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.

Description:
COPYRIGHT NOTICE 
       [0001]    A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever. 
       BACKGROUND 
       [0002]    When it comes to personal investments, individuals often seek the services of a bank to invest their money on their behalf. The money may be invested by the bank in mutual funds, or used to purchase various types of bonds or securities, for example. Although this kind of investment is usually safe, it may not provide large gains in the long run. On the other hand, while trading stocks may generate larger returns, the knowledge, skill and time required to successfully trade stocks may prevent the majority of individual investors from participating in stock trading activities. 
       SUMMARY OF THE INVENTION 
       [0003]    The present invention relates to a system and method for automated stock value prediction and trading. The solution proposed by the inventors is an automated stock trading system which utilizes a prediction module to predict the movement of a stock price based on an analysis of the movement of the stock price over time, and a decision module to determine when to buy or sell the stock. These modules may be integrated together with a brokerage trading account to allow individual investors to execute stock trade operations automatically. 
         [0004]    The inventors propose a novel use of a Least Mean Square (LMS) prediction algorithm to predict stock closing prices in a d+1 period, where d is a given increment of time (as measured in days, for example). More generally, the inventors propose the use of a transversal structure implemented M-order Finite Impulse Response (FIR) filter and an LMS prediction algorithm to adjust the filter coefficients. Based on the calculated predicted value resulting from the M-order FIR filter, the available funds in the investor&#39;s account, and the current price of a stock, the decision algorithm may be adapted to choose whether to hold, buy or sell the stock. Use of this automated system may give individual investors an improved chance of obtaining a better return on investment than may be achieved by ad hoc purchasing and selling of the stock. 
         [0005]    Thus, in an aspect of the invention, there is provided a method of predicting a 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. 
         [0006]    In an embodiment, the method further comprises, 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. 
         [0007]    In another embodiment, the method further comprises: 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. 
         [0008]    In another embodiment, the method further comprises: 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. 
         [0009]    In another embodiment, the method further comprises: 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. 
         [0010]    In another embodiment, the method further comprises: executing the buy order or sell order for a portion of the total stock holdings. 
         [0011]    In another embodiment, the time period is a day. 
         [0012]    In another aspect of the invention there is provided a system for predicting a 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. 
         [0013]    In an embodiment, the system further comprises 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. 
         [0014]    In another embodiment, the system further comprises: 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. 
         [0015]    In another embodiment, the system further comprises: 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. 
         [0016]    In another embodiment, the system further comprises: 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 a resulting gain or loss in total stock holdings is greater than the transaction cost. 
         [0017]    In another embodiment, the system further comprises: means for executing the buy order or sell order for a portion of the total stock holdings. 
         [0018]    In another embodiment, the time period is a day. 
         [0019]    In another aspect of the invention there is provided 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 a 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. 
         [0020]    In an embodiment, the data processor readable medium further comprises: 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. 
         [0021]    In another embodiment, data processor readable medium further comprises: 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. 
         [0022]    In another embodiment, the data processor readable medium further comprises: 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. 
         [0023]    In another embodiment, the data processor readable medium further comprises: 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 a resulting gain or loss in total stock holdings is greater than the transaction cost. 
         [0024]    In another embodiment, the data processor readable medium further comprises code for executing the buy order or sell order for a portion of the total stock holdings. 
         [0025]    These and other aspects of the invention will become apparent from the following more particular descriptions of exemplary embodiments. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0026]    The figures illustrate exemplary embodiments of the invention. 
           [0027]      FIG. 1  shows a generic data processing system that may provide a suitable operating environment. 
           [0028]      FIG. 2  shows a schematic block diagram of a system in accordance with an embodiment. 
           [0029]      FIG. 3  shows a more detailed schematic block diagram of the prediction module of  FIG. 2 . 
           [0030]      FIG. 4A  shows an illustrative example of a LMS prediction graph for a stock. 
           [0031]      FIG. 4B  shows an illustrative example of the evolution of LMS coefficients over time. 
           [0032]      FIG. 4C  shows another illustrative example of a LMS prediction graph for another stock. 
           [0033]      FIG. 4D  shows another illustrative example of a LMS prediction graph for another stock. 
