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

TL;DR

This paper introduces Gaussian-Chain filters designed to effectively handle heavy-tailed noise in financial data, enabling better detection of market movers and improving trading strategies.

Contribution

The paper develops a new heavy-tailed distribution model and corresponding filters, demonstrating their superiority over traditional methods in noisy, heavy-tailed environments.

Findings

GC filters outperform least-squares in heavy-tailed noise scenarios

The Ride-the-Mood strategy yields higher returns than benchmarks

Application to Hong Kong stocks confirms effectiveness

Abstract

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.