Mean-variance Analysis Research Articles

Mean–Variance Analysis, the Geometric Mean, and Horizon Mismatch

Mean–Variance Analysis, the Geometric Mean, and Horizon Mismatch

Implications of risk-averse manufacturer on coordination of green supply chain with mixed carbon policies

PurposeThis paper intends to address the decision-making and coordination of green supply chain (GSC) considering risk-averse manufacturers under mixed carbon policy.Design/methodology/approachThis paper focuses on a GSC consisting of a manufacturer and a retailer, in which the manufacturer is risk-averse (R-A). This paper employs Stackelberg game theory and mean variance analysis to assess the pricing decision-making process under various scenarios. Furthermore, cost-sharing contracts are introduced to coordinate the GSC.FindingsThe research results suggest that the green level of the product and the profit of the GSC under a centralized scenario are higher than those under a decentralized scenario, while the retail price is lower. Under the decentralized scenario, the green level of product, wholesale price and manufacturer’s profit in the R-A scenario are lower than the values in the risk-neutrality scenario, while retailer's profit is higher. In addition, when a cost-sharing contract is utilized for coordination in the GSC, it can lead to Pareto improvement, regardless of whether the manufacturer makes risk-neutrality or R-A decisions.Originality/valueThis research provides a deeper understanding of GSC decision-making and coordination strategy under mixed carbon policy with consideration of R-A from a theoretical perspective and provides decision support for enterprises to choose strategies in practice.

The effects of reducing renewable power intermittency through portfolio diversification

Intermittency of renewable power sources is a growing concern for grid reliability in the United States and it has not been properly addressed by public policy for very long. One simple way to mitigate this problem is to diversify the renewable energy portfolio. This work studies the case of Texas, a state that not only is the largest wind power producer in the US, but that also has faced considerable diversification in its renewable energy portfolio in the past ten years. A dispatch model is proposed to simulate how thermal generators respond to changes in the intermittency of renewable sources. Additionally, since higher renewable output usually means higher intermittency and vice-versa, a mean–variance analysis is used to study this trade-off. Results indicate that although intermittency decreases the reliability of the grid, reducing it can have adverse effects as increasing the dispatch of polluting power sources and decreasing the revenues of thermal generators, what can decrease important investment in dispatchable power sources in the long-term. Also, results show that the subsidy design adopted by the federal government is an obstacle to portfolio diversification.

Increased glutamatergic neurotransmission between the retinohypothalamic tract and the suprachiasmatic nucleus of old mice.

Circadian rhythms synchronize to light through the retinohypothalamic tract (RHT), which is a bundle of axons coming from melanopsin retinal ganglion cells, whose synaptic terminals release glutamate to the ventral suprachiasmatic nucleus (SCN). Activation of AMPA-kainate and NMDA postsynaptic receptors elicits the increase in intracellular calcium required for triggering the signaling cascade that ends in phase shifts. During aging, there is a decline in the synchronization of circadian rhythms to light. With electrophysiological (whole-cell patch-clamp) and immunohistochemical assays, in this work, we studied pre- and postsynaptic properties between the RHT and ventral SCN neurons in young adult (P90-120) and old (P540-650) C57BL/6J mice. Incremental stimulation intensities (applied on the optic chiasm) induced much lesser AMPA-kainate postsynaptic responses in old animals, implying a lower recruitment of RHT fibers. Conversely, a higher proportion of old SCN neurons exhibited synaptic facilitation,and variance-mean analysis indicated an increase in the probability of release in RHT terminals. Moreover, both spontaneous and miniature postsynaptic events displayed larger amplitudes in neurons from aged mice, whereas analysis of the NMDA and AMPA-kainate components (evoked by RHT electrical stimulation) disclosed no difference between the two ages studied. Immunohistochemistry revealed a bigger size in the puncta of vGluT2, GluN2B, and GluN2A of elderly animals, and the number of immunopositive particles was increased, but that of PSD-95 was reduced. All these synaptic adaptations could be part of compensatory mechanisms in the glutamatergic signaling to ameliorate the loss of RHT terminals in old animals.

