The impact of various learning functions to predict the stock data is discussed in this PPT. Here back propagation neural network is used to predict the closing value of Nifty stock data. The neural network model uses four different learning functions namely – Unipolar, sigmoid, bi-polar sigmoid, tanh, and radial basis function to train and test the model. It was observed that tanh() function outperformed the prediction task.

1. Impact of Learning Functions on Prediction of
Stock Data in Neural Network
By
Mrs. Shailaja
Associate Professor
BMSCE, Bangalore
Dr. Manjunath M
Assistant Professor
RVCE, Bangalore
Dr. Ummesalma M
Assistant Professor
CHRIST, Bangalore

2. Introduction
 Digitalization, globalization and urbanization has transformed the modern world
in all walks of life.
 The global world has become a digital market where a gigantic amount of data
is available in various forms, which when mined and analysed in a proper way
will turn into a profitable resource.
 One such form of valuable resource is stock data.
 Stock data is a time series data accumulated in an unprecedented rate every day.
 Since stock data is a financial data, proper handling and maintenance of stocks
can lead to tremendous financial growth and improved knowledge standards.

3. Literature Survey
 The stock market is a very complicated system because of its non-linear and
non-stationary nature [1].
 As demand for stock forecasting models increased, researchers started
developing hybrid models by combining traditional techniques with statistical
models and intelligent systems such as neural networks, fuzzy systems,
evolutionary computation, and genetic algorithms [1-4].

4. Experimental Study
 Data Collection
Stock data can be downloaded from the respective stock exchange or from
popular websites such as google finance, yahoo finance etc.
For our experimental study, we have downloaded the Nifty stock data from
Indian national stock exchange.
Stock data has attributes open, low, high, close values.
Here, we are trying to predict the closing value of Nifty.
 Data Preprocessing
For the study purpose we divided the Nifty data into two parts, one for training
which contains the 60% of the data (40% for training and 20% for
validation), and one for testing containing the remaining 40% of the data.
Data is normalized before giving it as an input to the neural network.

5. Experimental Study
 Neural Network
In this paper we have developed a stock data prediction model using back
propagation neural network.
Every neural network will have a learning function. Learning functions are
used in the neural network for learning the pattern so that the learnt pattern
can be used to develop the prediction/forecasting models.
In this paper we are testing the impact of four different learning functions on
stock data prediction model using neural network.

6. Experimental Study

7. Experimental Study
Results: Unipolar sigmoid

8. Experimental Study
Results: Unipolar sigmoid
 From Table, it is clear that the highest accuracy provided by bipolar sigmoid
function is 95.16% where a value is 0.9.

9. Experimental Study
Results: Bipolar sigmoid

10. Experimental Study
Results: Bipolar sigmoid
 From the Table, it is clear that the saturation point of bipolar sigmoid reached
when the value of a was 0.3 and the accuracy was approximately 91.34%.

11. Experimental Study
Results: Tanhyperbolic sigmoid

12. Experimental Study
Results: Bipolar sigmoid
 From the Table, it is clear that the saturation point of tan hyperbolic equation
reached when the value of a and b was 0.9 and 0.2 respectively, and the
accuracy was approximately 91.02%.

13. Experimental Study
Results: Radial Base Function

14. Experimental Study
Results: Radial Base Function
 From Table, it is clear that the highest accuracy provided by RBF is 87.53%
where sigma value is 0.9.

15. Conclusion
 The main objective of this paper is to analyse the impact of various learning
functions to predict the stock data.
 Here back propagation neural network is used to predict the closing value of
Nifty stock data.
 The neural network model uses four different learning functions namely –
 Unipolar, sigmoid, bi-polar sigmoid, tanh, and radial basis function to train and
test the model.

16. Conclusion
 The experimental study reveals that unipolar sigmoid learning function
produced an accuracy of 95.65%, bipolar sigmoid produced an accuracy of
91.34%, tan hyperbolic (tanh) function produced an accuracy of 91.02%, and
radial basis function produced an accuracy of 87.53%.

17. Conclusion
 The following are the reasons for the better performance of the unipolar sigmoid
activation function than the counterparts.
1. Unipolar sigmoid is simple.
2. Lesser computation and reusable values in the neural network.
3. Range covered is 0 to 1, which is the range more suitable for predictive data.
4. Probabilistic interpretation: When we consider the output it is an unnormalized
probability, which simplifies the neural networks task.
5. RBF outputs normalized log probabilities and includes more computation with
respect to mean and standard deviation.

18. Future work
 Hybrid Neural Network model for prediction of stock data.
 Use of sentiment analysis.

19. References
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Systems with Applications, vol. 38, no. 11, pp. 13 625–13 631, 2011.
3. J. L. Ticknor, “A bayesian regularized artificial neural network for stock market
forecasting,” Expert Systems with Applications, vol. 40, no. 14, pp. 5501–
5506, 2013.
4. A. Kazem, E. Sharifi, F. K. Hussain, M. Saberi, and O. K. Hussain, “Support
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