scholarly journals Nonlinear relationship between carbon market and energy market

2021 ◽  
Vol 275 ◽  
pp. 02046
Author(s):  
Yan Li
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Market Price ◽  
Futures Market ◽  
Carbon Market ◽  
Var Model ◽  
Energy Market ◽  
Nonlinear Relationship ◽  
Energy Futures ◽  
New Energy

This paper divides the energy market into energy futures market and new energy stock market. At the same time, the closing price of Shenzhen carbon emission rights is used to represent the carbon market price, the energy futures composite index of China Securities Exchange is used to represent the energy futures market price, and the stock price of new energy listed companies is used to represent the new energy stock market price. VAR model and MSVAR model are used to empirically study the relationship between the three variables and the nonlinear relationship between them. VAR model results show that there will be more complex relationship among carbon market price, energy company stock price and energy futures price. MSVAR model shows that the energy futures market, new energy stock market and carbon market present nonlinear and structural changes, and MSVAR model can better explain the nonlinear relationship among the three markets than traditional VAR model.

scholarly journals Time-frequency Connectedness between Coal Market Prices, New Energy Stock Prices and CO2 Emissions Trading Prices in China

2020 ◽  
Vol 12 (7) ◽  
pp. 2823 ◽  
Author(s):  
Chun Jiang ◽  
Yi-Fan Wu ◽  
Xiao-Lin Li ◽  
Xin Li
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Market Price ◽  
Market Prices ◽  
Frequency Bands ◽  
Coal Price ◽  
Time Frequency ◽  
New Energy ◽  
Coal Market ◽  
Wavelet Coherency

This paper aims to examine whether there is inherent dynamic connectedness among coal market prices, new energy stock prices and carbon emission trading (CET) prices in China under time- and frequency-varying perspectives. For this purpose, we apply a novel wavelet method proposed by Aguiar-Conraria et al. (2018). Specifically, utilizing the single wavelet power spectrum, the multiple wavelet coherency, the partial wavelet coherency, also combined with the partial phase difference and the partial wavelet gains, this paper discovers the time-frequency interaction between three markets. The empirical results show that the connectedness between the CET market price and the coal price is frequency-varying and mainly occur in the lower and higher frequency bands, while the connectedness between the CET market price and the new energy stock price mainly happen in the middle and lower frequency bands. In the high-frequency domain, the CET market price is mainly affected by the coal price, while the CET market price is dominated by the new energy stock price in the middle frequency. These uncovered frequency-varying characteristics among these markets in this study could provide several implications. Main participants in these markets, such as polluting industries, governments and financial actors, should pay close attention to the connectedness under different frequencies, in order to realize their goal of the production, the policymaking, and the investment.

scholarly journals Dynamic Spillovers and Asymmetric Spillover Effect between the Carbon Emission Trading Market, Fossil Energy Market, and New Energy Stock Market in China

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6438
Author(s):  
Dan Nie ◽  
Yanbin Li ◽  
Xiyu Li
Keyword(s):  
Stock Market ◽  
Carbon Emission ◽  
Spillover Effect ◽  
Emission Trading ◽  
Carbon Market ◽  
Carbon Trading ◽  
Fossil Energy ◽  
Trading Market ◽  
New Energy ◽  
Coal Market

In 2020, China proposed the goal of achieving carbon emission peaks by 2030 and carbon neutrality by 2060. For China, whose energy consumption structure has long been dominated by fossil energy, carbon trading and new energy are crucial for the realization of the emission target. By establishing a connectedness network model, this paper studies the static and dynamic spillovers between the Hubei carbon trading market, new energy stock market, crude oil market, coal market, and natural gas market in China, and draws the following conclusions: (1) the static spillover index of the carbon–energy–stock system is 3.57% and the dynamic spillover index fluctuates between 7.67% and 22.62%, indicating that the spillover effect of the system is low; (2) for the whole system, whether from a static or dynamic perspective, the carbon market always plays the role of net information receiver, while new energy is the net information transmitter; (3) the new energy stock market and the coal market always act as net information transmitters to the carbon market; and (4) the spillover effect of the system is asymmetric, wherein the system is more sensitive to negative information about price returns, and this asymmetry is much greater when the system is active.

scholarly journals Targeted Sentiments and Hierarchical Events based Learning Model for Stock Market Forecasting from Multi-Source Data

2020 ◽  
Vol 9 (4) ◽  
pp. 1763-1770
Keyword(s):  
Stock Market ◽  
Sentiment Analysis ◽  
Stock Price ◽  
Market Price ◽  
Movement Patterns ◽  
Event Extraction ◽  
Spotted Hyena ◽  
Price Movement ◽  
Training Time ◽  
Source Data

