Search Results (37)

Recent years have seen a rise in combining deep learning and technical analysis for stock price prediction. However, technical indicators are often prioritized over technical charts due to quantification challenges. While some studies use closing price charts for predicting stock trends, they overlook charts from other indicators and their relationships, resulting in underutilized information for predicting stock. Therefore, we design a novel framework to address the underutilized information limitations within technical charts generated by different indicators. Specifically, different sequences of stock indicators are used to generate various technical charts, and an adaptive relationship graph learning layer is employed to learn the relationships among technical charts generated by different indicators. Finally, by applying a GNN model combined with the relationship graphs of diverse technical charts, temporal patterns of stock indicator sequences are captured, fully utilizing the information between various technical charts to achieve accurate stock price predictions. Additionally, we further tested our framework with real-world stock data, showing superior performance over advanced baselines in predicting stock prices, achieving the highest net value in trading simulations. Our research results not only complement the existing applications of non-singular technical charts in deep learning but also offer backing for investment applications in financial market decision-making. Full article

Globalization and the spread of technological innovations have made world markets and economies increasingly unified and conditioned by international trade, not only for sales markets but above all for the supply of raw materials necessary for the functioning of the production complex of each country. Alongside oil and gold, the main commodities traded include industrial metals, such as aluminum and copper, mineral products such as gas, electrical and electronic components, agricultural products, and precious metals. The conflict between Russia and Ukraine tested the unification of markets, given that these are countries with notable raw materials and are strongly dedicated to exports. This suggests that commodity prices were able to influence the stock markets, especially in the countries most closely linked to the two belligerents in terms of import-export. Given the importance of industrial metals in this period of energy transition, the aim of our study is to analyze whether Industrial Metals volatility affects G7 stock markets. To this end, the BEKK-GARCH model is used. The sample period spans from 3 January 2018 to 17 September 2024. The results show that lagged shocks and volatility significantly and positively influence the current conditional volatility of commodity and stock returns during all periods. In fact, past shocks inversely influence the current volatility of stock indices in periods when external events disrupt financial markets. The results show a non-linear and positive impact of commodity volatility on the implied volatility of the stock markets. The findings suggest that the war significantly affected stock prices and exacerbated volatility, so investors should diversify their portfolios to maximize returns and reduce risk differently in times of crisis, and a lack of diversification of raw materials is a risky factor for investors. Full article

Forecasting financial time series is challenging due to their intrinsic nonlinearity, high volatility, and complex dependencies across temporal scales. This study introduces MSGformer, a novel hybrid architecture that integrates multi-scale graph neural networks (MSGNet) with Transformer encoders to capture both local temporal fluctuations and long-term global trends in high-frequency financial data. The MSGNet module constructs multi-scale representations using adaptive graph convolutions and intra-sequence attention, while the Transformer component enhances long-range dependency modeling via multi-head self-attention. We evaluate MSGformer on minute-level stock index data from the Chinese A-share market, including CSI 300, SSE 50, CSI 500, and SSE Composite indices. Extensive experiments demonstrate that MSGformer significantly outperforms state-of-the-art baselines (e.g., Transformer, PatchTST, Autoformer) in terms of MAE, RMSE, MAPE, and R². The results confirm that the proposed hybrid model achieves superior prediction accuracy, robustness, and generalization across various forecasting horizons, providing an effective solution for real-world financial decision-making and risk assessment. Full article

Enterprise performance in real-world markets is shaped by dynamic factors, including competitors, collaborators, and hidden associates. Existing models struggle to capture the interplay between time-varying network dynamics and financial asset price movements. Traditional energy cascading models rely on static network assumptions, while deep learning approaches lack the incorporation of key network science principles such as structural balance and assortativity degree. To address these gaps, we propose the Dynamic Energy Cascading Model (DECM), a framework that models the propagation of business influence within dynamic enterprise networks. This method first constructs a dynamic enterprise association network, then applies an energy cascading mechanism to this network, utilizing the propagated energy metrics as predictive indicators for stock price forecasting. Unlike existing approaches, DECM uniquely integrates dynamic network properties and knowledge structures, such as structural balance and assortativity degree, to model the cascading effects of business influences on stock prices. Through extensive evaluations using data from S&P 500 companies, we demonstrate that DECM significantly outperforms conventional models in predictive precision. A key innovation of our work lies in identifying the critical role of assortativity degree in predicting stock price movements, which surpasses the impact of structural balance. These findings not only advance the theoretical understanding of enterprise performance dynamics but also provide actionable insights for policymakers and practitioners from a network science perspective. Full article