MMED: GEOGRAPHICAL KNOWLEDGE ENHANCEMENT FOR GRADE 7 STUDENTS AT REINA MERCEDES VOCATIONAL SCHOOL

This action research investigates the effectiveness of the MMED (Map Me Educational Booklet) in enhancing the geographical knowledge of Grade 7 students at Reina Mercedes Vocational and Industrial School. Grounded in the interdisciplinary nature of social science, particularly the symbiotic relationship between geography and history, the study addresses challenges in Araling Panlipunan, revealing disinterest due to a lack of relevance and interactive learning experiences. Inspired by research on teaching aids and supplementary materials, the researcher developed the "Map Me Educational Booklet" as an evidence-based intervention. Employing a Quasi-Experimental design, the study randomly assigned students to control and experimental groups. Results indicate a substantial improvement in both groups, with the experimental group displaying a more significant enhancement. Statistical analyses support the effectiveness of the booklet, emphasizing its positive impact on student performance. The study contributes to existing literature by aligning with real-world studies and providing a practical, evidence-based platform. Utilizing mean scores, variance analysis, t-tests, and Cohen's D, the research establishes the significance of the intervention. The findings support the booklet's role in active learning and highlight its potential for educational practices, emphasizing the need for innovative tools to address challenges in geography education. The study recommends the implementation of the "Map Me Educational Booklet" as a valuable supplement to enhance geographical knowledge, fostering implications for teaching and learning modalities.

Performance Evaluation of a Drip Irrigation System inside the Automated Greenhouse in Huye Ecological Condition

This research, titled Performance Evaluation of a Drip Irrigation System inside the Automated Greenhouse in Huye ecological Condition, was carried out in the Research Center of Integrated Polytechnic Regional College (IPRC)-Huye Campus, which is located in the Ngoma Sector of Huye District of Rwanda, in 2023. The study has mentioned the specific objectives of determining the average emitter discharge of the irrigation system, evaluating Christiansen Coefficient Uniformity (CU), and evaluating the distribution uniformity (DU) of the system. The research design was a randomized complete block design with four replications. The experiment was conducted on 24 laterals and 288 drippers installed in the greenhouse. Each lateral had an equal distance of 36.5m and carried 94 total drippers. Data analysis of variance (ANOVA) and LSD for means were used as statistical tools in MINITAB software version 17. The confidence interval was 95%. The obtained results indicated an average discharge (q) of 3.98 L/h in the drip irrigation system, coefficient uniformity (CU) of 99.96%, and distribution uniformity (DU) of 57.72%. The research concludes that the performance of irrigation systems is excellent for water application. However, the way water is distributed in all systems is poor. In addition, the drip irrigation system may reach an adequate level of performance when it works in a shifty manner.

Automated market makers: mean-variance analysis of LPs payoffs and design of pricing functions

Automated market makers: mean-variance analysis of LPs payoffs and design of pricing functions

Gene action studies in sunflower (Helianthus annuus L.)

The present investigation was conducted to study the gene action in sunflower. Thirty-two hybrids developed by using Four CMS and eight restorer lines in L x T design. The hybrids and parents were evaluated inRBD at ORS, Latur during Rabi-2021-22 for ten quantitative traits. Analysis of variance for means revealed significant differences for all the ten characters studied. CMS-249A was found to be good general combiner for head diameter, 100-seed weight, volume weight, hull content, oil content and seed yield/plant. Among the testers R/HA-1 was found good general combiner for days to 50 % flowering, days to maturity, plant height, head diameter, seed filling (%), 100-seed weight, volume weight (g/100ml), hull content and seed yield/plant. For oil content IR-1-1 exhibited high gca effect. Out of 32 hybrids the hybrids CMS-47A x EC-279309-1 was with good sca effect for seed yield and component traits. The variance due to SCA was greater than that of GCA for all the characters except days to maturity and plant height. Which indicated that the non-additive gene action played important role for the inheritance of these traits whereas days to maturity and plant height were governed by additive gene action.

