Industrial Stock Research Articles

The use of aquatic bioresources as a factor in ensuring Ukraine’s food security: legal aspects

The article is dedicated to the study of the legal aspects of using aquatic bioresources in Ukraine and the impact of their usage on ensuring the country’s food security. It is argued that the issue of proper legal regulation of the use of aquatic bioresources is becoming increasingly relevant today. Attention is given to both legislative and scientific approaches to the issue of legal regulation of Ukraine’s food security. The necessity of adopting a separate law titled «On the Fundamentals of Food Security of Ukraine» is emphasized. It is noted that the state’s food policy should aim for the highest degree of self-sufficiency, as the quality and quantity of consumed food products directly impact the nation’s health, life expectancy, work capacity, and intellectual potential. The article examines the legal grounds for the usage of aquatic bioresources. The impact of the fishing industry on ensuring Ukraine’s food security is analyzed, with the conclusion drawn that the country’s food security largely depends on the state of aquatic bioresource usage. The level of fish consumption and its processed products serves as an indicator of the population’s food security, which should be maintained at a level conducive to physiological needs. It is stressed that the development of the fishing industry is one of the most important tasks for ensuring the post-war recovery of the country. The article identifies aquatic bioresources as a strategic state food reserve in the current context. The artificial reproduction of valuable fish species is aimed at both supporting aquatic ecosystems and building industrial stocks. The author asserts that today, food security is a condition for maintaining the state’s sovereignty, supporting high levels of public health, and developing the country’s economy. Emphasis is placed on the fact that the use of aquatic bioresources is crucial for the country in terms of ensuring food security, improving economic conditions, saturating the domestic market with quality food products, increasing employment and welfare, as well as preserving biodiversity and effectively using water and hydrobiological resources.

Technology Company Stock Trend Prediction Based on ARIMA Model

With the development of finance, more and more people are paying attention to stock price prediction, hoping to gain economic benefits from it. From the literature review, due to the complexity of stock trends, it is necessary to study the analysis of industry stocks. This paper studies the effectiveness of the autoregressive moving average model (ARIMA) for 5 stocks in the technology industry. Then, we will compare and parameterize the ARIMA model by using the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). After the data processing, we find that the development prospects of four head technology companies have steadily increased due to years of economic accumulation and stable operation. However, as a technology company that has emerged in recent years, OpenAI is extremely susceptible to external factors such as academic fraud and AI replacing manual labor. Its stock trend has fluctuated greatly and its prospects are sluggish.

Resilience and Adaptation: Investment Metrics of Indonesian Healthcare Companies Before and During the COVID-19 Pandemic

The impact of dividend policy and stock valuation (price earning proportion and price to book value) on the prices of healthcare industry stocks listed on the Indonesia Stock Trade somewhere in the range of 2016 and 2021 is examined in this study. The research data used in this quantitative analysis are secondary data. This study included eighteen healthcare sector companies in its sample. Purposive sampling is the methodology used in this study, and SPSS 24 is the analysis tool. We explained the relationship between stock prices, dividend policy, and stock valuation. We order the variables into pre-pandemic and during-pandemic categories subsequent to testing them for like clockwork. That's what the experimental results demonstrate, for all periods, the earning-per-share notation has a significant impact on stock prices. Stock valuation, on the other hand, has a significant impact on stock prices, as does the final dividend policy with dividend payout proportion and dividend per share. Meanwhile, results from the pre-pandemic time frame indicate that EPS and DPR didn't significantly influence results; results from the pandemic time frame indicate that stock valuation didn't significantly influence stock price. This research provides fresh insights into what the recovery of economic exposure means for corporate conduct in Indonesia, where persistent financial and economic pressures from stock market unpredictability give substantial sources of uncertainty.

Predicting Economic Trends and Stock Market Prices with Deep Learning and Advanced Machine Learning Techniques

The volatile and non-linear nature of stock market data, particularly in the post-pandemic era, poses significant challenges for accurate financial forecasting. To address these challenges, this research develops advanced deep learning and machine learning algorithms to predict financial trends, quantify risks, and forecast stock prices, focusing on the technology sector. Our study seeks to answer the following question: “Which deep learning and supervised machine learning algorithms are the most accurate and efficient in predicting economic trends and stock market prices, and under what conditions do they perform best?” We focus on two advanced recurrent neural network (RNN) models, long short-term memory (LSTM) and Gated Recurrent Unit (GRU), to evaluate their efficiency in predicting technology industry stock prices. Additionally, we integrate statistical methods such as autoregressive integrated moving average (ARIMA) and Facebook Prophet and machine learning algorithms like Extreme Gradient Boosting (XGBoost) to enhance the robustness of our predictions. Unlike classical statistical algorithms, LSTM and GRU models can identify and retain important data sequences, enabling more accurate predictions. Our experimental results show that the GRU model outperforms the LSTM model in terms of prediction accuracy and training time across multiple metrics such as RMSE and MAE. This study offers crucial insights into the predictive capabilities of deep learning models and advanced machine learning techniques for financial forecasting, highlighting the potential of GRU and XGBoost for more accurate and efficient stock price prediction in the technology sector.