Stock market price movement forecast from multi-source data has gained massive interest in recent years. Studies were focussed on extracting the events and sentiments from different source data and employ them in learning the stock price movement patterns. This approach provided accurate and highly reliable forecasting as it involves multiple stock price indicators. However, some aspects of sentiment analysis and event extraction increase the training time and computation complexity in big data stock analysis. To overcome these issues, the hierarchical event extraction and the target dependent sentiment analysis are performed in this paper to improve the learning rate stock price movement patterns. In this paper, the events are hierarchically extracted from news articles using Deep Restricted Boltzmann Machine (DRBM). The target based sentiments from the tweets are detected using Improved Extreme Learning machine (IELM) whose parameters are optimally selected using Spotted Hyena Optimizer (SHO). The stock indicators obtained from these two processes are used in the learning process performed using Tolerant Flexible Multi-Agent Deep Reinforcement Learning (TFMA-DRL) model for analysing the stock patterns and forecasting the future stock trends. The forecasting results obtained by using the TFMA-DRL model by combining the stock indicators of targeted sentiments and hierarchical events are trustworthy and reliable. Evaluations are performed using three datasets collected for 12 months period from three sources of Twitter, Market News and Stock exchange. Results highlighted that the proposed stock forecasting model achieved 90% accuracy with minimum training time.

scholarly journals Stock Market Prediction Using Twitter Sentiment Analysis

2021 ◽  
pp. 265-270
Author(s):  
Padmanayana ◽  
Varsha ◽  
Bhavya K
Keyword(s):  
Stock Market ◽  
Sentiment Analysis ◽  
Stock Price ◽  
Market Price ◽  
Future Price ◽  
Stock Market Prediction ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Media Platform ◽  
The Impact

Stock market prediction is an important topic in ?nancial engineering especially since new techniques and approaches on this matter are gaining value constantly. In this project, we investigate the impact of sentiment expressed through Twitter tweets on stock price prediction. Twitter is the social media platform which provides a free platform for each individual to express their thoughts publicly. Specifically, we fetch the live twitter tweets of the particular company using the API. All the stop words, special characters are extracted from the dataset. The filtered data is used for sentiment analysis using Naïve bayes classifier. Thus, the tweets are classified into positive, negative and neutral tweets. To predict the stock price, the stock dataset is fetched from yahoo finance API. The stock data along with the tweets data are given as input to the machine learning model to obtain the result. XGBoost classifier is used as a model to predict the stock market price. The obtained prediction value is compared with the actual stock market value. The effectiveness of the proposed project on stock price prediction is demonstrated through experiments on several companies like Apple, Amazon, Microsoft using live twitter data and daily stock data. The goal of the project is to use historical stock data in conjunction with sentiment analysis of news headlines and Twitter posts, to predict the future price of a stock of interest. The headlines were obtained by scraping the website, FinViz, while tweets were taken using Tweepy. Both were analyzed using the Vader Sentiment Analyzer.

scholarly journals Research on Time-Varying Two-Way Spillover Effects Between Carbon and Energy Markets: Empirical Evidence From China

2021 ◽  
Vol 9 ◽  
Author(s):  
Sen Qiao ◽  
Chen Xi Zhao ◽  
Kai Quan Zhang ◽  
Zheng Yu Ren
Keyword(s):  
Crude Oil ◽  
Spillover Effect ◽  
Energy Markets ◽  
Carbon Market ◽  
Energy Market ◽  
Time Varying ◽  
Carbon Markets ◽  
Oil Futures ◽  
New Energy ◽  
Crude Oil Futures

With the improvement of China’s carbon emission trading system, the spillover effect between carbon and energy markets is becoming more and more prominent. This paper selects four representative pilot carbon markets, including Beijing (BEA), Guangdong (GDEA), Hubei (HBEA) and Shanghai (SHEA). And three representative energy markets, including Crude Oil Futures (SC), power index (L11655) and China Securities new energy index (NEI). Combining the rolling window technology with DY spillover index, set a 50-weeks rolling window to measure the spillover index, and deeply analyze the time-varying two-way spillover effect between China’s carbon and energy markets. The results show that the spillover effect between China’s carbon and energy markets has significant time variability and two-way asymmetry. The time-varying spillover effect of different carbon pilot markets on the energy market has regional heterogeneity. The volatility spillover effect of Beijing and Shanghai carbon markets mainly comes from the crude oil futures market, Guangdong carbon market mainly comes from the new energy market, and Hubei carbon market mainly comes from crude oil and electricity market. The above research results contribute to the prevention of potential risk spillover between carbon and energy markets, which can promote the establishment of China’s unified carbon market and the prevention of systemic financial risks in energy market.

scholarly journals FOURIER ANALYSIS FOR STOCK PRICE FORECASTING: ASSUMPTION AND EVIDENCE

2016 ◽  
Vol 17 (3) ◽  
pp. 365-380 ◽  
Author(s):  
Bohumil STÁDNÍK ◽  
Jurgita RAUDELIŪNIENĖ ◽  
Vida DAVIDAVIČIENĖ
Keyword(s):  
Stock Market ◽  
Fourier Analysis ◽  
Stock Price ◽  
Market Price ◽  
Theoretical Background ◽  
Data Series ◽  
Price Forecasting ◽  
Similar Data ◽  
Price Series ◽  
Momentum Trading

