CSR and the Financial Results of Stock Companies from the Polish Stock Indexes mWIG40 and sWIG80

2021 ◽  
Vol XXIV (Issue 3) ◽  
pp. 303-315
Author(s):  
Agnieszka Moskal ◽  
Agnieszka Judkowiak
Keyword(s):  
Stock Indexes

scholarly journals A Reference Model of Sustainability Disclosure based on four Sustainability Stock Indexes

2016 ◽  
Vol 8 (4) ◽  
pp. 44 ◽  
Author(s):  
Hong Yuh Ching ◽  
Thiago Toste ◽  
Renan Tardelli
Keyword(s):  
Reference Model ◽  
Corporate Sustainability ◽  
Positive Signal ◽  
Socially Responsible ◽  
Responsible Investment ◽  
Sustainability Index ◽  
Fund Managers ◽  
Sustainability Disclosure ◽  
Dow Jones Sustainability Index ◽  
Stock Indexes

The study proposes to develop a reference model of sustainability disclosure based on the models and requirements of four sustainability indexes - Dow Jones Sustainability Index, Corporate Sustainability Index ISE, Frankfurt STOXX and Financial Times FTSE ESG. The approach employed to develop the model is a qualitative analysis of the complementarity among the Stock indexes above mentioned alongside a literature review on sustainability disclosure frameworks. There is no consensus around what should be measured and how. Yet, there is no study in the literature that has ever discussed the models of the sustainability stock indexes and the respective data required in each one of them or compared these models and their requirements. The present study attempts to fulfill this gap by examining the initiatives and requirements of four prominent sustainability indexes. This study contributes to the sustainability responsible investment literature. The inclusion of a firm in a sustainability index can be perceived as a positive signal by investors and this can be explained by signaling theory. This analysis can help investors and/or socially responsible fund managers to screen the stocks against this reference model and determine those firms that are more adherent to it.

scholarly journals Predicting Financial Prices of Stock Market using Recurrent Convolutional Neural Networks

2020 ◽  
Vol 12 (6) ◽  
pp. 21-32
Author(s):  
Muhammad Zulqarnain ◽  
◽  
Rozaida Ghazali ◽  
Muhammad Ghulam Ghouse ◽  
Yana Mazwin Mohmad Hassim ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Financial Time ◽  
Data Mining Approach ◽  
Learning Tasks ◽  
Financial Prices ◽  
Stock Indexes

Financial time-series prediction has been long and the most challenging issues in financial market analysis. The deep neural networks is one of the excellent data mining approach has received great attention by researchers in several areas of time-series prediction since last 10 years. “Convolutional neural network (CNN) and recurrent neural network (RNN) models have become the mainstream methods for financial predictions. In this paper, we proposed to combine architectures, which exploit the advantages of CNN and RNN simultaneously, for the prediction of trading signals. Our model is essentially presented to financial time series predicting signals through a CNN layer, and directly fed into a gated recurrent unit (GRU) layer to capture long-term signals dependencies. GRU model perform better in sequential learning tasks and solve the vanishing gradients and exploding issue in standard RNNs. We evaluate our model on three datasets for stock indexes of the Hang Seng Indexes (HSI), the Deutscher Aktienindex (DAX) and the S&P 500 Index range 2008 to 2016, and associate the GRU-CNN based approaches with the existing deep learning models. Experimental results present that the proposed GRU-CNN model obtained the best prediction accuracy 56.2% on HIS dataset, 56.1% on DAX dataset and 56.3% on S&P500 dataset respectively.

scholarly journals Factor analysis of the Lithuanian equity market indices

2009 ◽  
Vol 50 ◽  
Author(s):  
Svetlana Danilenko
Keyword(s):  
Factor Analysis ◽  
Stock Market ◽  
Stock Prices ◽  
Equity Market ◽  
Stock Index ◽  
Change Dynamics ◽  
Statistical Measures ◽  
Components Method ◽  
Main Components ◽  
Stock Indexes

