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

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.

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Analysis of Institutional Factors Affecting Share Prices: The Case of National Stock Exchange

Global Business Review ◽

10.1177/0972150917713865 ◽

2018 ◽

Vol 19 (3) ◽

pp. 707-721 ◽

Cited By ~ 2

Author(s):

Neeraj Nautiyal ◽

P. C. Kavidayal

Keyword(s):

Stock Prices ◽

Stock Price ◽

Transition Period ◽

Stock Exchange ◽

Market Price ◽

Negative Relationship ◽

Value Added ◽

Price Variation ◽

Wide Range ◽

The Impact

This study offers empirical findings on the impact of institutional variables on firm’s stock market price performance. In order to identify the influence of companies financial on NIFTY 50 Index, our sample consists of balanced panel of 30 actively traded companies (that becomes the study’s index representative) over a massive transition period, 1995–2014. Attempts have been made with a wide range of econometric models and estimators, from the relatively straightforward to (static) more complex (dynamic panel analyses) to deal with the relevant econometric issues. Results indicate that increasing debt in capital structure does not establish any significant relation with the stock prices. Earnings per share (EPS) shows a poor explanation of price variation. Economic value added (EVA) indicates a positive relation with current as well as previous year’s stock price performances. However, dividend payout (DIVP) and dividend per share (DPS) achieve negative relationship at moderately significant level. The present study confirms that performance of companies fundamental ratios will be essential and immensely helpful to investors and analysts in assessing the better stocks that belong to different industry groups.

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Left Frontal EEG Power Responds to Stock Price Changes in a Simulated Asset Bubble Market

Brain Sciences ◽

10.3390/brainsci11060670 ◽

2021 ◽

Vol 11 (6) ◽

pp. 670

Author(s):

Filip-Mihai Toma ◽

Makoto Miyakoshi

Keyword(s):

Stock Prices ◽

Stock Price ◽

Low Cost ◽

Time Frequency ◽

Scalp Eeg ◽

Delta Band ◽

Financial Bubble ◽

Eeg Power ◽

Simulated Market ◽

Theory Research

Financial bubbles are a result of aggregate irrational behavior and cannot be explained by standard economic pricing theory. Research in neuroeconomics can improve our understanding of their causes. We conducted an experiment in which 28 healthy subjects traded in a simulated market bubble, while scalp EEG was recorded using a low-cost, BCI-friendly desktop device with 14 electrodes. Independent component (IC) analysis was performed to decompose brain signals and the obtained scalp topography was used to cluster the ICs. We computed single-trial time-frequency power relative to the onset of stock price display and estimated the correlation between EEG power and stock price across trials using a general linear model. We found that delta band (1–4 Hz) EEG power within the left frontal region negatively correlated with the trial-by-trial stock prices including the financial bubble. We interpreted the result as stimulus-preceding negativity (SPN) occurring as a dis-inhibition of the resting state network. We conclude that the combination between the desktop-BCI-friendly EEG, the simulated financial bubble and advanced signal processing and statistical approaches could successfully identify the neural correlate of the financial bubble. We add to the neuroeconomics literature a complementary EEG neurometric as a bubble predictor, which can further be explored in future decision-making experiments.

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Stock Market Prices Prediction using Random Forest and Extra Tree Regression

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c4314.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 1224-1228

Keyword(s):

Machine Learning ◽

Stock Market ◽

Stock Prices ◽

Stock Price ◽

Stock Exchange ◽

Research Area ◽

Support Vector ◽

Market Prices ◽

Machine Learning Approach ◽

Innovative Work

Prediction of Stock price is now a day’s an existing and interesting research area in financial and academic sectors to know the scale of economies. There did not exists any significant set of rules to estimate and predict the scale of share in the stock exchange. Many evolutionary technologies are existing such as technical, fundamental, time, statistical and series analysis which help us to attempt the prediction process, but none of the methods are proved as reliable and accurate tool to the society in the estimation of stock exchange or share market scales. Here in this paper we attempted to do innovative work through Machine Learning approach to predict or sense the behaviour tracking of the stock market sensex. Linear regression, Support Vector regression, Decision Tree, Ramdom Forest Regressor and Extra Tree Regressor are the Machine Learning models implemented effectively in predicting the stock prices and define the activity between the exchanges the securities between the buyers and sellers. We predicted the price of the stock based on the closing value and stock price. An algorithm with high accuracy we do the process of comparison for the accuracy of each of the model and finally is considered as better algorithm for predicting stock price. As share market is a vague domain we cannot predict the conditions occur, and also share market can never be predicted, this job can be done easily and technically through this work and the main aim of this paper is to apply algorithms in Machine Learning in predicting the stock prices.

