6. Additional Tests
6.4 Developed vs. Developing Countries
[Insert Table 7 Here]
We compare in this specification the impact of our risk factors on the renewable-energy stock returns for a sub-sample of developed countries (Table 7, Model 7) versus a sub-sample of developing countries (Table 7, Model 8). We try in this part to have more insight about the differences in behavior of our country- and firm-risk factors in different economies. We base our classification of developed and developing countries on the World Economic Situation and Prospects 2017 published by the United Nations in 2017 (Table A2).
From Table 7, we note that in general that our risk factors have a similar impact on clean-energy stock returns in both developed and developing countries. Moreover, the results in this specification are consistent with our findings in Model (3).
In both models (7) and (8) in Table 7, changes in firm size and in market-to-book ratio are significant and positively influence with renewable-energy stock returns. The changes in debt are
significant for developed countries and have a negative impact and insignificant for the developing countries’ firms. The market excess return coefficient is also still positive and almost equal to 1.
We conclude then that the clean-energy sector is as risky as the market for both developing and developed economies. The oil price changes are positively correlated to the renewable-energy returns for both country classifications. Furthermore, developed countries appear to be more affected by oil prices fluctuations than developing countries (𝛽𝑜𝑖𝑙,𝑑𝑒𝑣𝑒𝑙𝑜𝑝𝑒𝑑 = 0.206 𝑣𝑠. 𝛽𝑜𝑖𝑙,𝑑𝑒𝑣𝑒𝑙𝑜𝑝𝑖𝑛𝑔= 0.134). Manufacturing changes appear to have a negative and significant impact on renewable- energy firms in developed countries, and are insignificant for the developing countries. Finally, interest rate changes appear to have positive impact on developed countries’ clean-energy firms and a negative one on the developing countries clean-energy firms.
This indicates that investors in developed countries view interest rate movements as an opportunity to invest more and generate more benefit, whereas investors in developed countries are more careful when dealing with interest rates movements.
Conclusion
For many years, oil and fossil fuels have been the main source of energy worldwide. However, following worldwide economic growth and increases in global energy demands, governments have become increasingly concerned about the limited fossil-fuel resources available to mankind and the harmful greenhouse emissions resulting from their use. This has encouraged various countries, together with investment agencies, to shift their interest towards clean and renewable sources of energy. This situation encouraged us to examine the dynamics and performance of green energy stocks worldwide.
In this study, we have addressed the relationship between alternative-energy stock returns and various risk factors for the period between 2000 and 2015 for companies listed in five main alternative-energy indexes. We followed Boyer & Filion’s (2007) methodology and used a GLS cross-sectional time series linear model to study these relationships, examining the influence of both country-level and firm-level risk factors on stock returns. Some of our findings are in line with the literature, whilst others provide some new insights into the behaviour of clean-energy returns and their determinants.
We document a positive and significant relationship between alternative-energy returns and market
market returns have a strong influence on determining the value of alternative-energy stocks and that the alternative-energy sector is riskier than the market in general. We also find a significant and positive relationship with the size of firms and book-to-market changes risk factors throughout our sample period and for different specifications.
An increase in oil prices seems to have a positive and significant impact on alternative-energy stock returns in some periods, and insignificant in others. This result broadly agrees with the existing body of work dedicated to this issue. On the other hand, natural gas changes seem not to affect renewable-energy stock returns in any of the specifications used in this study. Country-level risk factors also were significant in some periods and insignificant in others, which may be explained by the laws, regulations, and reforms implemented by governments during the studied period.
Finally, we document similar behavior to Inchauspe et al.’s (2015) findings concerning alternative-energy sector abnormal returns, namely the generation of positive excess returns prior to the financial crisis and after 2013, and negative returns in the period between 2007 and 2013. We invoke Bohl et al.’s (2013) and Inchauspe et al.’s (2015) reasoning in explaining this result.
The results of this study complement existing research into the factors that affect alternative-energy stock prices. We examine factors that were addressed in previous studies and add others, in particular a number of firm-specific variables that have not been investigated to date. We believe that our findings will contribute to the understanding of the dynamics of alternative-energy stock prices. However, we recognise that this study has some limitations that should be addressed in future investigations. In particular, we consider that the use of a longer time period would provide a clearer and more reliable assessment of the impact of the variables included in this study. In addition, the inclusion of measures of governmental environment policies and regulations might well help to explain some of the variance in clean-energy stock prices that could not be captured in the models included in this study.
