Probabilistic Framework for Stochastic Event Modeling in Financial Time Series
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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 financial time series data, lacking adequate modeling of temporal interactions and cross-correlations among events, which affects the accuracy of future stock return predictions.
Innovation Solution
A probabilistic framework is developed that incorporates stochastic event modeling using multivariate Hawkes processes and proximal graphical event models to capture event intensity, magnitude, and their effects on time series data, enabling more accurate predictions of stock returns and portfolio optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current techniques for stock price prediction are used, then the prediction process is simple, but the accuracy of future stock return predictions deteriorates due to failure to account for stochastic event impacts
Solution Approach 1:
The patent segments the complex prediction problem into distinct components: an event intensity model that processes stochastic event data, a time series model that processes price data, and a probabilistic framework that integrates them. This segmentation allows each component to specialize in specific aspects while maintaining overall system manageability despite the increased complexity.
Solution Approach 2:
The patent introduces a probabilistic framework as an intermediary layer between raw event data/time series data and final predictions. This framework uses multivariate Hawkes processes and proximal graphical event models as mediators to capture temporal interactions and cross-correlations, enabling accurate prediction without directly managing the full complexity of raw data relationships.
2Measurement precision
If stochastic event modeling is incorporated to capture temporal interactions and cross-correlations, then the accuracy of predictions improves, but the complexity of the modeling framework increases
Solution Approach 1:
The patent employs dynamic models including multivariate Hawkes processes that adaptively capture temporal interactions among events, and proximal graphical event models that dynamically represent cross-correlations. These dynamic components allow the framework to adjust to changing event patterns while providing accurate predictions, managing complexity through adaptive rather than static structures.
Solution Approach 2:
The patent creates a composite modeling framework that combines multiple specialized models (event intensity model, time series model, Hawkes processes, graphical models) into an integrated probabilistic system. Each component contributes specific capabilities for handling different aspects of stochastic events and time series data, with the composite structure managing overall complexity through modular integration.
3Reliability
If a probabilistic framework with multivariate Hawkes processes is used, then the modeling of event intensity and magnitude improves, but the computational requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing event data and time series data into structured formats suitable for the probabilistic framework. Training data is prepared in advance with event intensities and magnitudes pre-calculated where possible, reducing computational burden during actual prediction and portfolio optimization operations.
Solution Approach 2:
The patent utilizes parameter changes in the multivariate Hawkes processes and probabilistic models to balance computational efficiency with reliability. By adjusting model parameters and complexity levels based on data characteristics and computational constraints, the system maintains reliable portfolio optimization while managing computational resource usage through adaptive parameter selection.
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 related to the asset, wherein the event intensity model comprises a proximal graphical event model (PGEM), and the creating the event intensity model includes learning parameters of the PGEM 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.