Though its implementation remains polarizing amongst some industry analysts, the algorithmic trading market is poised for additional growth in the immediate future. In fact, global market growth has been recently projected to reach nearly $18.16 million by 2025, a compound annual growth rate (CAGR) increase of 8.7 percent from 2016’s $8.7 million.
That said, North America remains one of the most prominent regions for market expansion, contributing high amounts of revenue “due to technological developments and considerable application of algorithm trading;” this comes as little surprise, as an increasing amount of major industry players have shifted to an automated interface — a notion recently exhibited by Goldman Sachs, which now predicates much of its trading activity on “complex trading algorithms, some with machine learning (ML) capabilities.”
Moving forward, algorithmic trading looks to jump from a promising trend to a full-fledged paradigm shift across investing’s vast majority.
Revisiting the drawing points
At its core, the allure of algorithmic trading is as simple as that of any ML-based concept: automation is quicker and easier; it can streamline complex processes, therefore generating more turnover without sacrificing efficiency. However, the benefits run much deeper than a mere surge in industry convenience, and this fact will play a big part in the market’s increased adoption amongst reluctant companies.
Algorithmic trading is able to execute trading commands — some significantly layered — with accuracy while mitigating both latency and potential setbacks rooted in human folly. Now, this process is increasing in speed, “happening in the span of microseconds and going on to nanoseconds. Pair this notion with customization, cost-effectiveness, and anonymity, and you are left with widespread market appeal rooted in a variety of industry facets.
Regulations and growing capabilities
When looking at potential regulations for algorithmic trading, one must consider both the elimination of threats and the pace at which innovation is allowed to occur — that is, safety must be emphasized in both a firm but flexible manner. Regulators will need to be well-versed in algorithmic operations while remaining available to embrace and adapt to changing legislations — especially as the technology’s full capabilities become further recognized. Identified challenges, in this regard, currently include “insufficient risk valuation capabilities and operational efficiencies” within an already fragmented market.
As for what these future capabilities may look like, future algorithmic systems may reach the point of harnessing archived historical data from trading’s entire history, allowing us to better determine which approaches worked, which ones did not, and which ones might work in the future. Additionally, as QuantInsti observes, these systems have grown closer to a variety of other exciting possibilities, including, but not limited to:
- Self-adjusting systems capable of changing trading strategy on the fly, to keep pace with changing market conditions.
- Simultaneous checking of multiple markets worldwide, saving a significant amount of time in the process.
- Enhanced communication via algorithmic chips, creating the possibility of global regulation while opening the door for concepts like kill switches.
For now, the market remains healthily rooted in both traditional and algorithmic methodologies, but it is fair to assume that, as the benefits of algorithmic trading become increasingly realized, it will soon become more difficult to deny change in good faith.