vinay-project-report

Using Neural Networks to Explain Behavior of Indian Markets 06-October-2005
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Student: Vinay Avasthi
Roll No. 2003152
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IINNDDIIAANN IINNSSTTIITTUUTTEE OOFF MMAANNAAGGEEMMEENNTT BBAANNGGAALLOORREE
Towards partial fulfillment of the requirements for the Post Graduate Diploma in Software
Enterprise Management of the Indian Institute of Management Bangalore

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TTAABBLLEE OOFF CCOONNTTEENNTTSS
INTRODUCTION........................................................................................................................................... 3
VARIABLES EVALUATED ............................................................................................................................... 3
STRATEGY .................................................................................................................................................... 3
FACTORS ...................................................................................................................................................... 3
Factor 1 – RBI influence and Core sector................................................................................................. 3
Factor 2 – Foreign Exchange and Crude.................................................................................................. 3
Factor 3 – Agriculture, Total Domestic Product ....................................................................................... 3
Factor 4 – Company Financials ............................................................................................................... 3
Factor 5 – Company Ratios...................................................................................................................... 3
Factor 6 – Agriculture, Community services, debt structure with RBI........................................................ 3
Factor 7 – Company Capital structure, profitability ratios and other indicators........................................ 3
Factor 8 – Banking system residuals ........................................................................................................ 3
Factor 9 – Company Liquidity Ratios....................................................................................................... 3
Factor 10 – Company stock performance.................................................................................................. 3
Factor 11 – RBI balance sheet debt structure and errors .......................................................................... 3
Factor 12 – RBI balance sheet errors....................................................................................................... 3
Factor 13 – Company indicators (residuals)............................................................................................. 3
Factor 14 – Banking system residuals....................................................................................................... 3
Factor 15 – Company financial ratios, Residuals...................................................................................... 3
Factor 16 – Foreign Exchange, Crude and interest rate, Residuals........................................................... 3
Factor 17 – Company Financial Ratios, Residuals.................................................................................... 3
Factor 18 – Company Financial Ratios, Residuals.................................................................................... 3
Factor 19 – USD Forward Spot rate......................................................................................................... 3
Factor 20 – IDBI lending rate and crude prices........................................................................................ 3
COMPANIES .................................................................................................................................................. 3
CHOICE OF NEURAL NETWORK ............................................................................................................. 3
INPUTS AND OUTPUTS ................................................................................................................................... 3
HIDDEN LAYERS ........................................................................................................................................... 3
APPENDIX – FACTOR ANALYSIS.............................................................................................................. 3
TOTAL VARIANCE EXPLAINED ....................................................................................................................... 3
COMPONENT MATRIX.................................................................................................................................... 3
APPENDIX – NEURAL NET GENERATOR................................................................................................ 3

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SOURCE FOR NEURALNETSCREENER ............................................................................................................. 3
SOURCE FOR STOCKMARKETNEURALNETCREATOR ....................................................................................... 3

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IINNTTRROODDUUCCTTIIOONN
This project evaluates the impact of changes in macro and micro economic variables on
Indian financial markets. The data that is evaluated for this effort contains variables directly
controlled by Reserve Bank of India and other variables like price of crude etc.
The project uses a combination of statistical analysis and artificial intelligence techniques to
generate a model that could be used to predict the behavior of the markets.
Variables Evaluated
Following variables are evaluated as part of constructing this model.
• assetsWithBankingSystem – Total assets with the banking system
• bankCredit – Bank credit in India
• cash – Cash in hand
• investmentAtBookValue – Total bank investments at book value
• liabilitiesToBankingSystem – Total liabilities of banks to banking system
• liabilitiesToOthers – Total liability of banks other than banking system
• curcredit – Current account credit in INR
• curdebit – Current account debit in INR
• capcredit – Capital account credit in INR
• capdebit – Capital account debit in INR
• errcredit – Errors credit
• errdebit – Errors debit
• balcredit – Balance credit
• baldebit – Balance debit
• monmovcredit – Monetary movements credit
• monmovdebit – Monetary movements debit
• callMoneyHigh – Call money rate, High
• callMoneyLow – Call money rate, Low
• eps – Earning per share of the company
• ceps – Cash earning per share of the company

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• bookValue – Book value of the company
• div – Dividend paid per share of the company
• opProfitPerShare – Operating profit per share of the company
• netOperatingIncomePerShare – Net operating income per share of the company
• freeReserves – Free reserves with the company
• opm – Operating profit margin of the company
• gpm – Gross profit margin of the company
• npm – Net profit margin of the company
• ronw – Return on net work of the company
• debtToEquity – Debt to equity ratio of the company
• currentRatio – Current ratio of the company
• quickRatio – Quick ratio of the company
• interestCover – Interest cover of the company
• salesByTotalAssets – Sales by total assets of the company
• salesByFixedAssets – Sales by fixed assets of the company
• salesByCurrentAssets – Sales by current assets of the company
• noOfDaysOfWorkingCapital – No of days of working capital with the company
• cpi – Consumer price index
• br – Bank Rate
• idbiRate – IDBI minimum term lending rate
• maxCMR – Maximum Call Money Rate
• maxPLR – Maximum prime lending rate
• minPLR – Minimum Prime lending rate
• price – Crude price
• totalINRdebt – Total debt in Indian Rupees
• concessionalDebtAsPercOfTotal – Concessional debt as a percentage of total
• shortTermDebtAsPercOfTotal – Short term debt as a percentage of total
• affConstant – Agriculture, Forestry and Fishing, GDP factor cost, Constant prices
• affCurrent – Agriculture, Forestry and Fishing, GDP factor cost, Current prices

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• cspsConstant – Community social and personal services, GDP factor cost,
Constant prices
• cspsCurrent – Community social and personal services, GDP factor cost, Current
prices
• consConstant – Construction, GDP factor cost, Constant prices
• consCurrent – Construction, GDP factor cost, Current prices
• egwsConstant – Electricity, Gas and Water Services, GDP factor cost, Constant
prices
• egwsCurrent – Electricity, Gas and Water Services, GDP factor cost, Current
prices
• firebsConstant – Finance, Insurance, Real Estate and Business services, GDP
factor cost, Constant prices
• firebsCurrent – Finance, Insurance, Real Estate and Business services, GDP factor
cost, Current prices
• manuConstant – Manufacturing, GDP factor cost, Constant prices
• manuCurrent – Manufacturing, GDP factor cost, Current prices
• maqConstant – Mining and quarrying, GDP factor cost, Constant prices
• maqCurrent – Mining and quarrying, GDP factor cost, Current prices
• tdpConstant – Total domestic product, GDP factor cost, Constant prices
• tdpCurrent – Total domestic product, GDP factor cost, Current prices
• thrConstant – Trade, Hotel and Restaurant, GDP factor cost, Constant prices
• thrCurrent – Trade, Hotel and Restaurant, GDP factor cost, Current prices
• aff – Agriculture, Forestry and Fishing, GDP factor cost
• csps – Community social and personal services, GDP factor cost
• cons – Construction, GDP factor cost
• egws – Electricity, Gas and Water Services, GDP factor cost
• firb – Finance, Insurance, Real Estate and Business services, GDP factor cost
• manuf – Manufacturing, GDP factor cost
• min – Mining, GDP factor cost
• tdp – Total domestic product, GDP factor cost

