Volatile Ratio Research Articles

Portfolio selection revisited

AbstractIn 1952, Harry Markowitz formulated portfolio selection as a trade-off between expected, or mean, return and variance. This launched a massive research effort devoted to finding suitable inputs to mean-variance optimization. The estimation problem is high dimensional and a factor model is at the core of many attempts. A factor model can reduce the number of parameters that need to be estimated to a manageable size, but these parameters may incorporate substantial, hidden estimation error. Recent analysis elucidates the nature of this error, identifies a mechanism by which it can corrupt optimization and provides a method for its mitigation. We explore this analysis here by illustrating how to improve the volatility ratio of large optimized portfolios, leading to superior portfolio selection.$$^{*}$$ ∗

Shell Materials for Chewable Capsules, From Design of Experiments to Optimal Dosage Form Manufacturing

AbstractInnovations in chewable dosage forms provide solutions for swallowing difficulties faced by certain patient populations. Chewable softgel capsules (SGC) offer opportunities to expand the benefits of SGC, excelling in the delivery of lipid‐based formulations by optimizing the mechanical properties of the shell material without compromising industrial‐scale manufacturing. This study employs an original approach, with a target product profile that aims to combine processability and chewability, to develop optimized chewable dosage forms. Through a mixture design of experiments, key factors such as plasticizer content and the addition of acid‐modified thin‐boiling corn starch are explored, focusing on response parameters critical to achieving the desired properties of the capsule shell material. Optimal shell compositions—one starch‐free and another containing 9% w/w starch, with respective gelatin/non‐volatile plasticizer ratios of 0.81 and 0.67 are determined and evaluated by manufacturing chewable SGC at pilot scale using rotary die technology. The successful manufacturing and resulting capsules, which exhibit the intended properties, highlight the efficiency of this approach in striking the right balance between processability, soft texture, and quick disintegration time.

Behavioural Response of Aromia bungii Adults to Volatile Compounds Emitted by Prunus persica at Different Physiological Statuses

ABSTRACTAromia bungii is a serious wood borer damaging Prunus persica and other numerous economic tree species. Host plant volatiles play a key role in pest population monitoring and trapping. To ascertain the influence of volatile organic compounds emitted by P. persica at varying physiological statuses on the host‐searching behaviour of A. bungii adults, we initially collected and identified the volatiles emitted by P. persica leaves infested with A. bungii using gas chromatography–mass spectrometry. Subsequently, we evaluated the behavioural response of A. bungii adults to the crude extracts, single compounds and blends of these compounds by Y‐tube olfactometer bioassay. Results showed that volatiles emitted from P. persica infested with A. bungii larvae at Grade I were more attractive to females, but males were more attracted to volatiles from P. persica infested with A. bungii larvae at Grade IV. Five compounds were identified and showed relatively high levels from both infested plants at Grade I and Grade IV. Among them, 3‐carene, cis‐3‐hexen‐1‐ol and cis‐3‐hexenyl acetate were attractive to females, and a blend of the three volatiles in a ratio of 3:91:6 for Grade I infestation was more attractive to females than a ratio of 3:83:14 from Grade IV infestation. The compounds, 3‐carene, cis‐3‐hexenyl acetate, cis‐3‐hexene‐1‐ol and benzaldehyde, were attractive to males and a blend in a ratio of 3:81:13:3 from Grade IV infestation was more attractive to males than a ratio of 3:89:6:2 from Grade I infestation. The results show that the kinds and ratios of volatiles emitted from P. persica infested with A. bungii larvae play important roles in host‐searching behaviour of A. bungii adults.

Can long-term institutional owners improve market efficiency in parsing complex legal disputes?

Long-horizon institutional investors can help mitigate information asymmetries around securities class action (SCA) lawsuits. We find that the machine readability of SCA complaint filings can predict the outcome and duration of class actions. Long-term institutional investor ownership leads to a more positive post-SCA announcement price reaction and increases the volatility ratio of prices as a measure of price informativeness. Furthermore, there is a significant interaction effect between long-term institutional ownership and SCA complexity on price informativeness consistent with a superior information processing ability about complex corporate events affecting portfolio firms.