The Impact Ratio Finance, Role of Cash Flow As Moderating Variable, Property Industry Stock Prices

The purpose of this research is to examine (1) various elements that can influence the share prices of companies listed on the Indonesia Stock Exchange (BEI) and (2) whether free cash flow affects the share prices of property companies listed on the BEI. From 2018 to 2022; and (3) Do Current Ratio, Return On Equity, Debt to Equity Ratio, Earning Per Share, and Cash Flow simultaneously influence property company share prices on the IDX? and (4) Financial reports for 2018–2022 from eleven companies listed on the IDX are used as secondary data and samples.Method: Sampling was carried out purposively, and multiple linear regression analysis was used with the SPSS application to explain the data. Research and data analysis show that EPS has a significant effect and influences share prices, and several variables influence share prices positively or negatively. Likewise, free cash flow moderates the DER and EPS variables compared to share prices. The conclusion and implication of this research is that EPS has a positive and significant effect on stock prices. Furthermore, the mediation results show the Sig DER value and the Sig EPS value on share prices, which means that cash flow significantly moderates DER and moderates EPS on share prices.

Trading Strategy Based on the Leading Effect of Leading Stocks: LLR Model and Correlation Coefficient Effect

Stocks is one of the most popular financial tools around the world, However, it is essential to determine which stock to buy and the moment we buy in and sell out, only catch up with the proper opportunity, then we can make fortune from the stock market. So how can we find out those stocks which has great potential to grow in the future? Our study focuses on the leading stocks effect, which refers to in a specific industry, when some stocks in that industry have reached their price limit, which we call leading stocks, and in the following trading days, the stocks from the same industry will be affected by leading stocks and have a potential growth. In that case, if we can find out the leading stock in a specific industry and monitor it growth every day, then base on correlation coefficient, we can determine which stock to buy in and make profit via this strategy.

Safe haven properties of industrial stocks against ESG in the United States: Portfolio implication for sustainable investments

This study explores the safe haven role of industry sectors classified by the Global Industry Classification Standards (GICS) for global Environmental, Social, and Governance (ESG) stocks in the United States. Our study applies the novel cross-quantilogram and quantile time-frequency connectedness approach for daily time series data from November 2019 to October 2023. Our findings confirm that industry sectors, mainly financials, industrial, communication, information technology, energy, consumer staples, and consumer discretionary, are the safe haven avenues at down, normal, and up market conditions; however, these safe haven avenues vanish for the long-term investment horizon. We also explore the risk spillover patterns for the total time domain and the decomposed spillover level for the short- and long-term spillovers. Our connectedness finding shows that the net transmitters and net receiver patterns are similar for the extreme markets but dramatically change for the normal markets. Finally, based on the portfolio rebalancing strategy, the returns of our portfolio, comprised of industry sectors and the ESG index, outperform the overall market. Moreover, our portfolio yields lower negative returns and a higher Sharpe ratio when compared with the overall market returns. These findings confirm the benefits of diversification by reducing downside portfolio risk. This study is primarily relevant for socially responsible portfolio and risk management investors.

Monitoring of high-dimensional and high-frequency data streams: A nonparametric approach

ABSTRACT High-dimensional and high-frequency data streams (HHDS) have become widely available with the prevalence of data-acquisition technologies. Because HHDS have the characteristics of high dimensionality and temporal correlation, traditional statistical process control (SPC) techniques cannot be used directly to address the problem of monitoring HHDS. Most extant monitoring methods commonly assume that the process observations are independent or can be described by some parametric models and face the challenge posed by high-dimensional data. Little research has been conducted on the robust monitoring of HHDS that can simultaneously accommodate high dimensions and high frequencies. Therefore, in this paper, we propose a novel nonparametric approach for monitoring HHDS, based on data decorrelation, dimension reduction, and SPC. Specifically, process observations are first sequentially decorrelated and standardized using Cholesky decomposition, and then the k -nearest neighbors classification algorithm is applied to transform uncorrelated high-dimensional data into one-dimensional data. Finally, the traditional cumulative-sum procedure is used for online monitoring, based on an empirical log-likelihood ratio test. Numerical studies have shown that our monitoring scheme is sufficiently reliable and efficient for the online monitoring of HHDS. An illustrative example about the Dow Jones 30 industrial stock prices is presented to further validate the proposed approach.

