Multi level perspectives in stock price forecasting: ICE2DE-MDL.

This study proposes a novel hybrid model, called ICE2DE-MDL, integrating secondary decomposition, entropy, machine and deep learning methods to predict a stock closing price. In this context, first of all, the noise contained in the financial time series was eliminated. A denoising method, which utilizes entropy and the two-level ICEEMDAN methodology, is suggested to achieve this. Subsequently, we applied many deep learning and machine learning methods, including long-short term memory (LSTM), LSTM-BN, gated recurrent unit (GRU), and SVR, to the IMFs obtained from the decomposition, classifying them as noiseless. Afterward, the best training method was determined for each IMF. Finally, the proposed model's forecast was obtained by hierarchically combining the prediction results of each IMF. The ICE2DE-MDL model was applied to eight stock market indices and three stock data sets, and the next day's closing price of these stock items was predicted. The results indicate that RMSE values ranged from 0.031 to 0.244, MAE values ranged from 0.026 to 0.144, MAPE values ranged from 0.128 to 0.594, and R-squared values ranged from 0.905 to 0.998 for stock indices and stock forecasts. Furthermore, comparisons were made with various hybrid models proposed within the scope of stock forecasting to evaluate the performance of the ICE2DE-MDL model. Upon comparison, The ICE2DE-MDL model demonstrated superior performance relative to existing models in the literature for both forecasting stock market indices and individual stocks. Additionally, to our knowledge, this study is the first to effectively eliminate noise in stock item data using the concepts of entropy and ICEEMDAN. It is also the second study to apply ICEEMDAN to a financial time series prediction problem.

Do commodities offer diversification benefits during the COVID-19 pandemic crisis? Evidence from dynamic spillover approach.

This paper examines the diversification benefits of commodity indices during the COVID-19 pandemic by analyzing both static and dynamic risk spillovers for the period from January 2, 1998 to September 16, 2020. Using variance decomposition forecasting, we employed static and dynamic analyses based on the estimation of 50-day moving window spillover indices. Globally, the results show significant spillovers between markets during the COVID-19 pandemic crisis. The results show that stock markets are highly interdependent with other financial markets (in both directions), and that commodity markets (except energy) and the bond market are recipients of shocks emanating from stock markets. The main contribution of this paper is to study the return and volatility spillovers between stock and commodity indices before and during the pandemic. This study of shock transmission mechanisms will enable investors to develop optimal diversification and hedging strategies during the crisis. In this context, we found that commodities and US government bonds could offer diversification benefits to investors. In addition, some of these assets may serve as hedging instruments or safe havens during the COVID-19 crisis.

Brown trout in Oder estuary tributaries: genetic structure, stocking, and admixture.

Several rivers that are tributaries of the Oder estuary are inhabited by Salmo trutta L, the most important of which are Ina, Gowienica, and Wolczenica. Both forms of the species, sea trout and resident brown trout, are present. All rivers are traditionally stocked with either sea trout from the neighboring Pomeranian river Rega basin or resident brown trout from various locations. To examine populations in these rivers in terms of genetic structure, genetic diversity, and origin, they were analyzed using 13 microsatellite loci. Relatedness was also assessed for fish stocked in the same year. The obtained genotypes were compared with breeding stocks used for stocking in Poland. The analyses revealed a significant genetic distance between adult individuals from Ina and Rega Rivers and fish caught during electrofishing. Strong kinship relationships were identified in the sampled areas, with high proportions of fish originating from stocking and their dominance in numbers over wild juveniles, primarily in smaller tributaries. Additionally, clear separation in the origin of stocked individuals was observed. Adult trout from Ina and Rega are genetically closer to northern brown trout lineages, providing crucial information for the management and biodiversity conservation of Polish Salmo trutta populations.

Does US monetary policy uncertainty affect returns of Asian Developed, emerging, and frontier equity markets? Empirical evidence by using the quantile-on-quantile approach.

