scholarly journals Portfolio Construction for Pharmaceutical Industry

2021 ◽  
Vol 275 ◽  
pp. 03032
Author(s):  
Xiqing Sun ◽  
Baichuan Li ◽  
Huatian Pang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Pharmaceutical Industry ◽  
Efficient Frontier ◽  
Minimum Variance ◽  
Sharpe Ratio ◽  
Portfolio Construction ◽  
Human Beings ◽  
The Monte Carlo Method ◽  
Weighted Model

In finance area, portfolio construction is one of the most vital questions since the primary work of modern finance and attract numerous studies. In this paper, we focused on this issue in pharmaceutical industry since the industry is crucial for human beings. We adopted several methods for portfolio construction, like Equal Weighted Model, Monte Carlo simulation, and maximize Sharpe ratio etc. Specifically, five assets are selected. Then based on the Monte Carlo method, we constructed two optimized portfolios in the framework of the efficient frontier, i.e., portfolios with minimum variance and maximum Sharpe ratio. By analyzing the two portfolios, we found that the NVS accounts for the largest proportions in the optimized portfolio. The results in this paper may shed lights for certain investors who invest in pharmaceutical industry.

scholarly journals Portfolio Construction of Energy-Related Assets

2021 ◽  
Vol 275 ◽  
pp. 01001
Author(s):  
Yifei Feng ◽  
Kexin Li ◽  
Yingxuan Wang
Keyword(s):  
Monte Carlo ◽  
Financial Markets ◽  
Electric Power ◽  
Efficient Frontier ◽  
Minimum Variance ◽  
Portfolio Allocation ◽  
Ratio Method ◽  
Portfolio Construction ◽  
Minimum Variance Portfolio ◽  
Sharp Ratio

Portfolio construction is one of the most fatal issues of modern finance, which can effectively gain returns or reduce risks. This study constructs portfolios in energy-related assets. Specifically, the Monte Carlo simulations are carried out for a hundred thousand times in order to discover the efficient frontier and find the minimum variance and the maximum sharp ratio portfolio. According to the simulations, the American Electric Power possesses the largest share in minimum variance portfolio, while NextEra Energy for sharp ratio method. The results may benefit certain investor in financial markets and shed lights to focus more on portfolio allocation during constructing.

scholarly journals Changes in the Portfolio Management and Construction under the Pandemic Era

2021 ◽  
Vol 275 ◽  
pp. 01005
Author(s):  
Ruipeng Tan
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Stock Returns ◽  
Financial Market ◽  
Portfolio Management ◽  
Portfolio Construction ◽  
Sample Data ◽  
Before And After ◽  
Reduce Risk ◽  
The Impact

This paper focuses on comparing portfolio management and construction before and after the coronavirus. First, this paper presents the importance of building up portfolios for investors to diversify their risks. Theories on portfolio management are discussed in this section to show how they have been developed to help on investing and reduce risk. Then, the paper moves on to show the impact of the pandemic on the financial market and portfolio management. Sample data on tech stock returns are collected to perform a Monte Carlo simulation on portfolio construction to find out the efficient portfolio before and after the COVID-19 outbreak. The efficient portfolio is build based on the Markowitz theory to find the combination. Comparisons between these portfolio constructions are made to find out the changes in portfolio management and construction under the pandemic era. In conclusion, this paper presents how pandemic has changed and impacted the investments and lists recommendations on future portfolio management and construction.

scholarly journals Technical-Economic Analysis of Grapple Saw

2020 ◽  
Vol 41 (2) ◽  
pp. 219-229 ◽  
Author(s):  
Ricardo Hideaki Miyajima ◽  
Paulo Torres Fenner ◽  
Gislaine Cristina Batistela ◽  
Danilo Simões
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Probability Distributions ◽  
Simulation Method ◽  
Forest Harvesting ◽  
Production Costs ◽  
Wood Harvesting ◽  
Food And Agriculture ◽  
The Monte Carlo Method

