Algorithmic trading Issues and Recent developments

algo trading in India

Algorithmic trading has been shown to substantially improve market liquidity among other benefits. However, improvements in productivity brought by algorithmic trading have been opposed by human brokers and traders facing stiff competition from computers.

Cyborg finance

Technological advances in finance, particularly those relating to algorithmic trading, has increased financial speed, connectivity, reach, and complexity while simultaneously reducing its humanity. Computers running software based on complex algorithms have replaced humans in many functions in the financial industry. Finance is essentially becoming an industry where machines and humans share the dominant roles – transforming modern finance into what one scholar has called, “cyborg finance.”

Concerns

While many experts laud the benefits of innovation in computerized algorithmic trading, other analysts have expressed concern with specific aspects of computerized trading.

“The downside with these systems is their black box-ness,” Mr. Williams said. “Traders have intuitive senses of how the world works. But with these systems you pour in a bunch of numbers, and something comes out the other end, and it’s not always intuitive or clear why the black box latched onto certain data or relationships.”

“The Financial Services Authority has been keeping a watchful eye on the development of black box trading. In its annual report the regulator remarked on the great benefits of efficiency that new technology is bringing to the market. But it also pointed out that ‘greater reliance on sophisticated technology and modelling brings with it a greater risk that systems failure can result in business interruption’.”

UK Treasury minister Lord Myners has warned that companies could become the “playthings” of speculators because of automatic high-frequency trading. Lord Myners said the process risked destroying the relationship between an investor and a company.

Other issues include the technical problem of latency or the delay in getting quotes to traders,security and the possibility of a complete system breakdown leading to a market crash.

“Goldman spends tens of millions of dollars on this stuff. They have more people working in their technology area than people on the trading desk…The nature of the markets has changed dramatically.”

On August 1, 2012 Knight Capital Group experienced a technology issue in their automated trading system, causing a loss of $440 million.

This issue was related to Knight’s installation of trading software and resulted in Knight sending numerous erroneous orders in NYSE-listed securities into the market. This software has been removed from the company’s systems. … Clients were not negatively affected by the erroneous orders, and the software issue was limited to the routing of certain listed stocks to NYSE. Knight has traded out of its entire erroneous trade position, which has resulted in a realized pre-tax loss of approximately $440 million.

Algorithmic and high-frequency trading were shown to have contributed to volatility during the May 6, 2010 Flash Crash,when the Dow Jones Industrial Average plunged about 600 points only to recover those losses within minutes. At the time, it was the second largest point swing, 1,010.14 points, and the biggest one-day point decline, 998.5 points, on an intraday basis in Dow Jones Industrial Average history.

Recent developments

Financial market news is now being formatted by firms such as Need To Know News, Thomson Reuters, Dow Jones, and Bloomberg, to be read and traded on via algorithms.

“Computers are now being used to generate news stories about company earnings results or economic statistics as they are released. And this almost instantaneous information forms a direct feed into other computers which trade on the news.”

The algorithms do not simply trade on simple news stories but also interpret more difficult to understand news. Some firms are also attempting to automatically assign sentiment (deciding if the news is good or bad) to news stories so that automated trading can work directly on the news story.

“Increasingly, people are looking at all forms of news and building their own indicators around it in a semi-structured way,” as they constantly seek out new trading advantages said Rob Passarella, global director of strategy at Dow Jones Enterprise Media Group. His firm provides both a low latency news feed and news analytics for traders. Passarella also pointed to new academic research being conducted on the degree to which frequent Google searches on various stocks can serve as trading indicators, the potential impact of various phrases and words that may appear in Securities and Exchange Commission statements and the latest wave of online communities devoted to stock trading topics.

“Markets are by their very nature conversations, having grown out of coffee houses and taverns,” he said. So the way conversations get created in a digital society will be used to convert news into trades, as well, Passarella said.

“There is a real interest in moving the process of interpreting news from the humans to the machines” says Kirsti Suutari, global business manager of algorithmic trading at Reuters. “More of our customers are finding ways to use news content to make money.”

An example of the importance of news reporting speed to algorithmic traders was an advertising campaign by Dow Jones (appearances included page W15 of The Wall Street Journal, on March 1, 2008) claiming that their service had beaten other news services by two seconds in reporting an interest rate cut by the Bank of England.

In July 2007, Citigroup, which had already developed its own trading algorithms, paid $680 million for Automated Trading Desk, a 19-year-old firm that trades about 200 million shares a day. Citigroup had previously bought Lava Trading and OnTrade Inc.

In late 2010, The UK Government Office for Science initiated a Foresight project investigating the future of computer trading in the financial markets,led by Dame Clara Furse, ex-CEO of the London Stock Exchange and in September 2011 the project published its initial findings in the form of a three-chapter working paper available in three languages, along with 16 additional papers that provide supporting evidence. All of these findings are authored or co-authored by leading academics and practitioners, and were subjected to anonymous peer-review. Released in 2012, the Foresight study acknowledged issues related to periodic illiquidity, new forms of manipulation and potential threats to market stability due to errant algorithms or excessive message traffic. However, the report was also criticized for adopting “standard pro-HFT arguments” and advisory panel members being linked to the HFT industry.

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