Finance - Automated Trading Systems Thesis

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The algorithm can be your market eyes. it's effectively a trading assistant - a very diligent trading assistant... The downside is that it is also a very obedient trading assistant, so if you tell it to do something it might not have the intuition or the ability to veto you... obviously there are checks and balances to prevent anything bad from happening, but you do hear stories about people putting an order in with the wrong instruction, it moved the stock 10 per cent and then you get a call from the regulator" (Dey, 2006) in 2007, the Economist attributed a financially significant "wobble" suffered by the New York Stock Exchange on February 27, 2007 to the ad hoc combination of increasing capacity by adding more scalable hardware to a system that still relies substantially on floor-based trading, yielding a "hybrid" system with significant vulnerabilities. According to that journal, the anomaly produced in the NYSE system on February 27th, NASDAQ absorbed the shift, slowing down NASDAQ as well, simultaneously illustrating certain volume-based vulnerabilities in that system suggesting the need for upgrades to keep up with technological capabilities of automated trading (the Economist, 2007). Implications for the Future:

Current estimates are that more than half of all trades on the London Stock Exchange are already initiated automatically in conjunction with black box trading systems and several high-profile investment bank managers have suggested that this trend will continue to grow on a worldwide basis because of its demonstrated potential to increase both liquidity and price efficiency in securities markets (Dey, 2006).

Consequently, investment firms have accelerated their research and development programs to exploit the still-unrealized capabilities of artificial intelligence in the financial markets (Duhigg, 2006).

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In that regard, some of the most promising avenues relate to the recent development of non-linear decision-making processes that mirror the mechanisms by which living brains process information instead of traditional computer applications.

The limitations of linear computer processes are evident in the difficulties experienced by computer scientists in the realm of visual text recognition. Ray Kurzweil, a fund manager and inventor who has worked with visual text recognition programs for the blind explains that this is why computers powerful enough to analyze information through highly complex algorithmic processes cannot read a page of text as well as an average five-year-old (Duhigg, 2006).

Computer scientists like Kurzweil have pioneered the use of non-linear so-called genetic algorithms, named for their ability to evolve processes independently in much the same way as living brains. The distinction is best characterized by the difference between giving a computer sequential rules in the manner of traditional applications and giving computers "thousands of random rules [to] let the computer figure out what works" (Duhigg, 2006). In principle, the circuitry mimics the neural architecture of living brains that can imagine scenarios and learn from mistakes. In their financial investment applications, neural network computers are not limited to the realm of past stock performance patterns or even to the combination of stock performance patterns and news events. Instead, these systems actually "imagine" much more complex scenarios such as foreseeable shifts in the business focus of companies whose stocks are being traded (Duhigg, 2006).

Ultimately, these systems have the potential to further revolutionize the financial investment industries far beyond their present-day capabilities......

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