Over the last 2 decades, financial systems have been studied and analyzed from the perspective of complex networks, where the nodes and edges in the network represent the various financial components and the strengths of correlations between them. Here, we adopt a similar network-based approach to analyze the daily closing prices of 69 global financial market indices across 65 countries over a period of 2000–2014. We study the correlations among the indices by constructing threshold networks superimposed over minimum spanning trees at different time frames. We investigate the effect of critical events in financial markets (crashes and bubbles) on the interactions among the indices by performing both static and dynamic analyses of the correlations. We compare and contrast the structures of these networks during periods of crashes and bubbles, with respect to the normal periods in the market. In addition, we study the temporal evolution of traditional market indicators, various global network measures, and the recently developed edge-based curvature measures. We show that network-centric measures can be extremely useful in monitoring the fragility in the global financial market indices.

Overall, this project was meant test the relationship between social media posts and their short-term effect on stock prices. We decided to use Reddit posts from financial specific subreddit communities like r/wallstreetbets, r/investing, and r/stocks to see the changes in the market associated with a variety of posts made by users. This idea came to light because of the GameStop short squeeze that showed the power of social media in the market. Typically, stock prices should purely represent the total present value of all the future value of the company, but the question we are asking is whether social media can impact that intrinsic value. Our research question was known from the start and it was do Reddit posts for or against a certain stock provide insight into how the market will move in a short window. To solve this problem, we selected five large tech companies including Apple, Tesla, Amazon, Microsoft, and Google. These companies would likely give us more data in the subreddits and would have less volatility day to day allowing us to simulate an experiment easier. They trade at very high values so a change from a Reddit post would have to be significant giving us proof that there is an effect.

Next, we had to choose our data sources for to have data to test with. First, we tried to locate the Reddit data using a Reddit API, but due to circumstances regarding Reddit requiring approval to use their data we switched to a Kaggle dataset that contained metadata from Reddit. For our second data set we had planned to use Yahoo Finance through yfinance, but due to the large amount of data we were pulling from this public API our IP address was temporarily blocked. This caused us to switch our second data to pull from Alpha Vantage. While this was a large switch in the public it was a minor roadblock and fixing the Finance pulling section allowed for everything else to continue to work in succession. Once we had both of our datasets programmatically pulled into our local vs code, we implemented a pipeline to clean, merge, and analyze all the data. At the end, we implement a Snakemake workflow to ensure the project was easily reproducible. To continue, we utilized Textblob to label our Reddit posts with a sentiment value of positive, negative, or neutral and provide us with a correlation value to analyze with. We then matched the time frame of each post with the stock data and computed any possible changes, found a correlation coefficient, and graphed our findings.

To conclude the data analysis, we found that there is relatively small or no correlation between the total companies, but Microsoft and Google do show stronger correlations when analyzed on their own. However, this may be due to other circumstances like why the post was made or if the market had other trends on those dates already. A larger analysis with more data from other social media platforms would be needed to conclude for our hypothesis that there is a strong correlation.

The following dataset includes the Warsaw Stock Exchange market analysis using the Ljung-Box test. Partial autocorrelations up to the 5th order were analyzed, because it will allow to observe the relationship within one week of stock exchange quotations. In the case of the WIG index, the 1st and 2nd order correlation turned out to be statistically significant. It is the only index where two autocorrelations were significant. For the WIG20 index, only the 2nd order autocorrelation is important, and for the mWIG40 and sWIG80 indices, the 1st order. No higher order autocorrelation was recorded on any of the indexes.

Correlations insignificant at p < 0.05 are in italic.

This data was used to investigates the impact of climate change-induced monetary damage on the financial stability of SMEs in Africa, focusing on their overleveraging as measured by outstanding loan balances. Utilizing data from the World Bank Enterprise Survey (WBES) for 2023, we examine SMEs from Botswana, Côte d'Ivoire, and Mauritius. Our findings reveal a strong and statistically significant relationship between climate-related financial losses and SME indebtedness, with a correlation index of 0.61. The Ordinary Least Squares (OLS) regression model confirms that approximately 76.1% of the variance in SME overleveraging can be explained by climate-induced financial losses.