The effects of the Russia–Ukraine war and the Wagner Group coup on defense stocks in Europe: an event study analysis

Purpose This study aims to examine the impact of the beginning of the Russia–Ukraine war and the Wagner Group’s attempted military coup against Putin’s regime on the European defense sector, consisting of weapons manufacturers. Design/methodology/approach The authors use the event study methodology to quantify the impact. That is, the authors assume that markets are efficient, and abnormal stock returns around the event dates capture the magnitudes of the impacts of the two events studied on European defense sector companies. The authors use the capital asset pricing model and two different multifactor models to estimate expected stock returns, which serve as the benchmark necessary to obtain abnormal returns. Findings The start of the war on February 24, 2022, when the Russian forces invaded Ukraine, was followed by high positive abnormal returns of up to 12% in the next few days. The results are particularly strong if multiple factors are used to control for the risk of the defense stocks. Conversely, the authors find a negative impact of the rebellion initiated by the mercenary Wagner Group’s chief, Yevgeny Prigozhin, on June 23, 2023, on the abnormal returns of defense industry stocks on the first trading day after the event. Originality/value To the best of the authors’ knowledge, this is the first study of the impact of the Russia–Ukraine war on the defense sector. Furthermore, this is the first study to measure the financial implications of the military coup initiated by the Wagner Group. The findings contribute to a rapidly growing literature on the financial implications of military conflicts around the world.

Changes in Banking Industry Stock Prices: The Role of Risk, Good Governance, Earnings, and Capital

This research, with direct implications for banking professionals and investors, aims to empirically test and analyze the RGEC components, which consist of risk profile, good corporate governance, earnings and capital, and their influence on banking stock prices listed on the Indonesian Stock Exchange (BEI). The sample for this research was determined using a purposive sampling technique, resulting in a total sample of 21 companies over 3 periods, namely 2016-2018. The data analysis technique used is Partial Least Square (PLS). The research results, which have immediate relevance for investment strategies, show that earnings proxied using ROA positively affect stock prices, while risk profile, good corporate governance, and capital do not. This indicates that earnings are still a strong signal for investor investment decisions. Therefore, the company must succeed in maintaining and increasing its earnings to become an investment choice for investors. However, even though good corporate governance and capital do not influence stock prices, it doesn’t mean that banking companies should ignore their governance and capital. These two factors are essential in maintaining good performance.

Geopolitical risks, investor sentiment and industry stock market volatility in China: Evidence from a quantile regression approach

From an industry perspective, we apply the quantile regression to investigate the impact of investor sentiment (IS) and China’s/U.S. geopolitical risks (GPR) on Chinese stock market volatility. Considering the structural break of the stock market, we find that China’s and U.S. GPR/IS and their interaction effects have no significant impact on China’s stock market volatility at the market level. However, there has an asymmetric dependence between China’s and U.S. GPR/IS and stock market volatility, and the dependence structure is changing. At the industry level, the impact of geopolitical risk on industry stock market volatility is highly heterogeneous, and its significance mostly occurs in the upper and lower tails. Second, China’s and U.S. GPR/IS can exacerbate industry stock market volatility in bullish and bearish markets. In addition, China’s and U.S. GPR/IS and their interaction effects are heterogeneous and asymmetric, and the effects changes with the break point. Finally, compared with China’s GPR, the U.S. GPR has a larger impact on the industry stock market. The interactive effects of the U.S. GPR and IS can influence more industry stock market volatility.

Validation study of Yunnan ethnic culture industry stock model under epidemic situation based on chaotic particle swarm optimization neural networks

