Annual Rate Of Return Research Articles

Normal Asset Allocations and Their Statistical Properties

This study focuses on efficient asset allocations that properly include T-bills, T-bonds, and the S&P 500 stock index. It checks that their annual real rates of linear return are both normal and almost lognormal. It reexamines how efficient portfolios based on the rates of linear return may turn into efficient portfolios based on the rates of logarithmic return. It finds that each efficient asset allocation has the lowest possible standard deviation as well as the highest possible arithmetic and geometric means. It eventually reconsiders the relationship between the confidence interval of a geometric mean and an expected long-run capital accumulation. As a consequence, it bridges a gap in the scientific literature by enabling financial advisors to trade off the mean rate of return on a portfolio more rigorously against the value at risk.

Combined bio-methanol and power production from bio-oil: Proposing a clean bio-process through waste heat recovery and environmental optimization

Traditional methanol production from natural gas is well-studied, but there is a lack of research on environmentally optimizing bio-based methanol production. This study focuses on optimizing the combined production of bio-methanol and power from bio-oil using a comprehensive bio-process model. The research includes a novel framework for environmental, technical, and economic optimization of bio-oil to methanol and power to determine the most eco-friendly and cost-effective approach. An organic Rankine cycle (ORC) is also integrated for waste heat recovery. The results of this study revealed that the bio-based process with ORC showed a 22.6 % reduction in global warming potential impact (GWP) per kilogram of methanol, compared to the bio-based process without ORC. Also, by implementing the most environmentally friendly process, GWP will be reduced by 70.6 % per kg methanol compared to base sub-optimal operation. The results showed that for the process with the addition of ORC, after performing multi-objective optimization, under the optimal conditions, the economic annual rate of return increased from 19.6 % to 34.7 %. Finally, it was revealed that if the considered technology is commercialized, for every percentage of the methanol market it gains, it will contribute to 2.176 million tons CO2-eq/year less emissions.

Investment Weightings of Technology and Traditional Stocks: Investor Choice and Risk Management

In the era of big data, the financial landscape is undergoing transformative changes through the integration of advanced technologies. This research delves into the dynamic realm of asset portfolios within the information technology industry, traditional sectors, and amalgamated portfolios containing popular giant stocks. Employing Python and machine learning, the study focuses on predicting expected returns, comparing and analyzing diverse portfolios to identify optimal investment strategies for risk management. Evaluation metrics include annual return, annual volatility, Sharpe ratio, and statistical charts. The findings highlight the lucrative potential of investing in the information technology sector, revealing an impressive annual return rate of 40.1%. In contrast, traditional industry portfolios not only underperform but also exhibit high-risk profiles and diminished returns. This research underscores the critical role of technological advancements and data-driven methodologies in shaping contemporary financial strategies for robust portfolio management. his research advocates for the strategic incorporation of technology-driven insights in financial decision-making, emphasizing the significant outperformance potential of information technology portfolios in navigating the complexities of modern investment.

Portfolio Analysis of Various Financial Assets

Utilizing Monte Carlo Simulation and annualized data, this paper investigates portfolio establishment with currency, comparing the performance of the portfolio with the performance of equity. Specially, we choose S&P 500 to represent equity, while choosing Gold futures, Bitcoin, and CHF to represent different types of currency. Firstly we use Python to help us do the initial simulation of one million portfolios, analyzing the result of the simulation based on sharp ratio with Excel. We find that there are some regular patterns in the asset allocation of the portfolio and explain the appearance of the strange angle in our simulation. Then to increase the market shares of currency, we advocate adding an extra yield to the currency's annualized return and doing another two simulations on the four assets with a new annual rate of return. We then compare the results of new simulations with the performance of the initial simulation and S&P 500, finding that with extra yield it will be more easier for the portfolio including currency and equity to perform better than initial portfolios. We also find that the regular pattern of asset allocation which is discovered from the initial simulation is inapplicable in new simulations.

Sea ice concentration decline in an important Adélie penguin molt area.

