A Neural Network Approach to Predicting Stock Performance

John Piefer

ECE/CS 539

Project Presentation

Presentation Outline• Introduction

• Problem Description

• Neural Network Design– Data Format

– Program Description (code in Appendix B of report)

– Default Network Parameters

• Selected Results

• Discussion– Limitations of my model

– Comparison with model from www.stock100.com

• Conclusion

Introduction• Predicting stock performance is a very large

and profitable area of study

• Many companies have developed stock predictors based on neural networks

• This technique has proven successful in aiding the decisions of investors

• Can give an edge to beginning investors who don’t have a lifetime of experience

Problem Description• Collect a sufficient amount of historical stock data• Using this data, train a neural network• Once trained, the neural network can be used to predict stock behavior• Need to some way to gauge value of results – I will compare with

www.stock100.com as well as compare with what actually happened• Advantages

– Neural network can be trained with a very large amount of data. Years, decades, even centuries

– Able to consider a “lifetime” worth of data when making a prediction

– Completely unbiased• Disadvantages

– No way to predict unexpected factors, i.e. natural disaster, legal problems, etc.

Neural Network Design• I will use back propagation learning on a MLP with one hidden layer

– This decision is based on previous success– Due to time restraints, fairly easy to use

• This will be approached as an approximation problem, but with classification in mind– I will predict an exact value, but we really care about whether the

network predicts the general behavior correctly because that is how we make or lose money

– I believe the actual output will give a better idea of what to expect. If it is close to 0, that doesn’t tell us much. But if it is +5, that gives a better indication that it will increase – the network is more “sure”

• Training will be done using matlab programs, including a modified version of ‘bpappro.m’ written by professor Yu Hen Hu

• Network will have one output: the predicted value for the next day

Data Format• Stock data for various companies will be downloaded from

the S&P 500 data page– I will write a C++ program (datagen.cpp) to put the data into

matrix form to be used by matlab (See Appendix A of report for code)

• Only the stock price will be considered• The data will be input as the change in stock price from

open to close– Ex: $5 open, $6 close +1 is the actual value used– Ex: $5 open, $4 close -1 is the actual value used

• A certain number of samples will be used as inputs, e.g. 20 samples, user can specify in datagen.cpp

Data Format, cont’d

• The target value will be the last column, corresponding to the change in price the next day

• Target values will become inputs in subsequent samples as follows– Ex: [+1 -1 +0.5 -2] -2 is target– next sample is [-1 +0.5 -2 +3]– next sample is [+0.5 –2 +3 …] etc

• This allows for more training samples (apx. 250 for one year worth of data)

Default Network Parameters

• These are the default network parameters, determined by running experiments

Hidden Neurons (H): 18

Learning Rate (alpha): 0.4

Momentum Constant (mom): 0.75

Max Epochs (nepoch): 2000

Epoch Size (K): 24

Program Description – pred.m• Built on ‘bpappro.m’ by Yu Hen Hu

• Trains neural network and predicts the next day(s), giving an exact value for each prediction

– Usage: [Predicted,flagpct,flag1,flag2] = pred(Stockdata, H, alpha, mom, nepoch, K, days)

– Predicted: vector of the predictions - [day1 day2 …]

– flagpct: the percent of times it predicted the wrong behavior on the training set

– flag1: the number of “type 1 flags” – predicted an increase but it actually decreased the next day

– flag2: the number of “type 2 flags” – predicted a decrease but it actually increased the next day

– Stockdata: the matrix generated by datagen.cpp

– days: the number of days to predict (default: 1)

– All other intputs are the network parameters specified on the previous page

• Also outputs some statistics about the training set

Program Description – sp.m

• Driver program that calls ‘pred’• User inputs the number of trials to run, sp

calls pred that many times, each time getting a new prediction

• Outputs some statistics about all the trials to be used for making the decision– Also important to look at results from

individual trials for any odd behavior

Selected Results• Walmart (actual next day: -1.375)

– Over 50 trials, I predicted increases 32 times– Overall average of +0.6727– Type 1 flags: 12.3065% of the time– Type 2 flags: 6.2137% of the time– Discussion

• I would have recommended investing based on these results – would have lost money

• AT&T Corp (actual next day: +1.875)– Over 40 trials, I predicted increases 29 times – Overall average of +0.8563– Type 1 flags: 9.5959% of the time– Type 2 flags: 8.4884% of the time– Discussion

• I would have recommended investing based on these results – would have made money

Selected Results, cont’d• America Online (actual next day: -1.125)

– Over 50 trials, I predicted increases 25 times and decreases 25 times

– Overall average value of +0.6429– Type 1 flags: 5.2618% of the time– Type 2 flags: 8.051% of the time

• This is a very good overall classification rate (86.6872%)– Discussion

• Not consistent enough to make a decision• No majority of predicted increases or decreases• Overall value is close to 0, could easily drop below 0

• See report for six more companies and more detailed analysis of results

• Also see Appendix A of report for sample graphs for each company

Discussion• Overall results

– Predictions were generally not uniform over all trials, less consistent means the models are not very “sure” based on data

• This leads to more risky decisions– Shows a lot of promise - good classification rates (predicted

increases/decreases correctly)– Showed a tendency to predict more increases, even when actual

behavior was a decrease (see report for more discussion on this)• Limitations of my model

– Only considers stock price, and only one year’s worth of data– Only one output, maybe better to predict more than one day ahead

• Comaprison with www.stock100.com– Walmart: both predicted increase, actually decreased– AOL: I said uncertain, they predicted increase– AT&T: both predicted increase, actually increased

Conclusion

• My model shows promise, but needs improvement before becoming an effective aid– Needs more data, possibly more types of data

• No human or computer can perfectly predict the volatile stock market

• Under “normal” conditions, in most cases, a good neural network will outperform most other current stock market predictors and be a very worthwhile, and potentially profitable aid to investors

• Should be used as an aid only!