Momentum, volume and investor sentiment study for u.s. technology sector stocks-A hidden markov model based principal component analysis.

ABSTRACT: In this paper, we study the impact of momentum, volume and investor sentiment on U.S. tech sector stock returns using Principal Component Analysis-Hidden Markov Model (PCA-HMM) methodology. Price and volume are two well-known aspects in general equilibrium model. Momentum effect arises from the determination of prices in the market equilibrium. By studying momentum, volume and investor sentiment, we intuitively connect theoretical finance model with modern behavior finance topic. Instead of predicting future stock returns using machine learning models and doing comparisons, we apply the PCA-HMM method to reveal the hidden force in the financial and macroeconomic time series to calibrate different regimes. Combining the traditional financial study methods with modern machine learning techni

SUBMITTER: Li S 

PROVIDER: S-EPMC12419650 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature