To Be Invested or Not To Be Invested?

 As a minimal business investor and entrepreneur coach, Pulak Prasad’s upcoming book, What I Learned About Investing From Darwin, is a godsend. His first chapter alone is worth the price: combining Buffet’s advice, with statistical inference on the types of risk and how natural selection works, you get a great idea of how Prasad evaluates opportunities. As the author says, you might miss out on an Apple or Tesla, but you will avoid hundreds, thousands of JC Penney’s (in the 21st century) and the like.

The key is minimizing calling a bad investment a good one (type I error). Mistankenly putting money into a bad company is a losing risk and the biggest hindrance to having an overall good record. Whereas most investment funds chase lots of opportunities, they underperform against the stock exchange indexes (S&P 500, DJIA e.g.) This might be in contrast to Taleb’s Black Swan strategy—because you can’t predict winners, invest some in a lot of companies and the winners will overwhelm the losing multitudes. But then Prasad gives simple advice on how to simply identify a few solid investments. For those familiar with Stern Stewart’s Economic Value Added, you’ll resonate with Prasad’s analysis.

While you think evolution is only concerned with physiological traits, the author identifies some behavioral evolution and relates this to company leadership and strategies. 

I spotted one consideration worth changing on this upcoming book: Buffet’s guidelines are referred to as “diktat” but better is “dicta” since the former is considered pejorative as it refers to edicts from an external or distrusted authority.

Big Data: Help or Hindrance?

 Tumin and Want in their upcoming book, Precisely, highlight a necessary discussion regarding “big data” and its analysis. While they tout the many benefits of “precision systems”—reliable, reproducible, accurate output from data analysis—they do not overlook the past failures or forthcoming issues if imprecision is not exorcised from our data sets and algorithms. In this regard, this book builds on others like Weapons of Math Destruction. The authors discuss key successes in sports, criminology and business.

They warn against people who want to take action on analysis results before asking the tough, probing questions regarding the collation of data, the assumptions behind algorithms and so on. They caution that the “action vanguard” who blindly trust the data engine outputs will “fire, aim, ready” in that order. Part of the ready, which needs to be first, is ensuring high quality data integrity and freedom from corruption or misapplication.

There are some examples that we can find success by utilizing simpler tools. Many business processes (not just manufacturing) have benefited from timely Statistical Process Control analysis where outliers quickly instigate an investigation to determine if “something” has changed—or other patterns. Like any good business prospectus’ caveat, historical success does not guarantee future success, extrapolation of data should be “taken with a grain of salt.” Too many have been burned assuming projections are linear, only to discover there’s a performance plateau (e.g. market saturation). Whenever we’re dealing with markets and other phenomena of human behavior, it becomes less predictable; behavior changes when it’s observed, sort of like the quantum Heisenberg Uncertainty Principle. I’ve seen results often improve by ten percent just because people knew it was being measured. Consumer trends can run hot and cold in an instant and they become less predictable. Thus, precision systems might have their place in static processes and environments. I wish the authors would address this more completely.

For example, how would the authors build a precision system for most of our endeavors which can be characterized as an Infinite Game (Carse, 1987 and Sinek, 2019). In an infinite game, the rules may change, the competitors may change, the boundaries may not exist and there’s no time limit. In business, I always asked my staff to revisit policies, processes and procedures every six months—and I advise other business owners similarly—because what used to work may not work still: competitors, suppliers, service providers, regulations, customers, team members’ behaviors and skills, community resources, etc. have all changed. (As a Greek philosopher said many centuries ago, “You cannot step in the same river twice.”) 

In addition, we’ve probably all experienced that our metrics were leading us to the wrong behaviors, decisions and goals…and businesses go bankrupt when this happens. So Tumin’s and Want’s precision systems need to ensure that we’re not “putting the ladder against the wrong wall.” Their chapter on transformation, red zones and watching out for misleading correlations is worthwhile.

This is good addition to the data analysis conversation to move us toward reliable, reproducible and accurate results.