Trading profitability from learning and adaptation on the Tokyo Stock Exchange
研究了在东京证券交易所个股交易中，基于离散选择理论的动态切换交易策略（在过滤、移动平均和交易区间突破规则间切换）能否产生正收益，并发现其表现优于买入持有和非切换策略，但考虑数据窥探偏差后优势消失。
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.