Las Vegas Nevada examined the stock market algorithms flash crash theory postulated by Jim Cramer. This review is provided by Ross Aldridge Consultants and can be followed on Twitter @exoticfinance.
Manipulation of the stock markets are generally viewed as non-attainable, however; upon scrutinizing the past eight years of stock markets bulls and bears peaks and valleys a different picture is being formed. As many threats are being created and released faster that the monitoring systems can detect it is just a matter of time before history repeats itself. The most current pitfall may be in the algorithms that control those massive buy and sell orders that the average investor does not have access. Jim Cramer in an exclusive interview September 2015 indicated that the next flash crash may be the next catalyst for a massive correction. The initial testing of the mechanics of the flash crash was witnessed many quarters ago with a 800 drop in the stock market within minutes of the algorithms code being released into the system. Was this a dry run to find the defense codes that were in place and to circumvent the so called fix code to defend the upcoming adjustments? To forewarn is to forearm as the great general of the past do eloquently stated. In order to control the world economy, it is necessary to placate the general public into a serene sense of comfort. We believe that due diligence of stocks and companies behind the stocks is no longer relevant but a benchmark for taking a short stance.
As with the last crises, financial sectors were the catalyst and lest we not forget about history repeating itself. We recommend SKH at $44.50 and exit at $50.50 in blocks of 10,000 shares. In addition, further examination of the algorithms being utilized by those high frequency traders, hedge fund managers, financial institutions and mega dark net factors suggest it's not a matter of if but when this correction will occur. The expert opinions believe that a DOW 20,000 would be required to draw in the required global wealth to reset the global currency markets.
Far more controversial are algorithms that effectively prey on other algorithms. Some algorithms, for example, can detect the electronic signature of a big VWAP, a process called 'algo-sniffing'. This can earn its owner substantial sums: if the VWAP is programmed to buy a particular corporation's shares, the algo-sniffing program will buy those shares faster than the VWAP, then sell them to it at a profit. Algo-sniffing often makes users of VWAPs and other execution algorithms furious: they condemn it as unfair, and there is a growing business in adding 'anti-gaming' features to execution algorithms to make it harder to detect and exploit them . . .
Whatever view one takes on its ethics, algo-sniffing is indisputably legal. More dubious in that respect is a set of strategies that seek deliberately to fool other algorithms. An example is 'layering' or 'spoofing'. A spoofer might, for instance, buy a block of shares and then issue a large number of buy orders for the same shares at prices just fractions below the current market price. Other algorithms and human traders would then see far more orders to buy the shares in question than orders to sell them, and be likely to conclude that their price was going to rise. They might then buy the shares themselves, causing the price to rise. When it did so, the spoofer would cancel its buy orders and sell the shares it held at a profit. It's very hard to determine just how much of this kind of thing goes on, but it certainly happens. In October 2008, for example, the London Stock Exchange imposed a £35,000 penalty on a firm (its name has not been disclosed) for spoofing.
In a tip of the hat to science fiction scenarios of our algorithm overlords, he adds:
Tales of computers out of control are a well-worn fictional theme, so it's important to emphasise that it is not at all clear that automated trading is any more dangerous than the human trading it is replacing. If the danger had increased, one way it would manifest itself is in higher volatility of the prices of shares traded algorithmically. The evidence on that is not conclusive - like-for-like comparison is obviously hard, and the academic literature on automated trading is still small - but data we do have suggest, if anything, that automated trading reduces volatility. For example, statistical arbitrage algorithms that buy when prices fall and sell when they rise can normally be expected to dampen volatility.
MacKenzie goes on to explain exactly how algos caused the Flash Crash, but we're still left with a sense that in the end we're witnessing the beginning of a more rational and stable market (though a fairly complex one where trades happen at the subsecond level).
Basically we're entering the era of the Machines controlling everything. And it sounds like it's all to the good, just like in Isaac Asimov's vision of the world economy being stabilized by a A.I.s in the short story "The Evitable Conflict." As long as the algos don't go to war with each other and cause something even more difficult to diagnose than the Flash Crash.
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Disclosure: I am/we are long SKF.