References
Acharya, V., Philippon, T., Richardson, M., & Roubini, N. (2009). The financial crisis of 2007-2009: Causes and remedies. Financial Markets, Institutions & Instruments, 18(2), 89-137.
doi:10.1111/j.1468-0416.2009.00147_2.x
Apergis, N., & Payne, J. E. (2010). Renewable energy consumption and economic growth:
Evidence from a panel of OECD countries. Energy Policy, 38(1), 656-660.
doi:http://dx.doi.org/10.1016/j.enpol.2009.09.002
Banz, R. W. (1981). The relationship between return and market value of common stocks.
Journal of Financial Economics, 9(1), 3-18.
doi:http://dx.doi.org/10.1016/0304-405X(81)90018-0
Barber, B. M., & Lyon, J. D. (1997). Firm size, book-to-market ratio, and security returns: A holdout sample of financial firms. The Journal of Finance, 52(2), 875-883.
doi:10.1111/j.1540-6261.1997.tb04826.x
Beltratti, A., & Stulz, R. M. (2012). The credit crisis around the globe: Why did some banks perform better? Journal of Financial Economics, 105(1), 1-17.
doi:http://dx.doi.org/10.1016/j.jfineco.2011.12.005
Berk, J. B. (1995). A critique of size-related anomalies. The Review of Financial Studies, 8(2), 275-286.
Bhandari, L. C. (1988). Debt/Equity ratio and expected common stock returns: Empirical evidence. The Journal of Finance, 43(2), 507-528. doi:10.2307/2328473
Bleischwitz, R., & Fuhrmann, K. (2006). Introduction to the special issue on ‘hydrogen’ in
‘Energy policy’. Energy Policy, 34(11), 1223-1226.
doi:http://dx.doi.org/10.1016/j.enpol.2005.12.017
Boden, T. A., Marland, G., & Andres, R. J. (2017). Global, regional, and national fossil-fuel CO2 emissions. Oak Ridge, Tenn., U.S.A: Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy.
Bohl, M. T., Kaufmann, P., & Stephan, P. M. (2013). From hero to zero: Evidence of
performance reversal and speculative bubbles in German renewable energy stocks. Energy Economics, 37, 40-51. doi:http://dx.doi.org/10.1016/j.eneco.2013.01.006
Bondia, R., Ghosh, S., & Kanjilal, K. (2016). International crude oil prices and the stock prices of clean energy and technology companies: Evidence from non-linear cointegration tests with unknown structural breaks. Energy, 101, 558-565.
doi:http://dx.doi.org/10.1016/j.energy.2016.02.031
Bouri, E. (2015). Oil volatility shocks and the stock markets of oil-importing MENA economies:
A tale from the financial crisis. Energy Economics, 51, 590-598.
doi:https://doi.org/10.1016/j.eneco.2015.09.002
Boyer, M. M., & Filion, D. (2007). Common and fundamental factors in stock returns of Canadian oil and gas companies. Energy Economics, 29(3), 428-453.
doi:https://doi.org/10.1016/j.eneco.2005.12.003
BP. (2017). BP statistical review of world energy June 2017 (Annual Statistical Review No. 66).
United Kingdom: BP.
Bürer, M. J., & Wüstenhagen, R. (2009). Which renewable energy policy is a venture capitalist's best friend? empirical evidence from a survey of international cleantech investors. Energy Policy, 37(12), 4997-5006. doi:http://dx.doi.org/10.1016/j.enpol.2009.06.071
Chen, N. (1991). Financial investment opportunities and the macroeconomy. The Journal of Finance, 46(2), 529-554. doi:10.2307/2328835
Chiek, A. N., & Akpan, M. N. (2016). Determinants of stock prices during dividend
announcements: An evaluation of firms' variable effects in Nigeria's oil and gas sector.
OPEC Energy Review, 40(1), 69-90. doi:10.1111/opec.12063
Cong, R., Wei, Y., Jiao, J., & Fan, Y. (2008). Relationships between oil price shocks and stock market: An empirical analysis from China. Energy Policy, 36(9), 3544-3553.
doi:http://dx.doi.org/10.1016/j.enpol.2008.06.006
DePersio, G. (2015). Why did oil prices drop so much in 2014?
http://www.investopedia.com/ask/answers/030315/why-did-oil-prices-drop-so-much-2014.asp
Diaz, E. M., Molero, J. C., & Perez de Gracia, F. (2016). Oil price volatility and stock returns in the G7 economies. Energy Economics, 54, 417-430.
doi:http://dx.doi.org/10.1016/j.eneco.2016.01.002
Drew, M. E., & Veeraraghavan, M. (2002). A closer look at the size and value premium in emerging markets: Evidence from the Kuala Lumpur stock exchange. Asian Economic
Duy, N. T., & Phuoc, N. P. H. (2016). The relationship between firm sizes and stock returns of service sector in Ho Chi Minh city stock exchange. Review of European Studies, 8(4), 210-219.
