Data processing: artificial intelligence – Neural network – Learning task
Reexamination Certificate
2000-08-25
2004-05-11
Starks, Jr., Wilbert L. (Department: 2121)
Data processing: artificial intelligence
Neural network
Learning task
C706S030000, C706S925000
Reexamination Certificate
active
06735580
ABSTRACT:
FIELD OF THE INVENTION
The invention relates to a time series prediction system for financial securities utilizing Artificial Neural Network (ANN). More particularly, the invention relates to a processing system based on the recurrent ANN architecture capable of outputting upper and lower prediction bounds at any given confidence, which is based on the validation errors of the ANN. Specifically, the invention relates a prediction system that can be applied to any financial time series which can be called by any computer language and Web applications supporting the system.
BACKGROUND OF THE INVENTION
In the recent past, Recurrent Artificial Neural Networks have successfully improved the quality of forecasting of share movements in relation to its statistically based counterparts. The known recurrent neural networks (RNN) make a prediction of the appreciation potential of each stock based on the available historical data. The training process continues until at least one stopping criterion is met. Such criteria include the determination that the connections between the nodes of the net have reached a steady state, that the error between the predicted output and the actual target values is less than a certain threshold, or that a predefined time period has elapsed without any improvement in the net's performance. Once the neural nets for each stock of the capital market have been trained and tested on the available historical data the neural nets are tied to surpass the underlying market benchmark by predicting, the task becomes one of holding a smaller subset of all stocks of the market, such that this subset has a higher expected return and about the same level of risk as the market index. Such a task requires one to focus on individual stocks and their performance in relation to the index that serves as the underlying performance benchmark.
Individual stocks usually have their own unique performance characteristics some of which can be quantified. Clearly, however, the relationships among such data are complicated and frequently non-linear, making them difficult to analyze. In summary, an investment decision in the modern capital markets requires processing of large volumes of data and taking into account a number of factors which may exhibit significant non-linear relationships among different components of the data.
Computers, in general, are very adept at dealing with large amounts of numerical information. However, sophisticated techniques are required to analyze and combine disparate information that can potentially impact security prices. Several expert computer systems have been deployed in the domain of finance, including some in the area of investment management.
In the past several years, recurrent neural networks (RNN) have become popular in solving a variety of problems. Neural nets mimic the ability of the human brain to recognize recurring patterns that are not just identical but similar. A neural net can predict the value of one variable based on input from several other variables that can impact it. The prediction is made on the basis of an inventory of patterns previously learned, seeking the most relevant in a particular situation. In summary, RNNs can “learn” by example and modify themselves by adjusting and adapting to changing conditions. Several applications of neural nets to the domain of finance are already known in the art. Typically, the RNN prediction systems are “self” trained by adjusting weights and biases as a result of numerous repetitions. What the known systems typically do not do is to calculate an error function so the system's output can be adjusted or controlled in accordance with the determined error.
U.S. Pat. No. 5,109,475 to Kosaka et al. discloses a neural network for selection of time series data. This process is illustrated in a particular application to the problem of stock portfolio selection. In the first step of the proposed process, certain characteristics for each security are calculated from time series data related to the security. The characteristics to be computed include the historical risk (variance and co-variance) and the return. The following step involves the establishment of a performance function based on the calculated characteristics and, in the third step of the process, a Hopfield neural network is used to select a subset of securities from a predefined universe. Due to the fact that the Kosaka system only considers historical risk and return data, and implicitly assumes that the relationship between risk and return factors will remain stable in the future, in a typical rapidly changing market environment, it is unlikely to predict accurately price variations which are subject to complicated non-linear relationships.
U.K Pat. application 2 253 081 A to Hatano et al. discloses a neural net for stock selection using only price data as input. The main idea of the proposed system is to calculate runs (sequences) of price trends, increases and decreases, using a point-and-figure chart and using the maximum and minimum values from the chart to make a time-series prediction using a neural network. As in the previous case, the Hatano system only uses historic price data which places limitation on the type and quality of predictions that may be achieved. Additionally, the use of only the external points of the price chart obscures even further information about any time dependencies that might be present in the original data.
The above-described financial systems do not fully utilize the potential of the neural nets for stock selection. Notably missing is the possibility to develop the standard adaptive training procedure of the RNN to determine a prediction error or function in accordance to which the RNN output can be controlled. Further, many of the known investment management systems have not been able to effectively output the upper and lower error bounds at a given confidence level. Further, the movements of the stock prices, as well as price movements of other financial instruments, generally present a deterministic trend superimposed with some “noise” signals, which are, in turn, composed of truly random and chaotic signals, as illustrated in FIG.
1
. Deterministic trends can be detected and assessed by some maximum-likelihood processes. Although a truly random signal, often represented by a Brownian motion, is unpredictable, it can be estimated by its mean and standard deviation. The chaotic signal, seemingly random but with deterministic nature, proves predictable to some degree by means of several analysis techniques, among which the ANN techniques have proven most effective over the widest range of predictive variables. However, this trend is largely ignored by the above-discussed references. As a result, at least some of the known systems are fed with data including this deterministic trend that influences the training stage of the known systems. Overall, many of the known systems are limited for the prediction of specific types of securities and data, such as the price of a single stock and, thus, cannot be universally applied to any financial time series, price series and volatility series.
It is, therefore, desirable to provide a prediction system based on the recurrent Artificial Neural Network (ANN) architecture which is able to output upper and lower predictions bounds at any given confidence level. Also, an ANN prediction that can be applied to financial time series, price series and volatility series, for single securities and for portfolios of securities is desirable. A universal prediction system employing a pipeline recurrent neural network (PRNN), which provides the satisfactory accuracy of the nonlinear and adaptive prediction of nonstationary signal and time series processes is also desirable. Further, a universal ANN prediction system having high computation efficiency and multi-stage adaptive supervised training process is also desirable.
SUMMARY OF THE INVENTION
Accordingly, an inventive universal ANN prediction system motivated in its design by the human nervous system is capab
Li Bin
Li Liang
Tang Yi
Wu Xiaohua
Booker Kelvin
St. Onge Steward Johnston & Reens LLC
Starks, Jr. Wilbert L.
Westport Financial LLC
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