This paper examines the impact of the Monetary Policy Uncertainty (MPU) of the United States on Asian developed, emerging, and frontier stock markets using a Quantile-on-Quantile (QQR) approach by using monthly data from January 2006 to December 2022 of 14 Asian countries. The study finds that US monetary policy significantly negatively influences Asian stock markets. This is primarily due to the widespread use of the US dollar as a universal currency, resulting in substantial ripple effects on other nations through trade relationships. In Asian developed markets, MPU is negatively related to Australia and New Zealand. At the same time, it has a positive relationship with Hong Kong and Japan at the upper quantiles. Among Asian emerging markets, MPU negatively impacts Taiwan's, India's, and China's returns, increasing this negative relationship at higher MPU quantiles. Additionally, MPU has a significant negative relationship with Thailand, Indonesia, Korea, and Malaysia returns. In contrast, higher quantiles of MPU have no discernible impact on the Philippines stock returns. In Asian frontier markets, MPU negatively impacts Pakistan's and Sri Lanka's returns. The implications of these findings are twofold: for investors, this study provides valuable insights for hedging activities, allowing for more informed decisions based on the MPU of other countries to identify profitable stocks. For policymakers, this research aids in formulating effective monetary policy strategies. Furthermore, future studies can build upon these results by exploring other markets and comparing their outcomes with the findings presented in this study.

Protein Production at the 100L Scale.

The Baculovirus Expression Vector System (BEVS) has revolutionized the field of recombinant protein expression by enabling efficient and high yield production. The platform offers many advantages including manufacturing speed, flexible design, and scalability. In this chapter, we describe the methods including strategies and considerations to successfully optimize and scale-up using BEVS as a tool for production (Fig. 1). As an illustrative case study, we present an example focused on the production of a viral glycoprotein.

[Impact of Land Use Change on Carbon Storage in Urban Agglomerations in the Guanzhong Plain].

It is important to study the impact of land use change on terrestrial ecosystem carbon stocks in urban agglomerations for the optimization of land use structure and sustainable development in urban agglomerations. Based on the patch-generating land use simulation (PLUS) model and integrated valuation of ecosystem services and trade-offs (InVEST) model, a simulation was developed that predicted the land use change and carbon stock of the Guanzhong Plain urban agglomeration in 2040 under different scenarios and further analyzed the impact of land use change on carbon stock. The results showed that:① The land use types of the Guanzhong Plain urban agglomeration were mainly cultivated land, forest land, and grassland, which accounted for more than 90 % of the total study area. ② From 2000 to 2020, the carbon stock in the Guanzhong Plain showed a continuous downward trend, with cropland, woodland, and grassland being the main sources of carbon stock in the Guanzhong Plain, and the overall carbon stock declined by 15.12×106 t, with the spatial distribution presenting the distribution characteristics of "high in the north and south and low in the middle." ③ By 2040, the carbon stock would decrease the most under the urban development scenario, with a total reduction of 27.08×106 t, and the least under the ecological development scenario, with a total reduction of 4.14×106t. The research results can provide data support for the high-quality development and rational land use planning of the Guanzhong Plain urban agglomeration.

Fine Root Density Dynamics and Carbon Stock of Eucalyptus spp.: Interplay of Age, Genotype, and Edaphoclimatic Conditions.

Roots play a fundamental role in forest ecosystems, but obtaining samples from deep layers remains a challenging process due to the methodological and financial efforts required. In our quest to understand the dynamics of Eucalyptus roots, we raise three fundamental questions. First, we inquire about the average extent of the roots of two contrasting Eucalyptus genotypes. Next, we explore the factors that directly influence the growth and depth of these roots, addressing elements such as soil type, climate, and water availability. Lastly, we investigate how the variation in Eucalyptus species may impact root growth patterns, biomass, and carbon stock. In this study, we observed that the maximum root depth increased by an average of 20% when genotypes were grown on sites with higher water availability (wet site). E. urophylla stands had a higher biomass and carbon stock (5.7 Mg C ha-1) of fine roots when cultivated on dry sites (annual rainfall~727 mm) than the wet sites (annual rainfall~1590 mm). In E. grandis × E. camaldulensis stands, no significant differences were observed in the stock of fine root biomass (3.2 Mg C ha-1) between the studied environments. Our results demonstrated that genotypes with greater drought tolerance (E. grandis × E. camaldulensis) tend to maintain higher stocks of fine root biomass (3.2-6.3 Mg ha-1) compared to those classified as plastic (E. urophylla), regardless of the edaphoclimatic conditions of the cultivation site. Finally, our research helps understand how Eucalyptus trees adapt to their environment, aiding sustainable forest management and climate change mitigation. We also provide a practical tool to estimate underground biomass, assisting forest managers and policymakers in ensuring long-term forest sustainability.

