Regime Detection and Forecasting of Financial Indicators in Electric Transmission Sector Companies Using Hidden Markov Models
Abstract
: The Brazilian electric transmission sector operates under a regulated revenue regime, yet remains subject to financial volatility arising from internal corporate strategies and external macroeconomic shocks. This study compares four Hidden Markov Model (HMM) variants—Categorical, Gaussian (GHMM), Gaussian Mixture (GMM-HMM), and Autoregressive (AR-HMM)—to estimate financial regimes of four transmission companies listed on the Brazilian stock exchange (B3), using quarterly data from 2010 to 2024. The regulated nature of this sector provides a controlled environment with reduced speculative noise, enabling the comparison of regime-detection accuracy across model variants. Among continuous-emission models, the GHMM achieved the lowest forecasting error (NRMSE between 0.170 and 0.221), while the Categorical HMM attained one-step-ahead accuracies up to 0.609, exceeding the random-guessing baseline. The AR-HMM failed to converge for three of the four companies within the 35-quarter training window. The decoded regimes suggest that revenue-based indicators are more strongly associated with firm-specific dynamics, whereas operational expenses exhibited the highest cross-company synchronization rate (13.793%), consistent with common in-flationary pressures. Regime transitions were detected from Q4/2012 onward, a timing that is temporally consistent with the period following Provisional Measure No. 579/2012. These findings indicate that the GHMM is the most effective HMM variant within the data-sparse conditions examined.