on most of the time. However, focusing on the wrong metrics (in this instance “yield”) to the detriment of other considerations such as risks, ended up costing some banks billions of dollars.

Take a lesson from Michael Lewis. Follow the rules, but be wise to regularly examine changing business conditions and adjust rules based decision making accordingly.

Rules, it appears, are sometimes made to be broken.

Pithy Lessons Learned From Iceland


Pithy lessons learned from 2008 financial crash in Iceland – lessons I summarized from a Vanity Fair article by author Michael Lewis.

  1. Focus on your core competency – (hint: it usually isn’t investment banking)
  2. Leverage kills. Again, and again and again. Don’t we EVER learn?
  3. Speculation: if you play, you better know when to leave the casino. Preferably before you lose your shirt.
  4. It can happen to you
  5. Managers: Focusing solely on the numbers can kill you.  Investors: 14% returns should ALWAYS make you suspect.

Forget Derivatives – Hedge Risks with Innovation and Integrated Data

Puzzle piece

To protect against wild currency swings or volatile commodity costs, one surefire strategy employed by senior managers is hedging risk with derivative contracts. However, some companies are discovering alternative methods to guard against uncertainty via two strategies—product innovation and integrated data.

In a recent Fortune article, author Becky Quick cites methods for dealing with tumultuous changes in our global economy. In addition to “natural” risk hedges against currency storms such as bringing production of goods back to home markets, Quick mentions that companies look to hedge risks against commodity price fluctuations by purchasing futures, options, or similar financial products.

And while companies from soda manufacturers to global airlines use such structured products to protect themselves from perfect storms, derivatives aren’t a complete panacea, especially for un-sophisticated players. “Companies enter into transactions that are rarely understood,” says derivatives expert Satyajit Das. In addition, he says, “(These) complicated (derivative) structures make it hard for clients to price (them).”

Instead of trying to “out-think” markets, Quick gives an example of Proctor and Gamble relying on “good old fashioned engineering and science” to innovate its way out of commodity price risks. She says Proctor and Gamble doesn’t bother buying derivatives.  Instead, the company looks at alternative packaging options and sometimes substitutes for price sensitive ingredients. For example in one hair care product, biodegradable cornstarch was substituted for pricey petrochemical resins, saving millions in costs.

In addition to innovation, another risk management avenue is merging disparate data sources from sales, marketing, inventory, finance etc into an integrated foundation. With integrated data, company managers can peer deeply into various departments or divisions and perform cross functional analysis. Integrated data means that senior managers gain a 360 degree view of business conditions. Managers can now see how one decision impacts other aspects of the organization. And with everyone in the company marching to the same drum-beat—making decisions from a “single source of truth”—it’s much easier to produce coordinated and flexible responses when extreme events such as supply chain disruptions or other “once in a century events” occur.

Derivatives – a $60 trillion dollar business – aren’t going away any time soon. But innovation and integrated data provide much more than simple risk management.

Innovation provides an opportunity for cost take-out and game changing products or services. And maintaining integrated data across multiple functional areas (finance, operations, customers, suppliers etc) allows for increasing risk management sophistication as a business can be managed as “as a whole”.  Armed with a more complete picture of business conditions, executives can steer their company through chaotic economic conditions and find safe harbors in explosive market storms.

Unintended Consequences of Combining Speed with Technology

no speed limit 2

Technology is often hailed as innovation vehicle, productivity booster, and enabler of a higher standard of living for all global citizens. However, the field of finance provides an interesting backdrop for what happens when an industry is pushed to its technological limits in the pursuit of automation and speed.

Since advent of the telegraph, and all the way until early 1970s, stock prices were displayed on a ticker tape printed in near real time.  The ticker tape (via telegraph technology) was a drastic improvement in delivery of information, since brokers could gain stock prices with only a 15-20 minute delay from original quotation.

Setting the dial now to the year 2011, we now see super computers trading stocks—not with humans—but, with other super computers. Forget delays in minutes or seconds, today’s super computers trade in microseconds and are increasingly “co-located” near stock exchange servers to reduce the roundtrip time for electrons passing through networks. In fact, on most trading floors, human brokers are obsolete as algorithms are now programmed with decision logic to make financial instrument trades at near light speed.

We’ve come a long way since the decades of ticker tape, says Andrew Lo, professor at Massachusetts Institute of Technology (MIT). At a recent conference Professor Lo mentioned while technology has opened markets to the masses (i.e. day-trading platforms) and reduced price spreads, there are also downsides to automation and speed.

First, he says, there is the removal of the human element in decision making. As super computers trade with each other in near light speed, there are smaller and smaller windows of latency (between event and action) and therefore fewer opportunities for human intervention to correct activities of rogue algorithms or accidental “fat finger” trades.

Second, with fiber optic networks spanning ocean floors and super computers connecting global investors and markets, we’ve essentially taken a fragile system based on leverage and made it more complex. Automating and adding speed to an already “fragile” system generally isn’t a recipe for success (i.e. the May 6, 2010 Flash Crash).

Based on these trends, it’s easy to imagine a world where financial networks will intensify in complexity, capital will zip across the globe even faster, and relationships between market participants will increasingly grow more interconnected. Where loose correlations once existed between participants and events, markets will soon move in lockstep in a tightly coupled system.

To be sure, the confluence of technology and finance has been a boon to society in many respects. However, as Lo says, there are “unintended consequences” in the application of the most advanced and fastest technologies to an already fragile system.  Whereas the buffer of “time” to fix mistakes before or even as they occur once existed, now we’re left to clean up the mess after disaster strikes.

In addition, as markets become more tightly coupled and complex, the butterfly effect is more pronounced where the strangest and smallest event in a far away locale can potentially cause a global market meltdown.