  This article investigates a regime-switching investment strategy aimed at
mitigating downside risk by reducing market exposure during anticipated
unfavorable market regimes. We highlight the statistical jump model (JM) for
market regime identification, a recently developed robust model that
distinguishes itself from traditional Markov-switching models by enhancing
regime persistence through a jump penalty applied at each state transition. Our
JM utilizes a feature set comprising risk and return measures derived solely
from the return series, with the optimal jump penalty selected through a
time-series cross-validation method that directly optimizes strategy
performance. Our empirical analysis evaluates the realistic out-of-sample
performance of various strategies on major equity indices from the US, Germany,
and Japan from 1990 to 2023, in the presence of transaction costs and trading
delays. The results demonstrate the consistent outperformance of the JM-guided
strategy in reducing risk metrics such as volatility and maximum drawdown, and
enhancing risk-adjusted returns like the Sharpe ratio, when compared to both
hidden Markov model-guided strategy and the buy-and-hold strategy. These
findings underline the enhanced persistence, practicality, and versatility of
strategies utilizing JMs for regime-switching signals.