This study aims to identify the common indices having an impact on the SPV of BRICS countries during crises. To address this, the monthly data retrieved from the database of the Global Economic Monitor (GEM), World Bank, IMF International Financial Statistics data, and OECD in the period of January 2000 to December 2023 are analyzed in two phases. In the first phase, DM classification techniques are applied to the data to identify the best common classification technique in order to use this technique in the second phase to compare the results with Multiple Linear Regression (MLR) results. In the second phase, to account for the global financial crisis and COVID-19 crisis, the sample period is divided into two sub-periods. For those sub-periods, MLR and the best classification technique that was found in the first phase are utilized to find the common indices that have an impact on the stock price volatility during individual and both crises. The findings indicate that the Random Tree method commonly classified the data among the seven classification techniques. Regarding MLR results, no common indices were identified during the global financial crisis or the COVID-19 crisis. However, based on Random Tree classifications, the CPI price percent, National Currency, and CPI index for all items were common during the global financial crisis, whereas only the CPI price percent was common during the COVID-19 crisis. While some common indices were observed in individual crises for specific countries, no indices were consistently found across both crises. This variation is attributed to the unique nature of each crisis and the diverse economic and socio-political structures of different countries. These findings provide valuable insights for financial institutions and investors to refine financial and policy decisions based on the specific characteristics of each crisis and the indices affecting each country. Full article

This study uses real-world illustrations to explore the application of deep learning approaches to predict economic information. In this, we investigate the effect of deep learning model architecture and time-series data properties on prediction accuracy. We aim to evaluate the predictive power of several neural network models using a financial time-series dataset. These models include Convolutional RNNs, Convolutional LSTMs, Convolutional GRUs, Convolutional Bi-directional RNNs, Convolutional Bi-directional LSTMs, and Convolutional Bi-directional GRUs. Our main objective is to utilize deep learning techniques for simultaneous predictions on multivariable time-series datasets. We utilize the daily fluctuations of six Asian stock market indices from 1 April 2020 to 31 March 2024. This study’s overarching goal is to evaluate deep learning models constructed using training data gathered during the early stages of the COVID-19 pandemic when the economy was hit hard. We find that the limitations prove that no single deep learning algorithm can reliably forecast financial data for every state. In addition, predictions obtained from solitary deep learning models are more precise when dealing with consistent time-series data. Nevertheless, the hybrid model performs better when analyzing time-series data with significant chaos. Full article

The aim of this study is to examine the impact of green bond issuance on the stock market, based on the share prices of 29 companies located in different countries around the world. Using our financial map and applying clustering techniques, we study price fluctuations and identify the influences shaping them. Our contribution lies in methodological innovation through a Multidimensional Scaling approach. Based on this innovative approach, the results of this investigation revealed a complex dynamic in which various factors such as company size, issue volume, total number of issues, geographical location, country GDP, and even governance indices such as the corruption index interact significantly. Full article

Investigating the significant “roles” within financial complex networks and their stability is of great importance for preventing financial risks. On one hand, this paper initially constructs a complex network model of the stock market based on mutual information theory and threshold methods, combined with the closing price returns of stocks. It then analyzes the basic topological characteristics of this network and examines its stability under random and targeted attacks by varying the threshold values. On the other hand, using systemic risk entropy as a metric to quantify the stability of the stock market, this paper validates the impact of the COVID-19 pandemic as a widespread, unexpected event on network stability. The research results indicate that this complex network exhibits small-world characteristics but cannot be strictly classified as a scale-free network. In this network, key roles are played by the industrial sector, media and information services, pharmaceuticals and healthcare, transportation, and utilities. Upon reducing the threshold, the network’s resilience to random attacks is correspondingly strengthened. Dynamically, from 2000 to 2022, systemic risk in significant industrial share markets significantly increased. From a static perspective, the period around 2019, affected by the COVID-19 pandemic, experienced the most drastic fluctuations. Compared to the year 2000, systemic risk entropy in 2022 increased nearly sixtyfold, further indicating an increasing instability within this complex network. Full article