           [0034]      FIG. 5  shows a schematic flowchart of an illustrative method in accordance with an embodiment. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0035]    As noted above, the present invention relates to a system and method for automated stock value prediction and trading. 
         [0036]    The invention may be practiced in various embodiments. A suitably configured data processing system, and associated communications networks, devices, software and firmware may provide a platform for enabling one or more of these systems and methods. By way of example,  FIG. 1  shows a generic data processing system  100  that may include a central processing unit (“CPU”)  102  connected to a storage unit  104  and to a random access memory  106 . The CPU  102  may process an operating system  101 , application program  103 , and data  123 . The operating system  101 , application program  103 , and data  123  may be stored in storage unit  104  and loaded into memory  106 , as may be required. An operator  107  may interact with the data processing system  100  using a video display  108  connected by a video interface  105 , and various input/output devices such as a keyboard  110 , mouse  112 , and disk drive  114  connected by an I/O interface  109 . In known manner, the mouse  112  may be configured to control movement of a cursor in the video display  108 , and to operate various graphical user interface (GUI) controls appearing in the video display  108  with a mouse button. The disk drive  114  may be configured to accept data processing system readable media  116 . The data processing system  100  may form part of a network via a network interface  111 , allowing the data processing system  100  to communicate with other suitably configured data processing systems (not shown). The particular configurations shown by way of example in this specification are not meant to be limiting. 
         [0037]    Now referring to  FIG. 2 , shown is a schematic block diagram of a system  200  in accordance with an embodiment. As shown, the operator  107  of data processor  100  may be an investor wishing to participate in trading stock listed on a stock exchange  208  using the services of a bank or stock broker server  210 . The data processor  100 , stock exchange  208  and stock broker server  210  may be connected via the Internet  206 , for example, or some other suitable public or private network. 
         [0038]    Still referring to  FIG. 2 , the stock broker server  210  may include a user database  216  which includes a user account for investor  107 . This user database  216  may store information including the stocks currently held by investor  107 , and may update the value of the stock holdings of investor  107  by regularly receiving price values  214  from the stock exchange  208 . The stock broker server  210  may also be adapted to send purchase or sell orders  212  to the stock exchange  208  on behalf of the investor  107 . 
         [0039]    In an embodiment, the stock broker server  210  may include a prediction module  218  which may be adapted to predict future values of the stock held by investor  107 , and to provide the predicted value to a decision module  220 . The prediction module  218  will be described in more detail below. Based on the predicted movement of the stock value from the prediction module  218 , the decision module  220  may be adapted to hold the stock, or to buy or sell the stock on behalf of investor  107  by issuing a buy or sell order  212  sent to the stock exchange  208  via the Internet  206 . 
         [0040]    Now referring to  FIG. 3 , shown is a more detailed schematic block diagram of the prediction module  218  of  FIG. 2 . More generally, prediction module  318  may be adapted to predict the stock price value at n+1, given M previous values (n−M, n−M+1, . . . , n), by applying digital signal processing (DSP) techniques. As shown, the prediction module  218  may include a M-order FIR (Finite Impulse Response) filter  302  implemented as a transversal structure, and which receive an input comprising the closing values  304  of a stock for the past M previous time periods. A particular implementation of a transversal structure is taught, for example, by A. Oppenheim, R. Schafer and J. Buck in  Discrete - Time Signal Processing,  2 nd  Edition, Prentice Hall, at p. 367 and following. 
         [0041]    The M-Order FIR filter  302  may process the input closing values  304  into an output comprising the predicted next time period value  306 . For the purposes of this discussion, the time period in question will be assumed to be days. However, it will be appreciated that the time period may also be weeks, hours, minutes, or any standard length of time selected by a user. 
         [0042]    The predicted next day value  306  may be compared against the actual next day value  310  as retrieved from the stock exchange at comparison node  308 , and the difference may by output as a prediction error  312 . The prediction error  312  may then be provided as a feedback input into adaptive algorithm  314 , in order to adjust the M filter coefficients in the M-Order FIR filter  302  for the next iteration of stock value prediction using M previous days values. 