The processing of Eucalyptus logs is a stage that follows the full tree system in mechanized forest harvesting, commonly performed by grapple saw. Therefore, this activity presents some associated uncertainties, especially regarding technical and silvicultural factors that can affect productivity and production costs. To get around this problem, Monte Carlo simulation can be applied, or rather a technique that allows to measure the probabilities of values from factors that are under conditions of uncertainties, to which probability distributions are attributed. The objective of this study was to apply the Monte Carlo method for determining the probabilistic technical-economical coefficients of log processing using two different grapple saw models. Field data were obtained from an area of forest planted with Eucalyptus, located in the State of São Paulo, Brazil. For the technical analysis, the time study protocol was applied by the method of continuous reading of the operational cycle elements, which resulted in production. As for the estimated cost of programmed hour, the applied methods were recommended by the Food and Agriculture Organization of the United Nations. The incorporation of the uncertainties was carried out by applying the Monte Carlo simulation method, by which 100,000 random values were generated. The results showed that the crane empty movement is the operational element that most impacts the total time for processing the logs; the variables that most influence the productivity are specific to each grapple saw model; the difference of USD 0.04 m3 in production costs was observed between processors with gripping area of 0.58 m2 and 0.85 m2. The Monte Carlo method proved to be an applicable tool for mechanized wood harvesting for presenting a range of probability of occurrences for the operational elements and for the production cost.

scholarly journals Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport

2020 ◽  
Vol 10 (12) ◽  
pp. 4229 ◽  
Author(s):  
Alexander Heilmeier ◽  
Michael Graf ◽  
Johannes Betz ◽  
Markus Lienkamp
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
State Of The Art ◽  
Probability Distributions ◽  
Strategic Decisions ◽  
The Monte Carlo Method ◽  
Random Events ◽  
Example Variability

Applying an optimal race strategy is a decisive factor in achieving the best possible result in a motorsport race. This mainly implies timing the pit stops perfectly and choosing the optimal tire compounds. Strategy engineers use race simulations to assess the effects of different strategic decisions (e.g., early vs. late pit stop) on the race result before and during a race. However, in reality, races rarely run as planned and are often decided by random events, for example, accidents that cause safety car phases. Besides, the course of a race is affected by many smaller probabilistic influences, for example, variability in the lap times. Consequently, these events and influences should be modeled within the race simulation if real races are to be simulated, and a robust race strategy is to be determined. Therefore, this paper presents how state of the art and new approaches can be combined to modeling the most important probabilistic influences on motorsport races—accidents and failures, full course yellow and safety car phases, the drivers’ starting performance, and variability in lap times and pit stop durations. The modeling is done using customized probability distributions as well as a novel “ghost” car approach, which allows the realistic consideration of the effect of safety cars within the race simulation. The interaction of all influences is evaluated based on the Monte Carlo method. The results demonstrate the validity of the models and show how Monte Carlo simulation enables assessing the robustness of race strategies. Knowing the robustness improves the basis for a reasonable determination of race strategies by strategy engineers.

Secondary Fluorescence of 3D Heterogeneous Materials Using a Hybrid Model

2020 ◽  
Vol 26 (3) ◽  
pp. 484-496
Author(s):  
Yu Yuan ◽  
Hendrix Demers ◽  
Xianglong Wang ◽  
Raynald Gauvin
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Analytical Model ◽  
Hybrid Model ◽  
Three Dimensional ◽  
Heterogeneous Materials ◽  
X Ray ◽  
The Monte Carlo Method ◽  
Secondary Fluorescence ◽  
Good Agreement

AbstractIn electron probe microanalysis or scanning electron microscopy, the Monte Carlo method is widely used for modeling electron transport within specimens and calculating X-ray spectra. For an accurate simulation, the calculation of secondary fluorescence (SF) is necessary, especially for samples with complex geometries. In this study, we developed a program, using a hybrid model that combines the Monte Carlo simulation with an analytical model, to perform SF correction for three-dimensional (3D) heterogeneous materials. The Monte Carlo simulation is performed using MC X-ray, a Monte Carlo program, to obtain the 3D primary X-ray distribution, which becomes the input of the analytical model. The voxel-based calculation of MC X-ray enables the model to be applicable to arbitrary samples. We demonstrate the derivation of the analytical model in detail and present the 3D X-ray distributions for both primary and secondary fluorescence to illustrate the capability of our program. Examples for non-diffusion couples and spherical inclusions inside matrices are shown. The results of our program are compared with experimental data from references and with results from other Monte Carlo codes. They are found to be in good agreement.

scholarly journals C-beta energy converter efficiency modeling

2019 ◽  
Vol 222 ◽  
pp. 02012
Author(s):  
Oleg Kuznetsov ◽  
Viktor Chepurnov ◽  
Albina Gurskaya ◽  
Mikhail Dolgopolov ◽  
Sali Radzhapov
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Theoretical Models ◽  
Maximum Efficiency ◽  
Equivalent Circuits ◽  
Optimal Parameters ◽  
Energy Converter ◽  
Electron Hole ◽  
The Monte Carlo Method

