C4cast.com, Inc.

William Palmer Jennings

G Michael Phillips

M Chapman Findlay, Iii

Stephen A Klein

Mark E Rice

Gordon Michael Phillips

Kenneth M Leslie

The value of a target variable is predicted by obtaining historical values for the target variable at each of several time points and obtaining previously predicted values and currently predicted values for each of several predictor variables, the predictor variables being different from the target variable. Values are assigned to parameters of a forecasting model to obtain the best fit of the previously predicted values for the predictor variables to the historical values for the target variable. Finally, a value of the target variable is predicted from the currently predicted values for at least a subset of the predictor variables using the forecasting model and the values assigned to the parameters of the forecasting model.

Community-selected content

Provided are, among other things, systems, methods and techniques for forecasting information. One representative embodiment involves obtaining data that has been provided by each of a number of individual people; dividing the people into clusters by utilizing a statistical clustering technique; calculating statistics of the data in each of at least some of the clusters; and combining the statistics for such clusters so as to obtain a combination forecast.

Analysis and prediction of data using clusterization

New metrics are provided for evaluating a portfolio of financial assets and/or for determining whether or not the portfolio is properly tracking the performance of a desired index. The technique involves calculating a correlative value for the portfolio relative to each of a number of different factors having data values that are likely to be correlated with an aggregate market value for the portfolio, where the correlative value for an item relative to a given factor is a measure of a tendency of the aggregate market value of the item to change based on a change in the data value for such factor (e.g., in the nature of a sensitivity or elasticity). Such metrics often can be used, according to the present invention, in conjunction with other information to facilitate the adjustment of the portfolio to more closely track the performance of the desired index.

Asset portfolio evaluation

Measurements for comparing one financial asset to another are provided. For example, in one aspect a comparison measurement is generated between a model estimate and an observed market value for an asset and then such comparison measurements are compared for two different assets. In another aspect, tracking error and performance differences between two assets are measured simultaneously, thereby providing better isolation of important information.

Potential-based asset comparison

Provided is a technique for evaluating an asset, such as a share of stock in a corporation, by processing historical data for value of an asset and historical data values for various exogenous variables to obtain a formula for calculating a measure of a tendency of the value of the asset to change as a result of changes in the data values for the exogenous variables, the formula being a function of the exogenous variables. Projected data values are then obtained for the exogenous variables, and a measure of the tendency of the value of the asset to change based on a change in at least one of the exogenous variables is estimated using the formula and the input projected data values.

Sensitivity/elasticity-based asset evaluation and screening

Provided are, among other things, systems, methods and techniques for providing resources to participants over an electronic network. In one representative embodiment, a collection of resources is maintained, such that both the collection and the resources can be accessed by a participant over the electronic network at any given time; points are assigned to individual resources based on an amount of participant access of the individual resources over the electronic network; and the collection is modified based on the points assigned to the resources.

Techniques are provided for adjusting a portfolio of financial assets in order to properly track the performance of a desired index. The techniques generally involve calculating a correlative value for the portfolio, and obtaining a correlative value for the index, relative to each of a number of different factors having data values that are likely to be correlated with an aggregate market value for the portfolio, where the correlative value for an item relative to a given factor is a measure of a tendency of the aggregate market value of the item to change based on a change in the data value for such factor (e.g., in the nature of a sensitivity or elasticity). Then, the portfolio is adjusted in an attempt to move its correlative profile closer to the correlative profile of the index.

Asset portfolio tracking

Provided is a technique forecasting the direction in which the price of an asset will move by identifying a group of exogenous variables that are likely to influence observed prices of an asset. Then, historical data for values of the exogenous variables and historical data for the observed prices of the asset over a time period are processed to obtain a formula for calculating price estimates for the asset as a function of the exogenous variables. The formula is calculated using an input set of observed values for the exogenous variables at given point in time, so as to obtain a price estimate for the asset at the given point in time. Then, a similarity measure is determined by comparing the price estimate for the asset at the given point in time to the observed price for the asset at the given point in time. Finally, a direction in which the observed price of the asset will move is forecast based on the similarity measure.

Asset price forecasting

Provided is a technique for displaying information. Initially, a number of data points is obtained, each data point including an estimated statistic. A measure of the statistical significance for each estimated statistic also is obtained, and a graph of the data points is displayed. According to this aspect of the invention, a display characteristic (such as intensity, size, brightness, hue, saturation or any other color characteristic) of each data point is a function of the measure of statistical significance of the estimated statistic included in such data point.

Significance-based display

Forecasting using interpolation modeling

Provided is a technique for classifying stocks into business sectors by calculating, for each of multiple exogenous variables, a measure of a tendency for a value of a stock to change as a result of a change in a data value for each such exogenous variable. The foregoing step is then repeated for each of several different stocks. Finally, the different stocks are grouped into different sectors based on similarities of such measures of tendency to change.

Identifying industry sectors using statistical clusterization

Combination forecasts are generated using predictions obtained from a group of forecasters. The forecasters are first divided into a number of pre-defined clusters, which typically will have been formed using statistical clustering techniques. In particular, clusters of forecasters can be formed based on similarities of the forecasters' predictions. Then, statistical data are calculated for each pre-defined cluster (e.g., measures of central tendency and dispersion). Finally, the statistical data for all the pre-defined clusters are combined so as to obtain a combination forecast.

Combination forecasting using clusterization

Resources are provided to participants over an electronic network by maintaining a collection of resources that can be accessed by a participant over the electronic network at a given time. Points are assigned to each resource based on participant access of the resource and the collection is modified based on the points assigned to each resource. Alternatively, or in addition, participants may be permitted to rate the resources, with points assigned to each resource based on participant rating of the resource. The collection of resources is then modified based on assigned points for each resource.

Growing community of inventors

Simi Valley, CA, United States of America

William Palmer Jennings