Statistical measures that can reproduce the state of the stock market and the tendencies of its change dynamics are the stock indexes. Having in mind the more complicated state of the finance system it is important to answer the question of what impacts the fluctuations of the stock prices. The article discusses various factors that impact the fluctuations of the Lithuanian stock index OMXV ; also stock index factor analysis is performed. Factors are determined using the main components method.

scholarly journals Discriminating between Long Memory and Volatility Shifts

2016 ◽  
Vol 36 (4) ◽  
Author(s):  
Aonan Zhang ◽  
Robertas Gabrys ◽  
Piotr Kokoszka
Keyword(s):  
Long Memory ◽  
Change Point ◽  
Kernel Estimator ◽  
Practical Implementation ◽  
Point Model ◽  
Variance Estimators ◽  
Long Run ◽  
The Us ◽  
Change Point Model ◽  
Stock Indexes

We develop a practical implementation of the test proposed in Berkes, Horv´ath, Kokoszka, and Shao (2006) designed to distinguish between a change-point model and a long memory model. Our implementation is calibrated to distinguish between a shift in volatility of returns and long memory in squared returns. It uses a kernel estimator of the long-run variance of squared returns with the maximal lag selected by a data driven procedure which depends on the sample size, the location of the estimated change point and the direction of the apparent volatility shift (increase versus decrease). In a simulations study, we also consider other long-run variance estimators, including the VARHAC estimator, but we find that they lead to tests with inferior performance. Applied to returns on indexes and individual stocks, our test indicates that even for the same asset, a change-point model may be preferable for a certain period of time, whereas there is evidence of long memory in another period of time. Generally there is stronger evidence for long memory in the eight years ending June 2006 than in the eight years starting January 1992. This pattern is most pronounced for US stock indexes and shares in the US financial sector.

scholarly journals COVID-19 and Islamic Stock Index: Evidence of Market Behavior and Volatility Persistence

2021 ◽  
Vol 14 (8) ◽  
pp. 389
Author(s):  
Adil Saleem ◽  
Judit Bárczi ◽  
Judit Sági
Keyword(s):  
Stock Index ◽  
Market Model ◽  
Market Reactions ◽  
Health Crisis ◽  
Stock Indices ◽  
Thomson Reuters ◽  
Short Run ◽  
Study Methodology ◽  
Stock Market Reactions ◽  
Stock Indexes

The aftermath of the COVID-19 pandemic is not limited to human lives and health sectors. It has also changed social and economic aspects of the world. This study investigated the Islamic stock market’s reaction and changes in volatility before and during this pandemic. The market model of event study methodology was employed to analyze Islamic stock market reactions in nine different markets around the globe. To examine changes in volatility and persistence of risk, the generalized autoregressive conditional heteroscedasticity (GARCH) method was used. Nine Islamic stock indices were selected for this study from the Thomson Reuters data stream. The results suggest that, in the short run, the Islamic Australian stock index and Islamic GCC stock index remained stable for the first 15 days following news of the pandemic. The Islamic stock indexes of Qatar, UAE, ASEAN, MENA, MENASA, and Bahrain were significantly affected by the outbreak in the short-term. On the other hand, the volatility of Islamic stock indices was substantially amplified after the global health crisis was declared by the WHO. Moreover, volatility shocks tended to persist for a longer period after COVID-19.

scholarly journals The Impact of COVID-19 on the Dependence of Chinese Stock Market

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xianbo Wu ◽  
Xiaofeng Hui
Keyword(s):  
Stock Market ◽  
Strong Dependence ◽  
Sliding Window ◽  
Dependence Structure ◽  
Chinese Stock Market ◽  
Telecommunication Services ◽  
Window Method ◽  
Dependence Relationship ◽  
Stock Indexes ◽  
The Impact

