Trading profitability from learning and adaptation on the Tokyo Stock Exchange

Authored by Ryuichi Yamamoto

Date Published: 2016

DOI: 10.1080/14697688.2015.1091941

Sponsors: United States Centers for Disease Control and Prevention (CDC) Nikkei Media Marketing Grant-in-Aid for Scientific Research

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

This study proposes unexamined technical trading rules, which are dynamically switching strategies among filter, moving average and trading-range breakout rules. The dynamically switching strategy is formulated based on a discrete choice theory consistent with the concept of myopic utility maximization. We utilize the transaction data of the individual stocks listed on the Nikkei 225 from September 1, 2005 to August 31, 2007. We demonstrate that switching strategies produce positive returns and their performance is better than those from the buy-and-hold and non-switching strategies over our sample periods. We also demonstrate equivalent performance for switching with different learning horizons, implying that behavioural heterogeneity of stock investors arises from the coexistence of different strategies with varying degrees of learning horizons. Our result supports several research assumptions and results on agent-based theoretical models that successfully replicate empirical features in financial markets, such as fat tails of return distributions and volatility clustering. However, upon considering the effects of data-snooping bias superior performance disappears.

Tags

Market Behavioral heterogeneity bubbles Model Technical analysis Efficiency information Bootstrap Inflation-expectations Predictive ability