Algorithms and human judgment determine prices on the market today in equal measure every day. Previously, economic fundamentals have governed how investors participate in trading. Factors such as the cash flow, product lineup, and plans of mergers were part of the financial analysis performed on companies beside hikes in interest rates and performance of the dollar CITATION Jam15 \l 1033 (Surowiecki, 2015). However, with technological advancements today, computer algorithms have overtaken these traditional practices and instead trading is now dependent on short-term changes in the market often determined in seconds or less. Although the bots are much faster than humans in HFT (high frequency trading) and analyse information faster, there data they rely on is often insufficient to base decisions on CITATION Usm16 \l 1033 (Chohan, 2016).
The algorithms are capable of synthesizing the information contained in the order book and use the results in conjunction with the trends in price changes to trade CITATION Jam15 \l 1033 (Surowiecki, 2015). However, the sole focus these algorithms place on the buys and sales in the market makes them vulnerable to spoofing. This unethical practice entails placing several fake offers to sell or buy to simulate a pressure of buying or selling in the market. Market manipulation thus results because the practice creates conditions that enable traders to buy at low prices and sell at high prices CITATION Ken011 \l 1033 (Chang, 2001).
The speed at which the algorithms go about the bidding process is much faster than that used by humans. Consequently, in instances where I have lost bids to other traders I reserved suspicions that it may well have been an algorithm that beat me to it. The algorithms are however, not the problem in the market. Rather, the problem lies in the uses that the software have been programmed to do CITATION Usm16 \l 1033 (Chohan, 2016). Thus, it is possible for the bot to influence the behavior of humans and other bots trading online.
Computerized trading is advantageous to specialized traders over those with inadequate experience. The former are able to skim profits off the bids of slower traders and even influence their bids. Such acts can significantly destabilize a market as some cases such as the flash crash involving the Dow and S. & P. in 2010 among others have shown.
References
BIBLIOGRAPHY \l 1033 Chang, K. (2001, January 2). Science: First person; New age bidding: Against computers, humans usually lose. Retrieved October 21, 2017, from The New York Times: http://www.nytimes.com/2001/01/02/science/first-person-new-age-bidding-against-computers-humans-usually-lose.html
Chohan, U. W. (2016, January 31). The real problem with high-frequency trading. Retrieved October 21, 2017, from Business Insider: http://www.businessinsider.com/the-real-problem-with-high-frequency-trading-2016-1?IR=T
Surowiecki, J. (2015, May 18). New ways to crash the market. Retrieved October 21, 2017, from The New Yorker: https://www.newyorker.com/magazine/2015/05/18/new-ways-to-crash-the-market
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