The influence of investor sentiment on the green bond market

Chousa, Juan Piñeiro; López‐Cabarcos, M. Ángeles; Caby, Jérôme; Šević, Aleksandar

doi:10.1016/j.techfore.2020.120351

Cited by 139 publications

(48 citation statements)

References 42 publications

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“…In this regard, investors’ emotion assessment based on Google data search volume is superior than the predictability of others. Although numerous studies have looked on the impact of sentiment towards the bond market, there has been relatively scarce research on its impact towards the global economy (Piñeiro-Chousa et al 2021 ).…”

Section: Literature Reviewmentioning

confidence: 99%

How COVID-19 induced panic on stock price and green finance markets: global economic recovery nexus from volatility dynamics

Tan

Sadiq

Aldeehani

et al. 2021

Environ Sci Pollut Res

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This paper investigates the effect of different categories of essential COVID-19 data from 2020 to 2021 towards stock price dynamics and options markets. It applied the hypothetical method in which investors develop depression based on the understanding suggested by various green finance divisions. Furthermore, additional elements like panic, sentiment, and social networking sites may impact the attitude, size, and direction of green finance, subsequently impacting the security prices. We created new emotion proxies based on five groups of information, namely COVID-19, marketplace, lockdown, banking sector, and government relief using Google search data. The results show that (1) if the proportional number of traders’ conduct exceeds the stock market, the effect of sentimentality indexes on jump volatility is expected to change; (2) the volatility index component jump radically increases with the COVID-19 index, city and market lockdown index, and banking index; and (3) expanding the COVID-19 index gives rise to the stock market index. Moreover, all indexes decreased in jump volatility but only after 5 days. These findings comply with the hypotheses proposed by our model.

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Section: Literature Reviewmentioning

confidence: 99%

How COVID-19 induced panic on stock price and green finance markets: global economic recovery nexus from volatility dynamics

Tan

Sadiq

Aldeehani

et al. 2021

Environ Sci Pollut Res

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“…Copenhagen Accord introduced in 2009 points that financial innovation is a powerful way to defeat against global warming. Many economies are also in agreement that it is urgent to transform the economic development mode by attracting investors to green their portfolios, and one of the most effective ways is to provide more appealing green financial instruments (Piñeiro-Chousa et al, 2021). On the contrary, a few works also raise the different voices.…”

Section: Green Bond Marketmentioning

confidence: 99%

“…Zerbid (2019), on the other hand, finds there is a small negative premium on green bonds. From investors' perspective, the issuance of green bonds is likely to promote the disclosure of corporate ESG information (Piñeiro-Chousa et al, 2021). Flammer (2021) suggests that companies' long-term value and environmental performance can be enhanced after issuing green bonds, which will benefit long-term green investors.…”

Section: Green Bond Marketmentioning

confidence: 99%

Green Bond Index Prediction Based on CEEMDAN-LSTM

2022

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Green bonds, which are designed to finance for environment-friendly or sustainable projects, have attracted more and more investors’ attention. However, the study in this field is still relatively limited, especially in forecasting the market’s future trends. In this paper, a hybrid model combining CEEMDAN and LSTM is introduced to predict green bond market in China (represented by CUFE-CNI High Grade Green Bond Index). In order to evaluate the performance of our model, we also use EMD to decompose the green bond index. Our empirical result suggests that, compared with EMD-LSTM and LSTM models, CEEMDAN-LSTM is the most accurate model in green bond index forecasting. Meanwhile, we find that indices from the crude oil market and green stock market are both effective predictors, which also provides ground on the correlations between the green bond market and other financial markets.

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“…However, the law of stock price change is difficult to accurately grasp, and most investors usually depend on subjective judgment to conduct stock trading 1,2 . The stock market has always been affected by political factors, the industry specific factors, the world economic situation, and the price trends are highly nonlinear and nonstationary 3‐5 …”

Section: Introductionmentioning

confidence: 99%

“…1,2 The stock market has always been affected by political factors, the industry specific factors, the world economic situation, and the price trends are highly nonlinear and nonstationary. [3][4][5] Therefore, effective and efficient predicting the stock is an extremely challenging task for both investors and researchers. And thus, it is significant to research how to build a universal and effective model to forecast future movements of the stock market.There are a number of advanced methods that are applied for prediction of the stock market.…”

mentioning

confidence: 99%

Unsupervised feature extraction with convolutional autoencoder with application to daily stock market prediction

Xie

2021

Concurrency and Computation

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Due to the volatility and noise of the stock market, accurately obtaining the trend of the stock market is a challenging problem, and gets the attention of many researchers and speculators. Recently, convolutional neural network (CNN) has been used to automatically learn effective features and predict stock market trends. In CNN-based methods reported so far, less focus has been paid to time series information of the stock, but is very crucial for stock forecasting. In this study, an unsupervised feature extraction method with convolutional autoencoder (CAE) with application to daily stock market prediction is proposed, which has a higher prediction than traditional models.The proposed method mainly consists of the data processing part, unsupervised feature learning part, and the support vector machine model part. Data processing part includes time series data transform into two-dimensional data and data normalization. CAE network-based unsupervised feature learning is designed by fusing convolution and autoencoder. In order to verify the performance of the model, various initial financial and economic variables of stock indices are chosen for prediction experiments. The experimental results on different stock indices demonstrate a significant improvement in prediction's performance compared with the baseline methods. K E Y W O R D S convolutional autoencoder network, convolutional neural network, stock market prediction, support vector machine INTRODUCTIONThe stock market has become an important component for listed companies to raise funds from investors. However, the law of stock price change is difficult to accurately grasp, and most investors usually depend on subjective judgment to conduct stock trading. 1,2 The stock market has always been affected by political factors, the industry specific factors, the world economic situation, and the price trends are highly nonlinear and nonstationary. [3][4][5] Therefore, effective and efficient predicting the stock is an extremely challenging task for both investors and researchers. And thus, it is significant to research how to build a universal and effective model to forecast future movements of the stock market.There are a number of advanced methods that are applied for prediction of the stock market. Depending on the relationship between the historical behavior and future trend of the stock price is processed, forecasting methods can be divided into three categories. [6][7][8] Among statistical methodologies, due to the interpretability, linear regression, autoregression, and moving average are helpful in financial time series forecasting. Assume that the stock price trend is a result of historical behaviors, extracting effective features from the original stock data is the key of the prediction process. However, features are subjectively designed, and the accurate prediction of models mostly depended on the A preliminary version of this article has been published by the seventh International Conference on Information Science and Control Engineering.

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The influence of investor sentiment on the green bond market

Cited by 139 publications

References 42 publications

How COVID-19 induced panic on stock price and green finance markets: global economic recovery nexus from volatility dynamics

How COVID-19 induced panic on stock price and green finance markets: global economic recovery nexus from volatility dynamics

Green Bond Index Prediction Based on CEEMDAN-LSTM

Unsupervised feature extraction with convolutional autoencoder with application to daily stock market prediction

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