Appendix. Table A.1 (Part A) The Author(s) 2015 G. Chakrabarti and C. Sen, Green Investing, SpringerBriefs in Finance, DOI /

Similar documents
BEcon Program, Faculty of Economics, Chulalongkorn University Page 1/7

Appendixes Appendix 1 Data of Dependent Variables and Independent Variables Period

Economics 442 Macroeconomic Policy (Spring 2015) 3/23/2015. Instructor: Prof. Menzie Chinn UW Madison

Notes on the Treasury Yield Curve Forecasts. October Kara Naccarelli

Brief Sketch of Solutions: Tutorial 2. 2) graphs. 3) unit root tests

Brief Sketch of Solutions: Tutorial 1. 2) descriptive statistics and correlogram. Series: LGCSI Sample 12/31/ /11/2009 Observations 2596

Hasil Common Effect Model

Donald Trump's Random Walk Up Wall Street

Export and Import Regressions on 2009Q1 preliminary release data Menzie Chinn, 23 June 2009 ( )

Lampiran 1 : Grafik Data HIV Asli

Openness and Inflation

LAMPIRAN. Null Hypothesis: LO has a unit root Exogenous: Constant Lag Length: 1 (Automatic based on SIC, MAXLAG=13)

Financial Econometrics: Problem Set # 3 Solutions

LAMPIRAN PERHITUNGAN EVIEWS

Methods for A Time Series Approach to Estimating Excess Mortality Rates in Puerto Rico, Post Maria 1 Menzie Chinn 2 August 10, 2018 Procedure:

Lampiran 1. Data PDB, Pengeluaran Pemerintah, jumlah uang beredar, pajak, dan tingkat suku bunga

CHAPTER IV DATA COLLECTION AND ANALYSIS

Monetary Economics Portfolios Risk and Returns Diversification and Risk Factors Gerald P. Dwyer Fall 2015

FIN 533. Autocorrelations of CPI Inflation

The Relationship Between Internet Marketing, Search Volume, and Product Sales. Honors Research Thesis

Supplementary Materials for

Santi Chaisrisawatsuk 16 November 2017 Thimpu, Bhutan

LAMPIRAN 1. Retribusi (ribu Rp)

Appendices. Appendix 1 Buy ranges for each portfolio

Lampiran 1. Data Penelitian

Research on the Influencing Factors of Personal Credit Based on a Risk Management Model in the Background of Big Data

Kabupaten Langkat Suku Bunga Kredit. PDRB harga berlaku

Lampiran 1. Data Penelitian

UJI COMMON EFFECT MODEL

Lampiran I Data. PDRB (Juta Rupiah) PMA (Juta Rupiah) PMDN (Juta Rupiah) Tahun. Luas Sawit (ha)

Fall 2004 Social Sciences 7418 University of Wisconsin-Madison Problem Set 5 Answers

FBBABLLR1CBQ_US Commercial Banks: Assets - Bank Credit - Loans and Leases - Residential Real Estate (Bil, $, SA)

LAMPIRAN. A. Data. PAD (juta) INVESTASI (%) PDRB (juta) Kulon Progo. Bantul. Gunung Kidul. Sleman

Lampiran 1. Tabulasi Data

Photovoltaic deployment: from subsidies to a market-driven growth: A panel econometrics approach

Back from the Dead: the GFC and the Resurrection of Long Term Unemployment

Lampiran 1 Lampiran 1 Data Keuangan Bank konvensional

esia/perkembangan/

LAMPIRAN-LAMPIRAN. A. Perhitungan Return On Asset

Solution to Exercise E5.

POLYTECHNIC OF NAMIBIA SCHOOL OF MANAGEMENT SCIENCES DEPARTMENT OF ACCOUNTING, ECONOMICS AND FINANCE ECONOMETRICS. Mr.