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• thr – Trade, Hotel and Restaurant, GDP factor cost
• currencyWithPublic – Total currency with Public
• m3 – Money supply, also referred to as stock of legal currency in the economy
• timeDepositsWithBank – Total time deposits with the bank
• totalIncome – Total income of RBI
• totalExpenditure – Total expenditure of RBI
• netAvailableBalance – Net available balance in RBI
• surplusToCentralGovernment – Surplus payable to central government from RBI
• totalIssuesLiabilities – Total liabilities, Issues
• totalIssuesAssets – Total assets, Issues
• totalBankingLiabilities – Total liabilities, Banking
• totalBankingAssets – Total assets, Banking
• reserveMoneyLiabilities – Reserve Money, Liabilities
• reserveMoneyAssets – Reserve Money, Assets
• forwardCashSpot – Forward Cash Spot, USD forward premia
• forwardCashOneMonth – Forward Cash one month, USD forward premia
• forwardCashThreeMonth – Forward Cash three months, USD forward premia
• forwardCashSixMonth – Forward Cash six months, USD forward premia
• forwardCash12Month – Forward cash twelve months, USD forward premia
• referenceRate – RBI reference rate for USD
• rate – US interest rate
• quantitiy – Quantity of particular stock traded
• turnover – Total turn over of stock traded
We would use above macro and micro economic indicators establish the relationship of these
indicators with following data for each company and sensitive index for a specific day.
• Previous day close
• Day open
• Day high
• Day low
• Day close

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For each company and index, the model would be developed to predict prices four time
periods.
• Next day prices – +1d
• Prices after seven days – + 7d
• Prices after 15 days – +15d
• Prices after six months - +6m
Strategy
Since we are looking at very large number of input variables related to economic indicators
which may have heavy correlation between themselves, we will first use factor analysis to
identify a manageable set of factors that could be used as inputs for the neural network later to
develop the prediction model.
For each company four models would be constructed as follows.
• 1D model, which would predict the prices for next day given the stock price,
turnover and quantity for a day earlier to previous day.
• 7D model, which would predict the prices for next week given the stock price,
turnover and quantity for a week earlier
• 15D model, which would predict the prices 15 days down the line.
• 180D model, which would predict the prices six months down the line given the
stock price for a day.
Factors
After the factor analysis of the data, 96 inputs are reduced to 20 inputs with 95% of the
variance explained. These factors are as follows. As we can see from Appendix – Factor
Analysis, first 5 factors contribute 75% of the variance in the data while rest of the 15 factors
only adds approximately 20% of data.
As we go to later factors, these mostly cover the residual values from initial factors.
Factor 1 – RBI influence and Core sector
First factor signifies RBI’s influence and GDP related to the core sector on the market. It is
very highly correlated to the money supply variables and variables related to the RBI’s

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balance sheet. It is also correlated to GDP in construction, manufacturing, agriculture and
forestry.
Variable Correlation
Total bank investments at book value (investmentAtBookValue) 0.991
Money Supply, stock of legal currency in the economy (m3) 0.984
Total currency with public (currencyWithPublic) 0.983
Construction GDP at factor cost, current prices (consCurrent) 0.983
Bank Credit in India (bankCredit) 0.981
Finance, Insurance, Realestate and Business services, GDP at factor cost, current prices
(firebsCurrent)
0.981
Total time deposits with bank (timeDepositsWithBank) 0.977
Reserve Money Assets (reserveMoneyAssets) 0.977
Finance, Insurance, Realestate and Business services, GDP at factor cost, constant
prices (firebsConstant)
0.972
Current account debit (curdebit) 0.961
Manufacturing, GDP at factor cost, current prices (manuCurrent) 0.961
Construction GDP at factor cost, constant prices (consConstant) 0.955
Total banking liabilities (totalBankingLiabilities) 0.955
Consumer price index (cpi) 0.954
Trade, hotel and Restaurant, Current prices (thrCurrent) 0.946
Total assets (issues) (totalIssuesAssets) 0.945
Electricity, Gas and Water services, GDP at factor cost (Egws) 0.938
Total domestic product (Tdp) 0.937
Maximum prime lending rate (maxPLR) -0.925
Total expenditure of RBI totalExpenditure 0.904
Mining and quarrying, GDP at factor cost, current prices (maqCurrent) 0.878
Surplus payable to central government from RBI(surplusToCentralGovernment) -0.875
Call money rate, Low (callMoneyLow) -0.855
Agriculture, Forestry and Fishing (Aff) 0.846
Monetory movements, debit (Monmovdebit) 0.824

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Total debt in INR (totalINRdebt) 0.819
Cash with RBI (Cash) 0.813
Short term debt as percentage of total debt (shortTermDebtAsPercOfTotal) 0.577
Factor 2 – Foreign Exchange and Crude
The second factor primarily signifies impact of international events on markets. It correlated
highly with crude prices and prices of USD.
USD rate 0.542
Crude price 0.555
Tourism, Hotels and Restaurants -0.569
Capital account credit 0.607
Bank rate 0.615
Capital account credit (RBI) 0.691
RBI reference rate -0.811
Factor 3 – Agriculture, Total Domestic Product
Third factor has very high correlation with Agriculture, Forestry and Fishing. It also correlates
well with total domestic product.
Agriculture, Forestry and Fishing, GDP at factor cost, Constant prices (affConstant) 0.596
Community social and personal services, GDP at factor cost, constant prices (cspsConstant) 0.595
Total domestic product, constant prices (tdpConstant) 0.577
Agriculture, Forestry and Fishing, GDP at factor cost, Current prices (affCurrent) 0.515
Community social and personal services, GDP at factor cost, current prices (cspsCurrent) 0.502
Concessional debt as a percentage of total debt. (concessionalDebtAsPercOfTotal) -0.498
Factor 4 – Company Financials
Factor 4 seems to have very high correlation with the financials of the company.
Cash earnings per share (ceps) 0.903

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Operating profit per share (opProfitPerShare) 0.878
Book value of the company (bookValue) 0.770
Free reserves with the company (freeReserves) 0.762
Net operating income per share (netOperatingIncomePerShare) 0.679
Earnings per share (eps) 0.544
Dividend (div) 0.527
Factor 5 – Company Ratios
Factor 5 correlates well with the financial ratios of the company.
Gross profit margin(gpm) 0.859
Operating profit margin (opm) 0.818
Current ratio (currentRatio) 0.712
Number of days of working capital (noOfDaysOfWorkingCapital) 0.649
Quick ratio (quickRatio) 0.552
Factor 6 – Agriculture, Community services, debt structure with RBI
Variables Correlation
Concessional debt as percentage of total (concessionalDebtAsPercOfTotal) 0.616
Agriculture, Forestry and Fishing, GDP factor cost, Current prices (affCurrent) 0.535
Assets with banking system (assetsWithBankingSystem) -0.528
Community social and personal services, GDP factor cost, Constant prices (cspsConstant) -0.452
Factor 7 – Company Capital structure, profitability ratios and other indicators
Share capital (shareCapital) 0.802
Total outstanding shares (outstandingShares) 0.786
Sales by total assets (salesByTotalAssets) 0.532
Return on net worth (ronw) -0.505