Effect of pyrolysis temperature on migration characteristics of heavy metals during biomass pyrolysis

In this study, the distribution, morphology, and migration characteristics of heavy metals in the products obtained at different pyrolysis temperatures were studied. With an increase in the pyrolysis temperature, the heavy metals were more inclined to volatilize into bio-oil and syngas, and the volatilization ratio was Zn > Pb > Cr > Fe > Ni > Mn > Cu. At pyrolysis temperatures below 400 °C, heavy metals were transformed from the migratory states (F1, F2, F3) to the residual state (F4). When the pyrolysis temperature exceeded 500 °C, heavy metals in migration states (F1, F2, F3) migrated to the bio-oil and syngas. The residual states (F4) of Fe, Cu, Ni, and Mn were stable. Although Zn and Pb in the residual state (F4) volatilized at high temperatures, the volatilization ratio was lower than that in the migratory state (F1, F2, and F3). At a pyrolysis temperature of 900 °C, the potential risk factor (RI) of heavy metals decreased from 448.67 to 5.21, significantly reducing the environmental risk.

OPTIMIZATION AND FORECASTING OF A SECURITIES PORTFOLIO BASED ON MACHINE LEARNING METHODS

The article is devoted to the demonstration and analysis of the possibilities of connecting the Markowitz model, which is a classical model for forming a portfolio of securities, and forecasting methods based on machine learning data. The relevance of the chosen topic is justified by the fact that the use of machine learning methods makes it possible to simplify the activities of financial sector workers and the work of professional stock market players, as well as reduce the complexity of interpreting mathematical models and methods of their construction for private investors. The purpose of this work is to use machine learning methods to solve the problem of building an optimal securities portfolio and predicting its behavior. Tasks: - build a Markowitz model based on the selected data; - to build an optimal portfolio based on the proposed optimality criterion; - offer a time series forecast; - to assess the adequacy of the constructed model; - combine the Markowitz model, which belongs to the class of widely known classical models, with the construction of a prediction based on machine learning. Methods and models: machine learning methods are used in the work, the Markowitz model is built based on the selected data, then the Markowitz model is combined with the construction of a forecast based on machine learning. Research results: the optimal portfolio for AXP has been built and its forecast has been fulfilled. The formation of the portfolio was based on the Markowitz model. The data obtained were tested for adequacy and showed fairly good results. A portfolio was built, the optimality criterion of which is the ratio of average profitability and volatility of profitability (maximizing average profitability while minimizing its volatility). The Markowitz model, which belongs to the class of widely known classical models, was combined with the construction of a prediction based on machine learning.

Empirical Analysis of Portfolio Allocation During Market Turbulence Based on Efficient Frontier

The global financial markets have experienced considerable turmoil due to the COVID-19 pandemic, in this context, portfolio management becomes critically important. This paper employs Monte Carlo Simulation to determine Efficient Frontier of a portfolio comprising fourteen US technology stocks in the period of COVID-19 pandemic by python, which also allocates portfolios for Minimum Volatility and Maximum Sharpe Ratio, along with their key parameters. According to this study, Maximum Sharpe Ratio portfolio outperforms Minimum Volatility portfolio in terms of cumulative return. However, its important to note that Maximum Sharpe Ratio portfolio also experiences a greater maximum drawdown. Additionally, this paper visually depicts the optimal allocation of asset weights across different levels of risk aversion using a chart. Furthermore, it identifies NVDA as the optimal choice for investors who have low degree of risk-aversion due to its high volatility and expected return. Conversely, IBM and CSCO emerge as the preferred choices for risk-averse investors, as they effectively mitigate the portfolios entire risk.

