SYSTEMS AND METHODS FOR DEETERMINING A FAIR PRICE RANGE FOR COMMODITIES

• US 20140100989A1
• Filed: 10/03/2013
• Published: 04/10/2014
• Est. Priority Date: 10/04/2012
• Status: Abandoned Application

• Current Assignee: Valuerz, Inc.
• Original Assignee: Valuerz, Inc.
• Inventors: ZHANG, HAN, MIRZIKINIAN, WARWICK
• Application Number:
  US14/045,495
• Publication Number:
  US 20140100989A1
• Time in Patent Office: Days
• US Class Current: 705/26.61
• CPC Class Codes: G06Q 30/0283 Price estimation or determination

Abstract

A system and method for determining cross-market correlation factors which contribute to a response to a user request for a price. The system includes a database of plurality of commodities. The system includes a factor determination unit that, responsive to a user request, identifies inter-market and intra-market factors which contribute to a price determination for nearly all of the commodities. The system includes an evaluation unit that, responsive to the user request, evaluates the contribution of each of the inter-market and intra-market factors to identify candidate factors in a model of the commodity for which a price is requested. The system further includes a price response unit that responds to the request with a price for the asset, good or service based on the model. The system and method predict the price based on factors across multiple markets.

Claims

1. A system for determining cross-market correlation factors which contribute to a response to a user request for a price of a commodity, the system comprising:

a database of a plurality of commodities;

a factor determination unit that, responsive to a user request, identifies inter-market and intra-market factors which contribute to a price determination for nearly all of the commodities; and

a factor selection unit that, responsive to the user request, evaluates the contribution of each of the inter-market and intra-market factors to identify candidate factors in a model of the price of the commodity for which a price is requested; and

a price response unit that responds to the request with a price for the asset, good or service based on the model.

2. A method for pricing a commodity, the method comprising:

receiving a request from a user for pricing the commodity;

responsive to receipt of the request, and with respect to a database containing data for prices of commodities together with data for inter-market information and intra-market information relative to such commodities, extracting inter-market and intra-market correlations at least with the price of the commodity in the request;

further in response to the user request, differentiating correlations of significance from the extracted correlations;

calculating candidate factors from the correlations of significance;

predicting a fair price for at least the commodity identified in the user request, by using the calculated candidate factors and the correlations of the significance; and

providing the predicted price for the commodity identified in the user request to the user.

3. The method according to claim 2, wherein during the extracting, inter-market and intra-market correlations are extracted at least with prices of nearly all of the commodities in the database and during the predicting a fair price is predicted for nearly all of the commodities in the database.

4. A method for eliminating non-significant candidate factors from a pricing model for a selected commodity, the method comprising:

calculating cross-correlations in a database which stores data for the prices of commodities including the selected commodity, together with data for inter-market information and intra-market information relative to such commodities;

initializing a full model for the price of the selected commodity, the full model including a plurality of M candidate factors selected based on the calculated cross-correlations;

packaging M test packages of candidate models to be tested, wherein each candidate model comprises the full model with 1 to M factors of lowest significance eliminated;

distributing the M test packages to M processors for execution in parallel, and receiving a test result from each of the M processors, wherein the test result is indicative of the likelihood that 1 to M eliminated factors contribute to the significance of the full model;

in sequence starting from m=1 through m=M eliminated factors, determining if the test result is less than a predetermined threshold likelihood that non-eliminated factors contribute significantly to the model, and selecting the first of such test models in the sequence for which the test result is less than the predetermined threshold;

updating the full model by eliminating the m factors determined to be non-significant; and

repeating the above steps of packaging, distributing, determining, selecting and updating the full model, until all factors not eliminated return a test result exceeding a predetermined threshold of significance.

5. A method according to claim 4, wherein in packaging the test models, factors are eliminated based on those factors having lowest chi-squared factors, and wherein the test result received from each of the M processors comprises an average log-likelihood contribution of the eliminated factors, which is compared against the minimum chi-squared values of the remaining factors.