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Sales by fixed assets (salesByFixedAssets) -0.443
Sales by current assets (salesByCurrentAssets) -0.300
Factor 8 – Banking system residuals
Monetary movement of credit (monmovcredit) 0.577
Banks liability to others (liabilitiesToOthers) 0.527
Banks liability to banking system (liabilitiesToBankingSystem) -0.423
Factor 9 – Company Liquidity Ratios
Debt to equity (debtToEquity) 0.863
Sales by fixed assets (salesByFixedAssets) 0.772
Quick Ratio (quickRatio) 0.644
Factor 10 – Company stock performance
Earning per share (eps) 0.649
Dividend paid (div) 0.444
Interest cover (interestCover) 0.422
Net operating income per share (netOperatingIncomePerShare) -0.373
Book value of the company (bookValue) -0.339
Factor 11 – RBI balance sheet debt structure and errors
Errors in credit (errcredit) -0.584
Error in debit (errdebit) 0.539
Concessional debt as percentage of total debt (concessionalDebtAsPercOfTotal) -0.448

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IDBI lending rate (idbiRate) 0.395
Factor 12 – RBI balance sheet errors
Error in debit (errdebit) -0.566
Error in credit (errcredit) 0.410
Banks liabilities to banking system (liabilitiesToBankingSystem) -0.292
Factor 13 – Company indicators (residuals)
Net profit margin (npm) -0.614
Company identification (companyId) -0.447
Number of days of working capital (noOfDaysOfWorkingCapital) 0.429
Current ratio of company (currentRatio) 0.402
Factor 14 – Banking system residuals
Forward Cash Spot (forwardCashSpot) 0.416
Assets with banking system (assetsWithBankingSystem) 0.401
Liabilities with banking system (liabilitiesToBankingSystem) 0.329

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Factor 15 – Company financial ratios, Residuals
Sales by current assets (salesByCurrentAssets) 0.540
Return on net worth (ronw) 0.445
Sales by total assets (salesByTotalAssets) 0.249
Factor 16 – Foreign Exchange, Crude and interest rate, Residuals
Interest cover (interestCover) 0.300
Reference rate of RBI (referenceRate) 0.241
Price of crude (price) 0.235
Forward cash spot (forwardCashSpot) -0.227
Factor 17 – Company Financial Ratios, Residuals
Company identifier (companyId) 0.695
Net profit margin (npm) -0.369
Dividend paid (div) 0.236
Sales by current assets (salesByCurrentAssets) 0.201
Sales by total assets (salesByTotalAssets) -0.186
Debt to equity (debtToEquity) -0.133

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Factor 18 – Company Financial Ratios, Residuals
interestCover 0.491
salesByTotalAssets 0.299
opProfitPerShare -0.240
eps -0.226
npm 0.199
Factor 19 – USD Forward Spot rate
Forward cash spot (forwardCashSpot) 0.489
Banks liabilities to others (liabilitiesToOthers) -0.465
Factor 20 – IDBI lending rate and crude prices
Banks liability to others (liabilitiesToOthers) -0.281
Electricity, Gas and Water services, GDP at factor cost, current prices (egwsCurrent) -0.275
Crude oil price (price) 0.218
Companies
We would construct the models for majority of the companies in NSE-50 index. These
companies are listed below.
Reliance Industries TISCO SAIL

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IPCL State Bank of India ITC
Tata Motors Maruti Udyog Satyam
VSNL Infosys TCS
ONGC Bharati L & T
Zee HDFC Ranbaxy
M & M BPCL ICICI Bank
Hero Honda Glaxo Colgate
National Aluminum Dabur SCI
Tata Power Sun Pharma Tata Tea
BHEL ABB Grasim
Gujrat Ambuja Cement HCL Tech Tata Chemicals
MTNL Oriental Bank Reliance Energy
GAIL Wipro Punjab National Bank
Bajaj Auto CIPLA Dr. Reddy
ACC HDFC Bank Hindustan Petro
Two companies have been omitted which are part of NSE-50 because enough data is not
available for them.

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CCHHOOIICCEE OOFF NNEEUURRAALL NNEETTWWOORRKK
Inputs and Outputs
As shown in the Appendix – Factor Analysis, the economic indicators for model related to the
company have been factored into 20 factors that explain most of these numbers. Additional 3
inputs are company specific and are related to the past stock price data with respect to that
company.
• Previous Close
• Previous Turn Over
• Previous Quantity
These make up for the 23 variables that are used as inputs for neural network. Three different
neural networks are used for following three output variables
• High
• Low
• Close
Hidden Layers
It is assumed given the richness of the data that atleast 2 hidden layers would be required to
for a meaningful neural network. The neural network will have 23 inputs and will have 1
output.
Different neural networks would be created and a training run would be performed for a 1500
cycles of data set. At the end of sample run the best network would be chosen for further
training.
Neural networks that were evaluated are with
• 1 input layer with 23 inputs
• first hidden layer with nodes 31 to 351
• second hidden layer with nodes 8 to 31
• 1 output layer

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Figure I Training and validation errors for different NN architectures
As see in Figure I, the neural network with hidden layer 1 of 130 nodes and hidden layer 2 of
17 node comes with best error values to be further used. This NN architecture was used to
further train the network with following different data sets.
• Daily prices
o High
o Low
o Close
• Weekly prices
o High
o Low
o Close
• Fortnightly prices
o High
o Low
o Close

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• Six Monthly prices
o High
o Low
o Close

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AAPPPPEENNDDIIXX –– FFAACCTTOORR AANNAALLYYSSIISS
All the macro economic data and company financial information is captured by 96 variables
which is put through a factor analysis which reduces it to 20 variables.
Total Variance Explained
Initial Eigenvalues Extraction Sums of Squared LoadingsComponent
Total % of Variance Cumulative % Total % of Variance Cumulative %
1 48.290 50.302 50.302 48.290 50.302 50.302
2 6.825 7.109 57.411 6.825 7.109 57.411
3 5.012 5.221 62.633 5.012 5.221 62.633
4 4.691 4.887 67.519 4.691 4.887 67.519
5 3.976 4.142 71.661 3.976 4.142 71.661
6 2.638 2.747 74.409 2.638 2.747 74.409
7 2.576 2.683 77.092 2.576 2.683 77.092
8 2.356 2.455 79.547 2.356 2.455 79.547
9 2.339 2.437 81.983 2.339 2.437 81.983
10 1.825 1.901 83.885 1.825 1.901 83.885
11 1.752 1.825 85.710 1.752 1.825 85.710
12 1.512 1.575 87.284 1.512 1.575 87.284
13 1.356 1.412 88.697 1.356 1.412 88.697
14 1.091 1.136 89.833 1.091 1.136 89.833
15 0.969 1.009 90.842 0.969 1.009 90.842
16 0.962 1.003 91.845 0.962 1.003 91.845
17 0.861 0.897 92.742 0.861 0.897 92.742
18 0.739 0.770 93.512 0.739 0.770 93.512
19 0.684 0.712 94.224 0.684 0.712 94.224
20 0.633 0.659 94.883 0.633 0.659 94.883
21 0.605 0.630 95.513
22 0.544 0.567 96.080
23 0.445 0.464 96.543
24 0.433 0.451 96.994
25 0.357 0.372 97.366
26 0.326 0.340 97.705
27 0.296 0.308 98.014
28 0.287 0.299 98.313
29 0.188 0.196 98.509
30 0.186 0.194 98.703
31 0.170 0.177 98.879
32 0.151 0.157 99.037
33 0.120 0.125 99.162
34 0.107 0.111 99.273
35 0.104 0.108 99.381
36 0.094 0.098 99.479
37 0.072 0.075 99.554