Erkens, D. H., Hung, M., & Matos, P. (2012). Corporate governance in the 2007–2008 financial crisis: Evidence from financial institutions worldwide. Journal of Corporate Finance, 18(2), 389-411. doi:http://dx.doi.org/10.1016/j.jcorpfin.2012.01.005
Faff, R. W., & Brailsford, T. J. (1999). Oil price risk and the Australian stock market. Journal of Energy Finance & Development, 4(1), 69-87.
doi:https://doi.org/10.1016/S1085-7443(99)00005-8
Faff, R. W., & Chan, H. (1998). A multifactor model of gold industry stock returns: Evidence from the Australian equity market. Applied Financial Economics, 8(1), 21-28.
doi:10.1080/096031098333212
Fahlenbrach, R., Prilmeier, R., & Stulz, R. M. (2012). This time is the same: Using bank performance in 1998 to explain bank performance during the recent financial crisis. The Journal of Finance, 67(6), 2139-2185. doi:10.1111/j.1540-6261.2012.01783.x
Fama, E. F., & French, K. R. (1992). The cross-section of expected stock returns. The Journal of Finance, 47(2), 427-465. doi:10.1111/j.1540-6261.1992.tb04398.x
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds.
Journal of Financial Economics, 33(1), 3-56. doi:http://dx.doi.org/10.1016/0304-405X(93)90023-5
Fama, E. F., & French, K. R. (1995). Size and book-to-market factors in earnings and returns.
The Journal of Finance, 50(1), 131-155. doi:10.1111/j.1540-6261.1995.tb05169.x
Farhan, M., & Sharif, S. (2015). Impact of firm size on stock returns at Karachi stock exchange.
Available at SSRN: https://ssrn.com/abstract=2605460
Griffin, J. M. (2002). Are the Fama and French factors global or country specific? The Review of Financial Studies, 15(3), 783-803. Retrieved from http://www.jstor.org/stable/2696721 Gupta, K. (2016). Oil price shocks, competition, and oil & gas stock returns — global evidence.
Energy Economics, 57, 140-153. doi:http://dx.doi.org/10.1016/j.eneco.2016.04.019 Hammoudeh, S., & Li, H. (2005). Oil sensitivity and systematic risk in oil-sensitive stock
indices. Journal of Economics and Business, 57(1), 1-21.
doi:http://dx.doi.org/10.1016/j.jeconbus.2004.08.002
Haushalter, G. D. (2000). Financing policy, basis risk, and corporate hedging: Evidence from oil and gas producers. The Journal of Finance, 55(1), 107-152. Retrieved from
http://www.jstor.org/stable/222552
Henriques, I., & Sadorsky, P. (2001). Multifactor risk and the stock returns of Canadian paper and forest products companies. Forest Policy and Economics, 3(3–4), 199-208.
doi:https://doi.org/10.1016/S1389-9341(01)00064-8
Henriques, I., & Sadorsky, P. (2008). Oil prices and the stock prices of alternative energy companies. Energy Economics, 30(3), 998-1010.
doi:http://dx.doi.org/10.1016/j.eneco.2007.11.001
Inchauspe, J., Ripple, R. D., & Trück, S. (2015). The dynamics of returns on renewable energy companies: A state-space approach. Energy Economics, 48, 325-335.
doi:http://dx.doi.org/10.1016/j.eneco.2014.11.013
International Energy Agency. (2007). World Energy Outlook, IEA, Paris.