This dataset contains the prioritization provided by a panel of 15 experts to a set of 28 barriers categories for 8 different roles of the future energy system. A Delphi method was followed and the scores provided in the three rounds carried out are included. The dataset also contains the scripts used to assess the results and the output of this assessment. A list of the information contained in this file is: data folder: this folders includes the scores given by the 15 experts in the 3 rounds. Every round is in an individual folder. There is a file per expert that has the scores between -5 (not relevant at all) to 5 (completely relevant) per barrier (rows) and actor (columns). There is also a file with the description of the experts in terms of their position in the company, the type of company and the country. fig folder: this folder includes the figures created to assess the information provided by the experts. For each round, the following figures are created (in each respective folder): Boxplot with the distribution of scores per barriers and roles. Heatmap with the mean scores per barriers and roles. Boxplots with the comparison of the different distributions provided by the experts of each group (depending on the keywords) per barrier and role. Heatmap with the mean score per barrier weighted depeding on the importance of the role in each use case and the final prioritization. Finally, bar plots with the mean scores differences between rounds and boxplot with comparisons of the scores distributions are also provided. stat folder: this folder includes the files with the results of the different statistical assessment carried out. For each round, the following figures are created (in each respective folder): The statistics used to assess the scores (Intraclass correlation coefficient, Inter-rater agreement, Inter-rater agreement p-value, Homogeneity of Variances, Average interquartile range, Standard Deviation of interquartile ranges, Friedman test p-value Average power post hoc) per barrier and per role. The results of the post hoc of the Friedman Test per berries and per roles. The average score per barrier and per role. The mean value of the scores provided by the experts grouped by the keywords per barrier and role. P-value of the comparison of these two values. The end prioritization of the barrier for the use case (averaging the scores or fuzzy merging of the critical sets) Finally, the differences between the mean and standard deviations of the scores between two consecutive rounds are provided.

The stock market is the barometer of the economy that reflects the overall health and direction of the economic development and is affected by different factors including social, environmental and political. It is important to investigate the effect of the political instability on the stock market performance, especially on emerging economies. Therefore, we aim to study the relationship between political instability and stock market performance in Pakistan. To meet our objectives, we used past data from 1996 to 2021. Data are collected from the DataStream data base. MSCI indices are used as the proxy for the Stock market performance of the selected country. World governance six indicators are used in the study as the explanatory variable concentrating the political instability index as the main explanatory variable. Regression analysis is used but two-way robustness analysis was done for the accuracy of the findings through GMM methods and taking GDP as another endogenous variable. Our findings shows that the political stability has significant positive impact on the stock market performance while, political instability has negative impact on stock market performance. Moreover, other governance indicators has a significant positive impact on performance. However, political instability disrupts the operations and economical activities that leads to decrease the investor confidence and also decrease the foreign investment with the increment of the risk in the country. Moreover, our study has some implications for investors to develop the diversified portfolio to minimize the risk and policy makers can increase their foreign direct investment within the economy by controlling the political instability.

Recent advances in the classification of diverse fish species using state-of-the-art ML and DL.

Performance metrics results for BD-Freshwater-Fish, SmallFishBD, and FishSpecies datasets using SwinFishNet for each fold.

The dataset contains data illustrating the results of detecting and data-stamping price explosivity periods in the art market, represented by 35 indices from Art Market Research and 25 stock exchange indices (LSEG). The files contain:• 01_diagnostics_gsadf – summary of results for analysed time series.• 02_st_value – obtained statistical test value for GSADF procedure.• 03_st_cv – critical values from the Monte Carlo simulation with 2,000 repetitions.• 04_datestamp_gsadf_results – characteristics for detected price explosivity periods.• 05_dummy_results_gsadf – base for co-explosivity analysis.• 06_DS – value for descriptive statistics, which were calculated based on the raw data.• 07_mydata.coeff, 08_mydata.p, 09_mydata.n – correlation analysis results, phi coefficient values, p-value and number of observations, respectively.

Class-wise distribution of images in the SmallFishBD Dataset.