Different severe energy crisis episodes have occurred in the world in the last five decades. Energy crises lead to the deterioration of international relations, economic crises, changes in monetary systems, and social problems in countries. This paper aims to show the essential determinants of energy crises by developing a binary logit model that estimates the predictive ability of thirteen indicators in a sample that covers the period from January 1973 to December 2022. The empirical results show that the energy crises are mainly due to energy supply–demand imbalances (petroleum stocks, fossil energy production–consumption imbalances, and changes in energy imports by countries), energy investments (oil and natural gas drilling activities), economic and financial disruptions (inflation, dollar indices, and indices of global real economic activity) and geopolitical risks. Additionally, the model is capable of accurately predicting world energy crisis events with a 99% probability. Full article

The progress of financial markets depends on the way world investors foresee the market potential of the country of choice. Countries that are associated with favorable economic incentives are able to motivate investments in their respective stock markets. The objective of this paper is to examine the role of the many economic components which constitute the Market Potential Index in enhancing stock market progress. The methodology goes through testing and estimation. The tests include linearity versus nonlinearity (RESET), normality, and cointegration. The estimation includes cointegration regression and discriminant analysis to distinguish between high and low stock market progress. This study examines unbalanced panel data that covers the years 1996–2022 for 54 countries where a stock market exists. The results show the following: (a) increases in people’s expenditure result in decreases in consumption of investment in financial securities; (b) the investments in infrastructure technology is positively associated with stock market progress; (c) the positive effect of economic freedom indicates that further adaptive trading regulations are beneficial to stock market progress; (d) increases in imports consume large proportions of people’s income, coming at the expense of investment in financial securities; (e) stock markets that are associated with high country risk are characterized by a positive risk–return tradeoff, i.e., a high risk premium; (f) the stock markets listed in the MPI can reach high progress by improving three indicators, namely commercial infrastructure, market receptivity, and country risk. This paper offers a thorough and unique examination of the institutional arrangements and stock market progress. The paper offers a guide to policy makers about how economic institutional arrangements can be promoted in order to reach high stock market progress. Full article

Structural change tests aim to identify evidence of a structural break or change in the underlying generating process of a time series. The BDS test has its origins in chaos theory and seeks to test, using the correlation integral, the hypothesis that a time series is generated by an identically and independently distributed (IID) stochastic process over time. The BDS test is already widely used as a powerful tool for testing the hypothesis of white noise in the residuals of time series models. In this paper, we illustrate how the BDS test can be implemented also in a recursive manner to evaluate the hypothesis of structural change in a time series, taking advantage of its ability to test the IID hypothesis. We apply the BDS test repeatedly, starting with a sub-sample of the original time series and incrementally increasing the number of observations until it is applied to the full sample time series. A structural change in the unknown underlying generator model is detected when a change in the trend shown by this recursively computed BDS statistic is detected. The strength of this recursive BDS test lies in the fact that it does not require making any assumptions about the underlying time series generator model. We ilustrate the power and potential of this recursive BDS test through an application to real economic data. In this sense, we apply the test to assess the structural changes caused by the COVID-19 pandemic in international financial markets. Using daily data from the world’s top stock indices, we have detected strong and statistically significant evidence of two major structural changes during the period from June 2018 to June 2022. The first occurred in March 2020, coinciding with the onset of economic restrictions in the main Western countries as a result of the pandemic. The second occurred towards the end of August 2020, with the end of the main economic restrictions and the beginning of a new post-pandemic economic scenario. This methodology to test for structural changes in a time series is easy to implement and can detect changes in any system or process behind the time series even when this generating system is not known, and without the need to specify or estimate any a priori generating model. In this sense, the recursive BDS test could be incorporated as an initial preliminary step to any exercise of time series modeling. If a structural change is detected in a time series, rather than estimating a single predictive model for the full-sample time series, efforts should be made to estimate different predictive models, one for the time before and one for the time after the detected structural change. Full article

The stock market represents one of the most complex mechanisms in the financial world. It can be seen as a living being with complex ways to enact, interact, evolve, defend, and respond to various stimuli. Technical analysis is one of the most complex techniques based on financial data’s graphical aspects. News sentiment indices are very complex and highlight another important part of behavioral finance. In this study, we propose an integrated approach in order to determine the correlation between news sentiment indices, the stock market, and technical analysis. The research methodology focuses on the stock market’s practical and quantitative aspects. In this sense, we have used the graphical representation of technical analysis and econometric modeling techniques such as VAR and Bayesian VAR. The results of the empirical modeling techniques and analysis reveal some important connections between the stock market and news sentiment indices on the US stock market. The conclusions of this study highlight a strong connection between news sentiment indices, technical analysis, and the stock market which suggests that the behavioral finance aspect is a very important aspect in the analysis of the stock market. Full article