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38 0.067 0.070 99.625
39 0.055 0.058 99.682
40 0.043 0.045 99.728
41 0.040 0.042 99.769
42 0.038 0.040 99.809
43 0.030 0.031 99.840
44 0.029 0.031 99.870
45 0.023 0.024 99.894
46 0.016 0.016 99.911
47 0.015 0.015 99.926
48 0.012 0.013 99.939
49 0.011 0.011 99.950
50 0.009 0.009 99.959
51 0.007 0.007 99.967
52 0.006 0.006 99.973
53 0.005 0.006 99.978
54 0.005 0.005 99.984
55 0.004 0.004 99.988
56 0.004 0.004 99.992
57 0.003 0.003 99.996
58 0.002 0.002 99.998
59 0.001 0.001 99.999
60 0.001 0.001 100.000
61 0.000 0.000 100.000
62 0.000 0.000 100.000
63 0.000 0.000 100.000
64 0.000 0.000 100.000
65 0.000 0.000 100.000
66 0.000 0.000 100.000
67 0.000 0.000 100.000
68 0.000 0.000 100.000
69 0.000 0.000 100.000
70 0.000 0.000 100.000
71 0.000 0.000 100.000
72 0.000 0.000 100.000
73 0.000 0.000 100.000
74 0.000 0.000 100.000
75 0.000 0.000 100.000
76 0.000 0.000 100.000
77 0.000 0.000 100.000
78 0.000 0.000 100.000
79 0.000 0.000 100.000
80 0.000 0.000 100.000
81 0.000 0.000 100.000
82 0.000 0.000 100.000
83 0.000 0.000 100.000

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84 0.000 0.000 100.000
85 0.000 0.000 100.000
86 0.000 0.000 100.000
87 0.000 0.000 100.000
88 0.000 0.000 100.000
89 0.000 0.000 100.000
90 0.000 0.000 100.000
91 0.000 0.000 100.000
92 0.000 0.000 100.000
93 0.000 0.000 100.000
94 0.000 0.000 100.000
95 0.000 0.000 100.000
96 0.000 0.000 100.000
Extraction Method: Principal Component Analysis.

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Component Matrix
Component Matrix(a)
Component
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
assetsWithBankingSystem 0.001 -0.517 0.036 -0.018 0.006 -0.528 0.076 -0.353 0.092 0.006 -0.032 -0.269 -0.015 0.401 -0.039 -0.081 0.007 -0.016 -0.050 -0.104
bankCredit 0.981 -0.124 -0.007 -0.015 -0.003 -0.019 0.007 0.072 -0.016 -0.012 -0.013 -0.044 0.008 0.058 0.019 0.034 0.011 -0.016 0.012 -0.002
cash 0.813 -0.056 -0.302 -0.026 -0.024 0.091 -0.014 -0.197 0.033 -0.032 0.078 0.197 0.023 -0.006 -0.097 -0.148 -0.030 0.049 0.002 0.038
investmentAtBookValue 0.991 -0.048 -0.052 -0.010 -0.008 0.005 0.004 0.050 -0.011 -0.010 0.002 -0.014 -0.011 -0.022 -0.022 -0.033 -0.006 0.008 0.001 0.021
liabilitiesToBankingSystem -0.455 0.012 -0.131 -0.032 -0.006 -0.526 0.080 -0.423 0.117 0.042 -0.087 -0.292 -0.043 0.329 -0.077 -0.161 -0.011 0.040 0.020 -0.140
liabilitiesToOthers -0.199 0.237 -0.259 -0.049 -0.012 0.106 -0.004 0.527 -0.112 -0.041 0.126 0.033 0.028 0.299 -0.091 -0.129 -0.013 -0.036 -0.465 -0.281
curcredit 0.942 0.136 0.112 0.001 0.001 -0.129 0.027 0.087 -0.005 0.058 -0.169 -0.069 -0.007 0.023 0.021 0.031 0.008 -0.008 0.023 -0.013
curdebit 0.961 0.130 -0.096 -0.033 -0.010 -0.101 0.025 0.056 -0.003 0.008 -0.054 -0.112 -0.002 0.070 0.017 0.036 0.009 -0.029 0.001 0.065
capcredit 0.737 0.607 -0.025 0.001 -0.009 0.208 -0.028 -0.126 0.023 -0.015 0.046 0.045 0.017 -0.010 -0.011 -0.008 -0.004 0.009 0.034 -0.013
capdebit 0.552 0.691 -0.029 -0.002 -0.012 0.235 -0.025 -0.074 0.023 0.053 -0.149 -0.116 -0.015 0.061 -0.019 -0.034 -0.007 0.000 0.034 0.095
errcredit 0.183 0.454 0.183 0.039 0.003 -0.287 0.047 -0.079 0.052 0.249 -0.584 0.410 0.070 -0.048 -0.018 -0.014 -0.012 -0.003 -0.064 0.082
errdebit -0.333 0.065 -0.097 -0.013 -0.005 0.302 -0.045 0.036 -0.033 -0.217 0.539 -0.566 -0.151 -0.150 -0.030 -0.074 -0.015 0.042 0.132 0.057
balcredit 0.905 0.394 0.053 0.002 -0.004 0.030 0.002 -0.018 0.010 0.029 -0.081 -0.008 0.006 0.007 0.006 0.013 0.002 0.000 0.029 -0.012
baldebit 0.824 0.476 -0.070 -0.019 -0.013 0.092 -0.002 -0.013 0.011 0.029 -0.102 -0.143 -0.014 0.068 -0.003 -0.003 0.001 -0.014 0.024 0.092
monmovcredit -0.331 0.360 -0.396 -0.074 -0.018 0.125 -0.002 0.577 -0.123 -0.051 0.134 0.000 0.023 0.093 0.008 0.042 0.009 -0.062 -0.141 0.074
monmovdebit 0.824 0.144 0.270 0.039 0.014 -0.098 0.010 -0.005 0.000 0.019 -0.016 0.260 0.044 -0.111 0.023 0.043 0.005 0.027 0.027 -0.211
callMoneyHigh -0.790 0.348 -0.107 -0.018 0.000 0.044 -0.004 0.229 -0.047 -0.010 0.082 0.096 0.037 0.198 -0.042 -0.066 -0.006 0.002 0.029 0.034
callMoneyLow -0.855 0.325 -0.054 -0.015 0.003 -0.003 0.003 0.192 -0.037 -0.005 0.056 0.027 0.026 0.122 0.007 0.015 0.006 -0.016 -0.026 -0.049
companyId -0.004 -0.001 -0.005 -0.011 0.121 0.060 0.294 0.031 0.227 0.012 0.021 0.098 -0.447 0.069 -0.183 0.156 0.695 0.191 -0.046 0.052
eps 0.168 -0.081 -0.130 0.544 0.203 0.029 -0.061 0.013 0.038 0.649 0.203 -0.051 0.058 -0.017 -0.093 0.013 -0.061 -0.226 0.027 -0.002
ceps 0.057 -0.030 -0.122 0.903 -0.252 0.019 0.120 0.013 0.026 0.165 0.056 -0.007 -0.034 0.008 0.017 -0.005 -0.047 -0.110 0.009 0.000
bookValue -0.047 0.040 -0.067 0.770 -0.458 -0.008 0.180 0.015 0.038 -0.339 -0.096 0.028 -0.074 0.027 0.011 0.044 -0.042 0.036 -0.001 -0.005
div 0.138 0.011 -0.123 0.527 -0.019 0.020 0.046 -0.017 -0.054 0.444 0.156 -0.009 0.049 -0.044 -0.116 -0.024 0.236 0.106 0.000 0.013
opProfitPerShare 0.059 -0.042 -0.130 0.878 -0.005 0.032 0.139 0.040 0.165 0.176 0.058 -0.016 0.003 -0.013 0.043 -0.080 0.022 -0.240 0.025 -0.006
netOperatingIncomePerShare -0.061 0.041 -0.046 0.679 -0.542 -0.013 0.133 0.030 0.109 -0.373 -0.104 0.034 -0.065 0.022 0.114 -0.025 -0.004 0.080 -0.011 0.002
freeReserves -0.049 0.042 -0.066 0.762 -0.453 -0.012 0.197 -0.008 -0.083 -0.328 -0.095 0.017 -0.027 0.026 0.045 0.033 -0.052 0.095 -0.009 -0.004
opm 0.082 0.015 -0.101 0.228 0.818 0.041 0.265 -0.025 -0.067 -0.035 -0.006 0.071 -0.271 0.030 0.166 -0.068 -0.029 -0.050 0.004 -0.013
gpm 0.090 0.014 -0.113 0.276 0.859 0.030 0.190 -0.009 -0.005 -0.031 -0.005 0.054 -0.198 0.022 0.176 -0.083 -0.020 -0.041 0.002 -0.006

