Financial marketsFintechTechnologyElectronic Trading Dynamics.

Kennnedy MuturiDecember 6, 20219212 min
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Mathematicians have traditionally played an important role in risk management at financial institutions, but their expertise is increasingly being used to earn money rather than prevent it from disappearing. Companies hire talented academic statisticians to study patterns or trends in trading behavior and develop formulae to forecast future market moves. These equations are then fed into powerful computers, which purchase and sell automatically based on the algorithms’ triggers.

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These same quantitative trading systems underpin all high-frequency trading (HFT) trades, in which stocks are held for a fraction of a second. They’re also utilized in more typical trading, where the holding period might range from days to weeks to months. Some are completely automated, but the majority require human supervision to guarantee that nothing goes wrong. According to Scott Patterson, a Wall Street Journal reporter and author of The Quants, a plane on autopilot can fly automatically. Still, a specially trained pilot can take over at any time.

These systems are extremely strong, monitoring economic trends, chart analysis, and info flow in real-time and aspects of sustainability in fractions of a second artificial intelligence is even available to the most powerful, allowing them to adjust plans on their own. No one knows how successful these quant initiatives are, but according to Mr. Patterson, “they’ve been around long enough to presume they’re enormously profitable.” The fact that they are so numerous suggests as much.

According to one analyst, two of the largest HFT businesses, Tradebot and Getco, account for roughly 15% -20% of all equities trading in the US. It’s difficult to say how far their influence reaches because they’re private enterprises.  according to recent government-sponsored research in the United Kingdom, HFT accounts for between a third and half of all share trading in Europe and more than two-thirds in the United States. “The vast majority of firms use quantitative trading,” says Mr Patterson. “It drives almost everything that goes on Wall Street.

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Quant trading helped to reduce dealing costs and enhance liquidity, according to UK research commissioned by the Foresight program, and did not damage overall market efficiency (Quant trading helped to reduce dealing costs and enhance liquidity, according to UK research commissioned by the Foresight program, and did not damage overall market efficiency. Indeed, HFT and quant trading have “usually improved market quality,” according to the report.

However, it did bring up an essential point about self-reinforcing feedback loops, as they’re known in the industry. It means that a small trigger will set off a chain of similar occurrences, each one amplifying the previous one until the overall impact is significant. Indeed, HFT and quant trading have “usually improved market quality,” according to the report. However, it did bring up an important point about self-reinforcing feedback loops, as they’re known in the industry. This means that a small trigger will set off a chain of similar occurrences, each amplifying the previous one until the overall impact is significant.

Consider the case where a share drops in value, prompting a sale on one quant program, causing the share price to plummet even further. In turn, it prompts a deal on another program, driving the price even lower, and so on. The situation is made worse because numerous programs use the same formulae and invest in the same stocks. Order was restored, and the market bounced back within half an hour after the auto-pilot switches were turned off and the systems were overridden.

Some claim it was an unlucky one-off. Others point to much more severe implications, citing quant trading as a significant contributor to the enormous stock market sell-off in 2008 when the US market nearly halved in value. They claim that hedge funds sold shares quickly to offset substantial losses on their mortgage investments following the collapse of the US housing market, causing a domino effect across quant trading platforms with disastrous repercussions.

HFT has certainly upset markets, according to stock market historian David Schwartz. He claims, “I believe [some types of HFT] inflict a great deal of damage.” “During the latest sell-off, I’ve observed far too many occasions where a sudden flurry of frequent trades sent stock prices tumbling.”

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Proving that is the issue. Mr. Schwartz claims that no one knows who is making the trades and that the exchanges have no motive to discover out because they make a lot of money from them. Others contend that the issue is more fundamental. They claim that mathematicians have no understanding of markets. They work with absolutes rather than the illogical human behavior that drives so many financial decisions.

“Prices are set by supply and demand, not by mathematics,” claims one prominent actuary. Is it possible, then, those academic statisticians are genetically unsuited to the work for which they are compensated? Professor of quantitative finance Paul Wilmott has questioned if they are “capable of thinking beyond arithmetic and algorithms.”

“Are they aware of the human side of finance, of people’s swarming behavior, of unexpected consequences?” And if mathematicians don’t, the computer programs they design aren’t likely to. According to Foresight report’s conclusion: “Trading robots of the future will be able to adapt and learn with minimal human input. In the future, major financial markets will require many fewer human traders” Some may argue that this is a good thing, especially considering recent insider trading and fraud examples.

Still, Mr. Patterson believes that the expansion of quant trading is both “inevitable and dangerous. “Given the general contempt with which traders are currently viewed, it may seem far-fetched. Still, if mathematicians and their algorithm software prove to be a poor substitute, we may find ourselves clamoring for their return.

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Kennnedy Muturi

Ken is a Quantitative Trader with experience in investments, quantitative finance, financial modelling and algorithmic trading in Global Investable Markets (GIM). He enjoys using Bayesian Statistics, Time Series and Machine Learning in developing Robust consistent Alphas in Equities Market, FX, ETPs and Derivatives instruments. He enjoys deep dives in understanding High Frequency Trading infrastructures and improving how the African financial markets work. He holds a Bachelor's in Actuarial Science from Strathmore Institute of Mathematical Sciences : An Executive Program in Algorithmic Trading (EPAT) certificate in Algo Trading from QuantInsti : A current MSc student in Financial Engineering at World Quant University.

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