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Modelling Macroeconomic Determinants Of Stock Market Prices: Evidence From Namibia

Journal of Applied Business Research (JABR) ◽

10.19030/jabr.v28i5.7230 ◽

2012 ◽

Vol 28 (5) ◽

pp. 871 ◽

Cited By ~ 12

Author(s):

Joel Hinaunye Eita

Keyword(s):

Stock Market ◽

Interest Rates ◽

Money Supply ◽

Stock Prices ◽

Economic Activity ◽

Market Price ◽

Font Size ◽

Market Prices ◽

Econometric Methodology ◽

Macroeconomic Determinants

This paper investigates the macroeconomic determinants of stock market prices in Namibia. The investigation was conducted using a VECM econometric methodology and revealed that Namibian stock market prices are chiefly determined by economic activity, interest rates, inflation, money supply and exchange rates. An increase in economic activity and the money supply increases stock market prices, while increases in inflation and interest rates decrease stock prices. The results suggest that equities are not a hedge against inflation in Namibia, and contractionary monetary policy generally depresses stock prices. Increasing economic activity promotes stock market price development.

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Predicting Stock Price Using Artificial Neural Networks

Restaurant Business ◽

10.26643/rb.v118i8.6985 ◽

2019 ◽

Vol 118 (8) ◽

pp. 96-117

Author(s):

Dr. Nigama. K ◽

Dr. R Alamelu ◽

Dr. S. Selvabaskar ◽

Dr. K.G. Prasanna Sivagami

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Stock Market ◽

Stock Prices ◽

Stock Price ◽

Explicit Knowledge ◽

Stock Exchange ◽

Market Price ◽

Financial Investment ◽

Artificial Neural

Stock market facilitates the economic activities that contribute to a nation’s growth and prosperity. This is viewed as one of the lucrative avenues for financial investment. Although the stock market is a thrilling and potential opportunity to grow one’s money, it brings along with it certain challenges, because, there is no universal rule that suggests profitable investments. Investors, corporate and advisors employ several techniques like fundamental and technical analysis, trend analysis and other analysis to suggest stocks that will give best yields but such tools are neither consistent nor foolproof in the prediction of stock prices. But human exertions to convert the tacit knowledge into explicit knowledge has never found any alternate. More, the uncertainties, more the efforts to know them with certainty. Digital economy with its advanced technological tools aids the pursuit of not only understanding uncertainties but also predicting the future with maximum precision. The most prominent techniques in the technological realm includes the usage of artificial neural networks (ANNs) and Genetic Algorithms. This paper discusses the stock prices forecasting ability of Bombay stock exchange trend using genetically evolved neural networks, the input being the closing price of the previous five years and output being the price for the next day. Risk (Standard deviation), Average Return, variance and Market price are chosen as indicators of the performance. The objective of this study is to give an overview of the application of artificial neural network in predicting stock market.

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Examination of cybercrime and its effects on corporate stock value

Journal of Information Communication and Ethics in Society ◽

10.1108/jices-02-2018-0010 ◽

2019 ◽

Vol 17 (1) ◽

pp. 42-60 ◽

Cited By ~ 1

Author(s):

Katherine Taken Smith ◽

Amie Jones ◽

Leigh Johnson ◽

Lawrence Murphy Smith

Keyword(s):

Cyber Security ◽

Stock Prices ◽

Stock Price ◽

Preventive Measures ◽

Market Price ◽

Publicly Traded ◽

Content Type ◽

Publicly Traded Companies ◽

Original Manuscript ◽

The Impact

Purpose Cybercrime is a prevalent and serious threat to publicly traded companies. Defending company information systems from cybercrime is one of the most important aspects of technology management. Cybercrime often not only results in stolen assets and lost business but also damages a company’s reputation, which in turn may affect the company’s stock market value. This is a serious concern to company managers, financial analysts, investors and creditors. This paper aims to examine the impact of cybercrime on stock prices of a sample of publicly traded companies. Design/methodology/approach Financial data were gathered on companies that were reported in news stories as victims of cybercrime. The market price of the company’s stock was recorded for several days before the news report and several days after. The percentage change in the stock price was compared to the change in the Dow Jones Industrial average to determine whether the stock price increased or decreased along with the rest of the market. Findings Stock prices were negatively affected in all time periods examined, significantly so in one period. Practical implications This paper describes cases concerning cybercrime, thereby bringing attention to the value of cybersecurity in protecting computers, identity and transactions. Cyber security is necessary to avoid becoming a victim of cybercrime. Specific security improvements and preventive measures are provided within the paper. Preventive measures are generally less costly than repairs after a cybercrime. Originality/value This is an original manuscript that adds to the literature regarding cybercrime and preventive measures.