Jones, C. M., & Kaul, G. (1996). Oil and the stock markets. The Journal of Finance, 51(2), 463-491. doi:10.2307/2329368
Khoo, A. (1994). Estimation of foreign exchange exposure: An application to mining companies in Australia. Journal of International Money and Finance, 13(3), 342-363.
doi:http://dx.doi.org/10.1016/0261-5606(94)90032-9
Konijn, S. J. J., Kräussl, R., & Lucas, A. (2011). Blockholder dispersion and firm value. Journal of Corporate Finance, 17(5), 1330-1339.
doi:http://dx.doi.org/10.1016/j.jcorpfin.2011.06.005
Kumar, S., Managi, S., & Matsuda, A. (2012). Stock prices of clean energy firms, oil and carbon markets: A vector autoregressive analysis. Energy Economics, 34(1), 215-226.
doi:http://dx.doi.org/10.1016/j.eneco.2011.03.002
Lam, K. S. K. (2002). The relationship between size, book-to-market equity ratio, earnings–price ratio, and return for the Hong Kong stock market. Global Finance Journal, 13(2), 163-179.
doi:http://0-dx.doi.org.mercury.concordia.ca/10.1016/S1044-0283(02)00049-2
Le, T., & Chang, Y. (2015). Effects of oil price shocks on the stock market performance: Do nature of shocks and economies matter? Energy Economics, 51, 261-274.
doi:https://doi.org/10.1016/j.eneco.2015.06.019
Lintner, J. (1965). Security prices, risk, and maximal gains from diversification. The Journal of Finance, 20(4), 587-615. doi:10.2307/2977249
Maghyereh, A. I., Awartani, B., & Bouri, E. (2016). The directional volatility connectedness between crude oil and equity markets: New evidence from implied volatility indexes.
Energy Economics, 57, 78-93. doi:http://dx.doi.org/10.1016/j.eneco.2016.04.010
Malin, M., & Veeraraghavan, M. (2004). On the robustness of the Fama and French multifactor model: Evidence from France, Germany, and the United Kingdom. International Journal of Business and Economics, 3(2), 155-176.
Managi, S., & Okimoto, T. (2013). Does the price of oil interact with clean energy prices in the stock market? Japan and the World Economy, 27, 1-9.
doi:http://dx.doi.org/10.1016/j.japwor.2013.03.003
Maroney, N., & Protopapadakis, A. (2002). The book-to-market and size effects in a general asset pricing model: Evidence from seven national markets. European Finance Review, 6(2), 189-221.
McConnell, J. J., & Muscarella, C. J. (1985). Corporate capital expenditure decisions and the market value of the firm. Journal of Financial Economics, 14(3), 399-422.
doi:http://dx.doi.org/10.1016/0304-405X(85)90006-6
McCrone, A., Moslener, U., D’Estais, F., & Grüning, C. (2017). Global trends in renewable energy investment (Global Trends Reports. Germany: UN Environment, the Frankfurt School-UNEP Collaborating Centre, and Bloomberg New Energy Finance.
McDowall, W., & Eames, M. (2006). Forecasts, scenarios, visions, backcasts and roadmaps to the hydrogen economy: A review of the hydrogen futures literature. Energy Policy, 34(11), 1236-1250. doi:http://dx.doi.org/10.1016/j.enpol.2005.12.006
Merton, R. C. (1973). An intertemporal capital asset pricing model. Econometrica, 41(5), 867-887. doi:10.2307/1913811
Morck, R., Shleifer, A., & Vishny, R. W. (1988). Management ownership and market valuation:
An empirical analysis. Journal of Financial Economics, 20, 293-315.
Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica, 34(4), 768-783.
doi:10.2307/1910098
Organization of the Petroleum Exporting Countries (OPEC). (2017). OPEC Annual Statistical Bulletin (Annual Statistical Report No. 52). Austria: OPEC.
Park, J., & Ratti, R. A. (2008). Oil price shocks and stock markets in the U.S. and 13 European countries. Energy Economics, 30(5), 2587-2608.
doi:http://dx.doi.org/10.1016/j.eneco.2008.04.003
Patell, J. M. (1976). Corporate forecasts of earnings per share and stock price behavior:
Empirical test. Journal of Accounting Research, 14(2), 246-276. doi:10.2307/2490543 Reboredo, J. C. (2015). Is there dependence and systemic risk between oil and renewable energy
stock prices? Energy Economics, 48, 32-45.
doi:http://dx.doi.org/10.1016/j.eneco.2014.12.009
Reboredo, J. C., & Ugolini, A. (2016). Quantile dependence of oil price movements and stock returns. Energy Economics, 54, 33-49. doi:https://doi.org/10.1016/j.eneco.2015.11.015 Reinganum, M. R. (1983). The anomalous stock market behavior of small firms in January.
