Information cycles of stock market feedback, investment, innovation and mathematical trends

It seems that no matter how complex our civilization and society become, we humans are able to cope with ever-changing dynamics, find the cause in what seems chaos, and create order from what seems random. We run through life, watching each other, trying to find meaning – sometimes we are able, sometimes not, and sometimes it seems to us that we see patterns that may or may not be. Our intuitive minds try to make a rhyme out of the mind, but in the end without empirical evidence much of our theories behind how and why everything works or doesn’t work can’t be proven or disproved in some way.

I would like to discuss with you an interesting piece of evidence found by a Wharton Business School professor who sheds light on information flows, stock prices and corporate decision making, and then ask you, the reader, a few questions about how we could gain a better understanding of what is happening around us that we observe in our society, civilization, economy and business world every day. Okay, let’s talk?

On April 5, 2017, the Knowledge @ Wharton podcast had an interesting feature titled, “How the Stock Market Influences Corporate Decision Making,” and interviewed Wharton Finance Professor Ita Goldstein, who discussed evidence of feedback between information and the stock market. & corporate decision making. Back in October 2011, the professor, along with two other professors, James Doe and Alexander Gembel, wrote an article entitled: “Incentives to produce information in markets where prices affect real investment.”

In the article, he noted that when investing in stocks or mergers based on the amount of information received, there is an informational effect of strengthening. Producers of market information; investment banks, advisory companies, independent industry advisors and financial newsletters, newspapers and, I suppose, even TV segments on Bloomberg News, FOX Business News and CNBC, as well as on financial blogging platforms such as Seeking Alpha.

The document noted that if a company decides to go for acquisition or announces potential investments – the immediate growth of information suddenly appears from several sources, within the merger company, investment banks M&A, industry consulting companies, target company, regulators, which expect changes in the sector, competitors who may want to prevent mergers, etc. We all know this to be the case when reading and watching financial news, however, this paper contains real data and shows empirical evidence of this fact.

This infuriates both small and large investors who are now trading in rich information, whereas before they did not consider it, and there was no real serious information to talk about. In the podcast, Professor Itai Goldstein notes that the feedback cycle is created as the sector has more information, leading to increased trading, an upward shift, which causes more reporting and more information for investors. He also noted that people usually trade on positive information rather than negative. Negative information would force investors to stay away, positive information provides an incentive for potential gain. Answering the question, the professor also noted the opposite, that if information decreases, so does investment in the sector.

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

We are all familiar with the “hype curve” when it meets the “diffusion curve of innovation” where the early hype stimulates investment, but it is volatile due to the fact that it is a new technology that cannot yet live up to the hype expectations. So it rises up like a rocket and then falls back to earth, only to find a balance point of reality where technology meets expectations and a new innovation is ready to start ripening and then rises again and grows as new innovations should normally.

At the same time, the empirical evidence of Ita Goldstein et al. al., paper It would seem that the “information flow” or lack thereof is the driving factor if PR, information and excitement do not accelerate along with the trajectory of the “hype curve” model. This makes sense because new firms don’t necessarily continue to advertise or promote so aggressively once they have secured the first few rounds of venture funding or have enough capital to play with to achieve their temporary future goals in research and development of new technology. However, I would suggest that these firms increase their PR (perhaps logarithmically) and provide more and more information to avoid an early collapse in interest or drying up of initial investment.

Another way to use this knowledge, which may require further investigation, would be to find the “optimal information flow” needed to attract investment to new startups in the sector without building up a “hype curve” too high that does not crash the sector or with new potential a company-specific product. Since we now know our own feedback cycle, it would make sense to monitor it to optimize stable and long-term growth when new innovative products enter the market – easier to plan and invest cash flows.

Mathematically speaking, finding that the optimal speed of information flow is possible, the companies, investment banks with this knowledge could eliminate uncertainty and risk from the equation and thus promote innovation with more predictable profits, perhaps even staying just a few steps ahead of market imitators and competitors.

Additional questions for future research:

1.) Can we control the flow of investment information in emerging markets to prevent growth and decline cycles?

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

3.) Can we dwell on information flows operating at “industry association levels” as milestones when investments are made to protect the reverse side of the curve?

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

5.) Are there information flow algorithms that reconcile these identified correlations with investment and information?

6.) Can we improve software to trade derivatives to recognize and use information and investment feedback?

7.) Can we better track political races using information flow voting models? After all, voting your dollar for an investment is very similar to voting for a candidate and the future.

8.) Can we use “biased” mathematical models of social networks as a basis for predicting the trajectory of the information and investment course?

I would like you to think about all this and see if you see what I see here?