The role of centralization index in identifying momentum stage of stocks : empirical evidence from investor networks

This study uses a unique dataset of transactions at the account level to construct investor networks. These networks are then analyzed to examine the role of the network centralization index in identifying the stock momentum stages. The empirical results demonstrate that the early stage strategy of purchasing winner stocks with a low centralization index and selling loser stocks with a high centralization index outperform the simple momentum strategy. Conversely, the late-stage strategy of buying winner stocks with a high centralization index and selling loser stocks with a low centralization index underperforms the simple momentum strategy. Unlike prior research, the momentum effect in the Taiwanese stock market is particularly evident with an early stage strategy. Additionally, the regression analysis shows that the interaction between past cumulative returns and the centralization index significantly influences future returns, even after controlling for liquidity and investor attention variables. The impact of arbitrage frictions on momentum profits across different holding periods was also examined, with early stage strategies proving profitable for stocks facing severe arbitrage constraints. Moreover, this study investigates the influence of investor sentiment and market state on momentum, finding that early stage strategies perform better following periods of high sentiment and up-market states. Utilizing information networks can facilitate the identification of stock momentum stages. This research advances the understanding of momentum strategies in the Taiwanese stock market by examining investor behavior through information networks. Prior studies have struggled to observe the effects of momentum in Asian markets. However, this study innovatively uses the centralization index within investor networks to identify early-stage and late-stage momentum stocks. The findings show that early-stage momentum portfolios achieve significant price continuation. In contrast, late-stage portfolios do not exhibit significant momentum. The empirical results also highlight the importance of information diffusion patterns, arbitrage constraints, and investor sentiment in driving momentum profits. By leveraging information networks, the study contributes to the literature by providing a new lens to explain momentum profits, addressing the gap in previous research on the link between information flow and momentum. The findings have significant implications for investors and researchers, as they underscore the potential of information networks as a valuable tool for understanding market dynamics and enhancing investment strategies. The research also highlights the role of word-of-mouth communication in influencing stock performance, enriching the broader discourse on information evaluation in stock markets.