LAMPIRAN. Lampiran 1. Wilayah Tahun PAD JOW PDRB JH JR Yogyakarta

DATA PENELITIAN. Pendapatan Nasional (PDB Perkapita atas Dasar Harga Berlaku) Produksi Bawang Merah Indonesia MB X1 X2 X3 X4 X5 X6

Employment growth and Unemployment rate reduction: Historical experiences and future labour market outcomes

Business Survey and Short-Term Projection

Economy May Wake Up Without Consumers Prodding? Chart 1

Analysis of the Influence of the Annualized Rate of Rentability on the Unit Value of the Net Assets of the Private Administered Pension Fund NN

LAMPIRAN. Tahun Bulan NPF (Milyar Rupiah)

An Empirical Analysis of Labour Force Participation of Married Women in Adamawa State, Nigeria

1. A test of the theory is the regression, since no arbitrage implies, Under the null: a = 0, b =1, and the error e or u is unpredictable.

COTTON: PHYSICAL PRICES BECOMING MORE RESPONSIVE TO FUTURES PRICES0F

Social Network Analysis to Optimize Tax Enforcement Effort

23571 Introductory Econometrics Assignment B (Spring 2017)

DATA VARIABEL PENELITIAN

Financial Risk, Liquidity Risk and their Effect on the Listed Jordanian Islamic Bank's Performance

THE IMPACT OF BANKING RISKS ON THE CAPITAL OF COMMERCIAL BANKS IN LIBYA

Per Capita Housing Starts: Forecasting and the Effects of Interest Rate

Muhammad Nasir SHARIF 1 Kashif HAMID 2 Muhammad Usman KHURRAM 3 Muhammad ZULFIQAR 4 1

Chapter-3. Sectoral Composition of Economic Growth and its Major Trends in India

Forecasting the Philippine Stock Exchange Index using Time Series Analysis Box-Jenkins

Journal of Chemical and Pharmaceutical Research, 2014, 6(6): Research Article

ANALYSIS OF CORRELATION BETWEEN THE EXPENSES OF SOCIAL PROTECTION AND THE ANTICIPATED OLD AGE PENSION

Modeling Credit Rating for Bank of Eghtesade Novin in Iran

Okun s Law - an empirical test using Brazilian data

Poverty Alleviation in Burkina Faso: An Analytical Approach

SUSTAINABILITY PLANNING POLICY COLLECTING THE REVENUES OF THE TAX ADMINISTRATION

Quantitative evidence of post-crisis structural macroeconomic changes

Econometric Models for the Analysis of Financial Portfolios

Exchange Rate and Economic Performance - A Comparative Study of Developed and Developing Countries

Anexos. Pruebas de estacionariedad. Null Hypothesis: TES has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on SIC, maxlag=9)

What determines Paid Parental Leave Provisions in Collective Agreements in New Zealand?

Factor Affecting Yields for Treasury Bills In Pakistan?

9. Assessing the impact of the credit guarantee fund for SMEs in the field of agriculture - The case of Hungary

Impact of Capital Structure on Banking Profitability

The Impact of Credit Risk Management in the Profitability of Albanian Commercial Banks During the Period

An Investigation of Effective Factors on Export in Iran

VOLATILITY. Finance is risk/return trade-off.

LAMPIRAN. Variable Coefficient Std. Error t-statistic Prob.

The Frequency of Wars*

RESEARCH ON INFLUENCING FACTORS OF RURAL CONSUMPTION IN CHINA-TAKE SHANDONG PROVINCE AS AN EXAMPLE.

INFLUENCE OF CONTRIBUTION RATE DYNAMICS ON THE PENSION PILLAR II ON THE

LAMPIRAN LAMPIRAN. = Pengeluaran Konsumsi Masyarakat (milyar rupiah) = Jumlah Uang Beredar (milyar rupiah) = Laju Inflasi (dalam persentase)

An empirical study on the dynamic relationship between crude oil prices and Nigeria stock market

LAMPIRAN. Lampiran I

Interactions between United States (VIX) and United Kingdom (VFTSE) Market Volatility: A Time Series Study

The Impacts of Financial Crisis on Pakistan Economy: An Empirical Approach

Received: 4 September Revised: 9 September Accepted: 19 September. Foreign Institutional Investment on Indian Capital Market: An Empirical Analysis

COMMONWEALTH JOURNAL OF COMMERCE & MANAGEMENT RESEARCH AN ANALYSIS OF RELATIONSHIP BETWEEN GOLD & CRUDEOIL PRICES WITH SENSEX AND NIFTY