LSTM Prediction and Portfolio Optimization for Artificial Intelligence Industry

The launch of ChatGPT has overwhelmingly been revolutionizing the stock market. Of particular interests of stock traders and financial analysts, discovery about artificial intelligence stock market has become the main focus. The paper selected top worldwide artificial intelligence (AI) enterprises from Yahoo Finance and made future return forecasts with the long short-term memory networks (LSTM). The predicted information is employed in conducting portfolio optimization within the scope of mean-variance analysis to obtain an assessment of the portfolios performance. The outcomes illustrate that the utilization of the LSTM model exhibits aptness in forecasting the forthcoming returns of financial instruments. Furthermore, a favorable preference entails the inclusion of NVDIA and Microsoft stocks in the portfolio. These discoveries offer utility in proposing pioneering investment strategies and aligning with the prevailing tendencies of societal progression.

Opportunities for using investments in wine for portfolio diversification: Foreign and Russian practices

Subject. This article discusses the opportunities for using investments in wine for the purpose of portfolio diversification, taking into account Russian and foreign practices. Objectives. The article aims to assess the possibilities of using collectible wine to diversify the investment portfolio. Methods. For the study, I used the mean-variance analysis, covariance and correlation analyses, modeling, and the statistical method. Results. The article presents the results of the assessment of the investment characteristics of collectible wine as a class of alternative assets. It describes the main trends in the global wine investment market and identifies the prospects and problems of the development of wine investments in Russia. Conclusions and Relevance. The article concludes that investments in collectible wine are more profitable and less risky than traditional investment instruments, and they also have a weak correlation with classic financial market assets, which makes investing in wine an effective way to diversify a portfolio. The results of the study can be used by both private and institutional investors interested in diversifying their asset portfolio using alternative investments.

Portfolio Optimization Based on 5 Hong-Kong Stocks

The recent fluctuations in interest rates in the Hong Kong stock market have sparked a significant amount of attention, prompting investors to further intensify their research into portfolio optimization strategies. To determine the most suitable investment plan, this study carefully selected five Hong Kong stocks based on their market capitalization. Predictive data were utilized for portfolio optimization, with the mean-variance analysis framework used to measure portfolio performance. Our model revealed that asset allocation of 2800.HK held the highest proportion in the minimum variance model, with the five stocks holding ratio of 0.119952, 0.000517, 0.067936, 0.030762, and 0.822611 separately. The average cumulative return using mean variance on the testing dataset was 1.091263. The final findings of this study may be useful to investors seeking to optimize their investments in the Hong Kong stock market. The insights gained from this research could help investors make better decisions and achieve improved returns.

Implications of information sharing on blockchain adoption in reducing carbon emissions: A mean–variance analysis

Considering market uncertainties and risks, this study examines the implications of demand information sharing (DIS) on blockchain adoption (BA) in reducing carbon emissions, and further analyzes the impacts of DIS and BA on the welfare of consumers and the whole society. In this paper, we first develop analytical models based on the mean–variance theory to investigate a two-level supply chain’s optimal decisions in four scenarios (i.e., without and with DIS and BA). Furthermore, we examine the impacts of supply chain members’ DIS and BA decisions and risk attitude on consumer surplus and social welfare. Lastly, we extend our models to explore a general scenario, i.e., we consider the presence of imperfect information sharing, privacy-conscious consumers, consumer carbon sensitivity differences, carbon cap-and-trade regulations, and inflexible blockchain deployments. We find that retailers should consider more crucial factors (e.g., demand uncertainties and the risk sensitivity of supply chain members) rather than simply focus on the type (i.e., high- or low-type) of demand before sharing demand information. Moreover, DIS and BA practices can not only improve the business performance of supply chain members but also contribute to the circular economy and sustainability. Then, DIS practices can minimize the manufacturers’ misjudgments about the timing of BA. Finally, the welfare of consumers and the whole society can be improved by sharing demand information and using blockchain technology.

