It seems that no matter how complicated our civilization and society, we humans are able to overcome the ever-changing dynamics, to find reasons in chaos, to create order out of everything that seems accidental. We run through our lives, observing one after another, trying to find meaning. sometimes we can, sometimes we can’t, sometimes we think we see patterns that may or may not exist. Our intuitive minds try to rhyme the intellect, but in the end, without empirical evidence, most of our theories of why things work or do not work can not be proven or disproved.
I would 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, corporate decision making, and then ask you, the reader, a few questions about how. we can get a better idea of what is happening around us, what we see in our society, civilization, economy, business 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,” said Wharton Professor of Finance Ithay Goldstein, who discussed evidence of feedback across the stock market. & corporate decision making. The professor co-authored a paper with two other professors, James Dowey and Alexander Gamble, back in October 2011, entitled “Incentives for Information Production in Markets Where Prices Affect Real Investment.”
In the newspaper, he noted that there is an information effect of strengthening when investments are in shares, or the merger is based on the amount of information produced. Manufacturers of market information; Investment banks, consulting firms, independent consultants և financial bulletins, newspapers և, I suppose even TV sections on Bloomberg News, FOX Business News և CNBC, as well as financial blogging platforms like Searching Alpha.
The newspaper notes that when a company decides to go for a merger or announces a potential investment, the instantaneous growth of information suddenly appears from many sources: the merging company, participating M&A investment banks, industry consulting firms, target company, regulators expecting market breakthrough, competitors. who may want to prevent the merger և etc. We all know that this is the case when we read և look at the financial news, however, this document presents the real data և shows the empirical evidence for this fact.
This is causing the rage of both small and large investors, who trade on the basis of the abundant information now available, whereas in the past they did not consider it’s there was any real information that could be talked about. In Podcast, Professor Itay Goldstein notes that feedback is generated because the industry has more information, which leads to more trade, upward bias, more reporting, and more information for investors. He also noted that people mainly trade on the basis of positive information, not negative. Negative information will force investors to orient themselves clearly, positive information will stimulate potential profits. To the question, the professor also noticed the opposite, that when the information decreases, the investments in the sphere also decrease.
Well, that’s the point of a podcast և research article. Now, then, I would like to take this conversation, to assume that these truths also apply to new innovative technologies, to areas, to recent examples. 3-D printing, commercial drones, augmented reality headphones, wristwatch, etc.
We are all familiar with the Hype Curve when it meets the Innovation of Innovation Curve, where early hippies encourage investment, but it is unstable because it is a new technology that still does not live up to expectations. the noise. Thus, it flies like a rocket, “then” falls back to earth only to find the point of equilibrium of reality, where technology meets expectations, the new innovation is ready to begin to mature, then it rises again, it grows as usual. new innovation is needed.
With this famous, և Itay Goldstein empirical evidence և etc. It seems that “information flow” or lack thereof is the driving factor where PR, information և hip do not accelerate along the trajectory of the “hype curve” model. This makes sense because new companies do not have to continue to be so aggressive in advertising or PR when they have secured the first few rounds of venture financing or have enough capital to play to achieve their future research and development goals. However, I would suggest that these companies increase their PR (perhaps sliding), provide information more abundantly, and more frequently to avoid premature interest rates falling or the initial investment drying up.
Another way to use this knowledge, which may require further research, is to find the “optimal flow of information” needed to invest in start-ups without pushing the “hype curve” too high, which could lead to a crash. with a new potential product from a particular industry or company. As a specific feedback loop is now known, it makes sense to monitor it to optimize sustainable, long-term growth as new innovative products are brought to market, making it easier to plan for investment cash flow.
By finding out mathematically that the optimal rate of information flow is possible, ներով companies with this knowledge, investment banks can remove uncertainty and risk from the equation, thus boosting innovation with more predictable profits, perhaps even a few steps ahead of competitors և.
Further questions for further research.
1.) Can we control the flow of investment information in emerging markets to prevent boom cycles?
2.) Can Central Banks use mathematical algorithms to control information flows to stabilize growth?
3.) Can we repel information flows that cooperate at the “industry association level” as potential events when investments are made to protect the bottom of the curve?
4.) Can we program AI decision matrix systems into such equations to help managers maintain long-term corporate growth?
5.) Are there information burst algorithms that are consistent with this undisclosed ratio of investment to information?
6.) Can we upgrade derivative software to recognize and use information-investment feedback loops?
7.) Can we better track the political race through information flow voting models? After all, voting for investments in dollars is a lot like voting for a candidate: for the future.
8.) Can we use the “trend” mathematical models of social media as a basis for forecasting the trajectory of the information-investment process?
I would like you to think about all this և to see if you see what I see here.