It's not a title party, but it's not that we short, it's that we help clients short, and that's when I was a full-time lawyer in a law firm.
If you have a certain understanding of the Anti-Monopoly Law, you will know that if there is concentration between two (or more) enterprises (such as mergers and acquisitions) and the enterprises participating in the concentration reach a certain scale (such as the turnover reaches a certain standard), then the concentration must be declared first (such as to the Ministry of Commerce of China), and the concentration can be implemented only after the declaration is passed. The purpose of anti-monopoly declaration is to prevent a concentration from destroying the competitive order of the relevant market, thus damaging the interests of consumers. For example, if Coca-Cola and Pepsi-Cola are centralized and merged into one enterprise, then this concentration will most likely lead to the distortion and destruction of the competition order in the coke market-the competition in the coke market will disappear because of the disappearance of the two main competitors, and the price of coke will most likely soar, thus damaging the interests of consumers.
If Coca-Cola and Pepsi-Cola want to merge centrally, then this centralized merger must be reported to the relevant government departments, and at the same time, many brokers, hedge funds or others will decide whether to be long or short. If this concentration is likely to be approved, then the fundamentals of the stocks of these two companies will be great-although concentration may harm the interests of consumers, it will benefit both companies, and their share prices will rise, so the bulls will win. On the other hand, if the concentration is likely to be rejected, the fundamentals of shorting the stocks of these two companies will be great-because once the centralized declaration is rejected, the stocks of the companies participating in the concentration will fall, so shorting will win. Of course, taking these two coke giants as an example may be too typical and unrealistic for me, because it is almost certain that their concentration will be rejected. Then let's give an example of actual combat. But this example is still related to Coca-Cola.
On September 3rd, 2008, Coca-Cola announced its plan to acquire China Huiyuan Juice Group Co., Ltd. (0 1886. Hong kong dollars) cash. Coca-Cola Company suggested the tender offer price of HK$ 65,438+02.20 per share, and the issued convertible bonds and options were purchased at the same price. Coca-Cola has obtained an irrevocable acceptance promise signed by three shareholders of Huiyuan before the announcement, and the three shareholders * * * own 66% of Huiyuan's shares. If the proposed transaction is accepted, Coca-Cola will pay a consideration of about $2.4 billion. If the transaction is completed, it will be the largest acquisition of Coca-Cola in China at that time, and Huiyuan Juice will also be delisted.
After the announcement of the above news, the share prices of Huiyuan and Coca-Cola both rose sharply. But the problem is that Coca-Cola's acquisition of Huiyuan belongs to the concentration that should be declared in China's Anti-monopoly Law, and whether the concentration can be approved by the Ministry of Commerce becomes the X factor of this transaction. Hedge funds come to us for analysis, and we collect relevant data for analysis according to our routines and methods of doing this kind of business (not to mention what kind of data and analysis methods are here). Anyway, our final analysis result is that the Anti-monopoly Bureau of the Ministry of Commerce will not approve this concentration. Fortunately, our analysis results are correct. Correspondingly, customers who follow our advice to short are also making money.
When we did the above case analysis seven years ago, there were no such concepts as "big data" and "small data". Looking back now, what we did then (and now) was nothing more than data analysis. Of course, the total amount of data involved may not be that big, but it is large enough relative to specific projects. Of course, whether these data can be regarded as what we call "big data" at present may be debatable, and we will discuss it in another article later, which is why I put quotation marks on "big data" in the title of this article. In any case, considering that only two of the more than 1 000 anti-monopoly filing cases filed by the Ministry of Commerce so far have not been filed, we should be proud that we were able to accurately predict the incident with such a small probability at that time, which should be attributed to the accuracy of our data collection and proper analysis.
If the above successful short selling can be regarded as an effective analysis using "big data", then "big data" analysis seems to have the following characteristics. Here we try to summarize the so-called characteristics in order to attract jade:
-Big data analysis should be commodities first. Regardless of the method of data collection and analysis, the final product must be purchased with human money. Big data or big data analysis products that have no commercial value are worthless, in other words, they cannot be made.
-The development of big data analysis products should be aimed at customers. Different customers have different needs for big data analysis products. Take the above-mentioned legal industry big data as an example. Law firms and large international companies that have direct demand for big data and big data analysis products are basically foreign-related businesses, so the working languages of the above big data and big data analysis products are basically English.
-The vitality of big data analysis lies in its accuracy. Take our case above as an example. Coca-Cola's acquisition of Huiyuan was rejected, and Huiyuan's share price plunged 42% the next day. Prior to this, the news of Coca-Cola's sky-high acquisition of Huiyuan stimulated Huiyuan's share price to soar nearly 200 times. After Coca-Cola announced the acquisition of Huiyuan Juice, its share price on the NYSE rose strongly for a time, but fell by 20% in the following six months, which was not unrelated to its failure to acquire Huiyuan. It is conceivable that if our analysis at that time was inaccurate, then the customer would lose money. Of course, the success of our case cannot be said to be accidental, so is big data analysis fault-tolerant? I believe there is. If big data can't go wrong, it is equal to God, but the error rate of big data is too high, so it has no commercial value or even entertainment value.
At the end of the article, I ask a question: Is it malicious to short with the conclusion (big or small) of data analysis? Maybe this question is a bit "natural".
The above is what Bian Xiao shared for you about our shorting with "big data". For more information, you can pay attention to Global Ivy and share more dry goods.