Unlike in many polar regions, the spatial extent and duration of the sea ice season have increased in the Ross Sea sector of the Southern Ocean during the satellite era. Simultaneously, populations of Adélie penguins, a sea ice obligate, have been stable or increasing in the region. Relationships between Adélie penguin population growth and sea ice concentration (SIC) are complex, with sea ice driving different, sometimes contrasting, demographic patterns. Adélie penguins undergo a complete molt annually, replacing all their feathers while fasting shortly after the breeding season. Unlike most penguin species, a majority of Adélies are thought to molt on sea ice, away from the breeding colonies, which makes this period particularly difficult to study. Here, we evaluate the hypothesis that persistent areas of high SIC provide an important molting habitat for Adélie penguins. We analyzed data from geolocating dive recorders deployed year-round on 195 adult penguins at two colonies in the Ross Sea from 2017 to 2019. We identified molt by detecting extended gaps in postbreeding diving activity and used associated locations to define two key molting areas. Remotely sensed data indicated that SIC during molt was anomalously low during the study and has declined in the primary molt area since 1980. Further, annual return rates of penguins to breeding colonies were positively correlated with SIC in the molt areas over 20 y. Together these results suggest that sea ice conditions during Adélie penguin molt may represent a previously underappreciated annual bottleneck for adult survival.

A Deep Learning Approach with Extensive Sentiment Analysis for Quantitative Investment

Recently, deep-learning-based quantitative investment is playing an increasingly important role in the field of finance. However, due to the complexity of the stock market, establishing effective quantitative investment methods is facing challenges from various aspects because of the complexity of the stock market. Existing research has inadequately utilized stock news information, overlooking significant details within news content. By constructing a deep hybrid model for comprehensive analysis of historical trading data and news information, complemented by momentum trading strategies, this paper introduces a novel quantitative investment approach. For the first time, we fully consider two dimensions of news, including headlines and contents, and further explore their combined impact on modeling stock price. Our approach initially employs fundamental analysis to screen valuable stocks. Subsequently, we built technical factors based on historical trading data. We then integrated news headlines and content summarized through language models to extract semantic information and representations. Lastly, we constructed a deep neural model to capture global features by combining technical factors with semantic representations, enabling stock prediction and trading decisions. Empirical results conducted on over 4000 stocks from the Chinese stock market demonstrated that incorporating news content enriched semantic information and enhanced objectivity in sentiment analysis. Our proposed method achieved an annualized return rate of 32.06% with a maximum drawdown rate of 5.14%. It significantly outperformed the CSI 300 index, indicating its applicability to guiding investors in making more effective investment strategies and realizing considerable returns.

Research on the Applicability of Value Investment in China’s A-Share Market

Value investment has been widely respected and applied in the capital market of developed countries, and its effectiveness has also been verified in a large number of practices. However, since it was introduced to China in 1990s, the concept of value investment has been buried for a long time due to the lack of supervision and the backwardness of investment concepts. With the improvement of the market environment, the progress of supervision system and the update of investors’ ideas in recent years, does China’s A-share market have the conditions for value investment? This paper will discuss this problem theoretically and empirically. Firstly, this paper combs the development of value investment theory and expounds the views and propositions of value investment. Secondly, under the guidance of value investment theory, this paper selects the data of CASMR database 2018-2021. In the first stage, five sectors are selected as the low growth and high growth track of value investment by using multi-factor model. In the second stage, using EVA analysis, five leading stocks in each industry are selected for empirical test, combined with the fundamental analysis in each stage. The results show that the average annual rate of return of the top enterprises is over 50% and the average annual rate of return is over 30% in the high-growth industries screened out under the concept of value investment; This paper verifies that value investment is not only suitable for China A-share market, but also an excellent investment idea with low risk and high return, and puts forward some relevant investment suggestions.

Intelligent portfolio construction via news sentiment analysis

In this study, we apply deep learning and natural language processing methods to construct the view distribution in the Black–Litterman model. We implement this approach for portfolio allocation and perform statistical analysis to assess portfolio performance. The empirical analysis yields two main results. For the three deep learning models, we use mean square error to compare the model prediction results. The gated recurrent unit (GRU) model outperforms the other two models in the price prediction of seven stock assets. Moreover, it is more effective in capturing future trends and stock prices. The long short-term memory (LSTM) model outperforms the recurrent neural network (RNN) model. Moreover, in the comparison of the portfolio models, the Black–Litterman model, constructed by using Google’s Bidirectional Encoder Representations from Transformers (BERT) to measure news sentiment and by using the GRU model to predict stock prices, yields the highest annualized return rate of 46.6%. In addition, it has the highest Sharpe and Sortino ratios of 13.0% and 17.9%, respectively, which means that under a certain degree of risk, the Black–Litterman model still outperforms other constructed portfolios.

