What impact has the integration of AI in Algorithmic Trading had on the financial sector?
By Mr. Hemant Sood
In the midst of the ‘AI era,’ the financial industry is undergoing a transformation with the widespread incorporation of artificial intelligence (AI) across various sectors. Embraced as a tool for gaining a competitive edge, AI is increasingly utilized by financial organizations, with 57% acknowledging its potential, according to a collaborative report by PwC and FICCI.
The convergence of AI and algorithmic trading (algo trading) has been a pivotal development, reshaping market dynamics and strategies. Since the Securities and Exchange Board of India (SEBI) authorized algorithmic trading in 2008, its popularity among institutional investors, high-net-worth individuals, and individual traders has soared. Presently, individual traders harness algo trading for quicker and more efficient decision-making, contributing to 50–55% of trades influenced by algorithms.
The synergy between AI and algo trading has propelled advancements in data analysis, decision-making, and pattern recognition. AI-enhanced algo trading not only expedites data processing but also empowers traders to make informed decisions in real-time, offering a competitive advantage. As AI algorithms continuously learn and adapt, they prove to be valuable performance enhancers in the financial market landscape.
The proliferation of AI in trading can be attributed to expanded data accessibility and technological progress. The abundance of market information allows AI trading systems to swiftly and accurately process data, enabling traders to make well-informed decisions in real-time. This efficiency boost is reflected in a Boston Consulting Group report, indicating potential cost reductions of up to 25% and a 1.5% increase in portfolio returns through the use of AI in trading.
AI in algo trading addresses human emotional biases by making decisions based on data rather than emotions, contributing to more rational decision-making. Furthermore, it revolutionizes decision-making by introducing intelligent, dynamic algorithms that adapt to market conditions, thus improving accuracy and risk management.
The lowered barriers to entry into algorithmic trading, driven by improved trader productivity, reduced market impact, and transparent AI algorithms, have democratized learning and increased market participation. As AI becomes more accessible, the consolidated algo trading market is projected to grow at a CAGR of 10.5% from 2023 to 2028, offering new opportunities for a wider range of traders and investors.
While concerns about job displacement exist, the integration of AI in the financial sector has also created new avenues for employment. Proficiency in programming languages, machine learning/AI, and big data analytics is becoming essential for roles in data science, AI development, and algorithmic trading. The impact of automation on jobs is expected to be offset by the creation of new opportunities, fostering a need for individuals who can combine human expertise with AI accuracy.
Despite being in its nascent stage, AI in Indian algo trading markets holds immense promise for the financial sector. As brokerages focus on leveraging AI to enhance platform reliability and minimize latencies, the growth of the AI-based algo trading market in India is anticipated. The evolving landscape is expected to give rise to new use cases, trading strategies, and employment opportunities as the AI market matures in the Indian financial sector.
(The author is Mr. Hemant Sood, Founder of Findoc, and the views expressed in this article are his own)
