kinetic mind specializes in developing and implementing intelligent processes for trading, risk control and portfolio management by combining quantitative finance with predictive analytics and machine learning concepts
Statistical models based on neural networks allow to make sound predictions which help making better business decisions today.
Typical examples are:
Combining classical numerical methods with statistical learning techniques pioneer new and innovative solutions to common problems in engineering.
Use cases are:
Modern concepts that employ machine learning techniques advance trading and risk management operations.
Such techniques may be used:
Operating physical assets like power plants or gas storages can be very challenging in view of maximizing cash flows, while minimizing risks and simultaneously allowing for technical restrictions.
Intelligent systems may support:
The risk management of portfolios requires pre-processed market data (e.g. discount curves, forward curves and volatility surfaces). These bootstrapped inputs are often subject to errors arising from incorrectly finished processes, possibly causing severe losses.
Neural network models like Autoencoder provide a valuable tool for predictive error detection. By construction they validate the consistency of the entire data object going far beyond the usual plausibility checks.
The responsibilities of modern risk control covers a wide area of operations. In particular you will find recurring and standardized tasks among them. These may be outsourced to intelligent autonomous agents.
Possible tasks that can be automated: