It seems that no matter how complex our civilization and society is, we humans are able to cope with the ever-changing dynamics, find a reason in what appears to be chaos, and create some chaos. ; order from what appears to be random. We go through our lives making observation, one after another, trying to make sense – sometimes we can, sometimes not, and sometimes we think we see patterns that may or may not be so. Our intuitive minds try to rhyme reason, but ultimately without empirical evidence much of our theories as to how and why things work, or don't work, in some way cannot be proven or refuted.
I would like to discuss with you an interesting piece of evidence uncovered by a professor at Wharton Business School that sheds light on information flows, stock prices and business decision making, then ask yourself, the reader, a few questions about how we might gain a better insight into what is going on around us, the things we observe every day in our society, our civilization, our economy and our business world. Okay so, are we talking?
On April 5, 2017 Knowledge @ Wharton Podcast had a cool feature titled, "How the Stock Market Affects Business Decision Making", and interviewed Wharton Finance Professor Itay Goldstein who discussed evidence of a loop feedback between amount of information and stock market & business decision making. The professor had written an article with two other professors, James Dow and Alexander Guembel, in October 2011, titled: "Incentives for Producing Information on Markets Where Prices Affect Real Investment".
In the article, he noted that there is an information amplifying effect during an investment in a stock, or a merger based on the amount of information produced. Producers of market information; investment banks, consulting firms, independent industry consultants, financial newsletters, newspapers and I suppose even TV segments on Bloomberg News, FOX Business News and CNBC – as well as financial blogging platforms such as Seeking Alpha.
The document stated that when a company decides to embark on an M&A or announces a potential investment – an immediate surge of information suddenly appears from multiple sources, internally at the company. merger acquisition, participating M&A investment banks, sector consulting firms, target company, regulators anticipating movement in the industry, competitors who might want to prevent the merger, etc. We all inherently know this to be the case when we read and watch financial news, yet this article puts together real data and shows empirical evidence for that fact.
This causes a nurturing frenzy of investors small and large to exchange on the now abundant information available, whereas before they had not considered it and there was no real information. major strictly speaking. In the podcast, Professor Itay Goldstein notes that a feedback loop is created as the industry has more information, resulting in more trading, an upward bias, resulting in more reports and more information for investors. He also noted that people generally exchange on positive information rather than negative information. Negative information would make investors stay clear, positive information spurs potential gain. The professor, when interviewed, also noted the opposite, that when information goes down, so does investment in the sector.
Okay, that was the jist of the podcast and the research paper. Now I would like to take this conversation and speculate that these truths also relate to new technologies and innovative sectors, and recent examples might be; 3D printing, commercial drones, augmented reality headsets, wristwatch calculation, etc.
We all know the 'hype curve' when it meets the 'innovation diffusion curve' where the early hype drives investment, but is not sustainable because it This is new technology that cannot yet meet the hype of expectations. So it shoots like a rocket and then falls back to earth, to find a balancing point of reality, where technology meets expectations and new innovation is ready to start maturing, then it rises and develops normally. a new innovation should.
With this known and the empirical evidence of Itay Goldstein, et. al., paper, it would appear that "information flow" or the lack of it is the determining factor where PR, news, and hype is not accelerated with the trajectory of the "curveball" model. hype ". This makes sense, as new companies don't necessarily continue to hype or PR so aggressively once they get the first rounds of VC funding or after they get the first rounds of VC funding or after they get it. they have enough capital to play with to meet their temporary future R&D goals on new technology. Still, I would suggest that these companies increase their PR (perhaps logarithmically) and provide more and more frequent information to avoid an early collapse in interest or a drying up of the initial investment.
Another way to use this knowledge, which may require further investigation, would be to find the 'optimal information flow'. necessary to achieve investment for new start-ups in the industry without pushing the "hype curve" too high causing a crash in the industry. sector or with the potential new product of a particular company. Since there is an inherent feedback loop now known, it would make sense to control it to optimize stable, longer-term growth when bringing innovative new products to market – easier for planning and flows. of investment cash.
Mathematically finding that optimal information flow is possible and that companies, investment banks with this knowledge could remove uncertainty and risk from the equation and so foster innovation with more predictable profits, perhaps even staying a step away from market imitators and competitors.
Other questions for future research:
1.) Can we control the flow of information about investing in emerging markets to avoid boom and bust cycles?
2.) Can central banks use mathematical algorithms to control the flow of information to stabilize growth?
3.) Can we slow down the information flows collaborating at the 'industry association level' as milestones as investments are made to protect the negative side of the curve?
4.) Can we program AI decision matrix systems into such equations to help leaders maintain long term business growth?
5.) Are there any 'exploded' information flow algorithms that align with these discovered correlations with investment and information?
6.) Can we improve derivative trading software to recognize and exploit information-to-investment feedback loops?
7.) Can we follow political races better through information flow voting models? After all, voting with your dollar to invest is a bit like voting for a candidate and for the future.
8.) Can we use social media 'trending' mathematical models as the basis for information investment price trajectory predictions?
What I would like you to do is think about all of this, and see if you see, what I see here?