         [0043]    In selecting a suitable algorithm for the prediction module  218 , the inventors found that a LMS (Least Mean Square) algorithm is a good choice for modeling stock prices, as it considers only the current prediction error  312  value when minimizing mean square error. It is important to realize, however, that the LMS algorithm requires a high-order FIR filter. Testing by the inventors has shown that the filter order M and LMS step-size (mu) must also be adjusted for each different stock, as the stock graphs feature different statistical behavior and therefore, different variances which affects the adaptive algorithm. However, once the filter order M and the step-size mu are defined, the inventors found that these values need not be changed frequently, as the statistical properties of a specific stock graph rarely change abruptly. While the filter order M and LMS step-size mu remains relatively constant once determined, the M filter coefficients for the M-Order FIR filter may change frequently, depending on the level of prediction error  312 . This will be explained in more detail further below. 
         [0044]    In order to validate the stock value prediction model proposed for prediction module  218 , the inventors selected a number of stocks for testing purposes. Before the prediction module  218  is first used to predict a future value for a given stock, a sufficiently large sample history of N previous stock closing values were used in order to calibrate the filter order M and LMS step-size (mu) for the given stock. As an illustrative example, for testing purposes, a 400-day sample array of previous closing values were obtained for each stock. The first 300 samples in this array were used as a training sequence to calibrate the filter order M and LMS step-size (mu). After calibration using this training sequence, the remaining 100 samples in the array were used as a test sample to predict the next day values using M previous days values, where M is also the filter order. 
         [0045]    For testing purposes, the inventors first selected the stock prices for Petrobras PN (PETR4) from São Paulo Stock Exchange (BOVESPA) over a 400-day period. Upon running the training sequence using 300 samples, the values for the M-Order FIR filter were set at M=32 and mu=0.0000178. With these values set, the next day stock value prediction was simulated over 100 days, and graphed against the actual real values as shown in graph  400 A of  FIG. 4A . 
         [0046]    The inventors found that the calibrated prediction module  218  was able to predict the n+1 values for the stock with a small margin of error in most cases, and further found that filter coefficient values for the LMS algorithm quickly converge to a virtual steady state, as shown in  FIG. 4B  with a few illustrative coefficients w 0 , w 1  and w 2 . 
         [0047]    Referring back to  FIG. 4A , from the sample stock data obtained from BOVESPA, the initial price in this illustrative 100-day sample period is 45.29 per share. As PETR4 stock is available for purchase only in 100-share batches, the minimum purchase value is R$ 4529.00 in shares. Assuming that investor  107  has R$ 20,000.00 in his account, and purchases 400 shares valued at R$ 18,116.00, if the investor  107  buys the 400 shares and does nothing, then after 100 days the investor will have R$ 17,956.00. The final result (considering the remaining money in investor  107 &#39;s account) would be R$ 19,840.00, or a loss of 0.8%. In comparison, if the investor had used the prediction module  218  to automatically hold or buy the stock (if possible) while the prediction module  218  predicted that the prices will go up, and otherwise triggered an automatic sale, the investor would have had R$ 22,431.00 after 100 days, or a profit of 12.16%. 
         [0048]    In an embodiment, the decision to hold, buy or sell stock may be made at the end of each period (in this case, each day, since it&#39;s a daily-based graph). Also, in the preferred embodiment the decision algorithm may be configured to buy or sell 100% of the stock holdings if the prediction is for a higher or lower price, respectively. However, it will be appreciated that the decision algorithm may be configured to buy or sell less than 100% of the holdings if there are any applicable restrictions or trading rules governing the buying or selling of the holdings. 
         [0049]    As will be appreciated, if there are transaction costs associated with a buy or sell transaction, as charged by the broker for example, frequent buying and selling may impact upon the level of profit. The simulations in the present illustrative example do not consider the transaction costs of buying and selling, but these transaction costs may be added to the model as may be necessary. It will be appreciated, however, that the transaction costs may vary from country to country, and even from broker to broker. 
         [0050]    In an embodiment, in order to address the transactional costs, the buy or sell decision may be made after comparing the transactional cost to the expected gain or loss from buying or selling the holdings. Thus, if an expected gain is greater than the transaction cost associated with buying (additional) holdings, then a buying order may be triggered. And if an expected loss is greater than the transaction cost, then a sell order may be triggered. 
