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

Abstract

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.

Impact Statement

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.

Keywords:

• Momentum stage
• investor network
• centralization index
• arbitrage friction
• investor sentiment
• market state

JEL classification:

• G11
• G12
• G14

Reviewing Editor:

• David McMillan, University of Stirling, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND

Subjects:

• Finance
• Economics
• Business, Management and Accounting

Author contributions statement

Wen-Rang Liu: Conceptualization, Methodology, Software, Formal analysis, Validation, Writing – original draft, Writing – review & editing.

Data availability statement

The data that support the findings of this study are available on request from the corresponding author, W.R. Liu, upon reasonable request. The data are not publicly available due to the presence of information that may compromise privacy.

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1 Rouwenhorst (1998), Griffin et al. (2003), Chui et al. (2010), Asness et al. (2013), Israel and Moskowitz (2013), Li and Wei (2016), Chang et al. (2018), Berggrun et al. (2020), Gao et al. (2020), and Goyal et al. (2023) find momentum effects in the international stock markets.

2 John and Derek (2003) and Menkhoff et al. (2012) document momentum profits in foreign currency markets, Bhojraj and Swaminathan (2006) in equity indices, Erb and Harvey (2006) and Benavides Rosales (2017) in commodity markets, and Jostova et al. (2013) and Ho and Wang (2018) in bonds. Asness et al. (2013) find significant momentum profits in individual stocks, stock indices, commodities, and bond markets.

3 We express our gratitude to an anonymous reviewer for providing this highly insightful argument.

4 Screening criteria are devised in alignment with those of Hong et al. (2003). To ensure the robustness of our findings, we test the results to varying cutoff thresholds.

5 The center and the report can be accessed through the following URL: http://rcted.ncu.edu.tw/.

6 Ozsoylev et al. (2014) only consider a unidirectional network model. Because we propose that investor trading activities and information diffusion have leading and lagged relationships with each other, we also consider a directional network model.

7 This descriptive statistic is calculated based on the historical statistics of listed stocks provided by the Taipei Exchange (https://www.tpex.org.tw/web/).

8 We divide the stocks into 3 rather than 10 equal groups as Jegadeesh and Titman (1993) do due to the smaller sample size of the Taiwan stock market.

9 How investors anticipate Taiwan’s future may affect their choices and timing of trading to some extent. It is difficult to exclude the impact of contextual factors, such as political risk, on investors’ trading behavior when examining the momentum effect. We expect this influence to become even more pronounced with the US-China tension; rumors and speculations that the US and China may engage in a proxy war in Taiwan have been circulating in the market in recent years. This fear of potential conflicts stimulates Taiwan’s stock market. Furthermore, investor trading behavior and information dissemination behavior have also changed since the outbreak of the COVID-19 pandemic.

10 We thank an anonymous referee for providing this valuable input.

11 In the untabulated results, we verify that findings similar to the baseline results can be obtained if past-9-month or past-12-month cumulative returns are used as the ranking benchmark.

12 We thank an anonymous referee for providing this great suggestion.

13 For firm characteristic variables that are negatively correlated with arbitrage frictions, such as institutional ownership, number of shareholders, and dollar trading volume, we add a negative sign.

14 Prior research, notably Yu and Yuan (2011), Antoniou et al. (2013), and Antoniou et al. (2016), has extensively employed the investor sentiment index by Baker and Wurgler (2006) and Baker & Wurgler (2007) to identify different market sentiment periods. However, this index, based on the American market, is unsuitable for our analysis of the Taiwanese market. Wang et al. (2021)’s empirical findings suggest that the consumer confidence index effectively reflects investor sentiment. Thus, our study adopts the monthly Consumer Confidence Index as our sentiment measure. We are grateful for an anonymous reviewer’s recommendation.

Additional information

Funding

This work was supported by the National Science and Technology Council under Grant NSTC 110-2410-H-224-005.

Notes on contributors

Wen-Rang Liu

Dr. Wen-Rang Liu is an Assistant Professor in the Department of Finance at the National Yunlin University of Science and Technology, Taiwan. His primary research interests lie in empirical asset pricing, derivatives markets, and investment strategies.