Method for tracking and predicting the price of a mutual fund prior to the close of the market

Abstract

The present invention is a method for predicting mutual fund prices. The method of the present invention involves tracking the correlation of a mutual fund to an index, utilizing algorithms to determine the optimum numbers of days to average the correlation over and then predicting the price of the mutual fund prior to the close of the market based on the movement of the index. A typical prior art system monitors the price movement of a mutual fund and sounds an alarm when a tolerance is breached, but since mutual funds prices are only calculated at the end of each trading day one would not be able to take action until the close of the following day. The result could be increased losses and/or lost gains. To prevent this, the present invention discloses a system that can predict, based on the changes in the related index, where that mutual fund will close at the end of the day and action can be taken before the market closes. The method of the present invention enables a user to make buy and sell decisions based on current data more accurately and helps overcome the time-delay typical in buying and selling mutual funds. The method uses quality assurance methods to auto correct itself and can also be incorporated into automated portfolio management systems known in the art.

1 . A method for predicting mutual fund prices for use in an automated portfolio monitoring system wherein; 
 a mathematical algorithm is used to determine and monitor the correlation between a mutual fund and an assigned index;    said algorithm determines the optimum numbers of days to average the correlation over and then predicts the price of the mutual fund prior to the close of the market based on the movement of said index    said algorithm predicts where said mutual fund will close with respect to price, at the end of the trading day;    quality assurance methods are used by said algorithm to auto correct itself;    based on the changes in said index and where said mutual fund will close at the end of the trading day, action can be taken before the market closes.    
   
   
       2 . The method for predicting mutual fund prices for use in an automated portfolio monitoring system of  claim 1  comprising a first process wherein; 
 a first step where a specific mutual fund is associated with a relevant market index for which real-time pricing information is available;    a second step of calculating a first correlation ratio wherein: said first correlation ratio equals the associated index closing price of a first day divided by the mutual fund closing price of a first day    a third step of repeating said second step for multiple days resulting in correlation ratio (1 . . . n);    a fourth step in which the average of n days correlation ratios is calculated as follows:      ( CR (1)+ CR (2) . . . + CR ( n ) )/ n );    a fifth step in which the standard deviation of n days correlation ratios is similarly calculated resulting in two data points: n day moving average and n day standard deviation.    
   
   
       3 . The method for predicting mutual fund prices for use in an automated portfolio monitoring system of  claim 2  comprising a second process wherein; 
 a first step wherein the process of  claim 2  is repeated for multiple days resulting in a meaningful number of n day moving averages and n day standard deviations;    a second step that determines which of the standard deviations from step one is lowest.    
   
   
       4 . The method for predicting mutual fund prices for use in an automated portfolio monitoring system of  claim 2  wherein said second step defaults to initially five days worth of data resulting in a five-day average and five-day standard deviation.  
   
   
       5 . The method for predicting mutual fund prices for use in an automated portfolio monitoring system of  claim 4  wherein the average and standard deviations of said firsts data set is calculated resulting in a five-day average and five-day standard deviation.  
   
   
       6 . The method for predicting mutual fund prices for use in an automated portfolio monitoring system of  claim 3  further comprising a second process wherein; 
 said first, second, and third steps are repeated for every day between a period days between a first selected date and a second selected date; and    a determination of which day the standard deviation is smallest is made with said day becoming the default day.    
   
   
       7 . The method for predicting mutual find prices for use in an automated portfolio monitoring system of  claim 6  wherein said first, second, and third steps are repeated for every day between days five and day seventy-five.  
   
   
       8 . The method for predicting mutual fund prices for use in an automated portfolio monitoring system of claims  3  and  6  further comprising a third process wherein, on a daily basis, the system uses said default number of days to complete said first process that provides the correlation factor and standard deviation to use all day.  
   
   
       9 . The method for predicting mutual fund prices for use in an automated portfolio monitoring system of claims  3  and  6  further comprising a fourth process wherein daily quality control is accomplished by using the last two or more days of data points for the actual mutual fund closing price and predicted mutual fund closing price and following a predetermined mathematical algorithm.  
   
   
       10 . The method for predicting mutual fund prices for use in an automated portfolio monitoring system of  claim 10  wherein quality control is accomplished by using the last eight days worth of data points for the actual mutual fund closing price and predicted mutual fund closing price and following a predetermined mathematical algorithm.