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Dividend clientele, new insights, and new questions: the Brazilian case

RAE eletrônica ◽

10.1590/s1676-56482009000100002 ◽

2009 ◽

Vol 8 (1) ◽

Cited By ~ 4

Author(s):

Jairo Laser Procianoy ◽

Rodrigo S. Verdi

Keyword(s):

Stock Prices ◽

Stock Price ◽

Trading Volume ◽

Market Price ◽

Capital Gains ◽

Capital Gains Tax ◽

Abnormal Volume ◽

Abnormal Trading Volume ◽

Signaling Hypothesis ◽

Dividend Tax

This paper analyzes the dividend clientele effect and the signaling hypothesis in the Brazilian stock market between 1996 and 2000. During this period, the dividend tax was zero and the capital gains tax varied between zero and 10%. Brazilian firms face two information regimes, which allow us to test the signaling hypothesis. From a sample of 394 observations studied, 39% show a first ex-dividend day stock price higher than the price on the last cum-dividend day. The market price is higher for unanticipated dividends but, even with pre-announced dividends, stock prices are higher than the expected level, which is inconsistent with the clientele hypothesis. We also find evidence of a positive abnormal volume around the unanticipated dividend date, which is consistent with the signaling hypothesis, but no abnormal trading volume around pre-announced dividend dates. Our findings are inconsistent with the clientele hypothesis but provide support for the signaling hypothesis.

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The Impact of Stock Characteristics on Its Market Price in Jordanian Commercial Banks

Modern Applied Science ◽

10.5539/mas.v14n3p45 ◽

2020 ◽

Vol 14 (3) ◽

pp. 45

Author(s):

Belal Rabah Taher Shammout

Keyword(s):

Stock Prices ◽

Commercial Banks ◽

Stock Price ◽

Market Price ◽

Decision Makers ◽

Book Value ◽

Earnings Per Share ◽

Price Earnings Ratio ◽

Controlling Variables ◽

The Impact

The study aims at identifying the impact of stock characteristics represented by (Earnings Per Share (EPS), Book Value Ratio (BVR), Dividends Per Share (DPS), Dividends Payout Ratio (DPR), Market to Book Ratio (MBR), Price Earnings Ratio (PER), and Yield Per Share (YPE)) on the market stock price in the 13 commercial banks in Jordan during the period from 2005 to 2018. Multiple Linear Regression has been used to illustrate the impact of the independent variables and the controlling variables on the dependent variable. The study has found that there is a significant impact of stock characteristics on its market price at the Jordanian commercial banks. The study also found a statistically significant impact for each book value ratio, dividends per share, market to book ratio, price-earnings ratio, and yield per share on the market price at the Jordanian commercial banks. However, there was no statistically significant effect for each of the earnings per share and dividend’s payout ratio on the market price at the Jordanian commercial banks. The study recommends that investors, analysts, and decision-makers use the characteristics of stocks when carrying out analyses before making important investment decisions that can affect their wealth in the future through forecasting stock prices.

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Nonlinear relationship between carbon market and energy market

E3S Web of Conferences ◽

10.1051/e3sconf/202127502046 ◽

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.

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How Does Stock Prices Respond to Various Macroeconomic Factors? A Case Study of Pakistan

Journal of Management Info ◽

10.31580/jmi.v4i1.22 ◽

2014 ◽

Vol 1 (4) ◽

pp. 25-30

Author(s):

Ayaz Khan

Keyword(s):

Stock Market ◽

Stock Prices ◽

Stock Price ◽

Stock Exchange ◽

Correction Term ◽

Macroeconomic Factors ◽

Market Prices ◽

Distributed Lag ◽

Distributed Lag Model ◽

Long Run

Over the time everything flourished, at the same token the interrelationship among the stock market prices, returns and macroeconomic factors got attendance of the researchers in the field of finance and economics around the world. In this respect current study is an attempt to investigate the response of various macroeconomic factors (GDP, Money Supply, inflation, exchange rate and Size of firm) toward stock market prices in case of Karachi stock exchange over a period of 1971 to 2012. The study utilizes Autoregressive Distributed lag model (ARDL) technique. The results shows that in long run each factor significantly contribute to the stock price while in shot run some factors were significant while some were not but the error correction term shows significant convergence toward equilibrium. The findings of study suggest that for smoothness of stock market the current factors must be targeted.

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