Optimization of process parameters for selective chlorination and volatilization in FeO-Cu2O-CaCl2 system

Selective chlorination and volatilization is an effective approach for element separation in non-ferrous metallurgical processes, and has also been used for the separation of iron oxide and copper oxide within steel industry. In this work, to determine and optimize the conditions for copper and iron separation in FeO-Cu2O-CaCl2 system, a series of orthogonal experiments were carried out to investigate the effects of reaction temperature, holding time, mole ratio of CaCl2 to Cu2O (n(CaCl2)/n(Cu2O)), and gas flow rate on the separation efficiency. Results demonstrated that both reaction temperature and n(CaCl2)/n(Cu2O) take significant effects on the final copper volatilization ratio. In contrast, the gas flow rate and holding time shows a negligible influence on the effect of separation between copper and iron. By increasing the reaction temperature and n(CaCl2)/n(Cu2O), the copper volatilization ratio significantly increases, while the iron volatilization ratio remains at a low level. When the reaction temperature was 1273 K, the holding time was 90 min, n(CaCl2)/n(Cu2O) was 2.0, and the gas flow rate was 100 mL·min−1, the copper volatilization ratio reached 51.08 % and the iron volatilization ratio was only 6.52 %. The chlorination reaction rate of copper was controlled by chemical reaction and the apparent activation energyEa was 57.17 kJ/mol.

Investment Portfolio with Convex Optimization and Risk Adjustment Using Multi-Factor Model and Multi-Armed Bandit Algorithm

This paper examines the creation of investment portfolios through convex optimization, multifactor models, and the multi-armed bandit (MAB) algorithms, focusing on the KL-UCB strategy to optimize decisions in uncertain settings. It explores the impact of systematic risk factors using the Fama-French three-factor model, estimating the influence of market, size, and value premiums via linear regression. The use of Monte Carlo simulation is detailed for generating potential asset allocations and calculating their expected returns, volatility, and Sharpe ratios. The optimize minimize function from the SciPy library is employed to construct an efficient frontier and determine optimal asset allocation, aiming to maximize returns or minimize volatility across various risk levels. The findings suggest that the strategy of dynamic weight adjustments combined with the KL-UCB algorithm enhances portfolio returns, particularly during market volatility. The research also reveals a portfolio inclination towards large-cap growth stocks due to the negative impacts of size and value premiums. It concludes that dynamic weight adjustment strategies offer significant potential in optimizing portfolio performance in complex market conditions, though leveraging increases risk and should be carefully managed according to investor risk tolerance.

Self-sulfidation roasting and flotation of electroplating sludge: An innovative technology for metal recovery

Electroplating sludge is classified as a solid hazardous waste, which presents significant environmental and human health risks, while also resulting in substantial depletion of valuable metals. The study proposed an innovative technology of self-sulfidation roasting and flotation for the efficient recovery of valuable metal elements in electroplating sludge. The distinguishing feature lied in utilizing the calcium sulfate present in the electroplating sludge as the sulfidation reagent, thereby achieving self-sulfidation roasting without the need for additional sulfidation reagents. The decomposition process of calcium sulfate and influence factors during self-sulfidation roasting were investigated by thermogravimetric analysis and roasting experiments. Under optimized conditions, copper and nickel sulfidation ratios in the electroplating sludge reached 82.80% and 97.39%, respectively, while zinc volatilization ratio reached 87.98%. XRD and SEM-EDS techniques were employed to reveal phase evolution and surface morphology of the roasted products. Finally, copper-nickel concentrate achieved recovery ratios of 64.37% for copper with a grade of Cu 2.7%, and 67.38% for nickel with a grade of Ni 12.84%. This study presented a novel and effective approach towards secondary utilization of solid wastes.

Exploiting the sentiments: A simple approach for improving cross hedging effectiveness

This paper introduces a novel hedging strategy based on textual information for cross-hedging. The strategy allows the optimal hedge ratio to vary with the sentiment extracted from online oil news, leading to an improvement in cross-hedging effectiveness. We find that the sentiment hedging strategy outperforms traditional time-varying and static hedging strategies in both in-sample and out-of-sample scenarios, with statistical and economic significance. This superior performance benefits from the well-documented predictive power of sentiment on futures volatility. Furthermore, we confirm an asymmetric effect of sentiment on hedging effectiveness, with negative sentiment having a greater impact on hedge ratios. Finally, extreme sentiment can trade off the negative relationship between volatile hedge ratios and hedging performance, potentially leading to superior hedging performance compared to normal sentiment, which is robust when considering transaction costs. Our findings highlight the importance of sentiment for estimating the optimal hedge ratio and should be considered for risk management.

