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Twitter carbon information and cost of equity: the moderating role of environmental performance

Abstract

This study aims to examine the moderating role of a firm’s environmental performance, measured by its environmental strength and concern ratings, on the influences of Twitter dissemination of carbon-related information (Carbon_Tweets) on a firm’s cost of equity (COE). Our key focus is to provide an insight as to whether different levels of environmental strength and concern would influence the effect of Carbon_Tweets on the COE. Employing the sample of non-financial NASDAQ firms covering the period between 2009 and 2015, we found that the negative association of Carbon_Tweets and COE is strengthened for firms that have higher levels of environmental concerns, meanwhile the results stay the same for different level of environmental strength. These findings imply that although all firms can achieve lower COE by employing Twitter as a dissemination channel of Carbon information, firms with a concerning environmental status may benefit more by strategically disseminating via Twitter.

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Notes

  1. Firms are considered technological if SIC equals to 3570–3579, 3610–3699, 7370–7379, 3810–3849, 4800–4899, 4931, 4941, which take a value of 1 or 0 otherwise.

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Acknowledgements

This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Project No. GRANT419].

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Correspondence to Mohammed S. Albarrak.

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Appendices

Appendix 1: The measurements of Cost of Equity (COE)

COE estimates Formula
\(R_{OJ}\)
Ohlson and Juettner-Nauroth (2005)
\(R_{OJN} = A + \sqrt[ ]{{A^{2} + \left( {\frac{{E_{t} \left( {FEPS_{t + 1} } \right)}}{{P_{t}^{*} }}} \right) \left( {g_{2} - g_{lt} } \right)}}\)
\(A = 0.5 \left( {g_{lt} + \frac{{FDPS_{t + 1} }}{{P_{t}^{*} }}} \right)\)
FEPSt+1 = The median forecasted earnings per share for June next year
FDPSt+1 = Forecasted dividend per share for the next year or 6% of return on assets (ROA)
\(g_{2}\) = Growth rate from the long-term consensus analysts’ earnings forecasted (LTG) or the growth rate of short-term earnings (FEPSt+2/FEPSt+1 − 1)
\(g_{lt}\) = 10-year treasury bonds yield minus 3%
The model demands positive FEPSt+1 and FEPSt+2
\(R_{MPEG}\)
Modified Easton (2004) cost of equity model
\(P_{t} = \frac{{E_{t} \left( {FEPS_{t + 1} } \right)}}{{R_{MPEG} }} + \frac{{E_{t} \left( {FEPS_{t + 1} } \right)E_{t} \left[ {g_{st} - R_{MPEG} \times \left( {1 + {\text{FDIV}}} \right)} \right]}}{{R_{MPEG}^{2} }}\)
Pt = Share price in June
FEPS = The median value of forecasted future earnings per share
FDIV = Future dividend pay-outs ratio which equal to \(\left( {\frac{{\text{Dividend per share}}}{{\text{Earnings per share}}}} \right)\)
The model assumes FEPS to be positive and if FEPS is negative, we measure FDIV as 6% of ROA
\(R_{CT}\)
Claus and Thomas (2001)
\(P_{t}^{*} = B_{t} + \mathop \sum \limits_{i = 1}^{5} \frac{{[FEPS_{t + i} - R_{CT} { } \times B_{t + i - 1} ]{ }}}{{\left( {1 + R_{CT} } \right)^{i} }} + \frac{{\left[ {FEPS_{t + 5} - R_{CT} \times B_{t + 4} } \right]{ } \times \left( {1 + g_{lt} } \right){ }}}{{\left( {R_{CT} - g_{lt} } \right)\left( {1 + R_{CT} } \right)^{5} }}\) The model uses forecasted earnings per share (FEPS) by analysts for the first three years to measure the COE. The 4th and 5th forecasted earnings per share years are measured by multiplying the previous year forecast earnings per share by long term earnings growth rate (LTG). In case the LTG rate is missing, the growth rate of FEPS2 and FEPS3 is used. The glt in the model is measured as 10 years Treasury bonds minus 3%. The model uses clean surplus to measure future book value (Bt+i-1 = Bt + FEPSt+1—DPSt+1). The future dividend (DPSt+1) in the model is measured by multiplying FEPS by dividend pay-out ratio (FDIV)
\(R_{RGLS}\)
Gebhardt et al. (2001)
\(P_{t}^{*} = B_{t} + \mathop \sum \limits_{i = 1}^{T - 1} \frac{{\left[ {FROE_{t + i} - R_{GLS} } \right]{ } \times B_{t + i - 1} { }}}{{\left( {1 + R_{GLS} } \right)^{i} }} + \frac{{\left[ {FROE_{t + T} - R_{GLS} } \right]{ } \times { }B_{t + T - 1} { }}}{{\left( {1 + R_{GLS} } \right)^{T - 1} R_{GLS} }}\)
The model uses analyst forecast of return on equity (FROE) of the first 3 years to measure COE. Afterward, FROE is measured by using linter interpolation technique of previous ten years of industry specific FROE. In case the industrial FROE is lower than the risk-free (Rf) rate, we use Rf rate instead of industry FROE (Liu et al., 2002). After the 12 year, the model assumes industry FROE to stay constant. The model also uses accounting clean surplus to measure future book values ((Bt+i-1 = Bt + FEPSt+1—DPSt+1) where DPSt+1 = FEPSt+1 × FDIV
\(COE\) The arithmetic mean of four implied cost of equity measures (ROJ, RMPEG, RCT and RGLS)- risk-free

