What’s under the hood of aisot’s AI Insights Platform?

AI is everywhere these days, often tacked onto anything and everything. But at aisot, we focus on predictive AI – we don’t just manage data and information, we help you see what’s coming next. Our AI processes massive amounts of data at lightning speed, uncovering hidden patterns and predicting market trends, economic shifts, and sentiment. Wondering how aisot’s AI Insights Platform works? It's not magic – it’s advanced math, machine learning and generative AI working together. Here’s how:

Forecasting returns

Our proprietary model combines advanced machine learning (ML) with traditional forecasting techniques to predict potential investment returns. Developed by our expert research team, these models analyze market data, assess momentum and economic conditions and scan millions of news articles to identify trends and opportunities. Leveraging both AI and conventional methods, we assess the projected performance of various assets, including stocks, cryptocurrencies, and ETFs. Here’s an overview of the models we use and how they work:

• Traditional model – We use the Capital Asset Pricing Model that looks at how an asset moves with the market. It focuses on a single factor  – market risk  – to explain asset returns.
• AI Models: Machine learning digs deeper, looking at things like market trends, economic data, and even sentiment, identifying hidden patterns and forecasting returns. By incorporating generative AI, our systems can synthesize new insights and predict asset behavior by generating possible future scenarios based on historical data. This predictive capability extends beyond standard models, identifying complex relationships between factors.
  • We apply Arbitrage Pricing Theory (APT), which assesses the fair price of an asset by considering multiple factors or risk sources affecting its return.
  • Additionally, we use Statistical Machine Learning Ensembles, which combine predictions to enhance accuracy, and Deep Learning models, designed to mimic the complex decision-making capabilities of the human brain. Generative AI models, particularly large language models (LLMs), simulate decision-making based on intricate market signals and evolving economic trends.
  • Fine-tuned and time-boxed LLMs restrict predictions to only the data available at prediction time, ensuring greater accuracy, eliminating look-ahead bias, and improving auditability.

Combining the two – We blend AI models with the traditional model through a Bayesian framework, a statistical approach that enables more stable and reliable predictions of expected returns.

Example of an output of our predictive models: Alphabet (GOOGL) has a 90% chance of going up between +0.9% and +3.2%, with +2.4% being the most likely increase.This gives a clearer, data-driven forecast instead of just one possible outcome.

Managing risk (because no one likes surprises)

Investing isn’t just about seeking gains—it’s also about managing risk. We do this by using:

• Prior Shrinkage Covariance Estimation: Looking at how different investments move together.
• Posterior Covariance Estimation: Using machine learning to fine-tune risk estimates

Portfolio optimization

Now it’s time to optimize your portfolio. We use a method that balances risks and return, while considering your specific needs:

• Your preferred risk level.
• Any constraints you set (like only investing in tech stocks or keeping a certain amount in cash, benchmark, rebalancing schedule etc.).
• Optimization strategies to get the best risk-return balance.
• We even offer advanced options like risk parity and tracking error optimization for those who want extra control.

We use the Black-Litterman method, a mathematical model for portfolio allocation developed in 1990 at Goldman Sachs by Fischer Black and Robert Litterman, for portfolio optimization. This method allows us to blend all the information smoothly. 

Explainability: why we’re transparent

We know you and your clients want to understand how these predictions are made. That’s why we use Shapley values. This is a way to explain how each factor (like market trends or sentiment) affects the final prediction. In short, it makes our AI decisions easy to follow and clear. Watch our video.

Why this matters

Machine learning and large language models (LLMs) are transforming asset management by analyzing vast amounts of structured and unstructured data in real-time, uncovering market signals beyond human reach. At aisot, we harness AI-driven models that continuously process market, macroeconomic, fundamental, and alternative data (e.g., news), retraining them daily to refine asset price predictions.

At aisot, we’re not about mysterious “black box” AI. We blend AI with traditional finance to give you explainable and customized investment insights that help you optimize your portfolio. As a tech provider, we offer direct access to specialized AI models, fine-tuned for investment analysis and decision-making.

Our solution is entirely cloud-based, and we do not need to access any of your client data – your data remains yours. We focus on delivering advanced AI models without compromising privacy.

Are you a qualified investor and want to see it in action? Schedule a demo today to learn more.