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We propose a new heavy-tailed distribution --- Gaussian-Chain (GC)distribution, which is inspirited by the hierarchical structures prevailing insocial organizations. We determine the mean, variance and kurtosis of theGaussian-Chain distribution to show its heavy-tailed property, and compute thetail distribution table to give specific numbers showing how heavy is theheavy-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 thebenchmark least-squares algorithm when the noise is heavy-tail distributed.Using the GC filters, we propose a trading strategy, named Ride-the-Mood, tofollow the mood of the market by detecting the actions of the big buyers andthe 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 stocksover the recent two-year period from April 2, 2012 to March 31, 2014 shows thattheir returns are higher than the returns of the benchmark Buy-and-Holdstrategy and the Hang Seng Index Fund.

Gaussian-Chain Filters for Heavy-Tailed Noise with Application to Detecting Big Buyers and Big Sellers in Stock Market