The effects of environmental variability and forest management on natural forest carbon stock in northwestern Ethiopia.

Natural forests are crucial for climate change mitigation and adaptation, but deforestation and degradation challenges highly reduce their value. This study evaluates the potential of natural forest carbon stock and the influence of management interventions on enhancing forest carbon storage capacity. Based on forest area cover, a study was conducted in nine purposely selected forest patches across various forest ecosystems. Data on diameter, height, and environmental variables from various forest management approaches were collected and analyzed with R Ver. 4.1. The findings revealed a substantial difference (p .029) in carbon stock between environmental variables and management interventions. The findings revealed a strong connection between environmental variables and the overall pool of carbon stock within forest patches (p .029). Carbon stocks were highest in the Moist-montane forest ecosystem (778.25 ton/ha), moderate slope (1019.5 ton/ha), lower elevation (614.50 ton/ha), southwest-facing (800.1 ton/ha) and area exclosures (993.2 ton/ha). Accordingly, natural forests, particularly unmanaged parts, are sensitive to anthropogenic stresses, decreasing their ability to efficiently store carbon. As a result, the study highlighted the importance of sustainable forest management, particularly area exclosures and participatory forest management, in increasing forest carbon storage potential.

Assessing Stock Photos for Farmworker Health and Safety Education Materials.

OBJECTIVES: The aim of this study was to analyze how farmworkers are represented in stock photos available in commercial libraries for use in agricultural health and safety education materials. METHODS: We searched for images in five commercial stock photo libraries using the terms "farmworkers" and "women farmworkers" in April 2022. We used quantitative content analysis. We coded each image for containing a visible face, age, gender, skin tone, work activity, mechanization, presence of hazards, technology use, and protective equipment/clothing after establishing inter-coder reliability. We used descriptive statistics to characterize the available stock photos. RESULTS: We identified stock photos (n = 127) in three databases (Adobe Stock Images, Canva, and Getty Images). Two databases (Microsoft Office Image Library and Pixabay) had no relevant images at the time of the search. Only half of the photos analyzed contained a face. Light skin tones and young or middle-aged adults were more common. A majority of farming activities represented in photos were manual tasks (e.g., harvesting) with few depictions of equipment, hazards, and protective equipment/clothing. CONCLUSIONS: Health and safety professionals tasked with developing materials for education in agricultural settings face a severe lack of imagery pertinent to the realistic conditions of farmworkers in the United States. In the databases we reviewed, photos displaying human faces, photos showing a range of skin tones and ages, and photos displaying an array of different farm hazards are likely not sufficient for material development. Health and safety professionals may benefit from sharing photos from their own work with other professionals and allocating resources for professional photo shoots in their material development projects.

How does stock market react to environmental penalty announcements?

Environmental penalty announcement (EPA) has received increasing attention for its potential to convey valuable information and affect capital market performance. Using data on listed companies in China, this paper examines stock market reaction to environmental penalty announcements, the behavior of different types of investors, and the moderating factors of these responses. The findings show that (1) disclosure of EPA by listed companies results in negative abnormal returns, but this negative market reaction is not sustained. (2) Heavy polluters and non-state-owned enterprises are exposed to more negative abnormal returns when they disclose EPA. (3) Environmental reputation can mitigate the negative stock market reaction to EPA, while the participation of green investors will intensify this reaction. (4) Retail investors tend to sell stocks of companies that disclose EPA as media attention increases, while institutional investors increase their shareholding especially in companies that already have high holdings, high ESG scores, and in regions with low levels of green finance development. This paper serves as a reference for governments, firms, and stakeholders on stock market reaction to environmental information disclosures.

Topsoil dilution by subsoil admixture had less impact on soil organic carbon stock development than fertilizer form and erosion state.