24
Component Matrix(a)
Component
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
npm 0.136 0.043 -0.087 0.054 0.448 0.013 0.082 -0.053 -0.151 -0.030 0.005 0.156 -0.614 0.101 0.101 0.129 -0.369 0.199 -0.024 0.011
ronw 0.188 0.024 -0.133 0.241 0.282 -0.089 -0.505 -0.004 -0.060 0.256 0.108 0.006 -0.030 0.053 0.445 -0.142 0.078 0.072 -0.021 0.020
debtToEquity -0.008 -0.029 0.001 0.006 0.115 0.027 -0.114 0.175 0.863 -0.046 0.005 0.044 -0.196 0.019 -0.138 0.098 -0.133 -0.126 0.023 -0.014
currentRatio -0.091 0.029 -0.050 0.292 0.712 -0.003 0.152 -0.011 -0.096 -0.280 -0.105 -0.089 0.402 -0.052 -0.021 -0.049 0.090 0.106 -0.019 0.015
quickRation -0.057 0.010 -0.039 0.189 0.552 -0.012 -0.143 0.142 0.644 -0.228 -0.061 -0.038 0.171 -0.014 -0.105 0.064 -0.008 0.073 -0.002 -0.011
interestCover 0.106 -0.033 -0.097 0.389 0.089 -0.014 -0.043 -0.005 -0.118 0.422 0.149 -0.092 0.233 0.052 -0.162 0.300 -0.131 0.491 -0.048 -0.028
salesByTotalAssets 0.008 -0.092 0.055 -0.203 -0.099 0.091 0.532 0.060 0.210 0.117 0.049 -0.071 0.196 0.077 0.249 0.090 -0.186 0.299 -0.098 0.181
salesByFixedAssets 0.097 -0.023 -0.037 0.082 0.092 -0.040 -0.443 0.172 0.772 -0.029 0.005 -0.019 0.110 -0.009 -0.031 0.028 -0.093 0.100 -0.008 0.004
salesByCurrentAssets 0.087 0.024 -0.007 -0.093 -0.380 -0.034 -0.300 0.063 0.319 0.094 0.064 0.030 0.008 -0.008 0.540 -0.322 0.201 0.172 -0.036 0.002
noOfDaysOfWorkingCapital -0.035 0.025 -0.055 0.245 0.649 0.001 0.206 -0.018 -0.124 -0.313 -0.115 -0.085 0.429 -0.067 0.027 -0.129 0.128 -0.007 0.001 0.005
shareCapital -0.012 -0.018 0.064 -0.301 -0.133 0.149 0.802 0.040 0.284 0.196 0.092 0.007 0.071 -0.031 0.071 -0.094 -0.051 -0.075 0.034 -0.068
outstandingShares -0.004 -0.022 0.064 -0.329 -0.100 0.148 0.786 0.040 0.282 0.200 0.093 0.004 0.086 -0.031 0.100 -0.106 -0.026 -0.024 0.025 -0.063
cpi 0.954 -0.202 -0.062 -0.010 -0.008 0.100 -0.012 0.050 -0.019 -0.027 0.026 0.037 0.007 -0.055 0.006 0.022 0.002 -0.008 -0.002 0.027
br -0.705 0.615 -0.225 -0.039 -0.012 -0.096 0.023 -0.068 0.030 0.037 -0.070 -0.054 0.003 -0.004 0.028 0.054 0.006 -0.014 0.055 0.033
idbiRate -0.559 -0.082 -0.156 -0.036 0.003 -0.036 -0.007 -0.311 0.046 -0.139 0.395 0.272 0.141 0.110 0.109 0.257 0.039 -0.147 -0.055 0.252
maxCMR -0.741 0.186 0.010 0.005 0.006 0.081 -0.014 0.197 -0.044 -0.006 0.074 0.090 0.028 0.232 -0.060 -0.109 -0.010 0.025 0.115 0.047
maxPLR -0.925 0.070 -0.074 -0.017 0.005 0.013 -0.007 -0.023 0.000 -0.003 0.019 0.162 0.066 -0.020 0.096 0.167 0.029 -0.023 0.075 -0.109
minPLR -0.873 0.257 -0.032 -0.007 0.004 0.106 -0.016 0.049 -0.011 0.039 -0.099 0.093 0.039 -0.057 0.098 0.153 0.028 0.005 0.142 -0.168
price 0.401 0.555 -0.268 -0.087 -0.014 -0.288 0.065 0.120 0.002 0.050 -0.172 -0.202 0.041 0.204 0.123 0.235 0.048 -0.122 0.066 0.218
totalINRdebt 0.819 -0.336 -0.101 -0.033 -0.001 -0.057 0.003 -0.183 0.023 -0.097 0.215 0.130 0.074 0.026 0.075 0.154 0.027 -0.047 0.037 0.004
concessionalDebtAsPercOfTotal 0.046 0.058 -0.498 -0.070 -0.039 0.616 -0.070 0.154 -0.036 0.104 -0.448 -0.191 -0.006 0.059 0.071 0.108 0.026 -0.033 0.033 0.017
shortTermDebtAsPercOfTotal 0.577 0.305 -0.452 -0.076 -0.023 -0.259 0.043 -0.061 0.015 -0.083 0.276 0.289 0.087 0.092 -0.049 -0.021 -0.011 -0.052 -0.124 0.196
affConstant 0.177 0.311 0.596 0.104 0.029 0.503 -0.093 -0.393 0.061 -0.009 0.039 0.088 0.054 0.231 -0.025 -0.042 -0.002 -0.012 -0.074 0.006
affCurrent 0.318 0.251 0.515 0.091 0.024 0.535 -0.097 -0.408 0.060 -0.033 0.089 0.083 0.060 0.243 -0.021 -0.030 0.000 -0.021 -0.079 0.012
cspsConstant 0.480 0.169 0.595 0.071 0.039 -0.452 0.061 0.295 -0.050 -0.034 0.210 0.018 -0.033 -0.134 -0.021 -0.027 -0.013 -0.002 -0.033 0.096
cspsCurrent 0.675 0.126 0.502 0.059 0.031 -0.371 0.052 0.274 -0.047 -0.021 0.146 0.003 -0.032 -0.122 -0.020 -0.026 -0.012 -0.003 -0.031 0.097
consConstant 0.955 0.026 0.217 0.020 0.011 -0.061 0.009 0.099 -0.020 -0.002 0.012 0.070 0.028 0.071 0.007 0.017 0.006 -0.016 -0.032 -0.013
consCurrent 0.983 0.023 0.138 0.011 0.004 -0.041 0.008 0.078 -0.015 0.006 -0.023 0.014 0.008 0.036 -0.001 0.001 0.003 -0.005 -0.011 -0.006
egwsConstant 0.929 0.077 0.212 0.026 0.006 0.097 -0.010 0.130 -0.026 0.031 -0.118 -0.121 -0.035 -0.046 0.017 0.010 0.005 0.018 0.055 -0.071
egwsCurrent 0.838 0.245 0.074 -0.018 0.010 -0.007 -0.002 -0.002 -0.013 -0.099 0.240 0.041 0.055 0.012 0.112 0.187 0.040 -0.002 0.145 -0.275
firebsConstant 0.972 0.003 0.174 0.011 0.009 -0.088 0.014 0.089 -0.019 -0.020 0.047 -0.014 0.005 0.012 0.019 0.033 0.008 -0.005 0.024 -0.029
firebsCurrent 0.981 -0.088 0.105 0.005 0.002 -0.031 0.008 0.091 -0.017 0.005 -0.044 -0.063 -0.010 0.010 0.011 0.015 0.005 -0.005 0.013 -0.002

