C++ Neural Networks and Fuzzy Logic: Preface

A Turning Point Predictor

This neural network approach is discussed by Michitaka Kosaka et al. (1991).

They discuss applying the feedforward backpropagation network to develop buy/sell signals for securities.

You would gather time−series data on stock prices, and want to find trends in the data so that changes in the

direction of the trend provide you the turning points, which you interpret as signals to buy or sell.

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You will need to list these factors that you think have any influence on the price of a security you are

studying. You need to also determine how you measure these factors. You then formulate a nonlinear function

combining the factors on your list and the past however many prices of your security (your time series data).

The function has the form, as Michitaka Kosaka, et al. (1991) put it,

     p(t + h) = F(x(t), x(t −1), ... , f

1

, f

2

, ... )

     where

     f

1

, f

2

, represent factors on your list,

     x(t) is the price of your stock at time t,

     p(t + h) is the turning point of security price at time t + h, and

     p(t + h) = −1 for a turn from downward to upward,

     p(t + h) = +1 for a turn from upward to downward,

     p(t + h) = 0 for no change and therefore no turn

Here you vary h through the values 1, 2, etc. as you move into the future one day (time period) at a time. Note

that the detailed form of the function F is not given. This is for you to set up as you see fit.

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C++ Neural Networks and Fuzzy Logic:Preface

The Walk−Forward Methodology of Market Prediction

325

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