The new crown pneumonia epidemic is raging, in the context of global integration, the scope of the impact of this sudden event spread around the world, the stock market has not been spared, the financial risk has increased dramatically compared with the past, the emergence of the epidemic has led to the spread of investor panic, March 2020, the U.S. S&P 500 index appeared in the four plunge, and led to the market trading meltdown, the world’s financial markets have had an extremely serious impact. The study of the impact of Xin Guan Pneumonia on the company’s stock returns is not only conducive to enriching the theoretical study of public health emergencies, but also conducive to improving the coping strategy, stabilizing the general economic market, and enhancing the public’s awareness of risk response. This paper compares the effect of the four intelligent algorithms of chaotic particle swarm algorithm, chaotic bee colony algorithm, chaotic fruit fly algorithm and chaotic ant colony algorithm combined with neural network on the prediction of the stock price trend of Yunnan national culture, and the study shows that the speed of convergence of the chaotic particle swarm optimization neural network and the speed of descent is better than that of the two models of chaotic fruit fly and chaotic bee colony, and the coefficients of decision of the chaotic particle swarm optimization neural network are higher than that of the other three models, and the errors are lower than the other three models. Indexes are lower than the other three models and have high accuracy in stock prediction of Yunnan ethnic culture, this finding emphasizes the potential of PSO-BP model to provide robust stock market prediction, which is important for both investors and policy makers in dealing with volatile market conditions.

The impact of COVID-19 on the tourism industry stocks returns in Mauritius. Evidence from quantile regression analysis

<p>The tourism industry has been severely affected by the COVID-19 outbreak in Mauritius, where this industry is the backbone of the economy. Just like tourism revenue, tourism returns are also expected to be affected. Hence, this paper examines the impact of COVID-19 on tourism returns in Mauritius by using a quantile regression model and daily data from 18 March to 30 June 2020. The results show that the COVID-19 variables used in the study impact negatively on tourism stock returns. For instance, it is observed that COVID-19 confirmed cases exert negative effects on tourism returns at higher quantiles. Similar results were obtained for COVID-19 recovered cases. Also, a positive link was found between the government response stringency index and tourism returns. However, stock volatility is observed to reduce the industry’s return. This paper further provides useful insights for policymakers, investors, and managers in the tourism industry.</p>

The co-movement of fossil energy, new energy, rare earth, and carbon in China: Measurement and evolution analysis

The low-carbon transition of the energy system plays a crucial role in achieving carbon neutrality. However, the development of new energy heavily relies on rare earth resources, which in turn entail substantial energy consumption and carbon emissions. This “linkage dilemma” poses constraints on the realization of carbon neutrality objectives. In this study, we employ a combination of sample entropy, cross-sample entropy, and coarse-gained network modeling methods to uncover the internal complexity and mutual synchronization of the fossil energy stock market, new energy stock market, rare earth stock market, and carbon market in China. Our findings reveal several important insights. Firstly, we observe that the co-movements among the three industry stock markets and the carbon market are dynamic and closely tied to the efficiency of each market. Secondly, the co-movement between traditional and new energy stock markets remains high, with the supply chain stock market between energy and rare earth exhibiting a similar trend. This highlights the significant impact of inherent substitution or supply chain relationships on the level of synchronization. Thirdly, the stock market crashes and the COVID-19 pandemic have resulted in heterogeneous effects with notable changes observed in carbon-related market pairs. Notably, although the carbon market exhibits relatively low co-movement with other stock markets, the patterns of co-movement and their evolution are becoming increasingly complex. Lastly, based on our findings, we present conclusions and potential implications for policymakers to address the impact of the crisis on market interconnectivity, as well as guide investors to effectively allocate their investment portfolios.

The application of the propensity score matching method in stock prediction among stocks within the same industry.

Stock price prediction is crucial in stock market research, yet existing models often overlook interdependencies among stocks in the same industry, treating them as independent entities. Recognizing and accounting for these interdependencies is essential for precise predictions. Propensity score matching (PSM), a statistical method for balancing individuals between groups and improving causal inferences, has not been extensively applied in stock interdependence investigations. Our study addresses this gap by introducing PSM to examine interdependence among pharmaceutical industry stocks for stock price prediction. Additionally, our research integrates Improved particle swarm optimization (IPSO) with long short-term memory (LSTM) networks to enhance parameter selection, improving overall predictive accuracy. The dataset includes price data for all pharmaceutical industry stocks in 2022, categorized into chemical pharmaceuticals, biopharmaceuticals, and traditional Chinese medicine. Using Stata, we identify significantly correlated stocks within each sub-industry through average treatment effect on the treated (ATT) values. Incorporating PSM, we match five target stocks per sub-industry with all stocks in their respective categories, merging target stock data with weighted data from non-target stocks for validation in the IPSO-LSTM model. Our findings demonstrate that including non-target stock data from the same sub-industry through PSM significantly improves predictive accuracy, highlighting its positive impact on stock price prediction. This study pioneers PSM's use in studying stock interdependence, conducts an in-depth exploration of effects within the pharmaceutical industry, and applies the IPSO optimization algorithm to enhance LSTM network performance, providing a fresh perspective on stock price prediction research.

