Everything run by human psychology is bound to be beset with complexities beyond idiom, especially when money is involved. Now that the world is undergoing financial and economic recession, it would be even more difficult to imagine the complex, shifting gears of something like the stock market. Many known companies have already fallen to the tempest of crisis, and many more are poised to tumble. With such influential organizations rising and falling, stock traders need all the help they can get trying to make sense of stock market figures that might some might even try their luck in automated trading via trading software.

Putting a computer’s excellent data gathering and analysis skills to use, stock market trading software is one of the more useful things that had come out of the mesh of the World Wide Web that has today become commonplace. These software come in a variety of ranges: from observational systems designed to gather and organize data to analytic software that analyzes stock market information to actual AI traders that do the decision making as well. The data observation and gathering plus the analysis parts make such stock trading software virtual assistants to stock traders and are quite accurate and useful. But the decision making software is rather dubious.

While it is true that a computer program is the best suited to analyze such figures as stock market data, and also quite suited to perform according to a predefined set of principles like using technical or fundamental analysis, the stock market—like any other man-made and man-run complexity, can at times be drastically irrational. One example of such an irrational instance is the stock market crash of 1987 where the Dow Jones Index dropped 22.6% for no probable reason. No logical explanation was found. Even if today’s computers had been there, they could not have been able to foretell such an event happening. This is still the case today. Even if trends do occur in Gaussian distribution, no computer can accurately pinpoint an outlier possibility and thus make use of it. Furthermore, the Efficient Market Hypothesis of Professor Eugene Fama effectively negates a computer’s potential to break the bank, or in this case, beat the market. Stating that it is not possible to consistently outperform the market from information from the market, though the hypothesis has its drawbacks and contenders, is sound enough to ring true for the case of a stock investing software.

Finally, there is the psychological aspect wherein a computer can’t predict human over or under reaction that can cause over or under pricing. In the end, though computers are undoubtedly excellent in observation and analysis, humans should still have the final say.