Exponential Moving Average Stock Model Documents
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Main Document
written by
Matthew Mohorn
A primary application of econophysics is using digital signal processing techniques to filter and predict market data, which is theorized to exhibit random walk motion. An exponential moving average is one tool that physicists use to smooth data from an input signal to identify its trends. The Exponential Moving Average Stock Model implements three types of exponential moving averages and allows the user to change the parameters of each. The model allows the user to view the results of exponential moving averages computed on the New York Stock Exchange daily closing price of six familiar companies. It demonstrates one way that traders use causal filters to smooth market data and forecast the next day's price.
Last Modified June 6, 2014
 This file has previous versions.
Source Code Documents
The source code zip archive contains an XML representation of the Exponential Moving Average Stock Model. Unzip this archive in your EJS workspace to compile and run this model using EJS.
Last Modified June 6, 2014
This file has previous versions.
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Exponential Moving Average Sock Model
