Information Comment Loops In Stock as it turns out Markets, Investing, Innovation And Mathematical Trends

Interestingly, It seems that no matter how complex our civilization and society gets, we humans are able to cope out the ever-changing dynamics, uncover reason in what seems like chaos and create order with of what appears to be random. We run through our lives making observations, one-after-another, trying to discover meaning – sometimes we are able, sometimes not, and sometimes we think we see patterns which may ornot 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. Actually, Okay so, in modern times let’s talk shall we?

In fact, On April 5, 2017 Knowledge @ Wharton Podcast had an interesting capability titled: “How the Stock Niche 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 niche & corporate decision-making. The professor had written a paper with two other professors, James Dow and Alexander Guembel, return in October 2011 more than ever titled: “Incentives for Information Production in Markets where Prices Affect Real Investment.”

The market information producers; investment banks, consultancy companies, independent industry consultants, and financial newsletters, newspapers and CNBC suppose even TV segments on Bloomberg Report, FOX Business Report, and I – as well as financial blogs platforms such as Seeking Alpha. 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 paper indicated that when a corporation 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 enterprise, participating M&A investment banks, industry consulting firms, target corporation, 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 scan and watch the financial reportfactyet, this paper puts real-facts up and shows empirical evidence of this , .

This causes a feeding frenzy of both small large investors to trade on the now abundant information available, whereas before they hadn’t considered it and there wasn’tandany 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. Interestingly, Negative information would cause investors to steer clear, positive gives in modern times incentive forinformationpotential gain. The professor asked alsowhennoted the opposite, that when information as it turns out decreases, investment in the sector does too.

Asap then, I’d like as a matter of fact to take this conversation and speculate that these truths also relate to fresh innovative technologies and sectors, and recent might be; 3-D Printing, Commercial Drones, Augmented Reality Headsets, WristwatchexamplesComputing, etc. In fact, Okay so, this was the jist of the podcast and research paper.

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 tech that cannot yet meet the hype of expectations. Thus, it shoots up like a rocket and then falls an to earth, only to discover return equilibrium point of reality, where the tech is meeting expectations and the fresh innovation is ready to begin maturing and then it climbs go back up and grows as a normal new innovation should.

With this known, and the empirical evidence of Itay s’Goldstein, 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 recent 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 tomorrow goals for R&D of the novel technology. Yet, I would suggest that these firms increase their avoid (perhaps logarithmically) and provide information in more abundance and greater frequency to PR an early crash in interest or drying up of initial investment.

Another way to employ this knowledge, one which might require further inquiry, would be to uncover the ‘optimal information flow’ needed to attain investment for recent start-ups in the sector without pushing the “hype curve” too high causing a crash in the sector or with a particular enterprise’s fresh potential item. In fact, Since there is a instantly known inherent feed-go back loop, it would make sense to control it to optimize stable and longer condition increase when bringing fresh innovative products to industry – easier for planning and investment cash flows.

Mathematically speaking finding that optimal information flow-rate is, possible and companies, investment banks with that knowledge could take the uncertainty and risk out of the equation and thus foster innovation with more predictable profits perhaps even staying just a few paces ahead of field imitators and competitors.

As you may know, Further Questions TomorrowforResearch:

1.) Can we as it turns out control the investment information flows in Emerging Markets to prevent boom and bust cycles?
2.) Can Central Banks employ mathematical algorithms to control information flows to stabilize expansion?
3.) as a matter of fact 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?
4.) Can we program AI in modern times decision matrix systems into such equations to aid executives maintain long-clause corporate increase?
5.) Are there information ‘burstiness’ flow algorithms which align with these uncovered correlations to investment and information?
6.) investment we improve derivative trading software to recognize and exploit from another perspective information-Can response loops?
7.) Can we better monitor political races by way of information flow-voting models? After all, voting with your from another perspective dollar for investment is a lot like casting a vote for a candidate and prospect the.
8.) Can we employ social media ‘trending’ mathematical models as a basis for -investmentinformationcourse trajectory predictions?

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