Trading system

Abstract

An automated method, computer program product and system for using artificial intelligence based cognitive learning methods to enable the identification and optional implementation of trades for an organization security. The elements of value, components of value and categories of value of the organization are analyzed and modeled using predictive models that are developed by learning from the data associated with said organization. The output from these models is then used to calculate a market sentiment value that is used to determine the types of trades that will be recommended and optionally completed.

1 . An intelligent system for trading comprising:
 a computer with at least one processor having circuitry to execute instructions; a storage device available to each processor with sequences of instructions stored therein, which when executed cause the processors to:
 prepare a plurality of data representative of an organization that has one or more securities for processing where said organization comprises a plurality of components of value and a plurality of categories of value and where one or more elements of value have a net contribution to or an impact on a value of each of the components of value, 
 develop a predictive model for each of the components of value by learning from at least part of said prepared data where said learning comprises learning a linearity of the predictive model, 
 calculate a value of an organization current operation using the output of the predictive models for the components of value and a cost of capital, 
 determine a value for one or more organization real options using the prepared data, 
 calculate a market sentiment value for the organization by subtracting the combined value of the one or more real options and the value of the current operation from a market value of the one or more organization securities, and 
 identify and output at least one trade for the one or more organization securities based on the market sentiment value. 
   
     
     
         2 . The system of  claim 1 , wherein developing a linear or a nonlinear predictive model for each of the components of value by learning from at least part of said prepared data comprises:
 using a plurality of predictive models and a plurality of causal models to analyze and select a portion of the data to use as a value driver when modeling an impact of each of the one or more elements of value;   learning which model from a plurality of causal models comprises a best fit for modeling the net contribution of the elements of value to a value of each of the components of value when using the selected value driver data;   learning which algorithm from a plurality of linear and nonlinear predictive model algorithms to include in the model for each of the components of value in order to model the impact of each of the one or more elements of value on the value of each of the components of value; and   learning a relative contribution of each of the elements of value to the value of each of the components of value   where the plurality of causal models are selected from the group consisting of Tetrad, LaGrange, Bayesian and path analysis and where the plurality of predictive models are selected from the group consisting of classification and regression tree; projection pursuit regression; generalized additive model (GAM), redundant regression network; neural network, multivariate adaptive regression splines; linear regression; and stepwise regression.   
     
     
         3 . The system of  claim 1 , wherein the processors identify one or more changes that will optimize a total organization value. 
     
     
         4 . The system of  claim 1 , wherein the organization physically exists. 
     
     
         5 . The system of  claim 4 , wherein the one or more elements of value physically exist and are selected from the group consisting of: alliances, brands, channels, customers, employees, information technology, intellectual property, processes, vendors and combinations thereof. 
     
     
         6 . The system of  claim 1 , wherein the processors identify and output one or more factors that are driving the market sentiment value. 
     
     
         7 . The system of  claim 1 , wherein the at least one trade is completed automatically. 
     
     
         8 . A non-transitory computer program product tangibly embodied on a computer readable medium and comprising a program code for directing at least one computer to perform an intelligent trading method, comprising:
 prepare a plurality of data representative of an organization that has one or more securities for processing where said organization comprises a plurality of components of value and a plurality of categories of value and where one or more elements of value have a net contribution to or an impact on a value of each of the components of value,   develop a predictive model for each of the components of value by learning from at least part of said prepared data where said learning comprises learning a linearity of the predictive model,   calculate a value of an organization current operation using the output of the predictive models for the components of value and a cost of capital,   determine a value for one or more organization real options using the prepared data,   calculate a market sentiment value for the organization by subtracting the combined value of the one or more real options and the value of the current operation from a market value of the one or more organization securities, and   identify and output at least one trade for the one or more organization securities based on the market sentiment value.   
     