To construct beta converters with maximum efficiency it is necessary to carry out the theoretical calculation in order to determine their optimal parameters - the geometry of the structure, the thickness of the deposition of the radioisotope layer, the depth and the width of the p-n junction, and others. To date, many different theoretical models and calculations methods had been proposed. There are fairly simple theoretical models based on the Bethe-Bloch formula and the calculation of the rate of generation of electron-hole pairs, and on calculations by equivalent circuits. Also, the Monte-Carlo method is used for theoretical modeling of beta converters. This paper explores beta converter optimization using the Monte-Carlo method. The purpose of the study is to conduct Monte-Carlo simulation of the beta converter to determine its optimal parameters.

scholarly journals Mean-Variance Portfolio Selection with Tracking Error Penalization

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1915
Author(s):  
William Lefebvre ◽  
Grégoire Loeper ◽  
Huyên Pham
Keyword(s):  
Portfolio Selection ◽  
Tracking Error ◽  
Efficient Frontier ◽  
Minimum Variance ◽  
Sharpe Ratio ◽  
Portfolio Strategy ◽  
The Mean ◽  
Mean Variance Portfolio ◽  
Mean Variance ◽  
Variance Criterion

This paper studies a variation of the continuous-time mean-variance portfolio selection where a tracking-error penalization is added to the mean-variance criterion. The tracking error term penalizes the distance between the allocation controls and a reference portfolio with same wealth and fixed weights. Such consideration is motivated as follows: (i) On the one hand, it is a way to robustify the mean-variance allocation in the case of misspecified parameters, by “fitting" it to a reference portfolio that can be agnostic to market parameters; (ii) On the other hand, it is a procedure to track a benchmark and improve the Sharpe ratio of the resulting portfolio by considering a mean-variance criterion in the objective function. This problem is formulated as a McKean–Vlasov control problem. We provide explicit solutions for the optimal portfolio strategy and asymptotic expansions of the portfolio strategy and efficient frontier for small values of the tracking error parameter. Finally, we compare the Sharpe ratios obtained by the standard mean-variance allocation and the penalized one for four different reference portfolios: equal-weights, minimum-variance, equal risk contributions and shrinking portfolio. This comparison is done on a simulated misspecified model, and on a backtest performed with historical data. Our results show that in most cases, the penalized portfolio outperforms in terms of Sharpe ratio both the standard mean-variance and the reference portfolio.

Application of the Mean-Variance Theory and Resampling Technique for the Italian Energy Portfolio Settlement

2013 ◽  
Vol 869-870 ◽  
pp. 581-592
Author(s):  
Mauro Arnesano ◽  
Antonio Paolo Carlucci ◽  
Giovanni D'Oria ◽  
Alessio Guadalupi ◽  
Domenico Laforgia
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Estimation Error ◽  
Efficient Frontier ◽  
Energy Generation ◽  
Energy System ◽  
Estimation Errors ◽  
Energy Mix ◽  
The Mean ◽  
Mean Variance

The energy planning based on Mean - Variance theory, guides the investors in investment decisions, trying to maximize the return and minimize the risk of investment. However, this theory is based on strong hypotheses and, in addition, input data are often affected by estimation errors. Moreover, this theory determines poor diversification increasing return and risk of the portfolio, and strong variability of the outputs when inputs are varied.In the first part of the paper, the Mean - Variance theory was applied to the energy generation in Italy; in particular, the analysis was on the actual energy mix, but also assuming the use of nuclear technology and taking into account verisimilar improvement, of technologies in the future.On the other hand, in the second part of the paper, a methodology has been applied in order to limit the problems of Mean-Variance theory applied to the energy mix settlement. In particular, the input variables have been calculated using Monte Carlo simulation, in order to reduce the estimation error, and the Resampled EfficiencyTMtechnique has been applied in order to calculate the resulting new “average” efficient frontier. This methodology has been applied either not limiting or limiting the minimum and maximum percentage for every energy generation technology, in order to simulate constraints due, for example, to the technological characteristics of the plant, the availability of the sources and eventually to norms, to the territorial characteristics and to the socio-political choices. The application of Mean - Variance theory allowed to obtain energy portfolio, alternative to the actual, characterized by higher values of expected returns an lower values of risk.It was also shown that the application of the Resampled EfficiencyTMtechnique with data originated with the Monte Carlo simulation effectively tackles the problems of Mean - Variance theory; in this way, the decision maker is helped in making decisions in the energy system policy and development.Thanks to this approach, applied in particular to the Italian energy contest, it was also possible to evaluate the effectiveness of the introduced modifications to the Italian actual energy mix to achieve the 2020 European Energy Directive targets in particular concerning the reduction of CO2levels.

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