25
Component Matrix(a)
Component
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
manuConstant 0.929 0.113 0.281 0.028 0.013 -0.121 0.020 0.147 -0.025 0.001 0.013 -0.049 -0.014 -0.015 0.003 0.008 0.001 -0.011 -0.013 0.034
manuCurrent 0.961 0.115 0.173 0.016 0.005 -0.085 0.017 0.122 -0.019 0.011 -0.030 -0.071 -0.024 -0.025 -0.006 -0.012 -0.002 0.002 0.008 0.018
maqConstant 0.825 0.171 0.486 0.050 0.029 -0.024 0.000 0.137 -0.032 -0.029 0.091 -0.049 0.011 0.051 0.048 0.078 0.018 -0.023 0.013 -0.040
maqCurrent 0.878 0.039 0.290 0.010 0.018 -0.016 0.007 0.211 -0.042 0.009 -0.074 -0.163 0.007 0.100 0.096 0.151 0.036 -0.040 0.047 -0.076
tdpConstant 0.742 0.236 0.577 0.083 0.029 0.145 -0.032 -0.087 0.010 -0.016 0.077 0.028 0.019 0.097 -0.013 -0.019 -0.002 -0.011 -0.045 0.024
tdpCurrent 0.863 0.164 0.430 0.060 0.020 0.141 -0.027 -0.067 0.006 -0.017 0.055 -0.001 0.012 0.082 -0.009 -0.012 0.000 -0.011 -0.033 0.022
thrConstant 0.930 0.111 0.333 0.043 0.015 0.063 -0.011 0.024 -0.008 -0.011 0.030 -0.034 -0.003 0.031 -0.001 -0.002 0.001 -0.007 -0.012 0.011
thrCurrent 0.946 0.121 0.279 0.037 0.010 0.078 -0.012 0.002 -0.003 -0.005 0.008 -0.047 -0.010 0.021 -0.009 -0.016 -0.002 -0.001 -0.006 0.012
aff 0.846 -0.485 -0.121 -0.016 -0.013 0.094 -0.010 0.021 -0.009 0.006 -0.089 -0.033 -0.005 0.033 -0.012 -0.016 0.000 -0.016 -0.045 0.068
csps 0.946 -0.213 -0.221 -0.029 -0.018 0.052 -0.003 -0.011 -0.001 -0.010 -0.018 0.041 0.004 0.000 -0.027 -0.040 -0.006 0.011 -0.001 0.008
cons 0.950 -0.167 -0.233 -0.029 -0.019 0.049 -0.003 -0.019 0.001 -0.011 -0.008 0.056 0.002 -0.014 -0.037 -0.056 -0.009 0.019 0.004 0.001
egws 0.938 0.071 -0.295 -0.046 -0.020 -0.009 0.007 -0.030 0.004 -0.031 0.059 0.092 0.025 0.001 -0.010 -0.010 0.000 0.019 0.048 -0.063
firb 0.922 -0.316 -0.186 -0.023 -0.017 0.071 -0.006 -0.001 -0.004 -0.003 -0.046 0.016 -0.001 0.007 -0.026 -0.039 -0.006 0.003 -0.018 0.032
manuf 0.954 -0.125 -0.246 -0.032 -0.020 0.039 -0.001 -0.021 0.001 -0.015 0.004 0.063 0.006 -0.011 -0.032 -0.048 -0.008 0.019 0.012 -0.010
min 0.876 -0.288 -0.194 -0.062 -0.008 -0.010 0.008 0.048 -0.016 -0.035 -0.005 -0.054 0.061 0.167 0.111 0.199 0.046 -0.072 0.023 -0.033
tdp 0.937 -0.266 -0.205 -0.029 -0.017 0.056 -0.004 -0.002 -0.004 -0.009 -0.029 0.023 0.006 0.016 -0.016 -0.021 -0.002 0.001 -0.007 0.015
thr 0.749 -0.569 -0.080 -0.036 -0.004 0.055 -0.003 0.068 -0.020 -0.008 -0.089 -0.111 0.033 0.155 0.087 0.156 0.037 -0.078 -0.034 0.047
currencyWithPublic 0.983 -0.114 -0.038 -0.008 -0.007 0.018 0.001 0.034 -0.008 0.001 -0.035 0.001 -0.001 0.024 -0.018 -0.030 -0.003 0.006 -0.004 0.005
m3 0.984 -0.157 -0.031 -0.011 -0.006 0.013 0.002 0.046 -0.012 -0.009 -0.017 -0.029 -0.004 0.008 -0.001 -0.001 0.002 0.000 0.009 0.000
timeDepositsWithBank 0.977 -0.189 -0.029 -0.012 -0.005 0.012 0.002 0.051 -0.014 -0.017 -0.001 -0.025 0.001 0.003 0.011 0.022 0.006 -0.009 0.007 0.002
totalIncome -0.728 -0.460 0.342 0.070 0.016 0.110 -0.022 0.040 -0.008 0.061 -0.164 -0.130 -0.062 -0.038 -0.045 -0.087 -0.019 -0.011 -0.117 0.164
totalExpenditure 0.904 -0.211 -0.201 -0.003 -0.022 0.098 -0.012 -0.041 0.006 0.011 -0.041 0.082 -0.035 -0.100 -0.111 -0.186 -0.037 0.057 -0.026 0.047
netAvailableBalance -0.875 -0.255 0.326 0.052 0.020 0.043 -0.012 0.045 -0.008 0.041 -0.104 -0.126 -0.032 0.010 0.009 0.008 0.000 -0.030 -0.075 0.102
surplusToCentralGovernment -0.875 -0.255 0.326 0.052 0.020 0.043 -0.012 0.045 -0.008 0.041 -0.104 -0.126 -0.032 0.010 0.009 0.008 0.000 -0.030 -0.075 0.102
totalIssuesLiabilities 0.945 -0.224 -0.218 -0.030 -0.018 0.052 -0.003 -0.009 -0.002 -0.010 -0.020 0.037 0.005 0.005 -0.024 -0.034 -0.005 0.008 -0.002 0.008
totalIssuesAssets 0.945 -0.224 -0.218 -0.030 -0.018 0.052 -0.003 -0.009 -0.002 -0.010 -0.020 0.037 0.005 0.005 -0.024 -0.034 -0.005 0.008 -0.002 0.008
totalBankingLiabilities 0.955 -0.053 -0.267 -0.040 -0.019 0.017 0.003 -0.022 0.001 -0.023 0.027 0.070 0.017 0.003 -0.016 -0.020 -0.002 0.015 0.028 -0.034
totalBankingAssets 0.955 -0.053 -0.267 -0.040 -0.019 0.017 0.003 -0.022 0.001 -0.023 0.027 0.070 0.017 0.003 -0.016 -0.020 -0.002 0.015 0.028 -0.034
reserveMoneyLiabilities 0.977 -0.086 -0.062 -0.011 -0.009 0.010 0.003 0.027 -0.005 0.005 -0.044 -0.005 -0.005 0.034 -0.028 -0.050 -0.005 0.018 0.030 0.004
reserveMoneyAssets 0.977 -0.086 -0.062 -0.011 -0.009 0.010 0.003 0.027 -0.005 0.005 -0.044 -0.005 -0.005 0.034 -0.028 -0.050 -0.005 0.018 0.030 0.004
forwardCashSpot -0.329 -0.215 0.145 0.022 0.015 0.021 -0.008 0.388 -0.088 -0.002 0.052 0.220 0.055 0.416 -0.123 -0.227 -0.018 0.093 0.489 0.110
forwardCashOneMonth -0.743 -0.432 0.150 0.030 0.016 0.051 -0.016 0.271 -0.064 0.026 -0.048 0.204 0.033 0.066 -0.015 -0.041 -0.002 0.023 0.044 -0.005

