SYSTEM AND METHOD FOR AUTOMATED STOCK MARKET OPERATION

• US 20090089222A1
• Filed: 09/23/2008
• Published: 04/02/2009
• Est. Priority Date: 09/28/2007
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Ferreira De Castro, Rodrigo C., Franco, Lucas G., Seo, Carlos E., Pinto, Aline G.
• Application Number:
  US12/235,830
• Publication Number:
  US 20090089222A1
• Time in Patent Office: Days
• US Class Current: 705/36.R
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

A system and method for automated stock market investment. In an embodiment, the method includes: i) inputting M previous time period values for the stock into a M-order finite impulse response (FIR) filter, the M-order finite impulse filter having a filter order M, a least mean square (LMS) prediction algorithm with step-size mu, and M adjustable filter coefficients; ii) obtaining an output from the M-order FIR filter, the output from the M-order FIR filter being a predicted next time period value for the stock; iii) comparing the predicted next time period value for the stock with an actual next time period value for the stock to calculate a prediction error; iv) inputting the calculated prediction error into an adaptive algorithm to obtain an adjustment for the at least one adjustable filter coefficient; and v) applying the adjustment for the at least one adjustable filter coefficient and repeating all steps until halted.

Claims

1. A method of predicting the value of a stock, comprising:

i) inputting M previous time period values for the stock into a M-order finite impulse response (FIR) filter, the M-order finite impulse filter having a filter order M, a least mean square (LMS) prediction algorithm with step-size mu, and M adjustable filter coefficients;

ii) obtaining an output from the M-order FIR filter, the output from the M-order FIR filter being a predicted next time period value for the stock;

iii) comparing the predicted next time period value for the stock with an actual next time period value for the stock to calculate a prediction error;

iv) inputting the calculated prediction error into an adaptive algorithm to obtain an adjustment for the M adjustable filter coefficients; and

v) applying the adjustment for the M adjustable filter coefficients and repeating all steps until halted.

2. The method of claim 1, further comprising, prior to step i), obtaining a sample of N previous days values for a stock and utilizing the sample of N previous days values to obtain the filter order M and the LMS step-size.

3. The method of claim 1, further comprising:

receiving the predicted next time period value for the stock; and

in dependence upon the predicted next time period value, executing one of a hold, buy or sell order for the stock.

4. The method of claim 3, further comprising:

if the predicted next time value is higher than a present value, then executing a buy order for the stock;

if the predicted next time value is lower than the present value, then executing a sell order for the stock; and

if the predicted next time value is the same as the present value, then executing a hold on the stock.

5. The method of claim 4, further comprising:

considering a transaction cost of a buy order or a sell order; and

executing the buy order or sell order only if a resulting gain or loss in total stock holdings is greater than the transaction cost.

6. The method of claim 4, further comprising:

executing the buy order or sell order for a portion of a total stock holdings.

7. The method of claim 1, wherein the time period is a day.

8. A system for predicting the value of a stock, comprising:

means for inputting M previous time period values for the stock into a M-order finite impulse response (FIR) filter, the M-order finite impulse response filter having a filter order M, a least mean square (LMS) prediction algorithm with step-size mu, and M adjustable filter coefficients;

means for obtaining an output from the M-order FIR filter, the output from the M-order FIR filter being a predicted next time period value for the stock;

means for comparing the predicted next time period value for the stock with an actual next time period value for the stock to calculate a prediction error;

means for inputting the calculated prediction error into an adaptive algorithm to obtain an adjustment for the M adjustable filter coefficients; and

means for applying the adjustment for the at least one adjustable filter coefficient and repeating all steps until halted.

9. The system of claim 8, further comprising, means for obtaining a sample of N previous days values for a stock and utilizing the sample of N previous days values to obtain the filter order M and the LMS step-size.

10. The system of claim 8, further comprising:

means for receiving the predicted next time period value for the stock; and

means for executing one of a hold, buy or sell order for the stock in dependence upon the predicted next time period value.

11. The system of claim 10, further comprising:

means for executing a buy order for the stock if the predicted next time value is higher than a present value;

means for executing a sell order for the stock if the predicted next time value is lower than the present value; and

means for executing a hold on the stock if the predicted next time value is the same as the present value.

12. The system of claim 11, further comprising:

means for considering a transaction cost of a buy order or a sell order; and

means for executing the buy order or sell order only if the resulting gain or loss in total stock holdings is greater than the transaction cost.

13. The system of claim 11, further comprising means for executing the buy order or sell order for a portion of a total stock holdings.

14. The system of claim 8, wherein the time period is a day.

15. A data processor readable medium storing data processor code that when loaded onto and executed by a data processing device adapts the device to perform a method of predicting the value of a stock, the data processor readable medium comprising:

code for inputting M previous time period values for the stock into a M-order finite impulse response (FIR) filter, the M-order finite impulse filter having a filter order M, a least mean square (LMS) prediction algorithm with step-size mu, and M adjustable filter coefficients;

code for obtaining an output from the M-order FIR filter, the output from the M-order FIR filter being a predicted next time period value for the stock;

code for comparing the predicted next time period value for the stock with an actual next time period value for the stock to calculate a prediction error;

code for inputting the calculated prediction error into an adaptive algorithm to obtain an adjustment for the at least one adjustable filter coefficient; and

code for applying the adjustment for the at least one adjustable filter coefficient and repeating all steps until halted.

16. The data processor readable medium of claim 15, further comprising, code for obtaining a sample of N previous days values for a stock and utilizing the sample of N previous days values to obtain the filter order M and the LMS step-size.

17. The data processor readable medium of claim 15, further comprising:

code for receiving the predicted next time period value for the stock; and

code for executing one of a hold, buy or sell order for the stock in dependence upon the predicted next time period value.

18. The data processor readable medium of claim 17, further comprising:

code for executing a buy order for the stock if the predicted next time value is higher than a present value;

code for executing a sell order for the stock if the predicted next time value is lower than the present value; and

code for executing a hold on the stock if the predicted next time value is the same as the present.

19. The data processor readable medium of claim 18, further comprising:

code for considering a transaction cost of a buy order or a sell order; and

code for executing the buy order or sell order only if the resulting gain or loss in total stock holdings is greater than the transaction cost.

20. The data processor readable medium of claim 18, further comprising code for executing the buy order or sell order for a portion of a total stock holdings.