Automated Financial Trading Agent

An Automated Financial Trading Agent is a financial trading agent that is an automated intelligent agent and can perform an algorithmic financial trading task.

• AKA: Software-based Trader, Algorithmic Trader, AI Trader.
• Context:
  • They can perform Computer-Generated Trading Acts.
• Example(s):
  • one at CitiGroup Inc..
  • …
• Counter-Example(s):
  • a Financial Trader, performing a financial trading job.
• See: Electronic Trading Platform, Order (Exchange), Market Maker, Hedge Fund, Liquidity, High-Frequency Trading, Algorates, 2010_Flash Crash.

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References

2014

• (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/algorithmic_trading Retrieved:2014-12-2.
  • Algorithmic trading, also called automated trading, black-box trading, or algo trading, is the use of electronic platforms for entering trading orders with an algorithm which executes pre-programmed trading instructions whose variables may include timing, price, or quantity of the order, or in many cases initiating the order by automated computer programs. Algorithmic trading is widely used by investment banks, pension funds, mutual funds, and other buy-side (investor-driven) institutional traders, to divide large trades into several smaller trades to manage market impact and risk. Many types of algorithmic or automated trading activities can be described as high-frequency trading (HFT). As a result, in February 2012, the Commodity Futures Trading Commission (CFTC) formed a special working group that included academics and industry experts to advise the CFTC on how best to define HFT. HFT strategies utilize computers that make elaborate decisions to initiate orders based on information that is received electronically, before human traders are capable of processing the information they observe. Algorithmic trading and HFT have resulted in a dramatic change of the market microstructure, particularly in the way liquidity is provided. Algorithmic trading may be used in any investment strategy, including market making, inter-market spreading, arbitrage, or pure speculation (including trend following). The investment decision and implementation may be augmented at any stage with algorithmic support or may operate completely automatically. One of the main issues regarding HFT is the difficulty in determining how profitable it is. A report released in August 2009 by the TABB Group, a financial services industry research firm, estimated that the 300 securities firms and hedge funds that specialize in this type of trading took in a maximum of US$21 billion in profits in 2008, which the authors called "relatively small" and "surprisingly modest" when compared to the market's overall trading volume. In March 2014, Virtu Financial, a high-frequency trading firm, reported that during five years it made profit 1,277 out of 1,278 days, losing money just one day. A third of all European Union and United States stock trades in 2006 were driven by automatic programs, or algorithms, according to Boston-based financial services industry research and consulting firm Aite Group. As of 2009, studies suggested HFT firms accounted for 60-73% of all US equity trading volume, with that number falling to approximately 50% in 2012. In 2006, at the London Stock Exchange, over 40% of all orders were entered by algorithmic traders, with 60% predicted for 2007. American markets and European markets generally have a higher proportion of algorithmic trades than other markets, and estimates for 2008 range as high as an 80% proportion in some markets. Foreign exchange markets also have active algorithmic trading (about 25% of orders in 2006). Futures markets are considered fairly easy to integrate into algorithmic trading, with about 20% of options volume expected to be computer-generated by 2010. Bond markets are moving toward more access to algorithmic traders. Algorithmic trading and HFT have been the subject of much public debate since the U.S. Securities and Exchange Commission and the Commodity Futures Trading Commission said in reports that an algorithmic trade entered by a mutual fund company triggered a wave of selling that led to the 2010_Flash Crash. The same reports found HFT strategies may have contributed to subsequent volatility. As a result of these events, the Dow Jones Industrial Average suffered its second largest intraday point swing ever to that date, though prices quickly recovered. (See List of largest daily changes in the Dow Jones Industrial Average.) A July, 2011 report by the International Organization of Securities Commissions (IOSCO), an international body of securities regulators, concluded that while "algorithms and HFT technology have been used by market participants to manage their trading and risk, their usage was also clearly a contributing factor in the flash crash event of May 6, 2010.” However, other researchers have reached a different conclusion. One 2010 study found that HFT did not significantly alter trading inventory during the Flash Crash. Some algorithmic trading ahead of index fund rebalancing transfers profits from investors.

2013

• (Chaboud et al., 2013) ⇒ Alain Chaboud, Ben Chiquoine, Erik Hjalmarsson, and Clara Vega. (2013). “Rise of the machines: Algorithmic trading in the foreign exchange market.” In: Journal of Finance.
  • QUOTE: We study the impact of algorithmic trading in the foreign exchange market using a long time series of high-frequency data that specifically identifies computer-generated trading activity. Using both a reduced-form and a structural estimation, we find clear evidence that algorithmic trading causes an improvement in two measures of price efficiency in this market: the frequency of triangular arbitrage opportunities and the autocorrelation of high-frequency returns. Relating our results to the recent theoretical literature on the subject, we show that the reduction in arbitrage opportunities is associated primarily with computers taking liquidity, while the reduction in the autocorrelation of returns owes more to the algorithmic provision of liquidity. We also find evidence that algorithmic traders do not trade with each other as much as a random matching model would predict, which we view as consistent with their trading strategies being highly correlated. However, the analysis shows that this high degree of correlation does not appear to cause a degradation in market quality.

2011 =

• (Hendershott et al., 2011) ⇒ Terrence Hendershott, Charles M. Jones, and Albert J. Menkveld. (2011). “Does algorithmic trading improve liquidity?.” In: The Journal of Finance, 66(1). doi:10.1111/j.1540-6261.2010.01624.x
  • ABSTRACT: Algorithmic trading (AT) has increased sharply over the past decade. Does it improve market quality, and should it be encouraged? We provide the first analysis of this question. The New York Stock Exchange automated quote dissemination in 2003, and we use this change in market structure that increases AT as an exogenous instrument to measure the causal effect of AT on liquidity. For large stocks in particular, AT narrows spreads, reduces adverse selection, and reduces trade-related price discovery. The findings indicate that AT improves liquidity and enhances the informativeness of quotes.

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