Pairs trading of nearly identical twin stocks: The case of GOOGL versus GOOG

Abstract

Purpose: As computer technology and statistical methods advance, statistical arbitrage research focuses on developing more sophisticated trading methods. Twin stocks are stock pairs such as GOOGL and GOOG that trade as different stocks but are identical except one has voting rights. The two stocks should be identical, and standard arbitrage trading using these twin stocks should produce little if any profits. The purpose of this study is to test whether this hypothesis is true and whether the novel statistical arbitrage methods can be used on twin stocks to produce profits.
Design/methodology/approach: This article uses the Ornstein-Uhlenbeck process method to calculate the entry and exit timing and expected return under different transaction costs. We also use the factor model to test the impact of long- and short-term factors on the expected returns obtained by our statistical arbitrage trades.
Findings/results: Our test results show evidence of positive arbitrage profits, indicating that the hypothesis concerning twin stocks is not true. In addition, we find that the arbitrage profits are not affected by short-term risk factors.
Practical implications: Our research shows that profitable statistical arbitrage can be applied to asset pairs other than an asset and its derivative. Applying statistical arbitrage to twin stocks may be a good alternative for investors interested in arbitrage trading.
Originality/value: This is the first study that applies statistical arbitrage to twin stocks. We show investors can profit when applying statistical arbitrage to even highly correlated assets such as twin stocks still obtain excess returns.