Empirical Analysis of Private Investments: The Case of Pakistan

FTSE BIRR. ftserussell.com. FTSE Russell 1

The Influence of Leverage and Profitability on Earnings Quality: Jordanian Case

TURKISH STOCK MARKET DEPENDENCY TO INTERNATIONAL MARKETS AND EXCHANGE RATE

CHAPTER 5 MARKET LEVEL INDUSTRY LEVEL AND FIRM LEVEL VOLATILITY

An Examination of Seasonality in Indian Stock Markets With Reference to NSE

THE IMPACT OF INSURANCE ON ECONOMIC GROWTH IN NIGERIA

Ateyah Alawneh 1. Correspondence: Ateyah Alawneh, College of Business, Tafila Technical University, Jordan.

Influence of Macroeconomic Indicators on Mutual Funds Market in India

Übungsblatt 4. Please examine below OLS estimation results for the log earnings of Egyptian wage workers and answer the below questions:

The Credit Cycle and the Business Cycle in the Economy of Turkey

The Estimation Model for Measuring Performance of Stock Mutual Funds Based on ARCH / GARCH Model

Transcription:

Appendix Table A.1 (Part A) Dependent variable: probability of crisis (own) Method: ML binary probit (quadratic hill climbing) Included observations: 47 after adjustments Convergence achieved after 6 iterations Covariance matrix computed using second derivatives Variable Coefficient Std. error z-statistic Prob. C 7.507594 2.659024 2.823439 0.0048 Asset turnover 0.266978 0.212592 1.255823 0.2092 Financial charge cover ratio 0.000545 0.000386 1.413019 0.1577 Conditional correlation with market 0.475697 6.741296 0.070565 0.9437 Current ratio 0.736209 0.329633 2.233423 0.0255 Debt-equity ratio 0.003866 0.008454 0.457288 0.6475 DPS 0.043960 0.027033 1.626167 0.1039 EPS growth 0.005130 0.011096 0.462358 0.6438 Green 1.85942 0.845388 2.19948 0.0278 NPM (%) 0.284073 0.091929 3.090133 0.0020 RAR 0.588333 0.606710 0.969711 0.3322 Retention ratio 0.065239 0.026809 2.433488 0.0150 McFadden R-squared 0.503664 Mean dependent var 0.531915 S.D. dependent var 0.504375 S.E. of regression 0.384303 Akaike info criterion 1.196683 Sum squared resid 5.169107 Schwarz criterion 1.669061 Log likelihood 16.12204 Hannan Quinn criterion 1.374442 Deviance 32.24409 Restr. deviance 64.96422 Restr. log likelihood 32.48211 LR statistic 32.72013 Avg. log likelihood 0.343022 Prob (LR statistic) 0.000584 Obs with Dep = 0 22 Total obs 47 Obs with Dep = 1 25 The Author(s) 2015 G. Chakrabarti and C. Sen, Green Investing, SpringerBriefs in Finance, DOI 10.1007/978-81-322-2026-8 89

90 Appendix Table A.1 (Part B) Expectation-prediction evaluation for binary specification Success cutoff: C = 0.5 Estimated equation Constant probability Dep = 0 Dep = 1 Total Dep = 0 Dep = 1 Total P(Dep = 1) <= C 19 4 23 0 0 0 P(Dep = 1) > C 3 21 24 22 25 47 Total 22 25 47 22 25 47 Correct 19 21 40 0 25 25 % correct 86.36 84.00 85.11 0.00 100.00 53.19 % incorrect 13.64 16.00 14.89 100.00 0.00 46.81 Total gain a 86.36 16.00 31.91 Percent gain b 86.36 NA 68.18 Estimated equation Constant probability Dep = 0 Dep = 1 Total Dep = 0 Dep = 1 Total E(# of Dep = 0) 17.03 5.22 22.25 10.30 11.70 22.00 E(# of Dep = 1) 4.97 19.78 24.75 11.70 13.30 25.00 Total 22.00 25.00 47.00 22.00 25.00 47.00 Correct 17.03 19.78 36.81 10.30 13.30 23.60 % correct 77.42 79.12 78.32 46.81 53.19 50.20 % incorrect 22.58 20.88 21.68 53.19 46.81 49.80 Total gain a 30.61 25.93 28.12 Percent gain b 57.54 55.40 56.47 a Change in % Correct from default (constant probability) specification b Percent of incorrect (default) prediction corrected by equation

