  We propose a new heavy-tailed distribution --- Gaussian-Chain (GC)
distribution, which is inspirited by the hierarchical structures prevailing in
social organizations. We determine the mean, variance and kurtosis of the
Gaussian-Chain distribution to show its heavy-tailed property, and compute the
tail distribution table to give specific numbers showing how heavy is the
heavy-tails. To filter out the heavy-tailed noise, we construct two filters ---
2nd and 3rd-order GC filters --- based on the maximum likelihood principle.
Simulation results show that the GC filters perform much better than the
benchmark least-squares algorithm when the noise is heavy-tail distributed.
Using the GC filters, we propose a trading strategy, named Ride-the-Mood, to
follow the mood of the market by detecting the actions of the big buyers and
the big sellers in the market based on the noisy, heavy-tailed price data.
Application of the Ride-the-Mood strategy to five blue-chip Hong Kong stocks
over the recent two-year period from April 2, 2012 to March 31, 2014 shows that
their returns are higher than the returns of the benchmark Buy-and-Hold
strategy and the Hang Seng Index Fund.