Can Green Finance Policies Have Impact on Brown Sector Stocks?

In recent years, in response to climate change, China has introduced a series of green finance policies to support the low-carbon transformation of the economy, which may have a negative impact on the brown industry while promoting the development of green industries. This article uses the event study methodology to study the impact of the introduction of three typical green finance policies on the returns of brown industry stocks. The results show that there is consistency in the effects of different types of policies on brown industry stock returns. Overall, Green finance policy has a strong negative impact on the stock returns of brown enterprises in the short term, but has a significant positive impact on those enterprises with good financial performance in the long term.

Extreme connectedness between NFTs and US equity market: A sectoral analysis

This study examines the returns connectedness between NFTs and US sector stock markets. For this purpose, we use the recently developed technique of quantile-based regression to explore the dependence structure under various conditions. Our results support the view that connectedness between NFTs and sectoral markets is characterized by asymmetry and heterogeneity in the extreme conditions compared to the median quantile and mean-based approach. Under normal conditions, all NFTs except the ENJ are net recipients of the return spillover from the sectoral stock markets, whereas, financial, consumer staple, and industrial stocks are major net transmitters to the system. However, we observe heterogeneity at both tails, as all NFTs act as net transmitter (recipient) at the higher (lower) quantiles. This confirms the asymmetric dependence structure. We also compute the static optimal weights and hedge ratios using TVP-VAR model for the stocks/NFTs portfolios and show that investors and portfolio managers may consider including NFTs in their holdings to achieve diversification benefits.

The mitigating effect of new digital technology on carbon emissions: evidence from China.

This paper investigates the impacts of new digital technology on carbon emissions in China. To better capture the feature of new digital technology, we distinctively measure it using the invention patent stock of new generation information technology industry. The empirical results show that the development of new digital technology significantly reduces carbon dioxide (CO2) intensity. The results are still robust after addressing the endogeneity issues using instrumental variable regressions. The carbon reduction mechanisms of new digital technology include reducing energy intensity, optimizing energy structure, and advancing treatment technology. We further confirm the spatial spillover and nonlinear effects of new digital technology on CO2 intensity and the moderating effect of urban digitalization in carbon reduction. The study suggests that the potential of new digital technology in reducing carbon emissions should be highly attentioned by governments.

Dynamic asymmetric spillovers and connectedness between Chinese sectoral commodities and industry stock markets.

This study investigates the dynamic and asymmetric propagation of return spillovers between sectoral commodities and industry stock markets in China. Using a daily dataset from February 2007 to July 2022, we employ a time-varying vector autoregressive (TVP-VAR) model to examine the asymmetric return spillovers and dynamic connectedness across sectors. The results reveal significant time-varying spillovers among these sectors, with the industry stocks acting as the primary transmitter of information to the commodity market. Materials, energy, and industrials stock sectors contribute significantly to these spillovers due to their close ties to commodity production and processing. The study also identifies significant asymmetric spillovers with bad returns dominating, influenced by major economic and political events such as the 2008 global financial crisis, the 2015 Chinese stock market crisis, the COVID-19 pandemic, and the Russia-Ukraine war. Furthermore, our study highlights the unique dynamics within the Chinese market, where net information spillovers from the stock market to commodities drive the financialization process, which differs from the bidirectional commodity financialization observed in other markets. Finally, portfolio analysis reveals that the minimum connectedness portfolio outperforms other approaches and effectively reflects asymmetries. Understanding these dynamics and sectoral heterogeneities has important implications for risk management, policy development, and trading practices.

An Empirical Analysis on the China’s Medical Industry under Covid-19 Based on Fama-French model

The global financial markets have been seriously affected by the Covid-19 pandemic, causing widespread and deep-reaching effects since the latter part of 2019. The medical industry, being one of the fundamental sectors, has not been spared from its effects. This empirical study focuses on Chinese A-share medical industry companies as a sample and the Fama-French three-factor and five-factor models were evaluated to determine their effectiveness pre- and post-pandemic, aiming to investigate how the epidemic has impacted the stock prices of China’s medical industry. The results indicate that the epidemic has significantly affected the Chinese A-share medical industry, and the Fama-French model’s capability of explaining market returns has diminished following the outbreak of the epidemic. These findings provide guidance for investors and policymakers to make informed decisions regarding investments and policies related to the medical industry, as well as insights into examining the effectiveness of China’s medical industry stock price. These results also provide ideas and methods for related research in other fields.