Appendix 91 Table A.2 (Part A) Dependent variable: MKT_STRESS Method: ML binary probit (quadratic hill climbing) Sample (adjusted): 1 48 Included observations: 47 after adjustments Convergence achieved after 8 iterations Covariance matrix computed using second derivatives Variable Coefficient Std. error z-statistic Prob. C 0.681962 2.006131 0.339939 0.7339 Asset turnover 0.126999 0.171827 0.739108 0.4598 Financial charge cover ratio 3.71E-05 0.000335 0.110754 0.9118 Conditional correlation with market 11.95253 4.405112 2.709751 0.0120 Current ratio 0.460842 0.229540 2.007680 0.0447 Debt-equity ratio 0.045549 0.050002 0.910953 0.3623 DPS 0.017464 0.026240 0.665567 0.5057 EPS 0.003384 0.007230 0.468042 0.6398 Green 1.86978 0.845388 2.19948 0.0278 NPM (%) 0.054029 0.046926 1.151362 0.2496 RAR 1.241923 9.059794 0.137081 0.8910 Retention ratio 0.003531 0.019701 0.179250 0.8577 McFadden R-squared 0.302974 Mean dependent var 0.361702 S.D. dependent var 0.485688 S.E. of regression 0.446710 Akaike info criterion 1.422894 Sum squared resid 6.984250 Schwarz criterion 1.895272 Log likelihood 21.43801 Hannan Quinn criterion 1.600653 Deviance 42.87603 Restr. deviance 61.51278 Restr. log likelihood 30.75639 LR statistic 18.63675 Avg. log likelihood 0.456128 Prob (LR statistic) 0.067934 Obs with Dep = 0 30 Total obs 47 Obs with Dep = 1 17

92 Appendix Table A.2 (Part B) Expectation-prediction evaluation for binary specification Success cutoff: C = 0.5 Estimated equation Constant probability Dep = 0 Dep = 1 Total Dep = 0 Dep = 1 Total P(Dep = 1) <= C 27 8 35 30 17 47 P(Dep = 1) > C 3 9 12 0 0 0 Total 30 17 47 30 17 47 Correct 27 9 36 30 0 30 % correct 90.00 52.94 76.60 100.00 0.00 63.83 % incorrect 10.00 47.06 23.40 0.00 100.00 36.17 Total gain a 10.00 52.94 12.77 Estimated equation Constant probability Dep = 0 Dep = 1 Total Dep = 0 Dep = 1 Total E(# of Dep = 0) 23.09 7.26 30.35 19.15 10.85 30.00 E(# of Dep = 1) 6.91 9.74 16.65 10.85 6.15 17.00 Total 30.00 17.00 47.00 30.00 17.00 47.00 Correct 23.09 9.74 32.83 19.15 6.15 25.30 % correct 76.96 57.27 69.84 63.83 36.17 53.83 % incorrect 23.04 42.73 30.16 36.17 63.83 46.17 Total gain a 13.13 21.10 16.02 Percent gain b 36.31 33.06 34.69 a Change in % Correct from default (constant probability) specification b Percent of incorrect (default) prediction corrected by equation

Appendix 93 Table A.3 (Part A) General buy and sell strategy for 100 % green portfolio C 0.012214 0.000461 26.52174 0 R-squared 0 Mean dependent var 0.012214 Adjusted R-squared 0 S.D. dependent var 0.012201 S.E. of regression 0.012201 Akaike info criterion 5.9731 Sum squared resid 0.104362 Schwarz criterion 5.96661 Log likelihood 2,097.558 Hannan Quinn criterion 5.97059 Durbin Watson statistic 1.737959 Table A.3 (Part B) Trading strategy for 100 % green portfolio C 0.010284 0.000701 14.66988 0 BUY3060( 1) 0.0036 0.000987 3.647869 0.0003 R-squared 0.020369 Mean dependent var 0.012101 Adjusted R-squared 0.018838 S.D. dependent var 0.012621 S.E. of regression 0.012502 Akaike info criterion 5.9228 Sum squared resid 0.100026 Schwarz criterion 5.9089 Log likelihood 1,903.22 Hannan Quinn criterion 5.91741 F-statistic 13.30695 Durbin Watson statistic 1.781974 Prob (F-statistic) 0.000286