Investing Decision Model Based on Apriori Algorithm

Quantitative investments can be effective in reducing losses. It plays an important role in the pre-diction of the market. Due to this, more and more investors are paying attention to it. Trade Strategy Model and other models are used to predict price and make the decision. In Data Mining (DM), quantile values, advance rate, and decline rate are utilized to determine relative likelihood. Furthermore, the Apriori algorithm is used to calculate the number of continuous rises and falls that occur with a probability greater than 90%. To determine the amount of dollars to be purchased or sold in the shipment space, we establish a Position-Management Model (PMM). Finally, we introduce the Decision-Making Model (DMM) as a tool for trading decisions. We then plug $1000 into the model to arrive at $1958.37 in 2021. Based on the comparison of the predicted price and the real price from 2019-9-1 to 2019-10-31, we found a slight deviation between the predicted and the real price. As part of our verification process, we used the BackTesting method and selected the indicator "annual return rate," whose test showed an average of 17.687%, an excellent result. Afterward, we provide a summary of the strategy process for assisting its quantitative trading investment. To conclude, our models are distinguished by their precise values.

Stock market optimization amidst the COVID-19 pandemic: Technical analysis, K-means algorithm, and mean-variance model (TAKMV) approach

The Philippine stock market, just like most of its neighbors in the region, was seriously impacted by the global pandemic COVID-19. Investors remain hopeful while continuing to seek great ones in the damaged market. This paper developed a methodology for portfolio selection and optimization with the use of technical analysis, machine learning techniques, and portfolio optimization model. The combined methods of technical analysis, K-means clustering algorithm, and mean-variance portfolio optimization model will result in the development of the proposed TAKMV method. The study aims to integrate these three important analyses to identify portfolio investments. This paper uses the average annual risk and annual rate of return data for the years 2018 and 2020 to form the clusters and assessed the stocks that correspond to the investor's technical strategy such as Moving Average Convergence/Divergence (MACD) and Hybrid MACD with Arnaud Legoux Moving Average (ALMA). This paper solved the risk minimization problem on selected shares of the companies, based on the mean-variance portfolio optimization model. There are 230 and 239 companies for 2018 and 2020, respectively, listed in Philippine Stock Market, and all simulations were performed in MATLAB environment platform. Results showed that MACD strategy dominates the MACD-ALMA strategy in terms of the number of assets with a positive annual rate of return. The MACD works efficiently in the pre-COVID-19 condition while MACD-ALMA works efficiently during-COVID-19 condition, regardless of the number of assets with a positive annual rate of return. The results also show that the maximum expected portfolio return (RP) can be achieved using the MACD and MACD-ALMA in the pre-and during-COVID-19 conditions, respectively. The MACD-ALMA shows an advantage during high-risk market conditions and can also provide maximum RP. The performance of the TAKMV method was validated by applying its results and comparing it to the next year's historical price. The 2018 results were compared to 2019 data and the 2020 results were compared to 2021 data. For consistency, the comparison was applied to the same company per portfolio. Simulation results show that the MACD strategy is more effective compared to MACD-ALMA.

The gender gap in the ownership of promising land

Using millions of observations compiled from the public administrative data of Taiwan, we find a surprising gender inequity in terms of real estate: Men own more land than women, and the annual rate of return (ROR) of men's land outperform women's by almost 1% per year. The latter finding of gender-based ROR difference is in sharp contrast to prior evidence that women outperform men in security investment, and also suggests a quantity-and-quality double jeopardy in female land ownership which, given the heavy weight of real estate in individual wealth, has important implications for wealth inequality among men and women. Our statistical analyses suggest that such a gender-based difference in land ROR cannot be attributed to individual-level factors such as liquidity preferences, risk attitudes, investment experience, and behavioral biases, as described in the literature. Rather, we hypothesize parental gender bias-a phenomenon that is still prevalent today-to be the key macrolevel factor. To test our hypothesis, we partition our observations into two groups: an experimental group in which parents can exercise gender discretion, and a control group in which parents cannot exercise such discretion. Our empirical evidence shows that the gender difference with respect to land ROR only exists in the experimental group. For many societies with long-lasting patriarchal traditions, our analysis provides a perspective to help explain gender differences in wealth distribution and social mobility.