26
Component Matrix(a)
Component
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
forwardCashThreeMonth -0.786 -0.461 0.195 0.036 0.018 0.092 -0.024 0.174 -0.046 0.024 -0.056 0.146 0.026 0.035 0.010 0.000 0.004 0.012 0.036 0.003
forwardCashSixMonth -0.806 -0.470 0.217 0.038 0.019 0.112 -0.027 0.113 -0.033 0.022 -0.064 0.082 0.017 0.029 0.023 0.020 0.008 0.005 0.024 -0.012
referenceRate -0.319 -0.811 0.270 0.022 0.026 0.089 -0.026 0.028 -0.024 -0.021 -0.015 0.076 0.076 0.107 0.142 0.241 0.051 -0.079 -0.013 -0.057
rate -0.799 0.542 -0.130 -0.026 -0.004 -0.114 0.021 -0.096 0.033 0.026 -0.034 -0.060 0.005 0.039 0.028 0.043 0.008 -0.001 0.054 -0.057
Extraction Method: Principal Component Analysis.
a. 20 components extracted.

27
AAPPPPEENNDDIIXX –– NNEEUURRAALL NNEETT GGEENNEERRAATTOORR
Source for NeuralNetScreener
/*
* NeuralNetScreener.java
*
* Created on October 12, 2005, 8:46 PM
*
*/
package com.avasthi.stockneuralnetgenerator;
import java.text.SimpleDateFormat;
import java.util.*;
import java.io.*;
import javax.print.attribute.standard.Finishings;
import org.joone.engine.*;
import org.joone.engine.learning.*;
import org.joone.net.*;
import org.joone.util.DynamicAnnealing;
import org.joone.io.*;
import org.joone.util.*;
import org.joone.engine.weights.*;
/**
*
* @author binny
*/
public class NeuralNetScreener implements org.joone.engine.NeuralNetListener,
org.joone.net.NeuralValidationListener{
String baseFolder_;
int trainingDataPoints_;
int validationDataPoints_;
String trainingFile_;
String validationFile_;
int trainingStart_;
int validationStart_;
int testingStart_;
String inputColumnSelector_;
String outputColumnSelector_;
org.joone.net.NeuralNet net_;
int inputSize_;
int hidden1Size_;
int hidden2Size_;
int outputSize_;

28
String filename_;
PrintStream pStrm_;
ObjectOutputStream nnStrm_;
int totCicles_;
/** Creates a new instance of NeuralNetScreener */
public NeuralNetScreener(String baseFolder,
String trainingFile,
String validationFile,
int trainingDataPoints,
int validationDataPoints,
int inputSize,
int hidden1Size,
int hidden2Size,
int outputSize,
int totCicles) {
totCicles_ = totCicles;
baseFolder_ = baseFolder;
trainingFile_ = trainingFile;
validationFile_ = validationFile;
trainingDataPoints_ = trainingDataPoints;
validationDataPoints_ = validationDataPoints;
inputColumnSelector_ = new String("1-23");
outputColumnSelector_ = new String("24-30");
net_ = new org.joone.net.NeuralNet();
inputSize_ = inputSize;
hidden1Size_ = hidden1Size;
hidden2Size_ = hidden2Size;
outputSize_ = outputSize;
String filename = baseFolder + "/stockPrediction." + inputSize_ + "." + hidden1Size_ + "."
+ hidden2Size_ + "." +outputSize_;
System.out.println("File name :" + filename);
try {
filename_ = filename;
FileOutputStream fos = new FileOutputStream(filename + ".txt");
pStrm_ = new PrintStream(fos);
FileOutputStream stream = new FileOutputStream(filename + ".ser");
nnStrm_ = new ObjectOutputStream(stream);
}
catch (Exception ex) {
System.out.println("File count not be opened " + filename);
pStrm_ = System.out;
}
}
/**
* used to build a network
*/
public void buildNetwork(boolean singleOutput, String outputColumn) {
// build the input, hidden and output layer
LinearLayer input = new LinearLayer();
SigmoidLayer hidden1 = new SigmoidLayer();

29
SigmoidLayer hidden2 = new SigmoidLayer();
SigmoidLayer output = new SigmoidLayer();
input.setLayerName("input");
hidden1.setLayerName("hidden1");
hidden2.setLayerName("hidden2");
output.setLayerName("output");
input.setRows(inputSize_);
hidden1.setRows(hidden1Size_);
hidden2.setRows(hidden2Size_);
if (singleOutput) {
output.setRows(1);
}
else {
output.setRows(outputSize_);
}
// add the layers to the net.
net_.addLayer(input, NeuralNet.INPUT_LAYER);
net_.addLayer(hidden1, NeuralNet.HIDDEN_LAYER);
net_.addLayer(hidden2, NeuralNet.HIDDEN_LAYER);
net_.addLayer(output, NeuralNet.OUTPUT_LAYER);
// creating the synapses to link the layers
FullSynapse inputHiddenSynapse = new FullSynapse(); // In -> Hid
FullSynapse hiddenHiddenSynapse = new FullSynapse(); // Hid -> Hid
FullSynapse hiddenOutputSynapse = new FullSynapse(); // Hid -> Out
inputHiddenSynapse.setName("InputHidden");
hiddenHiddenSynapse.setName("HiddenHidden");
hiddenOutputSynapse.setName("HiddenOutput");
// wire them
input.addOutputSynapse(inputHiddenSynapse);
hidden1.addInputSynapse(inputHiddenSynapse);
hidden1.addOutputSynapse(hiddenHiddenSynapse);
hidden2.addInputSynapse(hiddenHiddenSynapse);
hidden2.addOutputSynapse(hiddenOutputSynapse);
output.addInputSynapse(hiddenOutputSynapse);
// add the monitor.
Monitor monitor = new Monitor();
monitor.getLearners().add(0, new String("org.joone.engine.BasicLearner"));
monitor.setLearningMode(0);
monitor.setUseRMSE(true);
// adding the monitor to the layers.