Enhancing the agroecosystems carbon (C) sink function for climate mitigation faced challenges, particularly with traditional measures with limited suitability for increasing soil organic carbon (SOC) stocks. Inducing a SOC undersaturation in the topsoil by abrupt subsoil admixture is a way to create an additional C sink. However, the deep tillage traditionally used for this topsoil dilution was not always successful. It was due to a lack of knowledge and suitable approaches to record the effect of all relevant factors in SOC recovery, including soil conditions and fertilizer forms. We addressed these problems by establishing a three-factorial experiment: I) "moderate topsoil dilution," II) "N fertilization form," and III) "soil erosion state," representing three soil types in the hummocky ground moraine landscape of NE Germany. SOC dynamics were determined over a year of winter rye cropping using a novel robotic chamber system capable of measuring CO2 exchange on 36 experimental plots with a reduced methodological bias than previous measuring systems. The averaged net ecosystem carbon balance, a proxy for SOC stock change, indicated that topsoil dilution only reduced further SOC losses. The N fertilizer form had a significantly stronger and more differentiated effect. While the mineral N fertilization consistently produced only C sources, the organic fertilization, in combination with the diluted topsoil, led to a C sink. This C-sink function was, however, more pronounced in the eroded soil than in the non-eroded soil. Overall, the results have made clear that the impact of topsoil dilution on the further development of the SOC stock is only possible if the effect of other relevant factors, such as N fertilizer form and erosion state, are taken into account.

A Future Growth Model for Building More Housing and Infrastructure with Less Embodied Greenhouse Gas.

Global demand for housing and the climate crisis have created a seemingly impossible choice between the need to build more and the need to emit less from construction materials. Here, we present the future infrastructure growth (FIG) model, a generalizable method for finding pathways to build enough housing and infrastructure while reducing material emissions, in line with climate commitments. FIG uses open data to quantify the emissions of existing neighborhoods as if they were built new; it then uses these quantifications to forecast future cradle-to-gate embodied emissions from new residential buildings and linear infrastructure construction. This novel approach allows for detailed analysis that scales to a city, region, and/or national level and captures variability in construction norms, designs, and codes. We demonstrate FIG on Canada, using the model to find neighborhood-level drivers of embodied emissions and the most effective reduction strategies through 2030 and 2050. Current construction practices will cause a 437% overshoot of Canada's climate commitments if housing growth targets are met. Avoiding this overshoot requires a near-total reliance on multiunit buildings and best-in-class design supported by improvements in material manufacturing, building within existing urban boundaries, and halving the use of new materials.

Supplemental greenhouse lighting increased the water use efficiency, crop growth, and cutting production in Cannabis sativa.

The expanding cannabis production sector faces economic challenges, intensified by freshwater scarcity in the main US production areas. Greenhouse cultivation harnesses sunlight to reduce production costs, yet the impact of greenhouse light levels on crucial production components, such as plant growth, branching, and water use efficiency (WUE), remains poorly understood. This study aimed to assess the effects of combined sunlight and supplemental lighting on the crop's main production components and leaf gas exchange of Cannabis sativa 'Suver Haze' in the vegetative stage. Within a greenhouse, LED lighting provided at intensities of ~150, 300, 500, and 700 µmol m-2 s-1 (18-hour photoperiod), combined with solar radiation, resulted in average daily light integrals of 17.9, 29.8, 39.5, and 51.8 mol m-2 d-1. Increasing light levels linearly increased biomass, leaf area, and the number of branches per plant and square meter, with respective rates of 0.26 g, 32.5 cm2, and 0.41 branches per mole of additional light. As anticipated, crop evapotranspiration increased by 1.8-fold with the increase in light intensity yet crop WUE improved by 1.6-fold when comparing the lowest and highest light treatments. Moreover, water requirements per unit of plant biomass decreased from 0.37 to 0.24 liters per gram when lighting increased from ~18 to 52 mol m-2 d-1, marking a 35% reduction in evapotranspiration. These results were supported by increments in leaf photosynthesis and WUE with light enhancement. Furthermore, our findings indicate that even 52 mol m-2 d-1 of supplemental lighting did not saturate any of the crop responses to light and can be economically viable for cannabis nurseries. In conclusion, light supplementation strongly enhanced photosynthesis and plant growth while increasing WUE. Additionally, a comprehensive discussion highlights the shared physiological mechanisms governing WUE in diverse plant species and their potential for water conservation under enhanced lighting conditions.

Identifying the source rookery of green turtles (Chelonia mydas) found in feeding grounds around the Korean Peninsula.

