Before computers evolved, all investment decisions were made by human experts who studied reams of information about how the markets moved in the past and how specific portfolio structures performed in specific market conditions. Without a crystal ball, however, predicting the future is difficult. It relies on the ability to process and gain accurate insights from the huge amounts of data in myriad categories that can move or be moved by the markets. Even the best human experts are limited by the amount of information they are able to absorb and understand.
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AI is the emerging technology that enables computers to not only process enormous amounts of data nearly instantaneously but also to learn from its own experiences, without direct human coding. Artificial Intelligence (AI) in finance is emerging at an unprecedented rate as a powerful tool for improving efficiency, enhancing risk management, revolutionizing the client experience, and supporting business growth. It is being effectively employed in fraud detection, AI lending, operations, marketing, product design and customer service.
AI’s superior capabilities in analyzing very large data sets, identifying patterns and learning from new information make it uniquely qualified as a tool for designing and implementing investment strategies.
1. Predictive Analytics for Market Trends
Machine learning – the part of artificial intelligence that imitates human learning – is especially good at identifying patterns in very large data sets like historical market data. This data can include historical price movements, trading volumes, liquidity levels, market indicators, and market behavior over a specific time period. Because market dynamics are shaped by a range of factors, it can also cover information on environmental regulations, technological advancements, economic conditions, and global demand. Investors have also traditionally used fundamental analysis of individual companies in making decisions, relying on metrics like earnings per share (EPS), price-to-earnings (P/E) ratio, P/E growth, and dividend yield.
A machine-learning powered program can look through large amounts of information and find correlations much faster than humans. One of its advantages is the ability to process data in real time, enabling investors to respond swiftly to new opportunities and current market conditions when delays and outdated information could mean greater exposure to investment risks.
AI is also providing new types of data. Non-traditional data sources include internet traffic, patent filings, satellite imagery, more detailed company data, etc. And one of the newest tools offered by AI is natural language processing or NPL. NPL reads and interprets text. It’s capable of analyzing multiple sources in multiple languages. It’s now possible to easily discover, for example, how the themes CEOs are focusing on in their earnings calls change over time. NPL can even gauge sentiment in text. The tone in which research reports, analyses and even news stories are written can provide additional nuanced clues.
Access to new types of data, along with the ability to capture and process that data rapidly, is offering new ways to capture investment themes, find opportunities and manage risk.
2. Algorithmic Trading
One important use of new AI technology is to automate the trading algorithms used to make informed investment decisions.
The early algorithms built by experts had limitations. As the markets zigged and zagged, the way they often do, the algorithms could be quickly left behind. They were not built to respond in real time and required a human to manually adjust their rules. Today, thanks to artificial intelligence in finance, new systems can learn on their own, and that makes a major difference. Armed with data including price, timing, volume, risk evaluation and historical market moves, they write their own adjustable rules for investment recommendations that can respond in real-time to changing circumstances.
Once it has built a model, an AI-based system is able to cross-reference new data against data it has already processed and predict when the models are likely to change. AI and machine learning can greatly improve our accuracy in predicting market trends. It’s easy to see how this can be used to produce insights into best practices for a particular market moment and enable investors to plan for the future with more confidence.
Timing the market has always been especially difficult, but AI’s ability to generate real-time insights and decisions may even improve the chances of executing trades at optimal times.
3. Risk Management
One of the critical aspects of designing and implementing investment strategies is risk management. How much an investor avoids losing is as important as how much they gain. By making forecasts of future market moves more accurate, AI replaces traditional guesswork with predictive science.
Predictive modeling uses algorithms to forecast potential risks, processing vast amounts of data on market volatility and the various types of risk inherent in the markets. In the same way that lenders can use AI lending models to assess the creditworthiness of borrowers and minimize defaults, investment firms can use AI models to minimize potential risks before they result in losses.
The ability of machine learning in finance to identify new patterns without human input is especially effective in improving risk detection. Financial markets are complex and ever-changing. In recent years alone, the markets have behaved in unexpected ways due to a pandemic, a work-from-home trend, supply chain challenges, fast-moving inflation, the rise of AI itself, Fed rate moves, meme stocks, yield curve inversions, unexpected patterns in consumer spending, threats of recession, etc.
AI monitors and analyzes traditional and nontraditional data from an array of sources to build detailed simulations of economic conditions and their potential impacts on investors. This proactive approach can recommend shifts in portfolio strategies to mitigate new risks before they become obvious and provide investors with greater stability in uncertain and volatile market conditions.
Bottom Line
As we look to the future, the role of AI in designing and implementing effective investment strategies is set to grow as the technology continues to advance. Data analysis, predictive modeling and risk mitigation are three areas in which the contributions of AI should become even more valuable over time. Financial firms that embrace AI are likely to gain a competitive advantage that sets them up for better investment outcomes, more satisfied customers and more business profitability in the future.
The need for firms to innovate quickly, or partner with providers of cutting-edge solutions is very clear. AI is likely to become more of a necessity than an opportunity in the future. And the objective is clear: integrate AI to get, and stay, ahead, not merely to keep pace.
Adhar Dhaval is experienced portfolio, program and project leader with demonstrated leadership in all phases of sales and service delivery of diverse technology solutions. He is a speaker sharing advice and industry perspective on emerging best practices in project leadership, program management, leadership and strategy. He is working for the Chair Leadership Co.
