It seems that no matter how complex our civilization and society are, we humans can cope with the changing dynamics, find reasons that seem like chaos, and create order from seemingly random things. We observe one by one in our lives, trying to find meaning - sometimes we have the ability, sometimes not, sometimes we think that the pattern we see may or not. Our intuitive thinking tries to rhyme rationally, but in the end there is no empirical evidence. We have a lot of theories about how and because things work or not, some way can't be proved, or it proves wrong on this issue.
I want to talk to you about an interesting piece of evidence discovered by Wharton professors, which reveals information flow, stock prices and company decisions, then asks you, readers, questions about how we can better understand what's happening around us. Things, what we observe every day in the social, civil, economic, and business worlds. Ok, let's talk about us?
On April 5th, 2017, the Knowledge @ Wharton podcast had an interesting topic: "How the stock market affects company decisions" and interviewed Wharton finance professor Itay Goldstein, who discussed the feedback loop between information volume and the stock market. evidence. And company decisions. The professor wrote a paper in October 2011 with two other professors, James Dow and Alexander Guembel, on the topic: "Prices influence the market information production incentives for actual investments".
In his article, he pointed out that when investing in stocks or based on the combination of the amount of information generated, there is an effect of magnifying information. Market information producers; investment banks, consulting firms, independent industry consultants, financial newsletters, newspapers, I even think Bloomberg News, Fox Business News and CNBC TV clips, and seeking financial blog platforms such as Alpha.
The document states that when a company decides to make a merger or acquire a potential investment - a merger within the acquisition company, participate in a merger and acquisition investment bank, the sudden emergence of information from multiple sources of industry consulting immediately rises to the company, the target company, the regulator expects the industry to Take action, competitors may wish to block the merger and so on. We all know this when we read and watch financial news. However, this article provides empirical evidence that the actual data is up and shows this fact.
This has led small and large investors to trade on the wealth of information available, without any important primary information before they are considered. In the podcast broadcast, Professor Itay Goldstein pointed out that as the industry has more information, it leads to more transactions, upward bias, more coverage and more information for investors, creating a feedback loop. He also pointed out that individuals usually trade with positive information rather than negative information. Negative information leads investors to be clear, and positive information can motivate potential benefits. The professors who were asked also noticed the opposite situation, that is, when the information was released, the investment in the department was also the same.
Ok, this is the main theme of podcasts and research papers. Now, I want to talk about this conversation and speculate that these truths also involve new innovative technologies and fields. Recent examples may be; 3D printing, commercial drones, augmented reality headsets, watch calculations, etc.
We are all familiar with the "hype curve". When it encountered "the proliferation of the innovation curve", the early hype promoted the investment, but it was unsustainable because it was a new technology that could not meet the needs of the hype. Expectation. Therefore, it shoots like a rocket and then returns to Earth, just to find a balanced reality, technology has reached expectations, new innovations are ready to mature, then it climbs again and normal growth new innovations should.
With this, and empirical evidence from Itay Goldstein et al. In addition, the paper seems to be "information flow" or its lack is PR, information and propaganda are not driven by the trajectory of the "hype curve" model. This makes sense, because once the new company gets the first few rounds of venture capital or has enough money to achieve their temporary future goals for new research and development, they will not unnecessarily continue to hype or public relations. technology. However, I recommend that these companies increase their PR [probably logarithm] and provide richer and more frequent information to avoid early collapse of interest or depletion of initial investment.
Another way to use this knowledge, which may require further querying, is to find the best information flow. You need to invest in startups in the industry without pushing the "hype curve" Too high, causing a collapse of new potential products in the industry or specific companies. Since the inherent feedback loop is now known, it makes sense to control new and innovative products to optimize stability and long-term growth when it comes to market – making it easier to plan and invest in cash flow.
Mathematically, it is possible to find the best information flow, companies, investment banks with this knowledge can exclude uncertainty and risk, and promote innovation with more predictable profits, even ahead of market imitators A few steps to the competition.
Further questions for future research:
1.] Can we control the flow of investment information in emerging markets to prevent a boom and bust cycle?
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2.] Can the central bank use mathematical algorithms to control the flow of information to stabilize growth?
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3.] Can we curb the flow of information at the industry association level? As a milestone in investment, is it to protect the bottom of the curve?
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4.] Can we incorporate an artificial intelligence decision matrix system into such equations to help executives maintain the company's long-term growth?
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5.] Is there a correlation between information and bursty traffic algorithms and these undiscovered investments and information?
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6.] Can we improve our derivatives trading software to identify and use information - the investment feedback loop?
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7.] Can we better track political risks through the information flow voting model? After all, investing in your dollar is like voting for a candidate and the future.
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8.] Can we use the social media 'trend' mathematical model as the basis for the trajectory prediction of the information investment course?
All I want to do is consider all of this and see if you see it. What have I seen here?
Orignal From: Information feedback loops in stock market, investment, innovation and math trends
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