This paper reassessed the hedging properties of four major safe-haven currencies (US dollar, Swiss franc, euro, yen) in international stock portfolios covering most representative world macroeconomic areas. The main contribution to the existing literature is the emphasis on optimal hedging and asset-allocation strategies. A further distinguishing feature is an accurate comparison, inside a multivariate framework, between value-at-risk simulations assuming equal or optimal asset weights in hedged global stock portfolios. The US dollar stands out as the best safe-haven currency, while adding the US currency to single-hedged global stock portfolios including either the Swiss franc or the euro yields smooth risk profiles during major financial crises, and average risk indicators lower than that of a benchmark fully hedged portfolio. Full article

Despite the spread and progress in the literature related to the disclosure of corporate social responsibility (CSR) performance around the world as one of the most essential tools for achieving sustainable development in society, its value relevance is still uncertain. Using a survey approach involving investors dealing in stocks of 60 enterprises listed on the Egyptian Stock Exchange (EGX) and included in the environmental, social, and governance index (S&P/EGX ESG index) and the equal-weight index (EGX100 EWI index), we empirically examine the importance of CSR financial performance disclosure by examining the extent to which it can influence investors’ choices. In addition, we assess whether company reputation acquired through online social media (OSM) influences the extent to which CSR performance disclosure influences such judgments. To examine these matters, we conduct two tests: the first examines the influence of disclosure of company environmental activities on investors’ decisions and the other examines the influence of disclosure of company social activities on investor decisions. Turning to our key results, we find that investment decision makers in both experiments tend to invest only in companies that have higher CSR performance scores. In the context of OSM, we provide and discuss empirical evidence that investment decision makers are more responsive to investing in companies included in the S&P/EGX ESG index, which have a positive e-reputation for CSR performance, than companies included in the EGX100 EWI index, which do not have such a reputation, which confirms that e-reputation, as one of the most important outputs of OSM, has a marginal impact on investment decisions and moderates the relation between disclosure of high CSR scores and investors’ decisions. Therefore, this paper presents a modern starting point for CSR experts and academics, particularly in the emerging markets. In general, our paper expands the CSR-related investment literature. In line with the affect-as-information theory, our paper also expands the OSM literature by indicating that the effects of OSM depend on the information context, where failure to provide information to investors or other stakeholders in a timely manner may render the information useless. Full article

Renewable energy sources are an attractive alternative to fossil fuels for several reasons. Firstly, these are ecological arguments, mainly reducing greenhouse gas emissions. Secondly, there are legal issues, particularly the obligations of the European Community countries in the field of climate as part of the implementation of the European Green Deal and the joint achievement of 40% of energy from renewable sources by 2030. Thirdly, these are international issues, primarily regarding reducing dependence on uncertain oil or gas markets. And finally, they may be economic reasons, such as diversification of energy supplies and associated costs, as well as opportunities for profits on the capital market. In Poland, over the last decade, a certain kind of boom in photovoltaics has been visible, both in terms of the number of companies dealing with solar collectors, as well as the annual increase in new capacity, or the level of installed capacity. Also, on the Warsaw Stock Exchange, photovoltaic companies have introduced much confusion in the tier of quotations in recent years. Solar energy has become a kind of gateway for companies to increase their results, stock exchange quotations, or acquire new customers. It is not surprising that more and more investors want to invest their money in this segment. Given the above, this article attempts to answer the question: Is there a risk of a stock market bubble among photovoltaic companies in the near future? For this purpose, we used the financial indicators of photovoltaic companies listed on the Warsaw Stock Exchange, and with the help of the Taxonomic Measure of Attractiveness of Investments, we created rankings of the investment attractiveness of these companies in 2017-2019. The leaders include companies listed on the main market as well as in the alternative trading system of the Warsaw Stock Exchange. It should be borne in mind that regardless of the undertaken diversification and analytical activities, the risk of an investment bubble has been and will remain an indispensable element in the functioning of every capital market in the world. Full article