Appendix 2: Variables definition and measurements

Variable Definition Measurement
COE The implied cost of equity The mean value of four implied cost of equity measures (ROJ, RMPEG, RCT and RGLS)
Carbon_Tweets Firm's carbon-related Tweets The natural logarithm of the total number of carbon-related tweets
iCarbon_Retweet Firm's carbon-related Tweets that are retweeted The natural logarithm of the total number of carbon-related tweets that are retweeted
ENV_STR Environmental strength The number of firm’s environmental strength rating at the year
ENV_CON Environmental concern The number of firm’s environmental concern rating at the year
SIZE Firm size Natural logarithm of firm’s total assets
LEV Financial leverage The ratio of debt to market equity value
DISP Analysts' forecast dispersion The standard deviation of one-year consensus earnings per share forecast (FEPS1)
BETA Firm beta coefficient Firm’s beta coefficient using market model of 60 with at least 24 months stock and market return
LTG The long term consensus growth forecast The natural logarithm of the mean of long-term growth rate of earnings forecast or FEPS2 minus FEPS1 divided by one year ahead average FEPS1&2
CD_NEWS News coverage Natural logarithm of total number of news articles that relate to carbon information
INST_OWN Percentage of institutional ownership The percentage of shares owned by institutions investors
BOD_IND The percentage of independent directors The percentage of independent directors in the board of directors
CSR_COMMITTEE Environmental Committee Dummy variable of whether a firm has an environmental committee
SALES_GROWTH Growth in Sales The change in sales from previous year scaled by previous year total sales
LOSS Losing firm Dummy variable of a firm reports negative earnings during the year
R&D Research and development The ratio of research and development expenses to total assets
MMT Price momentum The compounded rate of return of the last 12 months
MTB Market to book ratio Market to book value ratio
BOARD_SIZE Board size The natural logarithm of the number of board of directors members
CAPX Capital expenditure Total capital expenditure divided by total revenue
AGE Firm age The number of years since firms are listed in CRSP
SURP Earnings surprise Firm’s Consensus earnings forecast minus current earnings divided by share price

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Albarrak, M.S., Cao, N.D., Salama, A. et al. Twitter carbon information and cost of equity: the moderating role of environmental performance. Eurasian Bus Rev (2022). https://doi.org/10.1007/s40821-022-00225-0

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Keywords

  • Social media
  • Twitter
  • Climate change: carbon emission
  • Environmental performance
  • Cost of equity