The AI Value Chain

Artificial intelligence has found its way into stock trading and has helped streamline the processes while also helping investors and traders deal with huge amounts of data. In fact, Traditional AI is now making way for Generative AI to give a further edge to those who need to choose, buy and sell stocks at a much faster rate. Vaishnavi Chauhan takes a deeper look at how AI works as an evolutionary technology 

The other day, I found myself chatting with my nephew, and somehow our conversation veered to a topic related to the mid-19th century. In 1848, when gold was found at Sutter’s Mill, a lot of people rushed west hoping to strike it rich. They thought they would find gold easily and become wealthy quickly. But mining for gold was tough work and often didn’t pay off much. Instead of searching for gold themselves, some smart people realised they could make money by selling the deal to the miners. They opened stores where miners could buy tools like picks and shovels, or places like saloons where they could relax after a hard day’s work. 

This strategy, known as the ‘pick and shovel strategy’, worked really well. Miners needed good tools to find gold, along with other things like food and a place to rest. By providing these things, these smart entrepreneurs were able to build successful businesses. Just like what happened in the 19th century, we can see a similar trend in today’s business world. Instead of gold mines, we have companies in different fields, and instead of miners’ tools, we have AI. That’s right, artificial intelligence! 

AI is like the modern-day tool that helps companies do their work more efficiently. Just like miners needed picks and shovels to find gold, companies today need AI to help them with tasks like analysing data, making predictions and automating processes. So, just as entrepreneurs in the past found success by providing tools to miners, businesses today can succeed by using AI to improve their operations and stay competitive in their industries. When we talk about AI, we usually divide it into two main types: Traditional AI and Generative AI. 

Traditional AI is like the basic version of AI. It’s programmed to follow specific rules and instructions to perform tasks. It’s good at tasks that have clear rules and patterns, like playing chess or recognising faces in photos. On the other hand, Generative AI is more advanced. Instead of just following rules, it can create new things on its own, like generating new images or writing stories. It learns from examples and can come up with new ideas without being explicitly programmed to do so. So, while Traditional AI follows set rules, Generative AI can think more creatively and come up with innovative things all by itself. 

Here are a few key differences: 

Because Generative AI is seen as more advanced, companies are more likely to use it since it can be applied across all departments, not just a few. As an investor reading this article, you might wonder how you can seize this opportunity. As time progresses and we gather more data to analyse, coupled with advancements in algorithms, AI will continue to improve, much like everything else. Currently, there are several areas where AI is being utilised. One such area is the stock market, where AI is proving increasingly valuable. With over 5,000 stocks available, it can be daunting for investors to make decisions. AI comes to the rescue by screening and selecting stocks, simplifying the investing process for individuals. Let’s delve deeper into how AI is employed in the stock market to streamline investing. 

AI in Stock Screening

AI helps investors sort through the vast amount of data available for companies trading on the Indian stock markets. With AI, investors can quickly find stocks that match their preferences. Stock screeners, which are advanced tools, let investors filter stocks based on different criteria. These criteria can include things like financial ratios, trading volume and moving averages. There are many data points available, and stock screeners help investors narrow down their choices. 

AI in Building Portfolio

Robo-advisors simplify the process of working with a financial advisor to set investment goals, timeframes and risk levels. They use artificial intelligence to suggest the best mix of stocks for a portfolio, based on the investors’ preferences. These platforms also automatically adjust the portfolio if it strays too far from its target allocations. It therefore ensures that the portfolio stays on track with the chosen strategy 

AI for High-Frequency Trading

High-frequency trading (HFT) is a type of algorithmic trading where large volumes of stocks and shares are bought and sold automatically at extremely fast speeds. HFT is becoming increasingly popular among regulators and regular stock market investors. Algorithmic trading has revolutionised the trading process, making it faster and more efficient. Traders use algorithms to execute trades with higher speed and accuracy. The present scenario in trading is therefore quite unlike what you would have seen in the earlier days when traders cut deals on the trading floor while being present physically. 

These algorithms are becoming more sophisticated, using AI to adapt to different trading patterns. Machine learning (ML), a subset of computer science, plays a significant role in algorithmic trading. ML algorithms analyse large datasets to generate predictive models, helping traders make informed decisions in real time. The combination of algorithmic trading and machine learning often referred to as AI trading, holds immense potential for the future of trading. 

Trading and Trade Management

With the rise of fast trading and advanced computers, tools are available to automatically monitor trades and make decisions according to user-set criteria. For instance, they can purchase a stock if it meets certain conditions and then employ strategies like stop loss or profit targets to exit the position based on market movements. Effective use of AI helps traders minimise the emotional aspect of trading, which is crucial. 

Risk Management

Risk management involves using AI alongside modern portfolio theory and the efficient frontier to minimise risk in trading. This can include using advanced order options to manage risk in active trades. Additionally, AI can help reduce the risk of having too much exposure to individual stocks in a portfolio, or establish automated options strategies to assist with risk management. 

Exchange-Traded Funds

The rise of exchange-traded funds (ETFs) has transformed portfolio investment, with many ETFs being index funds that are passively managed, resulting in low expense ratios. These funds are simpler to run as they don’t require active security selection and can be managed mostly by computers. An example of an AI-powered ETF is the AIEQ, an equity exchange-traded fund powered by IBM’s artificial intelligence, Watson. This actively managed portfolio consistently outperforms the S and P 500, making it a unique offering in the market. Now a question that arises is whether AI is suitable for beginners due to its perceived complexity and advanced technology? 

Yes, investing with AI is absolutely suitable for beginners! Robo-advisors, which heavily rely on AI, are ideal for new investors. While some AI technology may seem complex, a lot of it is intuitive and user-friendly. In investing, AI simplifies tasks like stock selection through tools like stock screeners. These screeners use AI to quickly and accurately filter stocks based on investor criteria, much faster and more efficiently than a human could. Beginners, as well as experienced investors, can benefit from these powerful and easy-to-use AI investing tools. 

Further, given the abundance of scams online, some might also be concerned about the safety of using AI, especially for beginners. Investing with AI can be safe, but it depends on both the quality of the AI application and the user’s ability to use it effectively. AI tools can help identify risky or safe stocks, but the safety ultimately depends on the investor’s choices regarding risk and reward. Utilising strategies like modern portfolio theory to create a balanced portfolio can also enhance safety in investing decisions. 

However, there are potential risks associated with using AI for investing, such as faulty algorithms and the possibility of market movements due to many investors relying on the same AI-generated information. It is essential for investors to be aware of these risks and to use AI tools wisely in their investment strategies. Investors should explore the AI investing tools available on their current platform to ensure they meet their needs. If not, they may consider switching to a broker with more robust AI tools or using third-party AI software to supplement their investment strategy, such as a separate stock screener for selecting stocks. 

Conclusion

In the beginning, we discussed the ‘pick and shovel strategy’, where tool sellers always make profits regardless of whether mines succeed. However, for tool sellers to profit, people must be willing to mine. No mining means no need for tools. In today’s scenario, AI is undoubtedly a valuable tool that can help organisations thrive in new areas. But let’s get back to the basics. Without organisations—groups of individuals working together towards a common goal—AI wouldn’t have a purpose. Now is the time of the AI revolution. Like a child growing into adulthood, AI is evolving with more data, algorithms and skills. However, we can’t rely entirely on AI. While it’s a helpful tool, it’s not the ultimate solution. We still need human interpretation and involvement across all business departments.