February 24, 2022

Reimagine Investment Management with Artificial Intelligence

AI technologies can accelerate investment decision-making and process big data in a fraction of the time.

Reimagine Investment Management with Artificial Intelligence

Investment Management firms are expected to spend over $13B a year on Artificial Intelligence (AI) technologies by 2027. This technology is being leveraged to elevate investment decision-making and improve long-term portfolio performance.

In a world where data is growing exponentially, intelligent applications can create a competitive edge for investment managers in the technology's ability to process data faster and at scale. In financial markets, where excess return is scarce, going from data to decision faster can be a key differentiator.

A common challenge for investors is spotting the right investments quickly. With AI, larger data sets can be used to generate predictive models for applications like credit ratings, risk assessments, and investment thesis’. This results in higher accuracy and finding the best predictors of success in order for investment opportunities to be found and risks identified. With an effective application, AI tools help find the signal in the noise.

Here are three examples of how AI can be leveraged within the Investment Management firm:

Better Backtesting

Traditionally, investors rely on backtesting to identify trends in security performance. However, running random simulations can often inform future performance better than a backtest as it accounts for the inherent randomness in future outcomes.  Unprecedented regime changes like a pandemic can make random simulations challenging though and traditional models for financial data simulation, such as copulas, can be limited in their abilities. With machine learning generative models such as the Generative Adversarial Models (GANs), data can be simulated more effectively, and random simulations can be employed to provide more accurate insights into market trends amid highly volatile markets.

Understanding and Tracking Market Sentiment

Another important aspect of interpreting markets is sentiment.  For example, there are over 350,000 tweets a minute, 2 million posts on LinkedIn per day and 71% of Americans get their news from social media. This means sentiments can shift quickly, affecting market trends and investment portfolios. By employing Natural Language Processing (NLP), computers can quickly understand human language and translate it into data points.

AI tools like NLP conduct sentiment analysis on unstructured data (news, social media posts, etc.) and turn it into structured data that is quantitative and organized to predict change in trends and identify risks. With traditional methods, it would be impossible to quickly synthesize data from hundreds of sources to determine sentiment in a fraction of the time.

At AlphaLayer we leveraged NLP to understand sentiment changes in company earnings calls. By quantifying the sentiment of the call with Executive teams, we can support Investment Managers to quickly prioritize calls that require attention during the busy earnings season when sentiment changes are detected. Adding this solution to an investment research workflow allows Investment Managers to identify changes in executive and analyst attitudes and outlooks on the business. This new source of data is also useful for assessing performance along the tenure of the executive team and as the business matures.

Increased Understanding of Security Relationships

With the use of clustering technology, investment managers can find relationships between different securities and assess risks to returns. Traditional risk models use style factor performance and may miss the connection between the performance of similar stocks that have variable risk classifications. Cluster Analysis can show connections between securities based on price sensitivity, policy changes, return reports, and risk factors. Access to these relationships helps accelerate knowledge of financial markets for investment managers and uncover connections in portfolios that are not obvious.

With AI, investment managers can be sophisticated in diversifying their portfolio. Securities that show up in similar clusters can also be compared for risk exposure. Visualizing AI-generated clusters can allow for risk detection, and securities can be spread out to adjust factor exposure. At the same time, investment managers can discover new ideas in securities that may appear in unexpected clusters. With 3-D clustering models, bias or over-concentration can immediately be identified within the portfolio where it may not be obvious with traditional methods. This allows investment managers to avoid the associated unconscious risks and adjust their portfolios to reduce the overall risk profile.

AI Can Be a Key Part of the Investment Managers Toolkit

Stock picking using traditional methods is no longer a viable option. According to a recent report from Morningstar, over a 10-year period, only 27% of fund managers were able to outperform the index, while the rest either failed or underperformed. With AI-based tools that leverage NLP and Clustering, information can be synthesized into reports and connected across millions of data points for better predictions. AI tools allow investors to glean information from documents such as a 10-K and 10-Q, producing actionable insights and metrics. By combining easy-to-understand visualization, non-technical users are also able to participate in using AI to get more from data to generate more Alpha.

AlphaLayer is accelerating the understanding of the financial markets through artificial intelligence. We develop new and unique sources of investment return and build tools to scale the investment decision-making process.

Want to know more about AI and Investment Management? Head to our News section to read about AI in the investment world. You can also find out more about AlphaLayer here.

Chad Langager
Chad Langager
Follow us on: