It seems that no matter how complex our civilization and society becomes, 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 make reason rhyme, but in the end without empirical evidence many of our theories behind how and why things work, or don’t work, in a certain way cannot be proved or disproved.
I’d like to discuss with you some interesting evidence uncovered by a Wharton Business School professor that sheds some light on information flows, stock prices, and corporate decision-making, and then ask you, reader, some questions about how we could get more information about the things that happen around us, the things that we observe in our society, civilization, economy and business world every day. Okay, let’s talk, shall we?
On April 5, 2017, the Knowledge @ Wharton Podcast featured an engaging article titled, “How the Stock Market Affects Corporate Decision-Making,” and interviewed Wharton Finance Professor Itay Goldstein, who discussed the evidence of a cycle feedback 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 entitled: “Incentives for the production of information in markets where prices affect real investment.”
In the document, he pointed out that there is an effect of amplification of the information when an action is invested, or when a merger is carried out depending 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 document indicated that when a company decides to embark on a wave of mergers and acquisitions or announces a possible investment, there suddenly appears an immediate spike in information from multiple sources, internal to the merger acquisition company, investment banks of mergers and participating acquisitions, industry consulting firms, etc. target company, regulators who anticipate a movement in the sector, competitors who want to avoid the merger, etc. We all know intrinsically that this is the case as we read and watch financial news; however, this document presents real data and shows empirical evidence of this fact.
This causes a frenzy of investors small and large to trade with the now abundant information available, whereas before they had not considered it and there was no real important information to talk about. In the podcast, Professor Itay Goldstein points out that a feedback loop is created as the sector has more information, which generates more trade, an upward bias, which generates more reports and more information for investors. He also noted that people generally trade positive information rather than negative information. Negative information would keep investors clear, positive information would incentivize potential gains. The professor, when asked, also pointed out the opposite, that when the information decreases, investment in the sector also decreases.
Okay, this was the first part of the podcast and the research article. 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 comes across the “Innovation Diffusion Curve” where early hype drives investment, but it is unsustainable due to the fact that it is a new technology that cannot yet meet expectations. So it shoots up like a rocket and then falls back to earth, only to find a breakeven point of reality, where technology is meeting expectations and new innovation is ready to start maturing and then back. to rise and grow as usual. 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 public relations, information and advertising are not accelerated along with the trajectory of the “advertising curve” model. This makes sense because startups don’t necessarily continue to promote or PR as aggressively once they have secured the first rounds of venture funding or have enough capital to play with to achieve their temporary future goals for R&D. 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 drop 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’ necessary to achieve investments for new start-ups in the sector without pushing the ‘advertising curve’ too high and causing a collapse in the market. sector. sector or with the new potential product of a particular company. Since an inherent feedback loop is now known, it would make sense to control it to optimize stable and longer-term growth by bringing innovative new products to market, easier for planning and investing cash flows.
Mathematically speaking, finding that an optimal flow of information is possible and that companies, investment banks with that knowledge could remove uncertainty and risk from the equation and therefore encourage innovation with more predictable returns, perhaps even staying just 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 information flows by collaborating to ‘industry association levels’ as milestones as investments are made to protect the downside 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 “explosive” information flow algorithms that align with these discovered correlations with investment and information?
6.) Can we improve derivatives trading software to recognize and exploit investment and information feedback loops?
7.) Can we better track political careers 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 models of trends in social networks as a basis for predictions of the trajectory of the investment in information course?
What I would like you to do is think about all this and see if you see what I see here.