Evaluating the Effectiveness of Risk Management Strategies in the Indian Mutual Fund Industry

The study will employ a mixed-method approach, combining quantitative analysis of historical fund performance data with qualitative assessment of risk management processes and policies adopted by mutual fund companies. By examining key risk metrics such as volatility, beta, and sharpe ratio, the research aims to assess the impact of risk management strategies on the overall performance of mutual fund schemes in the dynamic and rapidly growing Indian mutual fund industry. As with any investment vehicle, mutual funds are subject to various risks that can impact investor returns, prompting mutual fund companies to employ various risk management strategies to mitigate these risks. This study seeks to evaluate the effectiveness of these strategies by analyzing different techniques employed by mutual fund companies, with the goal of identifying best practices for minimizing the impact of various risk factors on fund performance. The findings will provide valuable insights for both mutual fund companies and investors, aiding in understanding the effectiveness of risk management strategies in mitigating investment risks. By identifying and promoting best practices in risk management, this research aims to contribute to the development and sustainability of the mutual fund sector in India. Keywords: Risk management, mutual fund, market stability, investment risk

Rayleigh wave attenuation tomography based on ambient noise interferometry: methods and an application to Northeast China

SUMMARY Although ambient noise interferometry has been extensively utilized for seismic velocity tomography, its application in retrieving attenuation remains limited. This study presents a comprehensive workflow for extracting Rayleigh wave amplitude and attenuation from ambient noise, which consists of three phases: (1) retrieval of empirical Green's functions (EGFs), (2) selection and correction of amplitude measurements and (3) inversion of attenuation, site amplification and noise intensity terms. Throughout these processes, an ‘asynchronous’ temporal flattening method is used to generate high-quality EGFs while preserving relative amplitudes between stations. Additionally, a novel ‘t-symmetry’ criterion is proposed for data selection along with the signal-to-noise ratio. Furthermore, 2-D sensitivity kernels are utilized to estimate the focusing/defocusing effect, which is then corrected in amplitude measurements. These procedures are designed to deliver reliable attenuation measurements while maintaining flexibility and automation. To validate the effectiveness of the proposed noise-based attenuation tomography approach, we apply it to a linear array, NCISP-6, located in NE China. The obtained results correlate reasonably well with known geological structures. Specifically, at short periods, high attenuation anomalies delineate the location of major sedimentary basins and faults; while at longer periods, a notable rapid increase of attenuation is observed beneath the Moho discontinuity. Given that attenuation measurements are more sensitive to porosity, defect concentration, temperature, melt and volatile ratio than seismic velocities, noise-based attenuation tomography provides important additional constraints for exploring the crustal and upper mantle structures.

Evaluation of monthly changes in essential oil yield and components of cherry laurel (Prunus laurocerasus L.) leaf

This research was carried out to examine the monthly changes in both the volatile oil content and volatile components of cherry laurel leaves by taking samples every month for 12 months in Turkey. Harvest periods significantly affected volatile oil content (P < 0.01). Depending on harvest periods, volatile oil ratios ranged from 0.19 to 0.35%. The months of August, July, and September yielded the highest volatile oil ratios. Benzaldehyde, phenol, benzoic acid, benzeneacetonitrile, pentadecanone, 1,54-dibromotetrapentacontane and, tetrapentacontane were determined as components in the volatile oil. The ratio of benzaldehyde, the main active ingredient, varied between 83.89 and 94.41%, depending on the harvest time. The cherry laurel leaf should be harvested in July, August, and September for high essential oil ratios and in May, June, and July for high benzaldehyde ratios. Due to the high concentration of benzaldehyde in its volatile oil, cherry laurel evergreen leaf can be considered a valuable source of raw materials for the fragrance and pharmaceutical sectors.

