This document discusses using artificial neural networks (ANN) to predict stock market prices. It proposes a new ANN model for feature extraction and selection to more accurately predict stock exchange markets. The model involves preprocessing noisy stock data using technical indicators, extracting and selecting key features, then applying ANN and data mining techniques to accurately predict stock prices. Evaluation of the ANN model found that feature extraction and selection play an important role in the robustness and efficiency of predicting stock market movement.

1. Presented By:
Richa Handa
Asst. Professor

2.  The stock market is a complex and dynamic system with
noisy, non-stationary and chaotic data series.
 Prediction of a financial market is more challenging due to
chaos and uncertainty of the system. Soft computing
techniques are progressively gaining presence in the
financial world.
 This paper describes the application of Artificial Neural
Network (ANN) for the prediction of Stock Market using
some technical indicators..
 A new model is proposes of ANN for feature Extraction and
selection to get more accurate prediction of stock exchange
market.

3.  In this research work a framework is designed for an
optimal stock data prediction to develop an intelligent
decision support system.
 This developed system remove the non linearity that
exist in financial time series data using some feature
extraction and selection.
 These extracted features is apply to model of ANN and
data mining techniques to get the accurate prediction of
stock price.

4. EBPN
ANN Techniques
Supervised
Learning
Unsupervised
Learning
KSOM
RBFN

5. Radial Basis Function (RBF) Neural Network:
Radial basis functions are powerful techniques for interpolation
in multidimensional space. A RBF is a function which has built
into a distance criterion with respect to a center.
 Error Back Propagation Network (EBPN):
It is a supervised learning method, and is a generalization of the
delta rule. It requires a dataset of the desired output for many
inputs, making up the training set. It is most useful for feed-
forward networks

6.  Feature extraction method is transformative: that is we
are applying transformation to our data to project it into
new feature space with lower dimension.

7. One of the essential features of data mining is
feature selection, this technique is mostly based on
the machine learning for selection set of feature for
improving the efficiency of the prediction.
Feature selection techniques to automatically
discover the best features and it helps to solve the
problems of having too much data.

8.  The data used in this study consist of BSE30 and
BSE100 data collected from the historical data
available on the website yahoo finance.
 This dataset encompasses five years data. The collected
data is Non linear by nature, so preprocessing
technique has been done to make the data smoother.
For preprocessing of data some technical indicators are
used suggested by some researchers.

9. 1. Exponential Moving Average(EMA)
2. Moving Average Convergence-Divergence(MACD)
3. Relative Strength Index(RSI)
4. Stochastic Oscillator
5. Rate of Change(ROC)
6. Money Flow Index(MFI)
7. William %R
8. Accumulation Distribution Line(A/D)
9. On Balance Volume(OBV)
10. Chaikin Oscillator(CHO)
11. Average True Range
12. Average Directional Index(ADX)
13. Commodity Channel Index(CCI)
14. Chaikin Money Flow(CMF)
15. Percentage Price Oscillator(PPO)
16. Force Index(FI)

10.  In this approach the Bombay Stock Exchange(BSE)
data are collected including as opening price, closing
price, lowest price, highest price and volume.
 At the second stage, variables that had less significant
ability were removed and Feature extraction and
selection will be done.

11. Technical
Indicators
Technical
Indicators
Data SmoothingData Smoothing Feature ExtractionFeature Extraction
ANN ModelANN Model
Stock
Data
Stock
Data
Analyzed DataAnalyzed Data
Feature SelectionFeature Selection
RankingRanking

12. Stock Data
Feature Extraction
Normalize Data
Partition Data
Feature Selection
Analyze DataANN ModelANN Model
MAEMAE MAPEMAPE RMSERMSE

13. No Of FEATURES MAPE RMSE MAE
TRAINING
16 5.5138 0.03578 0.026
13 6.0853 0.0357 0.026
11 6.1105 0.0357 0.028
10 5.979 0.0344 0.025
9 5.5307 0.0342 0.025
8 5.5453 0.03428 0.025
7 5.4846 0.343 0.025

14. Best set of technical indicators will be
extracted through optimization techniques.
The empirical result show that feature
extraction and selection play a crucial role
in term of robustness and efficiency of
ANN model.