The strategy construction of major asset allocation based on macroeconomic cycle

Nowadays, in the investment market, the investment method with major asset allocation as the core has become the mainstream of various industries. This paper studies the data of various macroeconomic indicators, screens these indicators and observes their changing trends, so as to divide the economic cycle, and uses time series analysis and calculation of the relevant characteristics of risk and return, so as to maximize the benefits of asset allocation. According to the Merrill Lynch "investment clock" theory, select four economic indicators, observe the general change trend of these four indicators in one, and divide them into five different stages of economic status. The APIMA time series model was used to calculate the changing trend of macroeconomic environment. Through mathematical statistics, economics and other relevant principles to calculate the risk and return characteristics of the five stages, presented by the correlation coefficient heat map.This paper analyzes the impact of monetary policy on the return of major assets at different stages of the economic cycle, which is conducive to improving the investment ability of various asset management companies, promoting the effectiveness of asset allocation, improving the annual return rate of asset allocation portfolio, and thus increasing economic benefits.

Financial, Economic, and Environmental Analyses of Upgrading Reverse Osmosis Plant Fed with Treated Wastewater

One of the most effective strategies to mitigate water shortages worldwide is to reuse treated wastewater for freshwater production employing reverse osmosis (RO) technology. This strategy is appropriate in urban areas of arid or semi-arid regions as it can provide a sustainable and reliable water source close to the consumers. One of the drawbacks of RO is the high variability of production costs due to the electricity intensity. In addition, depending on the electricity source, it can also result in substantial environmental costs. This study showed that upgrading pumping and RO membrane systems of a wastewater reuse plant in Cyprus can significantly alleviate these drawbacks in terms cost, water recovery rate, and air pollution. The water-recovery rate of the upgraded RO plant increased from 43.2 to 75 percent, which resulted in a substantial net financial benefit due to the reduction in the quantity of wastewater purchased and the increase in potable water produced. The upgraded system also reduced the electricity requirement from 3.63 kWh/m3 to 1.92 kWh/m3. Pollution emissions decreased substantially because of the reduction in electricity requirements. The beneficiaries of these lower emission costs are the residents of Cyprus and global society. Overall, the benefit of upgrading the plant is highly attractive with more than 65 percent annual real internal rates of return in financial and economic terms. Positive net present values are realized for all the scenarios considered.

Productivity in Indonesian agriculture: Impacts of domestic and international research

AbstractTotal factor productivity growth contributed 38% of Indonesia's agricultural output growth from the mid‐1970s to the mid‐2000s. This study uses time series data analysed with an error correction mechanism to examine the contribution that Indonesian publicly funded agricultural research made to this outcome, allowing for other possible determinants of productivity growth, including international agricultural research, extension, government price policy and weather. The results imply a 27% real annual rate of return from a marginal increase in Indonesian agricultural research expenditure. Indonesia's public agricultural research explains virtually all of its agricultural total factor productivity growth between 1975 and 2006.

Mean Reversion Lessens Mean Blur: Evidence from the S&P Composite Index

This study makes use of a very long time series of the S&P Composite Index, checking once more that the rates of return benefit from aggregational normality. It performs unit root tests as well as elementary statistical tests that take advantage of normality. It finds that mean blur is not consistent with the hypothesis of random walk with constant parameters, because the means of the annual real rates of linear return can be estimated as usual. It gives further evidence that the rates of return on the S&P Composite Index are mean-reverting.

Economic benefit evaluation model of distributed energy storage system considering custom power services

This paper proposes an economic benefit evaluation model of distributed energy storage system considering multi-type custom power services. Firstly, based on the four-quadrant operation characteristics of the energy storage converter, the control methods and revenue models of distributed energy storage system to provide reactive power compensation, new energy consumption, peak-valley arbitrage and other customized power services are analyzed. Secondly, an economic benefit evaluation model of custom power services is formulated, considering the life cycle degradation cost, investment payback period, net present value, and internal return rate of energy storage. Finally, demonstrate the effectiveness and superior performance of the proposed methodology through comparative studies on the economic benefits and practical value. The influence of reserve capacity ratio of energy storage converter, additional price for power quality management, peak-valley price difference, battery cost and project cycle on the annual return and internal rate of return is revealed through the sensitivity analysis, which provides the decision-making reference for battery selection and capacity allocation of distributed energy storage system participating in various custom power services.