94 Appendix Table A.4 (Part A) General buy and sell strategy for 25 % green portfolio C 0.000574 0.000223 2.573593 0.0103 R-squared 0 Mean dependent var 0.000574 Adjusted R-squared 0 S.D. dependent var 0.005906 S.E. of regression 0.005906 Akaike info criterion 7.424322 Sum squared resid 0.02445 Schwarz criterion 7.417835 Log likelihood 2,606.937 Hannan Quinn criterion 7.421815 Table A.4 (Part B) Trading rule for 25 % green portfolio C 0.000155 0.000382 0.406533 0.6845 BUY150200( 1) 0.00085 0.000553 1.536426 0.1251 R-squared 0.004699 Mean dependent var 0.000562 Adjusted R-squared 0.002708 S.D. dependent var 0.0062 S.E. of regression 0.006191 Akaike info criterion 7.327388 Sum squared resid 0.019166 Schwarz criterion 7.310581 Log likelihood 1,841.174 Hannan Quinn criterion 7.320794 F-statistic 2.360605 Durbin Watson statistic 2.097381 Prob (F-statistic) 0.125066

Appendix 95 Table A.5 (Part A) General buy and sell strategy for 50 % green portfolio C 0.000543 0.000239 2.273588 0.0233 R-squared 0 Mean dependent var 0.000543 Adjusted R-squared 0 S.D. dependent var 0.006328 S.E. of regression 0.006328 Akaike info criterion 7.286211 Sum squared resid 0.028071 Schwarz criterion 7.279724 Log likelihood 2,558.46 Hannan Quinn criterion 7.283704 Durbin Watson statistic 2.275404 Table A.5 (Part B) Trading rule for 50 % green portfolio C 0.000293 0.000368 0.79541 0.4268 BUY150270( 1) 0.000918 0.000708 1.297469 0.1952 R-squared 0.0039 Mean dependent var 0.000542 Adjusted R-squared 0.001583 S.D. dependent var 0.00654 S.E. of regression 0.006535 Akaike info criterion 7.21862 Sum squared resid 0.018365 Schwarz criterion 7.199785 Log likelihood 1,561.222 Hannan Quinn criterion 7.211184 F-statistic 1.683426 Durbin Watson statistic 2.272725 Prob (F-statistic) 0.195165

96 Appendix Table A.6 (Part A) General buy and sell strategy for 75 % green portfolio C 5.00E-05 0.000257 0.19456 0.8458 R-squared 0 Mean dependent var 5.00E-05 Adjusted R-squared 0 S.D. dependent var 0.006813 S.E. of regression 0.006813 Akaike info criterion 7.138561 Sum squared resid 0.032538 Schwarz criterion 7.132074 Log likelihood 2,506.635 Hannan Quinn criterion 7.136054 Durbin Watson statistic 2.263722 Table A.6 (Part B) Trading rule for 75 % green portfolio C 0.0002 0.0004 0.4417 0.6589 BUY150270( 1) 0.0010 0.0007 1.3288 0.1846 R-squared 0.00409 Mean dependent var 9.68E-05 Adjusted R-squared 0.001774 S.D. dependent var 0.006884 S.E. of regression 0.006877 Akaike info criterion 7.116521 Sum squared resid 0.020339 Schwarz criterion 7.097686 Log likelihood 1,539.169 Hannan Quinn criterion 7.109085 F-statistic 1.765794 Durbin Watson statistic 2.327136 Prob (F-statistic) 0.184608

Appendix 97 Table A.7 (Part A) General buy and sell strategy for 100 % gray portfolio C 0.00015 0.000295 0.49628 0.6198 R-squared 0 Mean dependent var 0.00015 Adjusted R-squared 0 S.D. dependent var 0.007828 S.E. of regression 0.007828 Akaike info criterion 6.86086 Sum squared resid 0.042953 Schwarz criterion 6.85437 Log likelihood 2,409.161 Hannan Quinn criterion 6.85835 Durbin Watson statistic 1.536893 Table A.7 (Part B) Trading rule for 100 % gray portfolio C 0.00104 0.000465 2.23622 0.0258 BUY21270( 1) 0.00136 0.000825 1.648742 0.0999 R-squared 0.006282 Mean dependent var 0.00061 Adjusted R-squared 0.003971 S.D. dependent var 0.007994 S.E. of regression 0.007978 Akaike info criterion 6.81953 Sum squared resid 0.027372 Schwarz criterion 6.80069 Log likelihood 1,475.017 Hannan Quinn criterion 6.81209 F-statistic 2.71835 Durbin Watson statistic 1.561883 Prob (F-statistic) 0.099931