Determining the genetic diversity and source rookeries of sea turtles collected from feeding grounds can facilitate effective conservation initiatives. To ascertain the genetic composition and source rookery, we examined a partial sequence of the mitochondrial control region (CR, 796 bp) of 40 green turtles (Chelonia mydas) collected from feeding grounds around the Korean Peninsula between 2014 and 2022. We conducted genetic and mixed-stock analyses (MSA) and identified 10 CR haplotypes previously reported in Japanese populations. In the haplotype network, six, three, and one haplotype(s) grouped with the Japan, Indo-Pacific, and Central South Pacific clades, respectively. The primary rookeries of the green turtles were two distantly remote sites, Ogasawara (OGA) and Central Ryukyu Island (CRI), approximately 1,300 km apart from each other. Comparing three parameters (season, maturity, and specific feeding ground), we noted that OGA was mainly associated with summer and the Jeju Sea, whereas CRI was with fall and the East (Japan) Sea ground. The maturity did not show a distinct pattern. Our results indicate that green turtles in the feeding grounds around the Korean Peninsula originate mainly from the Japan MU and have genetic origins in the Japan, Indo-Pacific, and Central South Pacific clades. Our results provide crucial insights into rookeries and MUs, which are the focus of conservation efforts of the Republic of Korea and potential parties to collaborate for green turtle conservation.

Environmental consequences in lifecycle changes of container shipping vessels.

To estimate the average lifespan of container vessels, this study specified their most suitable lifespan distribution function using a comprehensive dataset comprising 2188 container vessels manufactured and retired between 1941 and 2018. The lifecycle CO2 emissions of vessels were estimated under different scenarios with varied average lifespans and average carbon intensity improvements per annum through stock-flow model analysis. The results indicated that a normal distribution best represented the lifespan distribution of container vessels, with an estimated average lifespan of approximately 24 years. Furthermore, scenario analyses revealed that shortening the lifespans of container vessels can effectively reduce lifecycle CO2 emissions. This study demonstrates the synergistic contribution of accelerating the replacement cycle of container vessels and implementing stronger energy efficiency regulations for emissions reduction, highlighting the importance of policies regulating vessel lifespans.

SF-Transformer: A Mutual Information-Enhanced Transformer Model with Spot-Forward Parity for Forecasting Long-Term Chinese Stock Index Futures Prices.

The complexity in stock index futures markets, influenced by the intricate interplay of human behavior, is characterized as nonlinearity and dynamism, contributing to significant uncertainty in long-term price forecasting. While machine learning models have demonstrated their efficacy in stock price forecasting, they rely solely on historical price data, which, given the inherent volatility and dynamic nature of financial markets, are insufficient to address the complexity and uncertainty in long-term forecasting due to the limited connection between historical and forecasting prices. This paper introduces a pioneering approach that integrates financial theory with advanced deep learning methods to enhance predictive accuracy and risk management in China's stock index futures market. The SF-Transformer model, combining spot-forward parity and the Transformer model, is proposed to improve forecasting accuracy across short and long-term horizons. Formulated upon the arbitrage-free futures pricing model, the spot-forward parity model offers variables such as stock index price, risk-free rate, and stock index dividend yield for forecasting. Our insight is that the mutual information generated by these variables has the potential to significantly reduce uncertainty in long-term forecasting. A case study on predicting major stock index futures prices in China demonstrates the superiority of the SF-Transformer model over models based on LSTM, MLP, and the stock index futures arbitrage-free pricing model, covering both short and long-term forecasting up to 28 days. Unlike existing machine learning models, the Transformer processes entire time series concurrently, leveraging its attention mechanism to discern intricate dependencies and capture long-range relationships, thereby offering a holistic understanding of time series data. An enhancement of mutual information is observed after introducing spot-forward parity in the forecasting. The variation of mutual information and ablation study results highlights the significant contributions of spot-forward parity, particularly to the long-term forecasting. Overall, these findings highlight the SF-Transformer model's efficacy in leveraging spot-forward parity for reducing uncertainty and advancing robust and comprehensive approaches in long-term stock index futures price forecasting.

Predicting stock price of construction companies using weighted ensemble learning.

