Tag Archives: hft

How Apple Can Cause Any Stock to Go Down

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On April 28th of this year, Carl Icahn (a billionaire hedge fund manager) announced that he sold his entire stake in Apple. He said, among other things, that he was worried about China and cautious on the U.S. stock market.  No big deal you would say. Sure: it might be bad for investors’ confidence in Apple, knowing that a man with a history of successfully anticipating the stock market shows not to have confidence in their company. But it would certainly not affect an apparently totally unrelated European company, such a BMW, right?

Wrong. Via a complex set of relations, it does. Stocks worldwide are closely interconnected; even though the rationale behind these relations might at best be hard to find. Let’s for example track the chain of events that caused BMW to decline on the 29th of April.

Icahn announced him selling his Apple shares on the 28th of April, after European trading hours (i.e., when the European markets were closed). Following his statement, Apple’s stock fell from $97.5 to $94.5 – around 3%. Apple, being the biggest company worldwide and the largest component of the S&P 500 index, to a large extent determines the S&P 500 index. So if Apple goes down, the S&P 500 goes down. Hence, after the announcement, the S&P 500 index declined from 2095 to 2075.

Now we arrive in Europe. European algorithms detect the decline in the S&P 500, and – being programmed to arbitrage around a positive correlation between the S&P 500 and the German DAX index – sell the DAX index future (possibly while going long the S&P 500, so called ‘statistical arbitrage‘). The result of the selling? The DAX index future plunges from 10321 to 10038, or +- 2.7%, an extraordinary big intra-day decline for the DAX.

Other algorithms, detecting the DAX index future to fall, and arbitraging around a relatively stable premium between the future and the index, sell-off the funds making up the DAX index, thereby causing the DAX index (which is just a collection of stocks of big German companies) to plunge accordingly. Since BMW is part of the DAX index, algorithms sell the BMW share. This causes BMW to decline from 83.94 to 80.5, more than 4%, a significant decline.

Hence Apple causes BMW to decline. See figure 1 for a graphical depiction of this chain of events.

Figure 1: how Apple going down causes BMW to go down

You might think this chain of events is too far-fetched. That it is some kind of conspiracy made up in a desperate attempt to explain what is in fact impossible to explain. But I doubt it. On the 28th and 29th of April, nothing exceptional occurred (besides the Apple event), or at least nothing that would justify a 2.7% fall in the DAX index. Usually, given a decline of this sort, there must at least be one relatively big event to which the decline can be ascribed. Hence in this case we have no better explanation for the plunge than Apple’s stock falling. Furthermore, assuming that algorithms do the tasks I described above, which are strategies known to be followed by algorithms, this chain of events is nothing but an utterly logical consequence.

Algorithms of course don’t care about Icahn’s opinion of the Apple stock, or the stock market in general. But what they do care about is relations between financial products, since this is where they make their profits. And it is by profiting from any significant deviation from historical relations between financial products that they keep intact such relations, and form the intricate web that is the stock market.

How High-Frequency Trading Affects Human Traders

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The machines have taken control

A lot has been written about high-frequency trading (HFT), especially since the 2010 flash crash, for which HFT is at least partially held responsible. HFT even caught Hilary Clinton’s eye, proposing a plan to tax cancelled trades, thereby hindering HFT’s business.

In my experience as a stock trader, who watches order books all day and follows the workings of HFT’s closely, you see many signs of the subtle workings of HFT’s. We, human traders, often complain about these ‘machines’; especially the more senior traders, who grew up in a time in which trading was something that happened between humans, sometimes get frustrated by the seemingly random price movements caused by the machines.

I want to give you some clues about how HFT has changed the job of a human trader. This might also shed some light on why today there are fewer and fewer human traders. Today, if you buy quite a large sum of stocks, let’s say 50.000 stocks ArcelorMittal or 10.000 stocks Shell, 9 in 10 times you will experience a sudden drop in the stock’s price (+- 1%). That’s right:  a drop, not an increase. This didn’t use to happen a couple of years ago, but from a HFT perspective, the price drop is easily explained.

For suppose you aggressively buy 50.000 stocks, meaning that you buy 50.000 stocks on offer. This implies that a certain HFT is short 50.000 stocks. Assuming the HFT wants to have a net position of zero, this means that it has to buy back 50.000 shares. But it doesn’t want to make a loss: it wants to buy back the shares at a lower price than they were sold for. Being a market maker, hence controlling the order book and therefore the share’s price, the HFT removes successive levels of best bid. Now it waits for other HFT’s to fill up the order book, until it detects an offer of 50.000 stocks at a price lower than the price the HFT sold the stocks for. Now the HFT buys back the shares, hence making a profit. For the human trader, who is on the other side of the trade, this means that he starts his trade with a loss.

Another way in which trading has changed, is in the extremity of price movements. A couple of years ago, human traders would prevent certain extreme price movements from happening – by buying when they deemed a stock oversold, and selling when it was overbought. Machines don’t follow this logic. They go with the flow, and if the flow is selling, they are selling too. Hence you see price movements that either go up or down continuously, without any correction. Furthermore, price movements get accelerated due to the high speed of HFT. This explains the increased volatility; another side effect of HFT.

Another issue that can be extremely frustrating to a trader, is that your orders do change the price of a stock – even if they are not executed. Let’s say you want to sell x number of stocks. If x is larger than a certain size, HFT’s will detect your order as being real, and use it build their order books around. Meaning: if you are best offer for 5.000 stocks at 4.241, a HFT will put an offer in front of yours at 4.240. Before you can blink your eyes, another HFT will lay down an offer at 4.239. Now the next person buying will pay 4.239 instead of your 4.241. Hence, HFT’s prevent your order from being executed, and cause the price of a stock to go down. You can of course sell your stocks at market, hence paying the spread, but always doing so significantly decreases your profits. There is of course nothing wrong with HFT’s offering stocks at a price lower than yours; it is that, when you put down an order, regardless of the price, the dynamics of the price will change in such way that your order will not be executed – no matter whether you are buying or selling. This process is also described in Flash Boys, Micheal Lewis’ book on HFT.

Adding up all such changes, you can imagine why the traditional way of trading has become increasingly difficult for humans, possibly explaining why relatively fewer and fewer humans trade.