Investigating the Symmetric Effects of Working Capital on Profitability in Turkish Banking: An ARDL Empirical Analysis

Abstract This paper carried out an empirical study on Return on Assets (ROA), total assets turnover, financial leverage, working capital, and debt-to-equity ratio. This study investigated commercial banks operating in Turkey. A secondary data collection method is used for the data collected from fifteen commercial banks. These banks' financial reports were examined from 2011 to 2022. A line graph is presented for each balance that tells us the volatility ratio in the variables, the unit root test applied to resolve the unit root problem, and the cointegration test used for long-term relationships among variables. The Autoregressive Distributed Lag (ARDL) bound test was performed to measure the short-run and long-run effects. Financial leverage, debt-to-equity ratio, and working capital have short- and long-run effects on ROA. Total asset turnover has only long-run effects on ROA in the Turkish banking industry. This study will be beneficial for investment managers, financial specialists, treasurers, and controllers of the banking industry.

Electron syntrophy between mixed hydrogenogens and Geobacter metallireducens boosted dark hydrogen fermentation: Clarifying roles of electroactive extracellular polymeric substances

To modulate the electron transfer behavior of hydrogen-producing bacteria (HPB) for enhanced hydrogen production, Geobacter metallireducens culture (GM) was introduced as an electron syntrophy partner and redox balance regulator in dark fermentation systems with hydrogen-producing sludge (HPS) as inoculum. The highest hydrogen yield was 306.5 mL/g-COD at the GM/HPS volatile solids ratio of 0.08, which was 65.2 % higher than the HPS group. The multi-layered extracellular polymeric substances (EPS) of GM played a significant role in promoting hydrogen production, with c-type cytochromes probably serving as electroactive functional components. The addition of GM significantly improved the NADH/NAD+ ratio, electron transport system activity, hydrogenase activity, and electrochemical properties of HPS. Furthermore, the microbial community structure and metabolic functions were optimized due to the potential syntrophic interaction between Clostridium sensu stricto (dominant HPB) and Geobacter, thus promoting hydrogen production. This study provided novel insights into the interactions among exoelectrogens, electroactive EPS, and mixed HPB.

De-chlorination of poly(vinyl) chloride using Fe2O3 and the improvement of chlorine fixing ratio in FeCl2 by SiO2 addition

Abstract A quantitative investigation of poly(vinyl) chloride (PVC) de-chlorination using Fe2O3, together with the impact of SiO2 addition on the co-pyrolysis of PVC and Fe2O3, was conducted below 673 K in an Ar atmosphere aiming to cut the emission of gaseous Cl⁻ products. It was found that chlorine in PVC can be fixed in FeCl2 by the reaction between PVC and Fe2O3. The co-pyrolysis of PVC and Fe2O3 proceeds in two stages with a temperature boundary of around 543 K. Below 543 K, a direct reaction occurs between PVC and Fe2O3, resulting in a small mass loss ratio and some extent of chlorine fixing ratio in FeCl2. However, above 543 K, PVC starts to decompose to release gaseous H2, HCl, etc., which react with Fe2O3 through two possible pathways to form FeCl2. In Pathway 1, first Fe2O3 is reduced to Fe3O4 by H2, followed by the chlorination of Fe3O4 to FeCl2 by HCl. In Pathway 2, first Fe2O3 is chlorinated to FeCl3 by HCl, followed by the reduction of FeCl3 to FeCl2 by H2. The chlorine fixing ratio in FeCl2 and the volatile generation ratio increase with decreasing PVC content in the initial mixtures. The addition of SiO2 promotes the chlorine fixing ratio in FeCl2 and volatile generation, and the impact gets stronger with decreasing PVC content in the mixtures. The chlorine fixing ratio is increased from 70.8 to 82.6% by SiO2 addition for the mixtures containing 25% PVC, whereas the difference in the chlorine fixing ratio in FeCl2 caused by SiO2 addition is negligible for the mixtures containing 90% PVC. Fayalite (Fe2SiO4) was not detected in the solid residues after the experiments. After separating FeCl2 using water leaching, the filter residue, a composite of iron oxide and conjugated polyene, can be used as a raw material for iron-making.