Resource-based cities often face problems such as resource scarcity and insufficient electricity to achieve complex high-quality growth. At present, there is relatively little research on the impact on the high-quality development of such cities. To study the key variables that affect the high-quality growth of resource-based cities, we adopt entropy weighted TOPSIS technology, spatial correlation analysis, and spatial econometric models. The main conclusions are as follows: (1) The overall high-quality development of resource-based cities in China is on the rise year by year; The cities with the highest growth rates are those that are mature, rejuvenated, growing, and declining. (2) Resource-based cities have a positive geographical correlation in high-quality development, and different numbers of clusters are displayed by changing the Moran I index score. (3) High quality development is strongly influenced by human capital, urbanization, technological innovation, and global market openness. There are significant differences in the ways in which these variables affect various types of resource-based cities. Policy makers who strive to reduce regional inequality and encourage high-quality growth in resource-based communities may benefit greatly from the insights provided by this study.

The accelerated progress in artificial intelligence encourages sophisticated deep learning methods in predicting stock prices. In the meantime, easy accessibility of the stock market in the palm of one’s hand has made its behavior more fuzzy, volatile, and complex than ever. The world is looking at an accurate and reliable model that uses text and numerical data which better represents the market’s highly volatile and non-linear behavior in a broader spectrum. A research gap exists in accurately predicting a target stock’s closing price utilizing the combined numerical and text data. This study uses long short-term memory (LSTM) and gated recurrent unit (GRU) to predict the stock price using stock features alone and incorporating financial news data in conjunction with stock features. The comparative study carried out under identical conditions dispassionately evaluates the importance of incorporating financial news in stock price prediction. Our experiment concludes that incorporating financial news data produces better prediction accuracy than using the stock fundamental features alone. The performances of the model architecture are compared using the standard assessment metrics —Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Correlation Coefficient (R). Furthermore, statistical tests are conducted to further verify the models’ robustness and reliability.

This work describes the design of a novel financial multiplex network composed of three layers obtained by applying the MST-based cross-correlation network, using the data from 465 companies listed on the US market. The study employs a combined approach of complex multiplex networks, to examine the statistical properties of asset interdependence within the financial market. In addition, it performs an extensive analysis of both the similarities and the differences between this financial multiplex network, its individual layers, and the commonly studied stock return network. The results highlight the importance of the financial multiplex network, demonstrating that its network layers offer unique information within the multiplex dataset. Empirical analysis reveals dissimilarities between the financial multiplex network and the stock return monoplex network, indicating that the two networks provide distinct insights into the structure of the stock market. Furthermore, the financial multiplex network outperforms the singleplex network of stock returns because it has a structure that better determines the future Sharpe ratio. These findings add substantially to our understanding of the financial market system in which multiple types of relationship among financial assets play an important role.

Correlation coefficients.

This study examines the impact of financialization and product market competition on the corporate social responsibility (CSR) engagements in sports goods manufacturing industry. Utilizing a dataset of China’s listed firms, we employ textual analysis to identify organizations within this sector and create a panel data model to analyze the determinants of CSR engagements. Our empirical findings reveal that financialization and product market competition positively influence shareholder-related CSR engagements. Additionally, product market competition enhances the effect of financialization on these engagements. Conversely, a negative correlation exists between product market competition and stakeholder-related CSR engagements. Moreover, firms not categorized as State-Owned Enterprises (SOEs) or within high-pollution industries show a positive response in CSR engagements to both financialization and product market competition. Our results also highlight that managerial compensation and financial constraints modify the impacts of financialization and product market competition on shareholder-related CSR engagements. Collectively, our findings shed light on the challenges that sports goods manufacturing firms face in aligning their primary goals with CSR commitments.

GARCH-BEKK currency, conventional stock index, and shariah stock index.

Correlations of latent variables

Regression results of a spatial econometric model of high-quality development level of various types of cities.

Abstract Background Hospital management is a complex area of administration since it has an organizational set where all sectors are interdependent and work at the same time. One of the main goals of these organizations is to provide quality services with the resources available to continuously improve the quality of services through improvements in the environment. Objective To analyze the relationship between economic and financial indicators with hospital quality indices. Method This is a descriptive, documentary and quantitative study based on the third sector hospitals in the south of the country. Management, nursing, and hospitalization reports are used for the quality indicators and the financial statements for the financial indicators. Univariate statistical tests (descriptive and Pearson correlation) were used for the analysis. Results The relationship between the indexes shows the existence of a significant correlation between the analyzed indicators, demonstrating which indicators should be observed by the hospital manager. Conclusion the more hospital entities providing a quality service, the greater the financial return, and therefore the greater the resources for investments to improve the quality of services.