It seems that no matter how complex our civilization and society gets, we humans are able to cope with the ever-changing dynamics, find reason in what seems like chaos and create order out of what appears to be random. We run through our lives making observations, one-after-another, trying to find meaning – sometimes we are able, sometimes not, and sometimes we think we see patterns which may or not be so. Our intuitive minds attempt to make rhyme of reason, but in the end without empirical evidence much of our theories behind how and why things work, or don’t work, a certain way cannot be proven, or disproven for that matter.
I’d like to discuss with you an interesting piece of evidence uncovered by a professor at the Wharton Business School which sheds some light on information flows, stock prices and corporate decision-making, and then ask you, the reader, some questions about how we might garner more insight as to those things that happen around us, things we observe in our society, civilization, economy and business world every day. Okay so, let’s talk shall we?
On April 5, 2017 Knowledge @ Wharton Podcast had an interesting feature titled: “How the Stock Market Affects Corporate Decision-making,” and interviewed Wharton Finance Professor Itay Goldstein who discussed the evidence of a feedback loop between the amount of information and stock market & corporate decision-making. The professor had written a paper with two other professors, James Dow and Alexander Guembel, back in October 2011 titled: “Incentives for Information Production in Markets where Prices Affect Real Investment.”
In the paper he noted there is an amplification information effect when investment in a stock, or a merger based on the amount of information produced. The market information producers; investment banks, consultancy companies, independent industry consultants, and financial newsletters, newspapers and I suppose even TV segments on Bloomberg News, FOX Business News, and CNBC – as well as financial blogs platforms such as Seeking Alpha.
The paper indicated that when a company decides to go on a merger acquisition spree or announces a potential investment – an immediate uptick in information suddenly appears from multiple sources, in-house at the merger acquisition company, participating M&A investment banks, industry consulting firms, target company, regulators anticipating a move in the sector, competitors who may want to prevent the merger, etc. We all intrinsically know this to be the case as we read and watch the financial news, yet, this paper puts real-data up and shows empirical evidence of this fact.
This causes a feeding frenzy of both small and large investors to trade on the now abundant information available, whereas before they hadn’t considered it and there wasn’t any real major information to speak of. In the podcast Professor Itay Goldstein notes that a feedback loop is created as the sector has more information, leading to more trading, an upward bias, causing more reporting and more information for investors. He also noted that folks generally trade on positive information rather than negative information. Negative information would cause investors to steer clear, positive information gives incentive for potential gain. The professor when asked also noted the opposite, that when information decreases, investment in the sector does too.
Okay so, this was the jist of the podcast and research paper. Now then, I’d like to take this conversation and speculate that these truths also relate to new innovative technologies and sectors, and recent examples might be; 3-D Printing, Commercial Drones, Augmented Reality Headsets, Wristwatch Computing, etc.
We are all familiar with the “Hype Curve” when it meets with the “Diffusion of Innovation Curve” where early hype drives investment, but is unsustainable due to the fact that it’s a new technology that cannot yet meet the hype of expectations. Thus, it shoots up like a rocket and then falls back to earth, only to find an equilibrium point of reality, where the technology is meeting expectations and the new innovation is ready to start maturing and then it climbs back up and grows as a normal new innovation should.
With this known, and the empirical evidence of Itay Goldstein’s, et. al., paper it would seem that “information flow” or lack thereof is the driving factor where the PR, information and hype is not accelerated along with the trajectory of the “hype curve” model. This makes sense because new firms do not necessarily continue to hype or PR so aggressively once they’ve secured the first few rounds of venture funding or have enough capital to play with to achieve their temporary future goals for R&D of the new technology. Yet, I would suggest that these firms increase their PR (perhaps logarithmically) and provide information in more abundance and greater frequency to avoid an early crash in interest or drying up of initial investment.
Another way to use this knowledge, one which might require further inquiry, would be to find the ‘optimal information flow’ needed to attain investment for new start-ups in the sector without pushing the “hype curve” too high causing a crash in the sector or with a particular company’s new potential product. Since there is a now known inherent feed-back loop, it would make sense to control it to optimize stable and longer term growth when bringing new innovative products to market – easier for planning and investment cash flows.
Further Questions for Future Research:
- Can we control the investment information flows in Emerging Markets to prevent boom and bust cycles?
- Can Central Banks use mathematical algorithms to control information flows to stabilize growth?
- Can we throttle back on information flows collaborating at ‘industry association levels’ as milestones as investments are made to protect the down-side of the curve?
- Can we program AI decision matrix systems into such equations to help executives maintain long-term corporate growth?
- Are there information ‘burstiness’ flow algorithms which align with these uncovered correlations to investment and information?
- Can we improve derivative trading software to recognize and exploit information-investment feedback loops?
- Can we better track political races by way of information flow-voting models? After all, voting with your dollar for investment is a lot like casting a vote for a candidate and the future.
- Can we use social media ‘trending’ mathematical models as a basis for information-investment course trajectory predictions?
What I’d like you to do is think about all this, and see if you see, what I see here?