AI-Driven Investing: The Rise of Robo-Advisors and Algorithmic Trading

By Sidhavelayutham M, Founder & CEO, Alice Blue

A broad category of automated and intelligent investment solutions that use artificial intelligence (AI) and data analytics to optimise portfolio performance, lower costs, and improve customer experience is referred to as “AI-driven investing.” With the emergence of AI and its incorporation into investing strategies, the financial industry has recently experienced a dramatic upheaval. The emergence of algorithmic trading and robo-advisors as a result has completely changed how people and institutions invest.

Robo-Advisors

Robo-advisors use mathematical algorithms to assess the risk profile, financial goals and preferences of the clients and then allocate their assets across different asset classes, such as stocks, bonds, ETFs, etc. Robo-advisors also monitor and rebalance the portfolios periodically to maintain the optimal risk-return trade-off.

Robo-advisors offer several benefits to investors, such as:

● Lower fees: The costs charged by robo-advisors are typically between 0.25 and 1 percent of the assets under management (AUM) per year, which is a small portion of what traditional human advisors charge. On a long-term basis, this can help investors save thousands of dollars.

● Higher accessibility: Robo-advisors are accessible to a wider range of investors due to their 24/7 availability through online or mobile platforms and frequent lower investment minimums compared to human advisors.

● Greater transparency: Investors are able to comprehend how their money is being invested and managed thanks to the clear and thorough information that robo-advisors provide about their investment strategies, techniques, assumptions, and performance.

● Reduced bias: Robo-advisors eliminate the potential for human errors, emotions, and conflicts of interest that may affect the quality of investment advice and decisions.

The Growth of Robo-Advisors

The popularity of robo-advisors has soared in recent years. Robo-advisors are expected to grow at a compound annual growth rate (CAGR) of 25.6% from 2020 to 2027, reaching $987.4 billion by 2027. This substantial growth can be attributed to several factors, including low fees, ease of use, and the ability to access professional investment services without the need for a human advisor. USA is the largest market for robo-advisors, accounting for 75% of the global AUM in 2019, followed by Europe (14%) and Asia-Pacific (6%)

Algorithmic Trading

The process of executing orders in the financial markets using pre-programmed rules and strategies depending on variables like price, volume, time, or market circumstances is known as algorithmic trading. Market making, arbitrage, trend tracking, and high-frequency trading are just a few of the uses for algorithmic trading. However, these traditional applications are not the only ones that can use algorithmic trading. We will examine some of the fresh issues and developments in algorithmic trading that are appearing in the current market climate in this viewpoint. According to Economic Times, algorithmic trading represents roughly 75% of worldwide stocks trading volume and about 60% of global currency and futures trading volume.

Some of the new trends in algorithmic trading are:

● Algorithmic trading of alternative assets: The use of algorithms in trading extends beyond conventional assets like stocks and bonds to non-traditional ones like derivatives, commodities, and cryptocurrencies. These assets differ from conventional assets in terms of their dynamics and attributes, such as more volatility, less liquidity, or intricate pricing schemes. To effectively capture the potential and hazards in these markets, algorithmic trading of alternative assets needs more advanced models, data sources, and execution platforms.

● Algorithmic trading with social media data: For traders and investors, social media sites like Twitter and Reddit have developed into significant sources of news and opinion. Social media data can shed light on market patterns, occasions, beliefs, or feelings that influence the value of stocks. Since important signals can be extracted from social media posts or comments using natural language processing (NLP), text mining, or machine learning techniques, algorithmic trading with social media data uses these signals to create or carry out trading strategies.

● Algorithmic trading with reinforcement learning: An agent can learn by its own behaviours and feedback in an unpredictable environment via reinforcement learning, a subfield of machine learning. Algorithmic trading can utilise reinforcement learning to develop adaptive and self-learning trading systems that continuously improve performance. Algorithmic trading systems can benefit from reinforcement learning in order to adapt to shifting market conditions or non-stationary data distributions.

The Surge in Algorithmic Trading

Algorithmic trading has experienced remarkable growth, driven by advancements in computing power and the availability of vast amounts of financial data. According to a study by Grand View Research, the global algorithmic trading market is expected to reach $25.7 billion by 2025, growing at a CAGR of 11.1% from 2019 to 2025. USA is the largest market for algorithmic trading, accounting for 52% of the global market share in 2018, followed by Europe (32%) and Asia-Pacific (12%)

Conclusion

AI-driven investing, powered by robo-advisors and algorithmic trading, has revolutionized the investment landscape. The growth and adoption of these technologies have reshaped the way individuals and institutions invest, offering cost-effective and efficient solutions. While challenges and ethical considerations remain, the continued advancement of AI in finance holds significant potential for enhancing investment outcomes and market efficiency in the years to come.