Idiosyncratic risk and stock price crash risk: The moderating role of discretionary income smoothing

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Given the growing significance of the capital market, investors tend to steer clear of stock price crashes. This study aims to examine how idiosyncratic risk affects the likelihood of a stock price crash and how discretionary income smoothing affects the relationship between them. This study uses a data panel to empirically examine the hypothesis. This study uses a data panel to empirically examine the hypothesis, using 1,203 firm-year observations from non-financial companies publicly traded on the Indonesia Stock Exchange from 2019 to 2021. The results show that firms with greater idiosyncratic risk do not significantly generate higher stock price crash risk. Nevertheless, this study also discovered that managing discretionary income smoothing is essential to increasing the risk of crashes. The test shows that the coefficient of discretionary income smoothing is 0.153 and significant with a t-value of 2.104. Moreover, the investigations also indicate that greater use of discretionary income smoothing can amplify the impact of idiosyncratic risk on the likelihood of stock price crashes. This is shown from the results where the moderation of the two variables has a positive coefficient of 0.087 and is significant at 10% with a t-value of 1.446. Based on the findings, this study concludes that the presence of idiosyncratic risk by itself may not substantially impact the probability of stock market crashes. However, combined with discretionary income smoothing, it can worsen the potential negative consequences. It implies that how a firm reports its income can affect its susceptibility to stock price crashes.

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    • Table 1. Descriptive statistics
    • Table 2. Effect of idiosyncratic risk and discretionary income smoothing on stock price crash risk
    • Table 3. Additional test: DUVOL as stock crash measurement
    • Table 4. Robustness test: company size and profitability
    • Table 5. Robustness test: binary logistic regression using CRASH
    • Conceptualization
      Jeanice Cecilia Setiawan, Felizia Arni Rudiawarni, Dedhy Sulistiawan, Valentin Radu
    • Data curation
      Jeanice Cecilia Setiawan
    • Formal Analysis
      Jeanice Cecilia Setiawan, Felizia Arni Rudiawarni, Dedhy Sulistiawan, Valentin Radu
    • Investigation
      Jeanice Cecilia Setiawan
    • Methodology
      Jeanice Cecilia Setiawan, Felizia Arni Rudiawarni, Dedhy Sulistiawan
    • Writing – original draft
      Jeanice Cecilia Setiawan
    • Project administration
      Felizia Arni Rudiawarni
    • Supervision
      Felizia Arni Rudiawarni, Dedhy Sulistiawan
    • Validation
      Felizia Arni Rudiawarni, Dedhy Sulistiawan, Valentin Radu
    • Writing – review & editing
      Felizia Arni Rudiawarni, Dedhy Sulistiawan, Valentin Radu
    • Visualization
      Dedhy Sulistiawan