Is cash flow growth helping stock performance during the COVID-19 outbreak? Evidence from Indonesia

  • Received December 15, 2021;
    Accepted February 22, 2022;
    Published March 18, 2022
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/imfi.19(1).2022.19
  • Article Info
    Volume 19 2022, Issue #1, pp. 247-261
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This work is licensed under a Creative Commons Attribution 4.0 International License

The COVID-19 pandemic is an unexpected event that causes stock market investors to panic so that their value drops drastically. Operating cash flow and free cash flow are indicators of a company’s financial statements that are used as a reference for investors’ decision making in the stock market. A firm’s cash flows reflect real changes in the firm’s value for money. Cash flow growth can provide information on how well the firm’s performance is in generating incremental cash inflows that can increase firm value. This study aims to explore the relationship between cash flow growth before the COVID-19 pandemic and after the COVID-19 outbreak on stock price performance. This study uses the OLS regression method with a total sample of 426 companies in the Indonesian capital market in the period March 2, 2020 to March 2, 2021. The results show that cash flow growth from operations and free cash flow growth had no significant effect on stock return after COVID-19 outbreaks in years 2020 to 2021. Sales growth, market capitalization and stock return before the COVID-19 outbreak from 2019 to 2020 had a significant negative correlation with the post COVID-19 outbreak stock return. Then, sectors whose stock performance is positively correlated after the COVID-19 outbreak are basic industry, chemicals, miscellaneous industry and infrastructure. This shows that the economic crisis caused by COVID-19 is an anomaly in the stock market. Therefore, cash flow is not relevant information for investors in predicting a company’s performance during the COVID-19 pandemic crisis.

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    • Figure 1. Research framework
    • Table 1. Sample selection criteria
    • Table 2. Variable operations
    • Table 3. Descriptive statistics
    • Table 4. Regression model 1 results
    • Table 5. Regression model 2 results
    • Table 6. Sectoral returns before and after the COVID-19 outbreak
    • Conceptualization
      Meliana Meliana, Hyacynthia Kesuma, Arief Rijanto
    • Data curation
      Meliana Meliana, Hyacynthia Kesuma, Desy Enjelina, Arief Rijanto, Dewi Savitri Saraswati
    • Formal Analysis
      Meliana Meliana, Arief Rijanto
    • Investigation
      Meliana Meliana, Arief Rijanto, Dewi Savitri Saraswati
    • Methodology
      Meliana Meliana, Arief Rijanto
    • Project administration
      Meliana Meliana, Hyacynthia Kesuma
    • Resources
      Meliana Meliana, Hyacynthia Kesuma, Desy Enjelina, Arief Rijanto
    • Software
      Meliana Meliana
    • Validation
      Meliana Meliana, Arief Rijanto, Dewi Savitri Saraswati
    • Visualization
      Meliana Meliana, Arief Rijanto
    • Writing – original draft
      Meliana Meliana, Hyacynthia Kesuma, Desy Enjelina, Arief Rijanto
    • Funding acquisition
      Arief Rijanto
    • Supervision
      Arief Rijanto, Dewi Savitri Saraswati
    • Writing – review & editing
      Arief Rijanto