         [0051]    Using the same method as described for the PETR4 stock in  FIG. 4A , the inventors selected another stock for IBM over a 100-day period, as shown in  FIG. 4C . In this case, the calculated values for the filter order and step-size were M=32 and mu=0.000004. Transactional costs for buying or selling were not considered, and it was assumed that 100% of the holdings wound be bought or sold based on the prediction model. Assuming a US$ 20,000.00 account in investor  107 &#39;s account on day 1, and a minimum buy of 100-share batches on day 1 at $7867*2=USD$15,734, after 100 days, an ad hoc purchase would have resulted in US$ 20,004.00, or a 0.04% profit. In comparison, using the automated prediction module  218 , the result would have been US$ 22,546.00, or a 12.73% profit. 
         [0052]    Now referring to  FIG. 4E , as another illustrative example, the inventors selected stock for Microsoft over a 100-day period. For this stock, the calculated values for the filter order and step-size were M=32, and mu=0.000045. Assuming US$ 20,000.00 in investor  107 &#39;s account, after 100 days, a one-shot purchase and hold strategy would have resulted in US$ 21,770.00, or 8.85% profit. In comparison, using the prediction module  218  and the same assumptions as used for buying and selling as used for  FIG. 4D , the investor  107  would have had US$ 22,548.00 after the 100-day period, or a 12.74% profit. 
         [0053]    Now referring to  FIG. 5 , and referring back to  FIG. 3 , shown is a schematic flowchart of a method  500  in accordance with an embodiment. As shown, method  500  begins and at block  502  where, as a preliminary step, method  500  obtains a sufficiently large sample of N previous days values for a given stock that an investor wishes to invest in. 
         [0054]    Next at block  504 , method  500  uses the sample of N previous days values in order to calibrate the M-Order FIR filter  302 , and to obtain values for the filter order M and the LMS step-size (mu) for the given stock. Once the M and mu values have been determined, an input array of M elements may be provided for the M-Order FIR filter  302  for all subsequent iterations. This array contains the M previous days closing prices (including today). 
         [0055]    Next, at block  506 , the sample of N previous days values is also used to train the adaptive algorithm  314 , and to prepare the filter coefficients to be applied to M-Order FIR filter  302 . 
         [0056]    Next, at block  508 , once the adaptive algorithm  314  has been trained, prediction of the future stock value may begin using M previous days values  304  as an input to M-Order FIR filter  302 , where M is the order size of the M-Order FIR filter  302 , and the output of the M-Order FIR filter  302  is the predicted next day value  306 . 
         [0057]    Method  500  may then proceed to block  510 , where the predicted next day value  306  output from the M-Order FIR filter  302  (i.e., from prediction module  218  of  FIG. 2 ) may be used by a decision module  220  ( FIG. 2 ) to determine whether to hold, buy, or sell the stock, depending on whether the next day prediction value is steady, increasing, or decreasing. As an illustrative example, in an embodiment, if the predicted next day value  306  is steady, then the decision module  220  may determine that the stock should be held. If the predicted next day value  306  is higher, then the decision module  220  may trigger a buy order  212  to purchase more stock if possible, given the investor&#39;s available funds. If the predicted next day value  206  is decreasing, then the decision module  220  may trigger a sell order  212  to sell stock. 
         [0058]    In an embodiment, the decision module  220  may be configured to consider any applicable transaction costs before a buy or sell order is triggered by decision module  220 . For example, if the expected gain or loss is greater than the transaction cost, then the buy or sell order may be triggered. 
         [0059]    Method  500  may then proceed to block  512  where, after closing of the stock exchange  208  the next day, the predicted next day value  306  may be compared with the actual next day value  310  (e.g., as received from the stock exchange) in order to calculate a prediction error  312 . 
         [0060]    Next, method  500  may proceed to block  514 , where the calculated prediction error  312  is used as an input to adaptive algorithm  314  in order to adjust the M filter coefficients for the M-Order FIR filter  302  for the next iteration. 
         [0061]    Method  500  may then proceed to decision block  516 , where method  500  may either return to block  510  to continue, or end. 
         [0062]    While various illustrative embodiments of the invention have been described above, it will be appreciated by those skilled in the art that variations and modifications may be made. Thus, the scope of the invention is defined by the following claims.