How AI and Machine Learning Are Changing Trading Strategies

AI trading or trading using artificial intelligence (AI) tools is the buzzword in the financial markets today. AI trading has brought about a sea change in trading strategy. Investments were earlier based on extensive research and manual analysis, along with gut feeling. Today, investors are more open to leveraging AI for trading stocks, with a focus on doing it instantly through a computer or smartphone by accessing swift analysis and predictive analytics. 

Evolution of Algorithmic Trading with AI

One of the biggest innovations in AI-based stock trading in India is the rise and evolution of algorithmic trading. It has gone up considerably over the last decade, with reports indicating that about 70% of the overall trading volume today is initiated through algorithmic trading. The global algorithmic trading market was valued at US$15.5 billion in 2021 and is anticipated to post a compound annual growth rate (CAGR) of 12.2% between 2022 and 2030. 

This form of AI-powered trading involves the usage of machine learning (ML) and artificial intelligence-driven algorithms to automate and execute trades, backed by instant analysis of huge datasets and identification of complex market patterns. AI models can also learn from market data and adapt to changing circumstances, making them a dynamic option for investors. Trades are often executed in milliseconds, thereby helping with high-frequency trading (HFT), which takes advantage of minor changes in prices. From sentiment analysis to portfolio optimisation and pattern recognition, several tools are used by algorithms to ensure more informed, efficient, and accurate decisions. Also, several algorithmic trading strategies are used to run these algorithms. 

Read More: How to Start Algorithmic Trading?

Key AI Techniques Used in Market Analysis

Several techniques and technical indicators come into play while using AI in stock trading. Some of these include: 

• Sentiment Analysis: Algorithms evaluate text data like reviews and social media to determine the sentiments behind the same, whether it is neutral, negative, or positive. 
• Predictive Analytics: These tools leverage historical data to predict future market trends and patterns, along with customer behavior and other relevant aspects. 
• Data-Based Insights: AI also helps in the extraction of invaluable insights from vast datasets, which can be used for personalised marketing, customer segmentation, and resource allocation. 
• Customer Journey Mapping: Customer interactions during the buying process can be visualised by AI tools, offering insights into preferences and behaviour. 
• Natural language processing (NLP): Natural language processing enables AI tools to suitably understand and process human language, facilitating these tools to analyse text data, execute sentiment analysis, and even answer questions. 
• Machine learning: ML algorithms learn from market data and improve predictive accuracy over a period of time, enabling more accurate recommendations and forecasts. 

Advantages and Challenges of AI-Based Strategies

AI trading in India offers several benefits, but it also comes with many hurdles. Let us look at them closely below. 

Benefits: 

• Lower research time: AI algorithms are tailored to gather and analyse huge volumes of historical market data, along with news, updates, and economic indicators. This helps reduce the time needed for research and decision-making, enabling fast trades in a well-informed manner. 

• Lower costs: AI-based automation helps enable cost savings through lower labour costs and the need for extensive physical infra and resource allocation. 
• Emotionless trading journey: Most of us end up making emotional and impulsive trading decisions which may lead to losses. AI systems do not have emotional biases and enable a detached and neutral trading environment that is backed by rational and disciplined strategies. 
• Pattern identification: AI-based pattern recognition abilities enable better market trend analysis, finding intricate trends, conditions, and other anomalies. This goes a long way towards enabling data-based decision making and better capitalisation of market movements. 
• 24-7 Trading Process: AI-based trading systems do not have human limitations, enabling them to function on a 24-7 basis, helping take advantage of opportunities in global markets and also continual vigilance in terms of updates and patterns. 

