Exploring Algorithmic Trading's Impact

Exploring Algorithmic Trading's Impact

June 5, 2018
9:10 am

Artificial intelligence has been around for over half a century, but it is just becoming impactful in a variety of different industries. A subset of artificial intelligence - machine learning - is leading the way. Machine learning is a machine’s ability to keep improving its performance without humans having to explain exactly how to accomplish all the tasks it’s given. We will explore algorithmic training, a deep learning application that is categorized as machine learning, and its potential impact in finance and the way securities are traded.

Algorithmic trading has the potential to disrupt finance, as hedge funds have already created algorithmic trading systems that consume millions of data points and make intuitive trading decisions regarding these data points. One hedge fund is able to make 1,800 days' worth of trading within a matter of minutes. If algorithmic trading has great potential, why aren’t more incumbent organization utilizing this technology?

Lack of Transparency

In the past, most software could be examined to determine its path and next steps. Software that wasn’t easily accessible could be accessed using a legal search warrant. Now, algorithms are developed, trained, and released to learn and grow without a whole lot of understanding of how these algorithms find certain patterns. A key component of artificial intelligence applications, like algorithmic trading models, is the neural networks. These networks are assimilating with the human
brain and have been coined a “black box” due to difficulty in comprehending their capabilities and proficiencies. Similar to the brain, neural networks are interconnected layers that understand machine translation, language understanding, image recognition, and speech recognition.

Due to the “opaqueness” of algorithmic training, there is fear surrounding the paths that could be taken by these algorithms. If fear exists in your organizations, simple trial and error could be implemented in order to test this new software. Only a portion of securities trades can be made in order to determine if this technology is a risk that is willing to be taken by your organization.

Unfair Trading Advantages

Skepticism has also risen regarding the advantages that are provided by using algorithmic
trading. Coined “stock market singularity,” select hedge funds that are using algorithmic trading are able to make millions of days of trades in a matter of an hour, which certainly provides advantage over those utilizing traditional trading. Along with the volumes of trades that can be made, algorithmic trading also incorporates millions of data points from social media remarks to major global news stories.

Organizations that are looking to incorporate algorithmic trading and other artificial intelligence ought to determine the legal and social impact that implementation of this technology may create. Research-based analysis can not only help determine the impact that algorithmic technology has on organizations, but also the legal, social, and customer implications.

Trading Errors

As mentioned above, a lack of transparency exists surrounding the ability to determine an
algorithm’s patterns and decision-making. This also makes it difficult to determine the cause of errors made in these artificial applications. Small application errors have caused multitudes of companies millions of dollars and wasted time in determining the error. An algorithmic trade has even caused a flash crash that resulted in a dramatic drop in share and future indices.

Organizations that are looking to utilize artificial applications need to develop systems that help mitigate risk and quickly determine errors.

Bâton Global has provided clients research-based insight in guiding organizations through change. Artificial intelligence such as algorithmic trading is finally impacting various functions in a variety of industries.

Are you aware of technologies that can innovate your organization?

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June 5, 2018
9:10 am
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