Modeling the behavior of stock price data has always been one of the challenging applications of Artificial Intelligence (AI) and Machine Learning (ML) due to its high complexity and dependence on various conditions. Recent studies show that this will be difficult to do with just one learning model. The problem can be more complex for companies in the construction sector, due to the dependency of their behavior on more conditions. This study aims to provide a hybrid model for improving the accuracy of prediction for the stock price index of companies in the construction section. The contribution of this paper can be considered as follows: First, a combination of several prediction models is used to predict stock prices so that learning models can cover each other's errors. In this research, an ensemble model based on Artificial Neural Network (ANN), Gaussian Process Regression (GPR), and Classification and Regression Tree (CART) is presented for predicting the stock price index. Second, the optimization technique is used to determine the effect of each learning model on the prediction result. For this purpose, first, all three mentioned algorithms process the data simultaneously and perform the prediction operation. Then, using the Cuckoo Search (CS) algorithm, the output weight of each algorithm is determined as a coefficient. Finally, using the ensemble technique, these results are combined and the final output is generated through weighted averaging on optimal coefficients. The proposed system was implemented, and its efficiency was evaluated by real stock data of construction companies. The results showed that using CS optimization in the proposed ensemble system is highly effective in reducing prediction error. According to the results, the proposed system can predict the price index with an average accuracy of 96.6 %, which shows a reduction of at least 2.4 % in prediction error compared to the previous methods. Comparing the evaluation results of the proposed system with similar algorithms indicates that our model is more accurate and can be useful for predicting the stock price index in real-world scenarios.

The impact of external auditors with forensic accounting competencies on auditee firm performance.

The purpose of this study is to examine how the presence of auditors with forensic accounting skills impacts the financial performance of audited companies. Using a quantitative approach, this research employs linear regression analysis and examines a sample of 74 companies from the industrial and service sectors listed on the Amman Stock Exchange between 2012 and 2021. The findings reveal that external auditors with forensic accounting competencies have a positive impact on the financial performance of audited companies. The insights presented in this study could serve as valuable tools for improving the financial performance of companies in the service and industry sectors, highlighting the importance of supporting and promoting the skills and competencies of forensic accountants.

Deep learning in finance assessing twitter sentiment impact and prediction on stocks.

The widespread adoption of social media platforms has led to an influx of data that reflects public sentiment, presenting a novel opportunity for market analysis. This research aims to quantify the correlation between the fleeting sentiments expressed on social media and the measurable fluctuations in the stock market. By adapting a pre-existing sentiment analysis algorithm, we refined a model specifically for evaluating the sentiment of tweets associated with financial markets. The model was trained and validated against a comprehensive dataset of stock-related discussions on Twitter, allowing for the identification of subtle emotional cues that may predict changes in stock prices. Our quantitative approach and methodical testing have revealed a statistically significant relationship between sentiment expressed on Twitter and subsequent stock market activity. These findings suggest that machine learning algorithms can be instrumental in enhancing the analytical capabilities of financial experts. This article details the technical methodologies used, the obstacles overcome, and the potential benefits of integrating machine learning-based sentiment analysis into the realm of economic forecasting.

Inertia of Technology Stocks: A Technology-Explicit Model for the Transition toward a Low-Carbon Global Aluminum Cycle.

Low-carbon technologies are essential for the aluminum industry to meet its climate targets despite increasing demand. However, the penetration of these technologies is often delayed due to the long lifetimes of the industrial assets currently in use. Existing models and scenarios for the aluminum sector omit this inertia and therefore potentially overestimate the realistic mitigation potential. Here, we introduce a technology-explicit dynamic material flow model for the global primary (smelters) and secondary (melting furnaces) aluminum production capacities. In business-as-usual scenarios, we project emissions from smelters and melting furnaces to rise from 710 Mt CO2-eq./a in 2020 to 920-1400 Mt CO2-eq./a in 2050. Rapid implementation of inert anodes in smelters can reduce emissions by 14% by 2050. However, a limitation of emissions compatible with a 2 °C scenario requires combined action: (1) an improvement of collection and recycling systems to absorb all the available postconsumer scrap, (2) a fast and wide deployment of low-carbon technologies, and (3) a rapid transition to low-carbon electricity sources. These measures need to be implemented even faster in scenarios with a stronger increase in aluminum demand. Lock-in effects are likely: building new capacity using conventional technologies will compromise climate mitigation efforts and would require premature retirement of industrial assets.