98 Appendix Table A.8 Coefficients of all possible trading rules: 100 % green 7 14 21 30 60 100 150 200 270 3 0.000791 0.000618 0.001601 0.001762 0.002769 0.003375 0.002659 0.002907 0.002894 7 0.000599 0.001261 0.00184 0.002637 0.003143 0.002333 0.003166 0.003237 14 0.002114 0.002378 0.003518 0.003716 0.003009 0.003013 0.003481 21 0.002861 0.003443 0.003499 0.00239 0.002637 0.003233 30 0.0036 0.002639 0.00256 0.00235 0.00297 60 0.001623 0.002706 0.001739 0.001739 100 0.003443 0.002741 0.002682 150 0.001657 0.001396 200 0.001453

Appendix 99 Table A.9 Coefficients of all possible trading rules: 75 % green 7 14 21 30 60 100 150 200 270 3 0.00016 0.00021 0.00042 0.00035 0.00032 0.00028 0.00029 0.00058 0.00063 7 0.00071 0.00088 0.00091 0.00060 0.00070 0.00066 0.00072 0.00007 14 0.00088 0.00067 0.00094 0.00051 0.00050 0.00065 0.00047 21 0.00017 0.00011 0.00029 0.00040 0.00070 0.00051 30 0.00032 0.00003 0.00048 0.00052 0.00028 60 0.00039 0.00023 0.00069 0.00051 100 0.00013 0.00016 0.00012 150 0.00085 0.00056 200 0.00001

100 Appendix Table A.10 Coefficients of all possible trading rules: 50 % green 7 14 21 30 60 100 150 200 270 3 0.00028 0.00046 0.00075 0.00054 0.00034 0.00035 0.00043 0.00048 0.00092 7 0.00108 0.00106 0.00080 0.00074 0.00065 0.00072 0.00048 0.00020 14 0.00115 0.00056 0.00080 0.00049 0.00058 0.00061 0.00057 21 0.00061 0.00018 0.00026 0.00024 0.00079 0.00025 30 0.00061 0.00018 0.00036 0.00050 0.00015 60 0.00029 0.00020 0.00056 0.00030 100 0.00012 0.00040 0.00025 150 0.00090 0.00092 200 0.00006

Appendix 101 Table A.11 Coefficients of all possible trading rules: 25 % green 7 14 21 30 60 100 150 200 270 3 0.00036 0.00014 0.00058 0.00031 0.00014 0.00011 0.00008 0.00005 0.00001 7 0.00043 0.00020 0.00029 0.00003 0.00016 0.00018 0.00016 0.00083 14 0.00093 0.00011 0.00063 0.00019 0.00043 0.00030 0.00014 21 0.00011 0.00004 0.00016 0.00005 0.00037 0.00007 30 0.00036 0.00001 0.00005 0.00028 0.00016 60 0.00023 0.00032 0.00090 0.00041 100 0.00009 0.00021 0.00020 150 0.00079 0.00099 200 0.00032

102 Appendix Table A.12 Coefficients of all possible trading rules: 100 % gray 7 14 21 30 60 100 150 200 270 3 0.00019 0.00046 0.00022 0.00015 0.00012 0.00011 0.00004 0.00024 0.00035 7 0.00014 0.00004 0.00032 0.00022 0.00036 0.00011 0.00004 0.00029 14 0.00012 0.00036 0.00020 0.00005 0.00024 0.00014 0.00022 21 0.00013 0.00061 0.00067 0.00071 0.00095 0.00136 30 0.00064 0.00032 0.00086 0.00054 0.00088 60 0.00104 0.00037 0.00013 0.00065 100 0.00026 0.00030 0.00036 150 0.00045 0.00112 200 0.00021