Innovative technology and mechanism for comprehensive recovery of copper, nickel, zinc and iron in electroplating sludge

Electroplating sludge is a product of the purification of the electroplating wastewater by neutralization or flocculation precipitation, which belongs to dangerous solid waste. In the study, copper, nickel, zinc and iron from electroplating sludge were recovered by an innovative sulfidation roasting-flotation-magnetic separation technology. The effects of the crucial parameters on the sulfidation ratio of copper and nickel and the volatilization ratio of zinc during the sulfidation roasting were investigated. Under the optimal condition of the sulfidation roasting, the volatilization ratio of zinc reached 78.10 % and the sulfidation ratios of copper and nickel were 88.55 % and 97.86 %, respectively. The migration rule of valuable metals in the roasting process were illustrated by XRD and SEM-EDS. By the flotation treatment for the roasting products, copper-nickel mixed concentrates containing copper 2.377 % and nickel 10.28 % were obtained, and the recoveries of copper and nickel reached 83.73 % and 85.00 %, respectively. After treatment by the flotation, magnetic separation was carried out to obtain iron concentrate with iron grade and recovery of 73.77 % and 12.41 %. Finally, the toxicity characteristic leaching procedure was employed to prove the excellent stability of the heavy metals in the tailing. The study realized the efficient, comprehensive recovery of copper, nickel, zinc and iron from electroplating sludge, and provided a new idea for the harmless and resource utilization of electroplating sludge.

Selection of Appropriate Portfolio Optimization Strategy

Portfolio optimization is a critical task in financial management, aiming to maximize expected returns while minimizing risk. This study compares the performance of Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Dynamic Programming (DP), and Differential Evolutionary Algorithm (DEA) in optimizing portfolios in the NIFTY 50 market. Using daily stock data from March 2023 to May 2023, we evaluate the algorithms based on performance metrics including the Sharpe ratio, expected return, volatility and Sortino ratio. Then an integrated multi-criteria decision making (MCDM) framework of Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) and Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS) methods has been used to compare the evolutionary algorithms for portfolio optimization. The results show that PSO outperforms the other algorithms in terms of Sharpe Ratio and Expected Return, while DEA exhibits the lowest portfolio risk. Furthermore, the efficient frontier analysis confirms PSO's ability to generate portfolios with higher expected returns at the same risk level. This research highlights the effectiveness of optimization algorithms in portfolio management and provides valuable insights for investors and portfolio managers in maximizing returns and managing risks.

Aspen Plus model of a downdraft gasifier for lignocellulosic biomass adjusted by Principal Component Analysis

The development and improvement of biomass gasification processes have been accelerated by the use of computational tools for process simulation, exploring their feasibility under different conditions and identifying key parameters that are validated through real experiments, contributing to maturing a gasification technology from laboratory to commercial scale. This work presents an Aspen Plus model of a small-scale downdraft gasifier using biomasses with different elemental and proximate analyses, combining stoichiometric and non-stoichiometric equilibrium approaches. The pyrolysis, oxidation, and reduction processes in the proposed model were separated into distinct steps, with the first one based on Gibbs energy minimization and the latter based on stoichiometric equilibrium models. A representative tar composition, with a production ratio to gas production based on literature, was considered as an input parameter to the model. The obtained data were analyzed using principal component analysis (PCA) to investigate the relations between equivalence ratio (ER), fixed carbon and volatile material ratio FCMV, the CO conversion through methanation (Mconv), carbon conversion via hydrogasification (Hconv), and the mean residual sum of square MRSS. The most significant results obtained from this study include the successful adjustment of the model to represent the gasification of different biomasses, particularly in correcting the H2 and CH4 concentrations. Notably, the adjustment resulted in a substantial reduction of the MRSS deviation from an average of 94.7% to an average of 7.2% concerning reported results in the literature. These findings demonstrate the accuracy of the model in simulating the gasification process and highlight the importance of considering CH4 reactions adjustment for more reliable predictions. The achieved adjustments are valuable insights for advancing gasification technology towards engineering applications.