Probabilistic Framework for Stochastic Event Modeling in Finance
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Solution Overview
Problem
Current techniques for stock price prediction and portfolio management fail to account for the impact of stochastic events on time series data, particularly in financial markets, and do not adequately capture the sophisticated temporal interactions among different stochastic events and their shock effects.
Innovation Solution
A probabilistic framework is developed that incorporates multivariate Hawkes processes and proximal graphical event models to model event intensity, magnitude, and their effects on financial time series, estimating a dynamic variance-covariance matrix to improve prediction accuracy and account for stochastic event impacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional time series models are used for stock price prediction, then the model structure is simple, but the prediction accuracy fails to account for stochastic event impacts
Solution Approach 1:
The patent combines traditional time series models with Hawkes process models to create a hybrid framework that captures both continuous price movements and discrete stochastic events. This merging allows the model to account for event impacts while maintaining a structured approach to prediction.
Solution Approach 2:
The Hawkes process acts as an intermediary component that specifically models stochastic events and their temporal interactions. This intermediary layer bridges the gap between simple time series models and the need to capture complex event-driven dynamics, improving prediction accuracy without completely replacing the traditional model structure.
2Reliability
If stochastic events are incorporated into the model, then the model captures event impacts better, but the computational complexity increases
Solution Approach 1:
The model segments the financial time series into event-driven components and continuous components. By separating the stochastic event modeling (Hawkes process) from the continuous price movement modeling, the system can process each segment with appropriate methods, improving reliability while managing computational complexity through modular processing.
3Measurement precision
If dynamic covariance matrix estimation is implemented, then asset correlations are more accurate, but the data processing requirements increase
Solution Approach 1:
The patent implements dynamic covariance matrix estimation that adapts to changing market conditions and event impacts. The covariance matrix is continuously updated based on incoming data and event occurrences, allowing the model to capture time-varying asset correlations accurately while processing data in a streaming fashion to manage computational load.
Data Source
AI summary
A method includes: creating a training data set based on user input, the training data set including time series data of a price of an asset and stochastic event data of events related to the asset; creating an event intensity model that models an event intensity parameter of one of the events, wherein the event intensity model is based on a multivariate Hawkes process, and the creating the event intensity model includes learning parameters of the event intensity model using machine learning and the training data set; creating a probabilistic time series model that predicts a probability distribution of a return of the asset, wherein the creating the probabilistic time series model includes learning parameters of the probabilistic time series model using machine learning and the training data set; and predicting a future return of the asset for a future time period using the probabilistic time series model.