Neural Network Forecasting with the S&P 500 Index Across Decades

LOYOLA UNIVERSITY CHICAGO QUINLAN SCHOOL OF BUSINESS

DMIN 2013, LAS VEGAS

Neural Network Forecasting with the S&P 500 Index Across Decades

M.E. Malliaris & A.G. MalliarisJuly 23, 2013

The 9th International Conference on Data Mining, Las Vegas

LOYOLA UNIVERSITY CHICAGO QUINLAN SCHOOL OF BUSINESSLOYOLA UNIVERSITY CHICAGO QUINLAN SCHOOL OF BUSINESS

QUESTIONS• Are there stable patterns of directional

movement in the S&P 500?• If so, can we use those patterns to

forecast?• Do these patterns and their

importance change over time?


DATA SET• Closing values of the S&P 500 from 1950

through 2010 [6 decades]• Derived variables:

– % change in closing– Moving averages– Patterns of Up and Down movement – Number of Ups


DATA SET DIVISIONS• Divided by Decade for analysis and

training• 10 Years used for training,

– e.g., 1950 - 1959• The Year immediately following a

Decade was used as a validation set– e.g., 1960


Two-Day Strings


Up Movement Only


Inputs

Input ExampleToday’s Closing Value 1132.99Percent Change in the Closing Value 1.60Today’s Closing Direction U4 Day Moving Average of Closing 1137.08Percent Change in the 4-day Mov. Avg. -0.0593Number of Up Closings in last day 0 or 1Number of Up Closings in last 2 days 1Number of Up Closings in last 3 days 2Number of Up Closings in last 4 days 2Number of Up Closings in last 5 days 3

2-day Up and Down pattern DU

3-day Up and Down pattern UDU

4-day Up and Down pattern DUDU

5-day Up and Down pattern UDUDU


Data Sets

Training SetValidation Sets

Jan 1 through Dec 31Jan 1, 1950 -- Dec 31, 1959 1960Jan 1, 1960 -- Dec 31, 1969 1970Jan 1, 1970 -- Dec 31, 1979 1980Jan 1, 1980 -- Dec 31, 1989 1990Jan 1, 1990 -- Dec 31, 1999 2000Jan 1, 2000 -- Dec 31, 2009 2010


METHODOLOGY• Neural Network

• Structure: Identical for all networks– 14 Inputs– One Hidden Layer with 9 nodes– 1 Output [tomorrow’s direction]– Random Seed: 229176228– 30% used to prevent over-fitting


Software: IBM’s SPSS Modeler 14


ResultsTraining Decade

Tr Percent Correct

Validation Year

Val Percent Correct

1950-59 59.00% 1960 59.92%

1960-69 59.90% 1970 62.99%

1970-79 59.94% 1980 58.10%

1980-89 55.18% 1990 54.94%

1990-99 56.25% 2000 52.78%

2000-09 52.96% 2010 47.22%


Percent Correct DirectionsTraining Set Validation Set

Down Up Down Up50s 53.90% 60.95% 61.68% 58.62%60s 57.94 61.00 67.02 60.6270s 58.88 60.94 53.25 60.2380s 53.21 56.16 63.16 54.2790s 53.43 57.98 56.70 50.3200s 50.67 55.01 40.74 54.70


Top Five InputsFirst PercChgClose PercChg4MA PercChgClose PercChg4MA PercChg4MA PercChg4MA

Second

Third

Fourth

Fifth

Decade 50s 60s 70s 80s 90s 00s



Second PercChg4MA NumUps4 PercChg4MA Close NumUps5 NumUps4

Third

Fourth

Fifth





Third String5Days PercChgClose Close String5Days PercChgClose MA4day

Fourth

Fifth






Fourth String4Days String4Days MA4day NumUps3 MA4day String5Days

Fifth






Fourth String4Days String4Days MA4day NumUps3 MA4day String5Days

Fifth NumUps1 String5Days NumUps2 PercChgClose String3Days Close



Summary• Neural Networks with the same structure

were trained for six decades• This identical structure,using the same

inputs, was useful for over six decades. • All variables were generated from the S&P

500 closing price• Variable importance shifted slightly over time• Successful forecasting was possible


Future Research• Future research might investigate a smaller

training time, say a rolling window of one or two years.

• This might enable us to see the importance of specific variables gradually shifting over time.

LOYOLA UNIVERSITY CHICAGO QUINLAN SCHOOL OF BUSINESS

Neural Network Forecasting with the S&P 500 Index Across Decades

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