Read more: Benefits of Algorithmic Trading in Stock Market

Challenges: 

• Data quality issues: AI-based predictive abilities depend hugely on historical data, although the markets are dynamic and shift unpredictably at times. Over-dependence on historical information may lead to a false sense of security, thereby leading to erroneous decision-making. 
• Transparency challenges: The complexity of AI algorithms may make their decision-making process non-transparent and uncertain for traders. 
• Lack of judgment: AI is proficient in analysing data and executing trades, although it could lack human intuition, contextual understanding, and judgment. It may not consider many external factors that humans would otherwise integrate into the decision-making process. 
• Risks of inaccuracies: AI is not completely devoid of errors and inaccuracies, especially in volatile and extreme market conditions. The strategy behind the algorithm should be adaptable, and this requires constant monitoring and adjustments. 
• No personalisation: These systems often implement standardised strategies throughout multiple assets and markets, which may not always sync with the personalised risk tolerance, investment goals, and market preferences of individual traders. 
• Cybersecurity and regulatory aspects: Market players face cybersecurity threats, hacking attempts, risk of data breaches, and other malicious operations. At the same time, regulatory aspects also come into play since algorithmic trading and HFT are subject to compliance and other regulatory aspects that have to be upheld. 

Case Studies: AI Success Stories in Trading

Let us look at a few AI trading success stories worth noting. 

Story 1- Jim Simons & the Medallion Fund

The well-known Medallion Fund was operated by Renaissance Technologies and was founded by James Harris Simons in 1982. The fund itself came into being around 1988 and is perceived to be the world’s most successful hedge fund, with complex algorithms and models to find and leverage inefficiencies in the market. 

The fund has offered returns to the tune of 66% per annum (before fees) over the last three decades, which is astounding, to say the least! What’s worked in favour of the firm’s algorithmic trading strategy is its focus on data analysis that creates predictive models behind the trading decisions, while the algorithms evolve over time, adapting to newer market conditions and data input changes. 

Story 2- Two Sigma Investments

Two Sigma Investments is a leading hedge fund and tech company that is known for applying machine learning (ML) successfully to algorithmic trading. Based in New York, it was created by David Siegel and John Overdeck in 2001 and has become one of the biggest hedge funds in the world, managing more than $60 billion. The firm’s success is largely due to its focus on processing and analysing vast amounts of structured and unstructured data, with its algorithms even using alternative sources like social media sentiment, satellite images, weather, and more. 

It enables better trading decisions by identifying patterns and correlations that may be missed by human traders. Two Sigma has performed better than regular hedge funds and even market indices over the last few years, giving high double-digit returns even in tough market conditions. 

Story 3- Virtu Financial 

When it comes to high-frequency trading (HFT), US firm Virtu Financial has become the master of the game. The firm was founded by Vincent Viola in 2008. It is one of the most successful electronic market-making firms today, offering ultra-fast algorithmic trading strategies. 

It executes trades at immensely high speeds and massive volumes, profiting from discrepancies that last for fractions of seconds. Its risk management approach has also been a differentiator, and in 2014, during its IPO filing, it announced that it had lost only a single trading day out of 1,300 such days in four years. The firm generated $2.5 billion in revenues in 2022, with net income touching $452 million. 

Read More: Algorithmic Trading with Python

Ethical and Regulatory Considerations

There are several regulatory and ethical aspects worth noting when it comes to using AI for trading stocks. These include the following: 

• Discrimination & Bias: AI systems may sometimes end up perpetuating unfair biases, leading to discriminatory outcomes in resource allocation. 
• Lack of transparency: Many algorithms are complex, making it hard for users to understand how the decisions are made, thereby hindering accountability and trust alike.
• Data security and privacy: AI systems process vast amounts of sensitive customer data, making them vulnerable to privacy risks.
• Fairness and Ethics: There are ethical frameworks required to ensure the responsible usage of AI in trading, especially in HFT scenarios. 
• Accountability & Compliance: Several countries and regulators are coming up with frameworks for AI-based trading, which have to be complied with. Clear accountability lines are also being set up for AI-driven trading decisions, ensuring that responsible parties will be accountable for negative consequences.

Read More: SEBI Regulations on Algorithmic Trading in India

Future Outlook: The Next Wave of AI in Trading

The future looks bright for AI-powered trading, and the next wave will emphasise greater transparency, efficiency, and accessibility. There will be further democratisation of the stock market, and more retail investors will come into the picture. There will be more refined risk management, analysis of diverse sources of data, and hyper-automation, along with sentiment analysis and a focus on regulatory and ethical trading norms. AI models are expected to get better at predicting market volatility and mitigating the same through the analysis of market indicators/signals, trends, and news sentiments.