Augmenting investment skills with AI

Financial time series forecasting predicts future values or trends of financial data by analyzing historical data and factors such as stock prices, interest rates or other indicators with statistical or machine learning techniques. aisot has developed proprietary AI technology (LSTM, long short-term memory networks) that captures various patterns in time series data with multiple variables and determines the impact of each variable on the prediction, allowing greater interpretability of the model's results.

aisot analyzes a constant stream of news and social media data with different natural language models, including large language models, and quantifies the propagation and absorption of large-scale publicly available news articles about financial markets.

Volatility plays a crucial role in investment choices, strategies and systemic risk assessments. To improve the accuracy of volatility forecasting, aisot has developed a proprietary custom temporal mixture model that uses both the historical patterns of volatility over time and the characteristics of the order book, which provides valuable insight into market intentions. Combining these factors gives more precise predictions of future volatility.