30
input.setMonitor(monitor);
hidden1.setMonitor(monitor);
hidden2.setMonitor(monitor);
output.setMonitor(monitor);
net_.setMonitor(monitor);
FileInputSynapse trainingInputData = new FileInputSynapse();
trainingInputData.setFileName(trainingFile_);
/* The output values are on the third column of the file */
trainingInputData.setAdvancedColumnSelector(inputColumnSelector_);
trainingInputData.setFirstRow(1);
FileInputSynapse validationInputData = new FileInputSynapse();
validationInputData.setFileName(validationFile_);
validationInputData.setAdvancedColumnSelector(inputColumnSelector_);
validationInputData.setFirstRow(1);
LearningSwitch ils = new LearningSwitch();
ils.addTrainingSet(trainingInputData);
ils.addValidationSet(validationInputData);
input.addInputSynapse(ils);
monitor.setLearningRate(0.8);
monitor.setMomentum(0.3);
monitor.addNeuralNetListener(this);
/* Setting of the file containing the desired responses, provided by a FileInputSynapse */
FileInputSynapse trainingOutputData = new FileInputSynapse();
trainingOutputData.setFileName(trainingFile_);
if (singleOutput) {
trainingOutputData.setAdvancedColumnSelector(outputColumn);
}
else {
trainingOutputData.setAdvancedColumnSelector(outputColumnSelector_);
}
trainingOutputData.setFirstRow(1);
FileInputSynapse validationOutputData = new FileInputSynapse();
validationOutputData.setFileName(validationFile_);
if (singleOutput) {
validationOutputData.setAdvancedColumnSelector(outputColumn);
}
else {

31
validationOutputData.setAdvancedColumnSelector(outputColumnSelector_);
}
validationOutputData.setFirstRow(1);
LearningSwitch ols = new LearningSwitch();
ols.addTrainingSet(trainingOutputData);
ols.addValidationSet(validationOutputData);
TeachingSynapse trainer = new TeachingSynapse();
/* We give it the monitor's reference */
trainer.setDesired(ols);
trainer.setMonitor(monitor);
output.addOutputSynapse(trainer);
monitor.setTrainingPatterns(trainingDataPoints_); /* # of rows contained in the input file
*/
monitor.setTotCicles(totCicles_); /* How many times the net must be trained on the input
patterns */
monitor.setLearning(true); /* The net must be trained */
input.start();
hidden1.start();
hidden2.start();
output.start();
monitor.Go(); /* The net starts the training job */
net_.join();
}
public void saveNetwork(int kount) {
try {
FileOutputStream stream = new FileOutputStream(filename_ + "-"+ kount + ".ser");
ObjectOutputStream nnStrm = new ObjectOutputStream(stream);
nnStrm.writeObject(net_);
}
System.out.println("Count not save neural network ");
}
}
public static NeuralNet restoreNeuralNet(String fileName) {
NeuralNet nnet = null;
try {
FileInputStream stream = new FileInputStream(fileName);
ObjectInputStream inp = new ObjectInputStream(stream);
nnet = (NeuralNet)inp.readObject();
}
catch (Exception excp) {
excp.printStackTrace();
}

32
return nnet;
}
public static void loadAndTestData(String nnFile, String inputFile, int inputDataSize,
String outputFile) {
try {
NeuralNet net = restoreNeuralNet(nnFile);
Layer input = net.getInputLayer();
input.removeAllInputs();
org.joone.io.FileInputSynapse inp = new org.joone.io.FileInputSynapse();
inp.setFileName(inputFile);
inp.setAdvancedColumnSelector("1-23");
inp.setFirstRow(1);
input.addInputSynapse(inp);
Layer output = net.getOutputLayer();
output.removeAllOutputs();
org.joone.io.FileOutputSynapse out = new org.joone.io.FileOutputSynapse();
out.setFileName(outputFile);
output.addOutputSynapse(out);
net.getMonitor().setTotCicles(1);
net.getMonitor().setTrainingPatterns(inputDataSize);
net.getMonitor().setLearning(false);
net.start();
net.getMonitor().Go();
}
System.out.println("Count not load neural network ");
}
}
public void netStarted(NeuralNetEvent ev) {
System.out.println("Net Started " +ev.toString());
}
public void netStopped(NeuralNetEvent ev) {
System.out.println("Net Stopped "+ev.toString());
}
public void netStoppedError(NeuralNetEvent ev, String str) {
System.out.println("Net Stopped Error "+ev.toString() + str);
}
public void errorChanged(NeuralNetEvent ev) {
}
/* Validation Event */
public void netValidated(NeuralValidationEvent event) {
// Shows the RMSE at the end of the cycle
NeuralNet NN = (NeuralNet)event.getSource();
pStrm_.println(",Validation Error,"+NN.getMonitor().getGlobalError());
}
public void cicleTerminated(NeuralNetEvent ev) {

33
Monitor mon = (Monitor)ev.getSource();
// Prints out the current epoch and the training error
int cycle = mon.getCurrentCicle()+1;
if (cycle % 100 == 0) { // We validate the net every 100 cycles
String msg = new String("Epoch #"+(mon.getTotCicles() - cycle));
msg += ",Training Error,"+mon.getGlobalError();
// Creates a copy of the neural network
net_.getMonitor().setExporting(true);
NeuralNet newNet = net_.cloneNet();
net_.getMonitor().setExporting(false);
// Cleans the old listeners
// This is a fundamental action to avoid that the validating net
// calls the cicleTerminated method of this class
newNet.removeAllListeners();
// Set all the parameters for the validation
NeuralNetValidator nnv = new NeuralNetValidator(newNet);
nnv.addValidationListener(this);
newNet.getMonitor().setValidation(true);
newNet.getMonitor().setValidationPatterns(validationDataPoints_);
nnv.start(); // Validates the
Monitor m = (Monitor) (ev.getSource());
pStrm_.print("Cicle Terminated " + msg);
if (cycle % 1000 == 0) {
saveNetwork(cycle);
}
}
}
}
Source for StockMarketNeuralNetCreator
/*
* StockMarketPredictorGenerator.java
*
* Created on October 12, 2005, 8:44 PM
*/
package com.avasthi.stockneuralnetgenerator;
/**
*
* @author binny
*/
public class StockMarketNeuralNetCreator {
/**
* @param args the command line arguments
*/
public static void main(String[] args) {
int inputs = 23;

34
int outputs = 7;
int trainingDataPoints = Integer.parseInt(args[3]);
int validationDataPoints = Integer.parseInt(args[4]);
int layer1 = Integer.parseInt(args[5]);
int layer2 = Integer.parseInt(args[6]);
int totCicles = 1500;
if (args.length >= 9) {
totCicles = Integer.parseInt(args[8]);
}
try {
System.out.println("Trying neural network with hidden layers "+layer1
+","+layer2);
NeuralNetScreener nns = new NeuralNetScreener(args[0],
args[1],
args[2],
trainingDataPoints,
validationDataPoints,
inputs,
layer1,
layer2,
outputs,
totCicles);
System.out.println("Base Directory" +
args[0] +
" Data File " +
args[1] +
" Number of Items " +
args[2]);
nns.buildNetwork(true, args[7]);
} catch(Exception ex) {
System.out.println(ex.toString());
}
}
}

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