Our expert AI and data science teams apply advanced AI and machine learning methodologies to deliver cutting-edge investment and trading solutions
How We Apply AI in Investment & Trading Solutions
A review of AI methodology in financial applications
Different AI Models
- Artificial Intelligence (AI) refers to computerized systems capable of performing tasks that typically require human intelligence, such as reasoning, problem-solving, planning, prediction, perception, and content generation.
- Machine Learning (ML) is a subset of AI that focuses on training algorithms to recognize patterns in data. By adjusting their internal parameters as they learn. Once trained, ML models can make predictions or decisions on new unseen data.
- It’s important to note that not all AI is machine learning. Other AI approaches include rule-based expert systems, symbolic reasoning, fuzzy logic, and robotics control systems that rely on programmed logic rather than pattern learning from data.
Generative AI
- Generative AI is a branch of Machine Learning focused on creating new content, such as text, images, audio, or video, that mimics patterns found in real-world data. Instead of producing a single numeric prediction, generative models learn the underlying structure of their training data and generate new outputs that are statistically similar.
- A well-known example is Large Language Models (LLMs), such as those used in ChatGPT, which generate coherent, human-like written content.
- Generative AI excels at creative language-based applications, but it is not used in our AI stock investment and trading solutions, which rely on predictive quantitative AI models designed to extract patterns from financial time series data to forecast some financial outcome.
We Use Predictive AIs
Predictive AI is a type of Machine Learning that learns from historical data to forecast future outcomes. A key area of application is financial time-series modeling and prediction, where AI models analyze past market behavior to predict future price movements, returns, and risk metrics.
In our AI investment and trading solutions, we leverage two complementary categories of predictive AI models:
- Deep Learning Models are neural networks with multiple layers that automatically learn hierarchical patterns in the data. They are powerful for capturing complex relationships but require large datasets and careful tuning.
- Ensemble Learning Models are tree-based models trained using gradient boosting, where during training and learning, each iteration reduces the previous model’s errors. They are lightweight, faster to train, and extremely effective for structured financial data such as prices, and fundamental, financial and technical indicator time series.
We employ these AI models in different learning modes:
- Supervised AI is used when the target outcome is known (labeled), for example, predicting a stock investment score from 0 to 9, or forecasting expected price movements.
- Unsupervised AI is used to discover hidden patterns when labeling is not possible, for example, clustering stocks by return behavior to identify groups with similar risk-reward profiles.
Financial feature selection
- It is well known that stock prices exhibit the characteristics of a random walk, meaning price movements are largely unpredictable when relying solely on historical prices. As a result, forecasting future prices by using only stock price time series data produces poor performance.
- Greate many research literature has been developed to use additional time-series inputs, named features, based on some fundamental or financial indicators for long-term stock classification, or technical indicators for short-term stock classification into scores or buy-sell signals. To bring financial data into relative scales, features used in AI modeling are typically financial ratios. For example, stock earnings is not used, rather earnings per share is used as a feature. These research typically select a handful set of financial Features and train AI models to make some predictions.
- Many novice AI trading and investment services boast about using massive quantities of fundamental and technical indicators. However, unlike image recognition where more images improve accuracy, financial time-series AI prediction demands precision, not sheer volume. Adding too many financial features introduces noise, confuses the AI, and leads to unreliable predictions.
Our AI Investment & Trading Methodology
- A key finding from the R&D of our large-scale AI prediction platform is that feature selection is not universal. The predictive power of financial and technical metrics varies significantly across different market sectors and industries. For example, features that are highly relevant for technology stocks may be far less meaningful, or even misleading, when applied to utilities or consumer staples.
- To ensure maximum accuracy and reliability, we have created a hybrid bottom-up AI trading architecture that meticulously engineers targeted technical, financial and fundamental metrics into finely tuned feature categories for AI Investment modeling (Profitability, Value, Risk, and Performance) and for AI Daily Trading Solutions. Each category powers a specialized AI solution dedicated to stock scoring, ranking, and trading signal generation within its specific domain. At the top layer of this hybrid AI architecture an advanced AI aggregation model synthesizes these domain-specific AI stock predictions into robust, unified AI Stock Scores, AI Trading, AI Investment and AI Portfolio selection services for day trading or long-term investment.
- Our targeted hybrid AI architecture enhances model performance by aligning the predictive inputs with the unique financial behavior of each market segment.