Looking For Short Term Signals In
Stock Market Data
Author(s)
A. Bocharov
Abstract
This paper offers a method for generating discrimination criteria that can be used
to identify strong patterns in stock market data. More effective criteria are
necessary as short-term trades are often driven by noise rather than valid market
signals - a viewpoint known as the "noisy trader" model of the stock market. To
demonstrate why traditional technical analysis falls short, we applied the
standard methods to a synthetic market generated by a random walk. Although
the market data is engineered to be pure noise, technical analysis "discovers"
strong features such as accumulation/distribution patterns, upward and
downward trends, support and resistance levels. These secondary features
represent noise in the technical indicators.
By comparing the performance of these indicators between a real market and
the synthetic noise market, real signals can be identified. We select a subset of
technical indicators {T} such that each indicator T in this subset has a
mesokurtic (usually, near-normal) distribution across the synthetic noise market.
It turns out that many of the common measures (e.g. accumulation, relative
strength, moving average trend direction) are mesokurtic for the noise market,
but have strongly leptokurtic distributions across the actual stock market. We
propose a method for using the kurtosis of such indicators T to separate the noise
from the real signal in the indicators. Specifically, having obtained a mesokurtic
baseline that corresponds to noise, we use the empirical real market distribution
for indicator T to compute discrimination thresholds beyond which a value of T
should be considered a strong signal.
The strong signal concept is validated using historical price/volume data for
the Standard & Poor’s 1500 (S&P 1500) set of stocks.
Keywords: market analysis, kurtosis risk, random walk, technical analysis.
Keywords
market analysis, kurtosis risk, random walk, technical analysis.
Related Book
Other papers in this volume
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