     
         9 . The computer program product of  claim 8 , wherein developing a linear or a nonlinear predictive model for each of the components of value by learning from at least part of said prepared data comprises:
 using a plurality of predictive models and a plurality of causal models to analyze and select a portion of the data to use as a value driver when modeling an impact of each of the one or more elements of value;   learning which model from a plurality of causal models comprises a best fit for modeling the net contribution of the elements of value to a value of each of the components of value when using the selected value driver data;   learning which algorithm from a plurality of linear and nonlinear predictive model algorithms to include in the model for each of the components of value in order to model the impact of each of the one or more elements of value on the value of each of the components of value; and   learning a relative contribution of each of the elements of value to the value of each of the components of value   where the plurality of causal models are selected from the group consisting of Tetrad, LaGrange, Bayesian and path analysis and where the plurality of predictive models are selected from the group consisting of classification and regression tree; projection pursuit regression; generalized additive model (GAM), redundant regression network; neural network, multivariate adaptive regression splines; linear regression; and stepwise regression.   
     
     
         10 . The computer program product of  claim 8 , wherein the organization physically exists. 
     
     
         11 . The computer program product of  claim 8 , wherein the method further comprises identifying one or more changes that will optimize a total organization value. 
     
     
         12 . The computer program product of  claim 10 , wherein the one or more elements of value physically exist and are selected from the group consisting of: alliances, brands, channels, customers, employees, information technology, intellectual property, processes, vendors and combinations thereof. 
     
     
         13 . The computer program product of  claim 8 , wherein the method further comprises identifying and outputting one or more factors that are driving the market sentiment value. 
     
     
         14 . The computer program product of  claim 8 , wherein the at least one identified trade is completed automatically. 
     
     
         15 . A intelligent trading apparatus, comprising:
 means for data processing,   means for data acquisition,   means for data storage,   means for preparing a plurality of data representative of an organization that has one or more securities for processing where said organization comprises a plurality of components of value and a plurality of categories of value and where one or more elements of value have a net contribution to or an impact on a value of each of the components of value,   means for developing a predictive model for each of the components of value by learning from at least part of said prepared data where said learning comprises learning a linearity of the predictive model,   means for calculating a value of an organization current operation using the output of the predictive models for the components of value and a cost of capital,   means for determining a value for one or more organization real options using the prepared data,   means for calculating a market sentiment value for the organization,   means for identifying and outputting at least one trade for the one or more organization securities based on the market sentiment value, and   means for optionally completing the at least one trade automatically.   
     
     
         16 . The apparatus of  claim 15 , wherein the means for developing a linear or a nonlinear predictive model for each of the components of value by learning from at least part of said prepared data comprises:
 means for using a plurality of predictive models and a plurality of causal models to analyze and select a portion of the data to use as a value driver when modeling an impact of each of the one or more elements of value;   mean for learning which model from a plurality of causal models comprises a best fit for modeling the net contribution of the elements of value to a value of each of the components of value when using the selected value driver data;   means for learning which algorithm from a plurality of linear and nonlinear predictive model algorithms to include in the model for each of the components of value in order to model the impact of each of the one or more elements of value on the value of each of the components of value; and   means for learning a relative contribution of each of the elements of value to the value of each of the components of value   where the plurality of causal models are selected from the group consisting of Tetrad, LaGrange, Bayesian and path analysis and where the plurality of predictive models are selected from the group consisting of classification and regression tree; projection pursuit regression; generalized additive model (GAM), redundant regression network; neural network, multivariate adaptive regression splines; linear regression; and stepwise regression.   
     
     
         17 . The apparatus of  claim 15 , wherein the organization physically exists. 
     
     
         18 . The apparatus of  claim 15 , wherein the apparatus further comprises means for identifying one or more changes that will optimize a total organization value. 
     
     
         19 . The apparatus of  claim 17 , wherein the one or more elements of value physically exist and are selected from the group consisting of: alliances, brands, channels, customers, employees, information technology, intellectual property, processes, vendors and combinations thereof. 
     
     
         20 . The apparatus of  claim 15 , wherein the apparatus further comprises means for identifying and outputting one or more factors that are driving the market sentiment value.