Rehabilitating Mean–Variance Portfolio Selection: Theory and Evidence

Recent research has proven that the application of mean–variance portfolio selection is justified if, and only if, asset returns follow a skew-elliptical generalized location and scale (SEGLS) distribution. This irrefutably corrects the widespread fallacy that mean–variance analysis can be used only for portfolios with normally or symmetrically distributed constituents. To make this important finding accessible to a wide range of academics and practitioners, the authors of this article present it in a nontechnical form and additionally highlight that, under the SEGLS distribution and some mild axiomatic requirements, mean–variance analysis and many alternative mean-risk approaches deliver the same optimal portfolios. In a numerical study, they illustrate the key features of the novel SEGLS distribution. In an empirical study, they emphasize its practical relevance by gathering existing and providing new evidence in its favor.

Research on Optimal Investment Strategy Combination Based on ARIMA Model and mean-variance analysis -- Taking Gold and Bitcoin assets as examples

Gold and Bitcoin are popular trading products in today's trading market. In order to build a trading portfolio that maximizes returns, the prices of two trading products need to be predicted first. This article utilizes ARIMA to deal with the non-stationarity and predict the future prices of gold and bitcoin. In this article, the choice of parameters is ARIMA (4, 1, 4) for both bitcoin and gold. To find the best timing to sell and buy the two assets, the article first rate them with well-designed rating system by three important factors: Changes in value, Moving averages, and Bias. Then based on these factors, the model further linearly composes the indicator for risk and trend. By utilizing the information, the model gets with the main factor to make trading decisions.

Incorporating the Theory of Planned Behaviour into Distracted Driving: Influencing Factors and Intervention Effects

This study focuses on the psychological characteristics and empirically tests of the factors influencing distracted driving behaviours. This information is used as a reference for an intervention on dangerous driving behaviours. First, a distracted driving scale is constructed based on the theory of planned behaviour (TPB). The questionnaires are distributed in Chongqing, China, and 321 completed questionnaires are obtained. Data are analyzed using mean-variance analysis, one-way ANOVA, T-test, and multivariate test by SPSS 26.0 to determine the significance of distracted behaviours and demographic variables. We use a structural equation model to determine the path coefficients of each latent variable. Finally, we select the drivers with high tendency of distraction from the results of the questionnaires, conduct a four-stage rational emotional behaviour therapy (REBT) experiment, and use a repeated measures ANOVA analysis to test the validity and persistence of the intervention method. Results show that subjective norm is the most influential psychological factor. There are significant differences between the experimental group (2.38, SD = 0.41) and the control group (2.89, SD = 0.40) in the scores of distractions. This indicates that the distracted behaviour intervention achieves adequate validity and consistency. Educational research on distracted driving behaviour can help identify and correct drivers with high distraction tendency.

Portfolio Shading up - A Portfolio composed by NVDA, NVO, KO AND C Using Efficient Frontier and Mean Variance Analysis

This article illustrates that since last century investment becomes more and more popular as a way for people to save or spend money, but during the recovery of the pandemic, how we could make a suitable investment. This article, it demonstrates how to make an optimal portfolio with high return and low risk, with the introduction of the Variance and Mean model and efficient frontier, accompanied with the aid of Excel functions, and let it helps to get the solution best fitted in our expectations. The companies we chose for this portfolio are NVDA, KO, C and NVO, which are outstanding firms from four different industries. And another method of shading up the portfolio is also mentioned in this article, Potter's five force analysis and ESG analysis and rating scale, in a way that could increase the practical usefulness and validity in the reality of our portfolio. And these measures are also expected to be used in the future portfolio shading up.

Application Analysis of Portfolio Theory in Financial Markets

Based on portfolio theory, this paper reviews and summarizes the role of investors and examines two aspects of portfolio theory: the mean variance analysis method and the efficient limit model of the portfolio. And its limitations are analyzed. So that investors can invest better. Indifference curves vary from person to person, and deviations will likely affect the resulting final maximum utility point. Although the research on financial market risk has been relatively mature, there are still some puzzles to be solved in the effective measurement of risk. Markowitz takes the ROI as a representation of ROI and the variance as a representation of ROI, which solves the problem of measurement of asset risk. He thinks that most investors are risk-averse; they will try to avoid risk at the same time as they seek high returns. Therefore, Markowitz has proposed a portfolio investment theory based on the analysis of the mean variance.

