1. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online),
INTERNATIONAL JOURNAL OF MANAGEMENT (IJM)
Volume 4, Issue 6, November - December (2013)
ISSN 0976-6502 (Print)
ISSN 0976-6510 (Online)
Volume 4, Issue 6, November - December (2013), pp. 217-220
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ANALYSIS OF THE PERFORMANCE OF ARTIFICIAL NEURAL
NETWORK TECHNIQUE FOR FORECASTING MUTUAL FUND
NET ASSET VALUES
Dr. E. Priyadarshini
Assistant Professor, Sathyabama University, Chennai-119.
ABSTRACT
In this paper, the Net Asset Values of four Indian Mutual Funds were predicted using
Artificial Neural Network after eliminating the redundant variables using PCA and the performance
was evaluated using standard statistical measures such as MAPE, RMSE, etc.
Keywords: Artificial Neural Networks, Principal Component Analysis, Net Asset Values,
Prediction.
1. INTRODUCTION
The Financial Market is the most complex area in an investment business where prediction
often misfires since the number of variables that cause a price change is too many. Shares should be
sold when their investment potential has deteriorated to the extent that they no longer merit a place in
the portfolio. The investors are torn between the desire to protect profits or minimize further losses
and the protection of price appreciation.
The selection of input data is an important decision that can greatly affect the model
performance. There are hundreds of financial and macroeconomic variables available for analysis.
However, many of the variables may be irrelevant or redundant to the prediction of stock returns.
Principal component analysis (PCA) is a variable reduction procedure and is widely used in data
processing and dimensionality reduction.
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2. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online),
Volume 4, Issue 6, November - December (2013)
2. DATA FOR ANALYSIS
The Net Asset Values of four of the top ten Indian Mutual Funds namely
1. HDFC Mutual Fund –Top 200(equity)
2. ING Mutual Fund - Dividend Yield Growth option(equity)
3. ICICI Mutual Fund-prudential Growth option(equity)
4. Reliance Mutual Fund –Regular savings(equity)
were modeled using ANN for a period of five years between 2006 and 2010 (Source: amfiindia).
Based on the literature review, regarding the data access restriction and on consultation with
Chartered Financial Analyst, 13 macro-economic and financial variables were selected as input
variables for the Indian Mutual Funds. The input variables used are: BSE Index , NSE Index , Crude
Oil (%) , Gold/Gram (Rs), Silver/ Gram (Rs), Dollar Equivalent (Rs), Inflation (%), 91 Day Treasury
Bill(Rs),Gross Domestic Product (%),Reserve Money(Rs) ,Unemployment Rate (%), Price/EarningsSensex , Dividend-Yield-Sensex.
3.
OBTAINING RELEVANT VARIABLES USING PCA
The collinearity statistics of the inputs clearly shows that the Variance Inflation Factor
(VIF) for most of the inputs is greater than 5, indicating potential multi-collinearity problem. In order
to identify relevant variables and redundant or irrelevant variables, PCA (Principal Component
Analysis) technique is used. High value of KMO (0.804>.05) indicates that a factor analysis is useful
for the present data. The next step in the process is to decide about the number of factors to be
derived. The rule of thumb is applied to choose the number of factors for which ‘Eigen values’ are
greater than unity. The Component matrix so formed is further rotated orthogonally using Varimax
algorithm which is the standard rotation method. Among the two factors identified, about 80% of the
total variance was explained. The Scree plot obtained for the components best explains the
prominence of the two factors. Using these two factors identified by PCA, the mutual funds are
modeled using Artificial Neural Network.
Error Estimate and Bias of the predicted NAV’s using ANN
Mutual
Funds
HDFC
ICICI
ING
RELIANCE
MAPE
0.0203
0.058
0.044
0.037
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3. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online),
Volume 4, Issue 6, November - December (2013)
Actual versus Neural Network Predicted Chart for the four mutual funds
4. CONCLUSION
From the model developed using Artificial Neural Networks for the NAV of the four Mutual
Funds namely ICICI Mutual Fund, ING Mutual Fund, HDFC Mutual Fund and Reliance Mutual
Fund, it has been observed that the network predictions agree with the actual values reasonably well
for all the other four schemes. The maximum value of bias is 0.829 for ICICI otherwise it is small for
the rest of the mutual fund. The plot of actual rates versus predicted rates indicates that the model fits
the given data well. The scatter plots of predicted and actual values for the four mutual funds
confirms this.
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4. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online),
Volume 4, Issue 6, November - December (2013)
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