Journal of Financial Economics, 12(1), 89-104.
doi:http://dx.doi.org/10.1016/0304-405X(83)90029-6
Roll, R. (1981). A possible explanation of the small firm effect. The Journal of Finance, 36(4), 879-888. doi:10.2307/2327553
Sadorsky, P. (1999). Oil price shocks and stock market activity. Energy Economics, 21(5), 449-469. doi:http://dx.doi.org/10.1016/S0140-9883(99)00020-1
Sadorsky, P. (2001). Risk factors in stock returns of Canadian oil and gas companies. Energy Economics, 23(1), 17-28. doi:https://doi.org/10.1016/S0140-9883(00)00072-4
Sadorsky, P. (2009a). Renewable energy consumption and income in emerging economies.
Energy Policy, 37(10), 4021-4028. doi:http://dx.doi.org/10.1016/j.enpol.2009.05.003 Sadorsky, P. (2009b). Renewable energy consumption, CO2 emissions and oil prices in the G7
countries. Energy Economics, 31(3), 456-462.
doi:http://dx.doi.org/10.1016/j.eneco.2008.12.010
Sadorsky, P. (2012). Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies. Energy Economics, 34(1), 248-255.
doi:http://dx.doi.org/10.1016/j.eneco.2011.03.006
Sari, R., Ewing, B. T., & Soytas, U. (2008). The relationship between disaggregate energy consumption and industrial production in the United States: An ARDL approach. Energy Economics, 30(5), 2302-2313. doi:http://dx.doi.org/10.1016/j.eneco.2007.10.002
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425-442. doi:10.2307/2977928
Singh, M. (2017). The 2007-08 financial crisis in review.
http://www.investopedia.com/articles/economics/09/financial-crisis-review.asp Walker, T. J., Lopatta, K., & Kaspereit, T. (2014). Corporate sustainability in asset pricing
models and mutual funds performance measurement. Financial Markets and Portfolio Management, 28(4), 363-407.
Wen, X., Guo, Y., Wei, Y., & Huang, D. (2014). How do the stock prices of new energy and fossil fuel companies correlate? evidence from china. Energy Economics, 41, 63-75.
doi:http://dx.doi.org/10.1016/j.eneco.2013.10.018
Zhu, H., Guo, Y., You, W., & Xu, Y. (2016). The heterogeneity dependence between crude oil price changes and industry stock market returns in china: Evidence from a quantile
regression approach. Energy Economics, 55, 30-41.
doi:https://doi.org/10.1016/j.eneco.2015.12.027
Tables and Figures Tables
Table 1: Data Collection
We report in this table our methodology for constructing our sample. We started by collecting the constituents of five main alternative-energy indexes, which provided us with 324 companies. Second, we deleted 115 duplicate firms that were listed in two or more indexes to keep only one observation for each firm. Finally, we deleted 23 companies for which data were not available on Compustat. Our final sample was composed of 186 clean-energy companies.
Firms Alternative Energy Companies from Bloomberg 324 Less
Duplicate Firms 115
Firms with No Data in Compustat 23
TOTAL 186
Table 2: Description of Variables
This table presents a brief description of the variables used in this study in addition to the sources used to obtain data for each variable.
Variable Source Description
Excess Return Bloomberg Excess return of a firm i over the one month US T-bill rate.
Market Excess Return
Bloomberg Market return for country j index over the one month US T-bill rate.
Total Assets Compustat Total assets of firm i which represents the firm size Capital
Expenditures
Compustat Capital expenditures of firm i which represents its investment opportunities.
Market-To-Book Ratio
Compustat Market to book value of firm i.
Long-Term Debt
Compustat Long term debt of firm i which represents the firm leverage.
Earnings Per Share
Compustat Earnings per share of firm i.
Capital Intensity
Compustat Capital Intensity of firm i defined as the ratio of total property plant and equipment to the total assets.
Oil Price Bloomberg Oil Prices Natural Gas
Price
Bloomberg Natural Gas Prices
Percentage of Generation
U.S. Energy Information Administration (EIA)
Percentage of energy generated using renewable sources of country j.
GDP Per Capita
World Bank Gross Domestic Product per Capita of county j.
Inflation Rate World Bank Inflation rate of country j.
Manufacturing World Bank The percentage of value added to the country j’s GDP from the manufacturing industries.
Pollution World Bank Mean annual concentration of PM2.5 in country j’s air.
Interest Rate World Bank Interest rate of country j.
