Author Topic: Comparative study of Financial Time Series Prediction by Artificial Neural Netwo  (Read 3008 times)

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Author : Arka Ghosh
International Journal of Scientific & Engineering Research Volume 3, Issue 1, January-2012
ISSN 2229-5518
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Abstract— Financial forecasting is an example of a signal processing problem which is challenging due to Small sizes, high noise, non-stationarity, and non-linearity,but fast forecasting of stock market price is very important for strategic business planning.Present study is aimed to develop a comparative predictive model with Feedforward Multilayer Artificial Neural Network & Recurrent Time Delay Neural Network for the Financial Timeseries Prediction.This study is developed with the help of historical stockprice dataset made available by GoogleFinance.To develop this prediction model Backpropagation method with Gradient Descent learning has been implemented.Finally the Neural Net ,learned with said algorithm is found to be skillful predictor for non-stationary noisy Financial Timeseries.

Key Words—.  Financial Forecasting,Financial Timeseries Feedforward Multilayer Artificial Neural Network,Recurrent Timedelay Neural Network,Backpropagation,Gradient descent.
 
I.   INTRODUCTION
Over past fifteen years, a view has emerged that computing based on models inspired by our understanding of the structure and function of the biological neural networks may hold the key to the success of solving intelligent tasks by machines like noisy time series prediction and more[1]. A neural network is a massively parallel distributed processor that has a natural propensity for storing experiential knowledge and making it available for use. It resembles the brain in two respects: Knowledge is acquired by the network through a learning process and interneuron connection strengths known as synaptic weights are used to store the knowledge[2]. Moreover, recently the Markets have become a more accessible investment tool, not only for strategic investors but for common people as well. Consequently they are not only related to macroeconomic parameters, but they influence everyday life in a more direct way. Therefore they constitute a mechanism which has important and direct social impacts. The characteristic that all Stock Markets have in common is the uncertainty, which is related with their short and long-term future state. This feature is undesirable for the investor but it is also unavoidable whenever the Stock Market is selected as the investment tool. The best that one can do is to try to reduce this uncertainty. Stock Market Prediction (or Forecasting) is one of the instruments in this process. We cannot exactly predict what will happen tomorrow, but from previous experiences we can roughly predict tomorrow. In this paper this knowledge based approach  is taken.

The accuracy of the predictive system which is made by ANN can be tuned  with help of different network architectures. Network is consists of input layer ,hidden  layer & output layer of neuron, no of neurons per layer can be configured according to the needed result accuracy & throughput,there is no cut & bound rule for  that.the network can be trained by using sample training data set,this neural network model is very much useful for mapping unknown functional dependencies between different input & output tuples.In this paper two types of neural network architecture,feed forward multilayer network & timedelay recurrent network is used for the prediction of the  NASDAQ stock price.A comparative error study for both network architecture is introduced in this paper.

In this paper gradient descent backpropagation learning algorithm is used for supervised  training of  both  network architectures. The back propagation algorithm was developed by Paul Werbos in 1974 and it is rediscovered independently by Rumelhart and Parker. In backpropagation  learning  atfirst the network weight is selected as random small value then the network output is calculated & it is compared with the desired output,difference between them is defined by error .The goal of efficient network training is to minimize this error by monotonically tuning the network weights by using gradient descent method.To compute the gradient of error surface it takes mathematical tools & it is a iterative process.

ANN is a powerful  tool  widely used in soft-computing techniques for forecasting  stock price.The first stock forecasting approach was taken by White,1988 ,he used IBM daily stock price to predict the future stock value[3].When developing  predictive model for forecasting Tokyo stock market , Kimoto, Asakawa, Yoda, and Takeoka 1990  have reported onthe effectiveness of alternative learning algorithms and prediction methods using ANN[4]. Chiang, Urban, and Baldridge 1996 have used ANN to forecast the end-of-year net asset value of mutual funds[5]. Trafalis (1999) used feed-forward ANN to forecast the change in the S&P(500) index. In that model, the input values were the univariate data consisting of weekly changes in 14 indicators[6].Forecasting of daily direction of change in the S&P(500) index is made by Choi, Lee, and Rhee 1995[7]. Despite the wide spread use of ANN in this domain, there are significant problems to be addressed. ANNs are data-driven model (White, 1989[8]; Ripley, 1993[9]; Cheng & Titterington, 1994[10]), and consequently, the underlying rules in the data are not always apparent (Zhang, Patuwo, & Hu, 1998[11]). Also, the buried noise and complex dimensionality of the stock market data makes it difficult to learn or re-estimate the ANN parameters (Kim & Han, 2000[12]). It is also difficult to come with an ANN architecture that can be used for all domains. In addition, ANN occasionally suffers from the overfitting problem (Romahi & Shen, 2000[13])[14].

II.   DATA ANALYSIS AND PROBLEM DESCRIPTION

This paper develops two comparative ANN models step-by-step to predict the stock price over financial time series, using data available at the website http://www.google.com/finance. The problem described in this paper is a predictive problem. In this paper four predictors have been used with one predictand. The four predictors are listed  below

•Stock open price
•Stock price high
•Stock price low
•Stock close price
•Total trading volume

The predictand is next stock opening price.

All these four predictors of year X are used for prediction of stock opening price of year ( X+1). Whole dataset comprises of 1460 days NASDAQ stock data. Now first subset contains early 730 days data (open,high,low,close,volume) which is the inputseries to the neural network predictor.Second subset has later 730 days data(only open) which is the target series to the neural network predictor.Now the network learns the dynamic relationship between those previous five parameters (open, high, low, close, volume)to the one final parameter(open),which it will predict in future.

A.   Data Preprocessing

Once the historical stock prices are gathered ,now this is the time for data selection for training,testing and simulating the network.In this project we took 4 years historical price of any stock ,means total 1460 working days data.We done R/S analysis  over these datafor predictability(Hurst exponent analysis).Now The Hurst exponent (H) is a statistical measure used to classify time series. H=0.5 indicates a random series while H>0.5 indicates a trend reinforcing series. The larger the H value is, the stronger trend. (1) H=0.5 indicates a random series. (2) 0<H<0.5 indicates an anti-persistent series. (3) 0.5<H<1 indicates a persistent series. An antipersistent series has a characteristic of “mean-reverting”, which means an up value is more likely followed by a down value, and vice versa. The strength of “meanreverting” increases as H approaches 0.0. A persistent series is trend reinforcing, which means the direction (up or down compared to the last value) of the next value imore likely the same as current value. The strength of trend increases as H approaches 1.0. Most economic and financial time series are persistent with H>0.5. Now we took the dataset timeseries having hurst exponent >0.5 for persistency in good predictability.

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