Dynamical Models of Stock Prices Based on Technical Trading Rules Part I: The Models
Authored by Li-Xin Wang
Date Published: 2015
DOI: 10.1109/tfuzz.2014.2327994
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Abstract
In this paper, we use fuzzy systems theory to convert the technical
trading rules commonly used by stock practitioners into excess demand
functions which are then used to drive the price dynamics. The technical
trading rules are recorded in natural languages where fuzzy words and
vague expressions abound. In Part I of this paper, we will show the
details of how to transform the technical trading heuristics into
nonlinear dynamic equations. First, we define fuzzy sets to represent
the fuzzy terms in the technical trading rules; second, we translate
each technical trading heuristic into a group of fuzzy IF-THEN rules;
third, we combine the fuzzy IF-THEN rules in a group into a fuzzy
system; and finally, the linear combination of these fuzzy systems is
used as the excess demand function in the price dynamic equation. We
transform a wide variety of technical trading rules into fuzzy systems, including moving average rules, support and resistance rules, trend line
rules, big buyer, big seller, and manipulator rules, band and stop
rules, and volume and relative strength rules. Simulation results show
that the price dynamics driven by these technical trading rules are
complex and chaotic, and some common phenomena in real stock prices such
as jumps, trending, and self-fulfilling appear naturally.
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