The moderating role of FSIS and IO on FCRE-stock liquidity nexus
| Amihud | HLS | Amihud | HLS | |
|---|---|---|---|---|
| Variables | (1) | (2) | (3) | (4) |
| FCRE | −0.0017*** | −0.0004*** | −0.0018*** | −0.0002* |
| (−16.59) | (−2.68) | (−13.98) | (−1.75) | |
| FSIS | 0.0028*** | 0.0011** | ||
| (21.58) | (2.07) | |||
| IO | 0.0148*** | 0.0013*** | ||
| (7.48) | (4.20) | |||
| FCRE*FSIS | −0.0003*** | −0.0002*** | ||
| (−6.83) | (−9.37) | |||
| FCRE*IO | 0.0144*** | 0.0009*** | ||
| (6.71) | (9.04) | |||
| Constant | −0.0642*** | −0.0227*** | −0.0638*** | −0.0229*** |
| (−17.86) | (−6.16) | (−17.05) | (−6.38) | |
| Observations | 9,320 | 9,320 | 9,320 | 9,320 |
| Baseline controls | Yes | Yes | Yes | Yes |
| Year-fixed effects | Yes | Yes | Yes | Yes |
| Industry-fixed effects | Yes | Yes | Yes | Yes |
| Adj. R-squared | 0.520 | 0.479 | 0.498 | 0.477 |
| Amihud | HLS | Amihud | HLS | |
|---|---|---|---|---|
| Variables | (1) | (2) | (3) | (4) |
| FCRE | −0.0017*** | −0.0004*** | −0.0018*** | −0.0002* |
| (−16.59) | (−2.68) | (−13.98) | (−1.75) | |
| FSIS | 0.0028*** | 0.0011** | ||
| (21.58) | (2.07) | |||
| IO | 0.0148*** | 0.0013*** | ||
| (7.48) | (4.20) | |||
| FCRE*FSIS | −0.0003*** | −0.0002*** | ||
| (−6.83) | (−9.37) | |||
| FCRE*IO | 0.0144*** | 0.0009*** | ||
| (6.71) | (9.04) | |||
| Constant | −0.0642*** | −0.0227*** | −0.0638*** | −0.0229*** |
| (−17.86) | (−6.16) | (−17.05) | (−6.38) | |
| Observations | 9,320 | 9,320 | 9,320 | 9,320 |
| Baseline controls | Yes | Yes | Yes | Yes |
| Year-fixed effects | Yes | Yes | Yes | Yes |
| Industry-fixed effects | Yes | Yes | Yes | Yes |
| Adj. | 0.520 | 0.479 | 0.498 | 0.477 |
Note(s): This table reports the panel fixed effect regression results of the moderating effect of FSIS (firm-specific investor sentiment as per Aboody et al. (2018) measure) and IO (institutional ownership) on the nexus between FCRE (firm-level climate risk exposure based on textual analysis) and two stock liquidity proxies such as Amihud (opposite of Amihud’s (2002) illiquidity measure) and HLS (opposite of high low spread). We control the baseline model control variables, industry (2-digit NIC code), and year-fixed effects. Table A1 describes all the variables in detail. The sample consists of 9,320 firm-year observations from FY 2003–2004 to 2022–2023. t-statistics are reported in parentheses. ***, ** and * denote statistical significance at the 1, 5 and 10% levels, respectively
Source(s): Table by authors
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