Nonlinearity Characteristic for Clean Energy Stock Market: An Integrated Exploration Approach

2014 ◽  
Author(s):  
Huiling Lv ◽  
Fengmei Yang ◽  
Ling Tang
Keyword(s):  
Stock Market ◽  
Clean Energy ◽  
Nonlinearity Characteristic

scholarly journals Nexus between green bonds, financial and environmental indicators

2021 ◽  
Vol 10 (3) ◽  
pp. 191-199
Author(s):  
Ngo Thai Hung
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Granger Causality ◽  
Multilayer Perceptron ◽  
Co2 Emission ◽  
Clean Energy ◽  
Environmental Indicators ◽  
Energy Price ◽  
Causal Direction ◽  
Bidirectional Relationship

This study uses a novel perspective to examine the causal connectedness between green bonds and other conventional assets, including clean energy, price of CO2 emission allowances, Bitcoin, and the S&P 500 stock market covering from January 2013 to March 2019. We apply the Multilayer Perceptron Neural Network Non-linear Granger causality and Transfer Entropy to detect possible changes in the causal direction between green bonds and other considered variables. We find a bidirectional relationship between green bonds, S&P 500, and Bitcoin markets, while green bonds have a unidirectional connection with the price of CO2 emission allowances.

Air Products Debuts Clean Energy Lab

2010 ◽  
pp. 10073010152446
Author(s):  
Marc Reisch
Keyword(s):  
Clean Energy

ARPA-E to Fund New Clean Energy Projects

2011 ◽  
pp. 042811132534
Author(s):  
Rajendrani Mukhopadhyay
Keyword(s):  
Clean Energy ◽  
Energy Projects

Predicting Stock Market Performance

2020 ◽  
pp. 1-10
Author(s):  
Thomas Plieger ◽  
Thomas Grünhage ◽  
Éilish Duke ◽  
Martin Reuter
Keyword(s):  
Stock Market ◽  
Risk Taking ◽  
Life Stress ◽  
Price Volatility ◽  
Individual Characteristics ◽  
Self Report ◽  
Financial Behavior ◽  
Market Simulation ◽  
Stock Market Performance ◽  
Risk Taking Behavior

Abstract. Gender and personality traits influence risk proneness in the context of financial decisions. However, most studies on this topic have relied on either self-report data or on artificial measures of financial risk-taking behavior. Our study aimed to identify relevant trading behaviors and personal characteristics related to trading success. N = 108 Caucasians took part in a three-week stock market simulation paradigm, in which they traded shares of eight fictional companies that differed in issue price, volatility, and outcome. Participants also completed questionnaires measuring personality, risk-taking behavior, and life stress. Our model showed that being male and scoring high on self-directedness led to more risky financial behavior, which in turn positively predicted success in the stock market simulation. The total model explained 39% of the variance in trading success, indicating a role for other factors in influencing trading behavior. Future studies should try to enrich our model to get a more accurate impression of the associations between individual characteristics and financially successful behavior in context of stock trading.

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