By calculating the mutual information of stock indexes of 10 primary industry sectors in China, this paper analyzes the dependence relationship among Chinese stock sectors during the COVID-19 and the dynamic evolution of the relationship by using the sliding window method. According to the actual situation of the development of COVID-19 in China, the samples were divided into three stages, namely, calm period, pandemic period, and post-pandemic period. The results show that the dependence relationship among Chinese stock sectors is significantly enhanced in the pandemic period, but it decreases in the post-pandemic period and the dependence structure is similar to that in the calm period. The industrials sector is most closely connected with other sectors in the pandemic period. The information technology sector and telecommunication services sector maintain strong dependence in the three periods and share little contact with other sectors. In the pandemic period, the dependence between the consumer staples sector and other sectors is significantly enhanced, and consumer staples sector and health care sector maintain a strong dependence. From the results of the sliding window, the Chinese stock market is sensitive to the impact of COVID-19, but the duration of the impact on the dependence among the stock sectors is not long.

Analysis of Time Series Based on a New Entropy Plane by Using Weighted Dispersion Pattern

2021 ◽  
Vol 31 (09) ◽  
pp. 2150128
Author(s):  
Guyue Qin ◽  
Pengjian Shang
Keyword(s):  
Time Series ◽  
Financial Markets ◽  
Stock Markets ◽  
Simulated Data ◽  
Tsallis Entropy ◽  
Dispersion Pattern ◽  
Complex Time ◽  
Plane Method ◽  
Stock Indexes ◽  
Analysis Of Time Series

Complexity is an important feature of complex time series. In this paper, we construct a weighted dispersion pattern and propose a new entropy plane using past Tsallis entropy and past Rényi entropy by using weighted dispersion pattern (PTEWD and PREWD, respectively), to quantify the complexity of time series. Through analyzing simulated data and actual data, we have verified the reliability of the entropy plane method. This entropy plane successfully distinguishes American and Chinese stock indexes, as well as developed and emergent stock markets. We introduce PTEWD and PREWD into multiscale settings, which could also well distinguish different stock markets. The results show that the new entropy plane could be used as an effective tool to distinguish financial markets.

Evolution and Diversification of the Going Concern Uncertainties Disclosed in the Auditor’s Report

2021 ◽  
Vol 19 (163) ◽  
pp. 516-527
Author(s):  
Camelia-Daniela HATEGAN ◽  
◽  
Carmen-Mihaela IMBRESCU ◽  
Keyword(s):  
Financial Statements ◽  
Point Of View ◽  
Stock Exchanges ◽  
Going Concern ◽  
Reduced Frequency ◽  
Legal Perspective ◽  
Accounting Principle ◽  
Threat Reporting ◽  
Stock Indexes ◽  
The Impact

The going concern of an entity's activity is a fundamental accounting principle. The practical application of this principle has accounting, legal and financial implications. From an accounting point of view, the management of the entities shall be responsible for drawing up the financial statements in accordance with this principle. From a legal perspective, entities that go into liquidation are no longer obliged to respect the going concern principle. When auditing financial statements, auditors shall be responsible for assessing the adequacy of compliance with the principle of going concern and for including the appropriate references in their report. The objective of the paper is to analyse the reasons for including in the auditors' report the paragraph on going concern uncertainties, in the light of their evolution over time, their frequency and diversification. The sample included 120 companies listed on European stock exchanges, included in the main stock indexes for the period 2010-2020. The data was gathered from reports published by auditors that were included in the Audit Analytics database. The results showed that there was an average trend of 20 reported situations per year, but with a significant increase over the last two years analysed mainly due to the situations arising from the impact of the Covid-19 pandemic. The most common reasons were liquidity risk, substantial liabilities and the refinancing of activities. In recent years there has been a diversification of reasons, but with a reduced frequency, such as the working capital, the decrease in stockholder equity and competitor threat. Reporting on going concern issues is of particular importance so that increasing transparency in the publication of this information can contribute to a higher degree of investor confidence in the entities' financial statements.

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