The research addressed the relevant question whether the Fourier analysis really provides practical value for investors forecasting stock market price. To answer this question, the significant cycles were discovered using the Fourier analysis inside the price series of US stocks; then, the simulation of an agent buying and selling on minima and maxima of these cycles was made. The results were then compared to those of an agent operating chaotically. Moreover, the existing significant cycles were found using more precise methods, suggested in the research, and based on the results of an agent buying and selling on all possible periods and phases. It has been analysed whether these really existing cycles were in accordance with the significant cycles resulting from the Fourier analysis. It has been concluded that the Fourier analysis basically failed. Suchlike failures are expected on similar data series. In addition, momentum and level trading backtests have been used in a similar way. It has been found that the level trading does provide a certain practical value in comparison to the momentum trading method. The research also simplifies the complicated theoretical background for practitioners.

scholarly journals Stock Market Price Forecasting by Using Deep Learning

2018 ◽  
Vol 7 (3.12) ◽  
pp. 627
Author(s):  
Madhusudan Reddy ◽  
Arun Gade ◽  
Sreekarreddy . ◽  
P Prabhu
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Market Price ◽  
Price Forecasting ◽  
Price Forecast ◽  
Corporate Capital ◽  
Financial Products ◽  
New Information ◽  
The Future ◽  
Future Value

Stock market forecasts are an attempt to determine the future value of corporate capital or other financial products consumed in the stock market. If the future stock price forecast succeeds, you can gain great profit. The efficient market presents all the current stock price information, which shows that price fluctuations are not the basis for unnecessary new information. Others disagree that people who have these ideas have many methods and techniques to help them get future information. [1]  

scholarly journals Predicting Stock Market Price Movement Using Sentiment Analysis: Evidence From Ghana

2020 ◽  
Vol 25 (1) ◽  
pp. 33-42
Author(s):  
Isaac Kofi Nti ◽  
Adebayo Felix Adekoya ◽  
Benjamin Asubam Weyori
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Prediction Accuracy ◽  
Time Window ◽  
Market Price ◽  
Investor Sentiment ◽  
Google Trends ◽  
Price Movement ◽  
The Future ◽  
Web News

AbstractPredicting the stock market remains a challenging task due to the numerous influencing factors such as investor sentiment, firm performance, economic factors and social media sentiments. However, the profitability and economic advantage associated with accurate prediction of stock price draw the interest of academicians, economic, and financial analyst into researching in this field. Despite the improvement in stock prediction accuracy, the literature argues that prediction accuracy can be further improved beyond its current measure by looking for newer information sources particularly on the Internet. Using web news, financial tweets posted on Twitter, Google trends and forum discussions, the current study examines the association between public sentiments and the predictability of future stock price movement using Artificial Neural Network (ANN). We experimented the proposed predictive framework with stock data obtained from the Ghana Stock Exchange (GSE) between January 2010 and September 2019, and predicted the future stock value for a time window of 1 day, 7 days, 30 days, 60 days, and 90 days. We observed an accuracy of (49.4–52.95 %) based on Google trends, (55.5–60.05 %) based on Twitter, (41.52–41.77 %) based on forum post, (50.43–55.81 %) based on web news and (70.66–77.12 %) based on a combined dataset. Thus, we recorded an increase in prediction accuracy as several stock-related data sources were combined as input to our prediction model. We also established a high level of direct association between stock market behaviour and social networking sites. Therefore, based on the study outcome, we advised that stock market investors could utilise the information from web financial news, tweet, forum discussion, and Google trends to effectively perceive the future stock price movement and design effective portfolio/investment plans.

scholarly journals Determinant of Stock Market Prices in Nepal: A Case of Commercial Banks

2021 ◽  
Vol 12 (2) ◽  
pp. 1
Author(s):  
Sudip Wagle
Keyword(s):  
Stock Market ◽  
Commercial Banks ◽  
Stock Price ◽  
Market Price ◽  
Positive Association ◽  
Annual Reports ◽  
Secondary Source ◽  
Significant Positive Association ◽  
The Government ◽  
Price Instability

<p>Equity share investment is one of the key investment paths that provide significant returns for investors but, unusual stock price instability makes confusion for them, as well as troubles for policymakers and the government authorities. This study aims to identify the empirical variables that influence the stock market price in commercial banks for 2015/16 to 2019/20 using a set of dependent and independent variables. The study is based on 130 observations from 26 commercial banks (out of 27) in Nepal using a secondary source and the information obtained from annual reports. The descriptive and causal-comparative research design was employed. For that, mean, standard deviation, correlation and regression analysis techniques have been used. The results revealed that Market to Book proportion (M/B), Price-earnings proportion (P/E) and Earning Yield proportion (E/Y) have a significant positive association with the stock market price. In contrast, the Dividend Yield proportion (D/Y) has a positive but insignificant impact on the stock market price. The finding of this study is valuable to the curious investors, concerned bankers, academicians and government authorities, which help them to more about the stock market’s returns and likelihood in the country.</p>

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