Table 3: Summary Statistics
We report in this table the summary statistics for our variables before calculating the changes, and before (Panel A) and after winsorizing (Panel B) each one at the 5th and 95th percentiles to reduce the influence of outliers. Looking at Panel B, we notice that the excess yearly return for the alternative-energy companies in our sample has a mean of 18.2% and a median of 4.5%, which may indicate that most of the companies in this sample have a positive excess return during the period studied. The market excess return varies has a mean of 0.84% and a median of -0.01%, which indicates that most of the economies used in this study generated positive returns on average between 2000 and 2015. Also, the market appears to be less volatile (standard deviation = 5.24%) compared to the alternative energy firms (standard deviation = 67.9%).
Panel A
Variables N. Obs. Mean Standard
Deviation
Minimum Median Maximum
Excess Return (%) 2023 18.204 67.909 -69.396 4.496 201.269
Panel B
Variables N.
Obs.
Mean Standard
Deviation
Minimum Median Maximum
Excess Return (%) 2023 18.204 67.909 -69.396 4.496 201.269
2334 35558.1 16364.29 3471.25 39677.2 56115.72
Inflation Rate (%) 2334 2.092 1.536 -0.653 2.069 5.263
Manufacturing (%) 2159 16.119 7.824 1.189 13.517 36.927
Pollution 2139 17.111 15.475 7.2 10.44 56.48
Interest Rate (%) 1966 2.999 2.209 -1.061 2.840 8.236
Table 4: Correlation Matrix
We report in this table the Pearson/Spearman correlation coefficients between each risk factor and the renewable-energy stock excess return, and between each pair of risk factors.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Excess Return 1
Market Excess Return 2 0.142
Oil Price Change 3 0.269 0.052
Natural Price Change 4 0.127 -0.040 0.480
Total Assets Change 5 0.220 0.015 0.059 -0.051
Capital Expenditures Change 6 0.064 0.002 -0.025 -0.092 0.461 Market-To-Book Ratio Change 7 0.691 0.118 0.221 0.118 0.105 0.004
Long-Term Debt Change 8 -0.009 0.036 -0.047 -0.095 0.319 0.164 -0.024 Earnings Per Share Change 9 0.012 0.018 -0.076 -0.071 0.072 0.083 -0.031 -0.002 Capital Intensity Change 10 -0.081 0.062 0.066 -0.038 -0.080 0.301 -0.070 0.118 -0.023 Percentage of Generation Change 11 -0.105 -0.023 -0.069 -0.094 -0.073 -0.061 -0.133 0.029 -0.016 0.054
GDP Per Capita Change 12 0.052 0.116 0.058 -0.012 0.224 0.174 0.027 0.105 0.052 0.135 -0.093 Inflation Rate Change 13 -0.091 -0.030 -0.211 0.018 -0.035 0.035 -0.089 -0.019 0.027 0.027 0.039 0.091
Manufacturing Change 14 0.013 -0.034 0.006 0.016 0.102 0.098 0.011 0.018 0.018 -0.012 0.039 0.156 -0.112 Pollution Change 15 0.023 0.014 -0.133 0.140 -0.021 0.040 0.026 0.008 0.043 -0.008 0.090 0.029 0.179 0.087 Interest Rate Change 16 -0.016 -0.057 0.028 0.108 -0.003 0.046 -0.029 0.033 -0.066 0.014 -0.135 -0.035 0.076 0.098 -0.199
Table 5: Summary of Regression Results for Models (1), (2), and (3)
This table presents the results for our main regressions models. In model (1), we study the effects of country-related risk factors on the excess returns of renewable-energy firms. In model (2), we investigate the effect of firm-related risk factors on the returns.
Finally, model (3) combines both country and firm risk factors to quantify the risk of all risk factors on clean-energy returns, as a robustness test.
Dependent Variable: Excess Return Country Specific Factors
(1)
Firm Specific Factors (2) Country and Firm Factors Combined (3) Market Excess Return 2.242***
(0.000)
1.062***
(0.000)
0.994***
(0.000)
Total Assets Change 0.432***
(0.000)
Long-Term Debt Change -0.039**
(0.025)
-0.022 (0.190)
Earnings Per Share Change 0.002
(0.747)
0.010 (0.283)
Capital Intensity Change 0.023
(0.772)
-0.061 (0.612)
Oil Price Change 0.481***
(0.000)
0.219***
(0.000) Natural Price Change 0.005
(0.562) GDP Per Capita Change -0.781*
(0.562) GDP Per Capita Change -0.781*