Investor sentiment measurement based on technical analysis indicators affecting stock returns: Empirical evidence on VN100

  • Received October 18, 2021;
    Accepted December 1, 2021;
    Published December 3, 2021
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/imfi.18(4).2021.25
  • Article Info
    Volume 18 2021, Issue #4, pp. 297-308
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This work is licensed under a Creative Commons Attribution 4.0 International License

The purpose of this study is to examine whether investor sentiment as measured by technical analysis indicators has an impact on stock returns. The research period is from 2015 to mid-2020. 1-year government bond yields, financial data, transaction data of 57 companies in the VN100 basket, and VNIndex are analyzed. The investor sentiment variable is measured by each technical analysis indicator (Relative Strength Index – RSI, Psychological Line Index – PLI), and the general sentiment variable is established based on extracting the principal component from individual indicators. The paper uses two regression methods – Fama-MacBeth and Generalized Least Square (GLS) – for five different research models. The results show that sentiment plays an important role in stock returns in the Vietnamese stock market. Even controlling the factors such as cash flow per share, firm size, market risk premium, and stock price volatility in the studied models, the impact of sentiment is significant in both the model using individual technical indicators and the model using the general sentiment variable. Furthermore, investor sentiment has a stronger power to explain excess stock returns than their trading behavior. The implication from the results shows that the Vietnamese stock market is inefficient, in which psychology is a very important issue and participants need to pay due attention to this factor.

Acknowledgment
This study was funded by the Industrial University of Ho Chi Minh City (IUH), Vietnam (grant number: 21/1TCNH03).

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    • Table 1. Descriptive statistics
    • Table 2. Correlation results between pairs of variables
    • Table 3. Principal component analysis
    • Table 4. Regression results according to the Fama-MacBeth method
    • Table 5. Regression results according to GLS
    • Conceptualization
      Lai Cao Mai Phuong, Vu Cam Nhung
    • Data curation
      Lai Cao Mai Phuong, Vu Cam Nhung
    • Formal Analysis
      Lai Cao Mai Phuong
    • Funding acquisition
      Lai Cao Mai Phuong, Vu Cam Nhung
    • Methodology
      Lai Cao Mai Phuong
    • Validation
      Lai Cao Mai Phuong
    • Writing – original draft
      Lai Cao Mai Phuong
    • Writing – review & editing
      Lai Cao Mai Phuong
    • Resources
      Lai Cao Mai Phuong, Vu Cam Nhung