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Using AI for Investing

Artificial Intelligence is at the heart of almost every activity we do today. From our phone’s facial recognition software to personalized amazon shopping recommendations, from Siri to google maps. AI is even modernizing our money; making our lives easier through mobile banking apps, helping us make better investment decisions, and optimizing our investment portfolios for success. Investment managers are using AI to invest smarter, faster, easier, and cheaper than ever before.

Technology firms and venture capitalists have spent billions acquiring AI start-ups and investing in companies that are developing and commercializing AI-focused technology and products. Investments like these have resulted in a lot of press coverage on the subject. This has been further fuelled by the trending claims that computers could one day become more intelligent than humans, replace our jobs, and even take over the world. However, to understand the use of AI in investing, we need to demystify some of these claims.  

Demystifying AI

AI, in a nutshell, is a broad area of computer science that involves simulating human intelligence in machines. It refers to a machine’s ability to solve issues, accomplish tasks, or perform other cognitive processes that humans can.   

It is a blanket term that can apply to a wide variety of functions, just as the term ‘manufacturing’ can apply to everything from the production of cars, to buttons, to biscuits. However, using the blanket term ‘AI’ can downplay the importance of its specific purpose.   

There are two types of AI:   

Narrow AI, commonly referred to as Weak AI, is the type of AI that we experience on a day-to-day basis. Examples include everything from chatbots to facial recognition to google translate to self-driving cars.  

Narrow AI refers to artificial intelligence that has been designed to execute a specific task, such as weather monitoring the weather, data processing, or analysis of investment risk. It is not conscious or fuelled by emotion like humans are. Narrow AI systems can only carry out limited data collection and operate within a pre-defined objective, so they cannot operate beyond the particular task they were created for.  

General AI, otherwise known as Strong AI, refers to machines that demonstrate human-level intelligence. In other words, it has the capacity to carry out any intellectual task that a human can. This is the kind of AI we see in science fiction movies where humans interact with sentient robots that are driven by consciousness and emotion. This is a wide definition of AI; it is ambiguous and often misrepresents the types of AI we engage with day-to-day, which would fall under the “narrow” category.   

The Application of AI in Financial Services

Financial institutions are among the earliest adopters of Narrow AI and machine learning. When Wall Street statisticians realized they could process millions of data points in real-time and harvest information that traditional statistical models couldn’t, they were quick to apply AI to many areas of finance. Financial institutions are already utilizing AI to detect fraud and anomalous transactions, manage risk, collect and analyze data, personalize customer services, and assist in credit decision-making.  

The fundamental concept of investing is being revolutionized thanks to this technology. What was once a relationship-driven practice accessible exclusively to the wealthy few is now an inclusive, democratized activity available to a much wider consumer base.  

We’re seeing automated systems play a more prominent role in the investment process, especially as their ability to outperform humans in the markets continues to improve. The most exciting component of this role is undoubtedly machine learning (ML).  

ML is an AI application that uses data to learn, adapt, and improve investing decisions. ML systems can be trained to rapidly detect security mispricings with algorithms and exposure to large amounts of relevant data, e.g., transactional data and historical market prices. This means that investors and fund managers have a better chance of gaining high returns. Quant Infinity’s ART fund is an example of this kind of AI machine learning.  

So, how has AI contributed to the transformation of the investment industry specifically? Here is a list of some of its most significant contributions.  

Accurate predictions

Accurate predictions make for better investment choices. Thanks to AI and ML, asset managers can incorporate new information into their portfolio development processes much quicker and more accurately. Additionally, with increasingly advanced processing power, the financial industry is better able to acquire and analyze data using increasingly comprehensive statistical models.  

This has contributed to better forecasting of future economic outcomes, helping investors better allocate their money to the most profitable opportunities as well as manage their portfolio risk. In other words, smarter robots result in smarter investors. 

Optimized portfolios

Portfolio optimization before AI relied solely on the capacity of humans, which was time-consuming and did not guarantee a thorough collection of all data sources. Humans aren’t capable of evaluating every single one of the market aspects that influence a portfolio’s success. Money management systems can use algorithmic programming to track particular indicators and adjust portfolios automatically. As a result, investors experience optimized returns as they benefit from faster response times to economic, global, and market developments.   

Low-cost, high-quality advice

AI provides higher-quality advice at a reduced cost to both customers and financial firms. Tedious tasks including precursory data collection, research, and compliance adherence, can be delegated to Robo-advisors. Advisors and asset managers can then devote more time to developing superior strategies and packages for individual clients. This also means AI services enable firms to provide a greater range of financial services to customers of various income levels, further democratizing the investment industry.  

Improved understanding of risk

Asset managers are now using AI and ML to improve their understanding of the risks associated with investing. Historical data provides subtle indications and autonomous patterns that can be utilised to forecast future patterns with astonishingly high accuracy in financial risk analysis. Asset managers, as a result, will be able to increase their performance and risk/reward ratio significantly.  

Emotionless decision making

AI increases the odds of investment success when it comes to emotional bias. The emotional bias we encounter as investors (both institutional and retail) can be damaging to our long-term expected returns. This is because humans aren’t always rational and we tend to feel more pain from our losses than pleasure from our gains. Since AI does not experience these emotions, it can eliminate many of the behavioural biases that cause us to fail.  

Better customer relations

Thanks to AI, financial advisers can now automate key aspects of client engagements, such as first conversations, risk profiling, and all legal paperwork associated with the client-advisor relationship.  

Rapid communication

For retail customers, AI chatbots are now the initial point of contact. They’re available 24/7, and though they can’t always answer all our questions, each question answered successfully by a bot is one less query that needs to be forwarded onto a human. This means AI communication lowers the cost of investing for both businesses and investors. It also makes communication more efficient and pleasurable for individuals who prefer digital communication.   

During the next decade, data-driven investing using AI and ML will become indispensable for investors as algorithms become increasingly sophisticated and alternative datasets continue to prove their value in creating alpha.  

Given these evident advantages, one might wonder if these quickly evolving machines could completely replace human investors. However, as investor Paul Tudor Jones famously stated: “No human is better than a machine, but no machine is better than a human with a machine.” This is especially evident in the Robo-advisory industry, where investors appear to prefer hybrid models that combine the benefits of a digital algorithm with the personal touch provided by real-life investment advisors. 

Ultimately, AI will continue to have a transformative impact on the investment process. In fact, people are going to receive financial help before they even know it with the help of AI, as it becomes better at predicting our spending habits, saving habits, and lifestyle choices.  

With AI machines possessing the capacity to evolve, adapt and search for patterns and trends, they have an enormous advantage for both asset managers investors when used to optimize investments. We’ll likely see the deployment of more AI across the financial spectrum in the future, and combining the strengths of both AI and humans will undoubtedly be the way forward. 

If you’re interested in finding out how AI can improve and optimize your investment strategy, check out arty. arty is an AI-powered Robo-advisor that designs optimized portfolios based on your chosen level of risk and desired target returns.   

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