The effectiveness of alternative investments in the era of geopolitical changes

Purpose: The purpose of the article is to identify the volatility of the rates of return, as well as the profitability of investments in precious metals in relation to traditional investments during the unstable epidemic and geopolitical situation, including the identification of the impact of the method of calculating the simple rate of return on investment decisions. Design/methodology/approach: The article uses literature analysis and a statistical research method as research methods. A review of the world literature on the changes taking place on investment markets in the context of the COVID-19 pandemic and the war in Ukraine was carried out. The empirical part of the article presents the results of the analysis of the level of return on metals, compared to traditional investments, in the period 20.03.2018-21.03.2023. Findings: The level of annual rates of return differed from the level of average annual rates of return. Therefore, the conclusions about the profitability of individual investments depend on the chosen method of calculating simple rates of return. Research limitations/implications: This article does not exhaust the examined issue. In subsequent studies, it is worth reviewing the literature not only of the most recent publications, but also of older ones. It is also worth analyzing other types of alternative investments. In future analyses, it is also worth considering the use of statistical tests. Practical implications: Identifying changes in ores markets compared to traditional investment markets and indicating the impact of the simple rate of return calculation method on its value may be useful in making and evaluating investment decisions, as well as in better understanding of the changes taking place in these markets. Originality/value: The paper compares two methods of calculating the rate of return during an uncertain economic situation. The choice of the method of calculating the rate of return affects the results obtained, which translates into investment recommendations. Keywords: rate of return, alternative investments, precious metals, COVID-19 pandemic, war in Ukraine. Category of the paper: Research paper.

Ефективність встановлення субекономайзера у котельні УСГК

One of the issues that is to be solved by means of modernization of coke dry cooling facilities at coke oven and by-product plant of one of the foreign enterprises was ensuring the required temperature level of cooled coke in case coke production capacity is increased. In order to increase the efficiency of coke cooling, SE "GIPROKOKS'' has analyzed different methods of temperature reduction for circulating gases at the outlet of the cooling chamber. The most optimal is the installation of a sub-economizer, which, if installed in the coke dry cooling plant (CDCP), allows increasing the energy efficiency of the CDCP units (heating the return water from 70 to 110 °C gives the possibility to have an additional amount of heat (more than 3000 Gcal/year) for the energy supply system of the enterprise, which allows to reduce the amount of steam at the enterprise respectively)) and increase their productivity in terms of cooled coke by approximately by 10 % while maintaining the required temperature and quality of the produced coke. As part of the research and development of scientific and technical documentation, an analysis was made of the validity of the sub-economizer installed in the CDCP boiler house. It is shown that for the mentioned project, the cost of heat generation at the enterprise will be 44,4 % of the capital investment, the non-discounted pay-back period for capital investment will be 3,7 years, and the simple annual internal rate of return – 27 %. Considering the fact that the pay-back period is 16,3 years less than the period of its realization (the life cycle of CDCP is equal to the life cycle of coke ovens and according to the project makes up 20 years) and expected level for internal rate of return is 9 % higher than the normative (18 %), installation of sub-economizer in the CDCP boiler houses of coke-oven battery is not only technologically expedient, but also economically efficient for implementation. Due to obtaining positive results from the implementation of new technical solutions of SE "GIPROKOKS'' for improving the process of dry coke cooling, including improving stability and increasing efficiency, the installation of a sub-economizer in the CDCP boiler house is in increasing demand from Ukrainian and foreign coke oven and by-product plants. Keywords: economic efficiency, payback period, simple rate of return, sub-economizer, CDCP, coke oven and by-product plant. Corresponding author S.V. Chaplianko e-mail: chaplianko_sv@ukr.net

Fundamental Quantitative investment research based on Machine learning

In recent years, the status of quantitative investment in China's capital market has been improving, and fundamental quantification has emerged as a promising approach that integratesfundamental analysis and quantitative investment successfully. Hence, this kind of intelligent quantitative investment method has garnered significant attention. In this paper, eight machine learning algorithms, including Lasso regression, ridge regression, partial least squares regression, elastic network regression, decision tree, random forest, support vector machine and K-nearest neighbor method, are used to construct the stock return prediction model. The empirical results show that linear machine learning algorithm outperforms nonlinear machine learning algorithm. The annual return rate of CSI 300 index in the same term is 1.47%, while the investment strategy based on OLS model has an annualized return rate of 35.96%, and the maximum withdrawal rate is only 29.61%, showing its strong return capacity. In this paper, machine learning is introduced in the field of fundamental quantitative investment, which provides investment reference for all kinds of investors and is helpful for the country to promote quantitative investment.

Site Fidelity, Annual Return Rate, Site Switching and Movement of Shorebirds Wintering in Thane Creek, West Coast of India, with Conservation Implications

Site Fidelity, Annual Return Rate, Site Switching and Movement of Shorebirds Wintering in Thane Creek, West Coast of India, with Conservation Implications