Single Stock Analyst Score via Payoff Function
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Solution Overview
Problem
Existing systems fail to accurately measure and score the performance of securities analysts' recommendations on a single stock basis, as they often rely on portfolio calculations that do not account for individual stock performance, separate benchmarks, and the accuracy of predictions, and struggle to evaluate neutral recommendations effectively.
Innovation Solution
A system and method using payoff functions that consider the performance of the stock, selected benchmark, and contributor recommendations to derive scores, ensuring specific characteristics such as neutral recommendations scoring higher in certain scenarios, and allowing for normalization and aggregation of scores to provide a comprehensive performance indicator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If portfolio calculation approach is used to measure contributor performance, then performance can be measured across multiple stocks, but accurate scoring of single stock recommendations cannot be achieved
Solution Approach 1:
The patent segments the portfolio evaluation approach into individual stock-level evaluations. Instead of treating all stocks as a aggregated portfolio, the system creates separate payoff functions for each stock that independently measure the accuracy of contributor recommendations on that specific stock, while still allowing aggregation across multiple stocks for overall performance measurement.
2Power
If percent return is used as performance metric, then return performance can be calculated, but accuracy of analyst prediction cannot be properly assessed
Solution Approach 1:
The patent changes the measurement parameter from percent return to a payoff function that directly measures prediction accuracy. The payoff function takes as input the contributor's recommendation, the actual stock performance, and the benchmark performance, and outputs a score that reflects how accurate the contributor's prediction was, rather than just the raw return generated.
3Adaptability or versatility
If neutral recommendations are evaluated using percent return, then they can be included in analysis, but their predictive value is underestimated
Solution Approach 1:
The patent inverts the traditional evaluation approach for neutral recommendations. Instead of expecting neutral recommendations to generate positive returns to be considered accurate, the payoff function recognizes that neutral recommendations are accurate when the stock performs close to the benchmark (i.e., when avoiding large losses is the successful outcome). This reverses the conventional wisdom that only positive returns indicate good predictions.
4Device complexity
If separate benchmarks are not used for separate stocks, then simpler analysis is possible, but accurate single stock performance measurement is compromised
Solution Approach 1:
The patent applies local quality by allowing each stock to have its own appropriate benchmark selected based on its characteristics (e.g., market cap, industry, liquidity). This enables accurate measurement of each stock's performance relative to its specific peer group or market segment, rather than using a single universal benchmark for all stocks, thereby improving measurement precision without requiring overly complex uniform structures.
Data Source
AI summary
A system and method for measuring and creating a score for the performance of one or more contributor recommendations on a single stock. According to one embodiment, the score may be derived via a payoff function that depends on a variety of factors. For example, the factors may include one or more of: i) the performance of the stock; ii) the performance of a selected benchmark; iii) the recommendation of the contributor for the stock; and/or iv) other factors. According to one embodiment the payoff function may be designed such that certain desired characteristics are satisfied.