Privacy-Preserving Hybrid Cloud Framework for Real-Time TCL-Based Demand Response

Widespread advanced metering infrastructure and wide-area monitoring systems generate a significant amount of electricity load consumption data, which can facilitate eliciting end users temperature flexibility for demand response programs. However, the direct delivery of users load profiles is a threat to users privacy. So this paper proposes a privacy-preserving hybrid cloud framework for TCL-based demand response programs, composed of user private clouds and aggregation cloud. User clouds store users load profiles and elicit temperature flexibility by the proposed stable temperature-related regression model. In the aggregation cloud, this paper proposes the slope-priority flexibility aggregation method for the mean-variance analysis of aggregate flexibility and the XGBoost-accelerated disaggregation model for real-time selecting users based on users fitting coefficients. Hybrid cloud achieves privacy-preserving by separating flexibility eliciting models and aggregation/disaggregation methods into user private clouds and aggregation cloud. Numerical experiments verify that: 1) in user clouds, the stable regression model achieves less predict errors; 2) in aggregation cloud, the slope-priority method can achieve higher aggregate flexibility, and XGBoost-accelerated disaggregating reduces the solving time by nearly three orders of magnitude.

The informativeness of dividend ratios and their economic predictive value towards equity premium

Purpose This paper aims to examine and compare the dividend ratios’ statistical and economic ability to predict the equity premium in the UK and US markets and two US sub-indices (S&P 500 Growth and S&P 500 Value). Design/methodology/approach In this paper, the authors use the linear regression models to examine the dividend ratios’ statistical ability to predict the equity premium. The in-sample and out-of-sample approaches, including Diebold and Mariano (1995) statistics, and Goyal and Welch’s (2003) graphical approach, are used. Also, the mean-variance analysis is used to test the economic significance. Findings The paper findings indicate that the dividend ratios have in-sample and out-of-sample predictive abilities in both UK and US markets and both US sub-indices. However, the results show that the dividend ratios have a less impressive predictive ability in the US market compared to the UK market and less in the US value index than the US growth index. This could indicate that there is no relation between the number of companies that distribute dividends in each index and the informativeness of dividends ratios. Furthermore, the tests show the dividend ratios’ predictive ability departure during particular periods and in some indices. Research limitations/implications Results and implications of this research are exclusively applied to the US and UK markets. These results can also be applied with caution to other markets, taking into consideration the distinctive characteristics of these markets. Practical implications Results revealed in this paper imply that the investors in any of the indices may experience economic gain by adopting a dynamic trading strategy using the information content of the dividend ratios prediction models instead of the benchmark model, which is the prevailing simple moving average model. Originality/value This paper adds value through testing the prediction models’ economic significance in two well-developed markets, in addition to exploring the relationship between the number of companies distributing cash dividends and the dividends ratio prediction ability. Unlike most of the previous studies in which dividend ratios’ prediction ability is attributed to the number of companies that distribute dividends in the market, this paper denied this interpretation by studying two S&P 500 sub-indices. To the best of the authors’ knowledge, this is the first study to test the prediction models’ ability for these sub-indices.

Portfolio Optimization Analysis in American Industry

Portfolio optimization, as a process of reorganizing investment and dispersing risks by considering the established target return and risk tolerance, is widely used in the financial field. This paper analyzes the asset allocation of six kinds of assets. It uses three methods, namely, mean-variance analysis, CAPM, and the FF3F model, to optimize the investment portfolio. At the same time, the proportion of assets in the portfolio should be considered under the conditions of maximizing the return or minimizing the risk at a specific risk level. The results show that in the mean-variance model, the AEP and Gold ETF have the most considerable weight in the combination of maximum Sharpe ratio and minimum variance, respectively. In the FF3F model, AT&T and Gold ETF have the most prominent weight in the combination of maximum Sharpe ratio and minimum variance, while in CAPM, Google and Gold ETF have the most significant weight in the combination of maximum Sharpe ratio and minimum variance respectively. This research may help potential investors interested in power, communication, science and technology, gold, and automobile industries.