ML Architecture for Financial Instrument Pricing
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Solution Overview
Problem
The primary market for securities, particularly in bond origination and over-the-counter trading, is inefficient and manual, leading to information asymmetry, decentralization, and a lack of transparency, which hampers capital allocation and access for investors.
Innovation Solution
A computer-implemented method and platform that uses a processor to generate a user interface with tabs for pricing, issuance, and matching, enabling real-time financial instrument pricing outputs, propensity score calculations for issuer issuance, and algorithmic matching of target buyers with financial instruments based on past patterns and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes and paper-heavy operations are used in primary market securities trading, then flexibility and human judgment are maintained, but efficiency and transparency deteriorate
Solution Approach 1:
The patent replaces manual mechanical processes (phone calls, email communications, paper document exchange, spreadsheet-based pricing) with an automated electronic platform that uses software algorithms for pricing calculations, machine learning models for demand forecasting, and digital workflow systems for deal execution. This substitution dramatically improves trading efficiency while reducing the complexity of coordination between market participants.
Solution Approach 2:
The platform integrates multiple functions into a single unified system: pricing calculation engines, demand forecasting models, deal execution workflows, document management, communication tools, and compliance tracking. This multi-functionality eliminates the need for separate manual processes for each task, improving overall productivity while standardizing operations across the board.
2Loss of information
If decentralized manual data operations are used between counterparties, then data autonomy is maintained, but information asymmetry and market efficiency deteriorate
Solution Approach 1:
The patent introduces a centralized electronic platform as an intermediary between market participants (issuers, investors, dealers). This platform acts as a neutral mediator that collects, standardizes, and distributes market data to all participants in real-time, eliminating information asymmetry while maintaining data security and participant autonomy through controlled access mechanisms.
Solution Approach 2:
The system implements real-time feedback loops where pricing data, trading volumes, and market sentiment are continuously collected from all participants, processed through analytical engines, and immediately fed back to market participants. This creates a transparent information environment where all parties have access to the same data, reducing information asymmetry while maintaining automated data collection and distribution.
3Loss of time
If point-in-time market information is used, then data simplicity is maintained, but real-time decision-making capability deteriorates
Solution Approach 1:
The patent implements continuous real-time data collection and processing across the platform. Pricing engines continuously calculate valuations as market conditions change, demand forecasting models continuously update predictions based on new data, and the system continuously monitors trading activity. This continuous operation eliminates time delays between data generation and availability, enabling real-time decision-making while automating the complex processing required.
Solution Approach 2:
The system performs preliminary calculations and preparations in advance: pricing models are pre-configured with multiple scenarios, demand forecasting is continuously run in the background, and deal execution workflows are pre-established. When market conditions change or trading decisions are needed, the system can immediately deploy pre-computed information, reducing decision-making time while the background processing handles the computational complexity.
Data Source
AI summary
A computer-implemented method for forecasting the pricing of at least one financial instrument, the method comprising a processor and a memory, the method comprising the operations of: generating, by the processor, a user interface on a display, said user interface comprising a user-selectable pricing tab; wherein selecting the pricing tab causes the processor to at least: receive raw data in a plurality of disparate formats; scrub the raw data for anomalies and null values using a set of rules and generate a structured data set; and wherein selecting said pricing tab causes the processor to measure best-fit correlations with respect to a company's fundamental valuation and secondary market pricing for the company's at least one financial instrument across sector peers and market conditions and generate at least one financial instrument pricing output, in real-time.