It seems that no matter how complex our civilization and society become, humans are capable of coping with ever-changing dynamics, finding reason in what appears to be chaos, and creating order out of what appears to be random. We go through our lives making observations, one after another, trying to find meaning; sometimes we are capable, sometimes we are not, and sometimes we think we see patterns that may or may not be. Our intuitive minds try to rhyme with reason, but in the end, without empirical evidence, much of our theories behind how and why things work or don’t work are somehow unprovable or disprovable.

I’d like to discuss with you an interesting piece of evidence discovered by a Wharton Business School professor that sheds some light on information flows, stock prices, and corporate decision-making, and then ask you, the reader. , some questions about how we could get more information about the things that happen around us, things that we observe in our society, civilization, economy and business world every day. Okay, so let’s talk, okay?

On April 5, 2017, the Knowledge @ Wharton Podcast published an interesting article titled: “How the Stock Market Affects Corporate Decision Making” and interviewed Wharton Finance Professor Itay Goldstein, who discussed the evidence for a feedback loop. between the amount of information and the stock market. and corporate decision making. The professor had written an article with two other professors, James Dow and Alexander Guembel, in October 2011 titled: “Incentives for the production of information in markets where prices affect real investment.”

In the paper, he noted that there is an information amplification effect when investing in a stock or merging based on the amount of information produced. Producers of market information; investment banks, consulting firms, independent industry consultants, and financial newsletters, newspapers, and I guess even TV segments on Bloomberg News, FOX Business News, and CNBC, as well as financial blogging platforms like Seeking Alpha.

The paper noted that when a company decides to go on an M&A acquisition spree or announces a potential investment, there is suddenly an immediate surge in information from multiple sources, internally within the M&A acquisition company, participating M&A investment banks. , industry consulting firms, target company, regulators anticipating a move in the sector, competitors who may want to avoid the merger, etc. We all intrinsically know this to be the case when we read and watch financial news, however this paper presents real data and provides empirical evidence for this fact.

This causes a feeding frenzy from small and large investors to trade on the now abundant information available, whereas before they had not considered it and there was no real important information to speak of. In the podcast, Professor Itay Goldstein points out that a feedback loop is created as the sector becomes more informed, leading to more trading, an upward bias, leading to more reporting and more information for investors. He also pointed out that people generally trade positive information rather than negative information. Negative information would keep investors away, positive information gives incentives for potential profit. When consulted, the professor also pointed out the opposite, that when information decreases, investment in the sector also decreases.

Well, this was the essence of the podcast and the research work. Now I would like to take this conversation and speculate that these truths also relate to new technologies and innovative sectors, and recent examples could be; 3D printing, commercial drones, augmented reality headsets, wristwatch computing, etc.

We are all familiar with the “Hype Curve” when it meets the “Innovation Diffusion Curve”, where early hype drives investment, but is unsustainable due to the fact that it is a new technology that cannot yet deliver. the exaggeration of expectations. So it shoots off like a rocket and then falls back to earth, only to find a balance point of reality, where the technology meets expectations and the new innovation is ready to start maturing and then back again. go up and grow like a normal. the new innovation should.

With this known, and the empirical evidence of Itay Goldstein, et. al., paper, it would appear that “information flow” or lack thereof is the driving factor where PR, information, and hype are not accelerating along with the “hype curve” model trajectory. This makes sense because startups don’t necessarily continue to advertise or advertise as aggressively once they’ve secured early rounds of venture funding or have enough capital to play with and hit their temporary future R&D goals of the new technology. However, I would suggest that these companies increase their PR (perhaps logarithmically) and provide information more abundantly and frequently to avoid an early dip in interest or depletion of the initial investment.

Another way to use this knowledge, which might require further research, would be to find the “optimal information flow” needed to drive investment for new start-ups in the sector without pushing the “hype curve” too high and causing a collapse in the sector. market. sector or with the potential new product of a particular company. Since an inherent feedback loop is now known, it would make sense to control it to optimize longer-term and stable growth when bringing innovative new products to market, thus facilitating planning and investment cash flows.

Mathematically speaking, finding the optimal information flow rate is possible and companies, investment banks with that knowledge could remove uncertainty and risk from the equation and thus encourage innovation with more predictable returns, such time even staying a few steps ahead of market imitators and competitors.

More questions for future research:

1.) Can we control investment information flows in emerging markets to avoid boom-bust cycles?

2.) Can central banks use mathematical algorithms to control information flows to stabilize growth?

3.) Can we reduce collaborative information flows at ‘industry partnership levels’ as milestones as investments are made to protect the bottom of the curve?

4.) Can we program AI decision matrix systems into such equations to help executives sustain long-term corporate growth?

5.) Are there information ‘burst’ flow algorithms that align with these discovered correlations with investment and information?

6.) Can we improve derivatives trading software to recognize and exploit information trading feedback loops?

7.) Can we better track political races through information flow voting models? After all, voting with your investment dollar is a lot like casting a vote for a candidate and the future.

8.) Can we use mathematical “trend” models of social media as a basis for information investment course trajectory predictions?

What I would like you to do is think about all this, and see if you see, what do I see here?