Excel Based Financial Modeling for Making Portfolio Management Decisions

The Excel based financial model proposed in this paper provides a very simple but powerful method for portfolio selection. Apart from a simple and powerful tool for making portfolio management decisions, the paper also proposes an easy to use technique for calculating portfolio standard deviation without using correlation coefficients. The model uses â€œExcel Solver Add-Inâ€ to create an optimum portfolio by maximizing the Sharpe ratio. Benefits of Sharpe style optimization are demonstrated using data on monthly returns from 1999 to 2010 covering 30 stocks.

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Petroleum Exploration Risk in Prospect Portfolio Selection

Advances in Logistics, Operations, and Management Science - Novel Six Sigma Approaches to Risk Assessment and Management ◽

10.4018/978-1-5225-2703-9.ch003 ◽

2017 ◽

pp. 40-65

Keyword(s):

Standard Deviation ◽

Portfolio Selection ◽

Six Sigma ◽

Efficient Frontier ◽

Sharpe Ratio ◽

Petroleum Exploration ◽

Management Approach ◽

Portfolio Optimisation ◽

Exploration Risk ◽

Return Variance

Presented method is applied to petroleum exploration for prospect portfolio selection to achieve investment objectives controlling risk. DMAIC framework applies stochastic techniques to risk management. Optimisation resolves Efficient Frontier of portfolios for desired range of expected return with initially defined increment. Simulation measures Efficient Frontier portfolios calculating mean return, variance, standard deviation, Sharpe Ratio, and Six Sigma metrics versus pre-specified target limits. Analysis considers mean return, Six Sigma metrics and Sharpe Ratio and selects the portfolio with maximal Sharpe Ratio as initially the best portfolio. Optimisation resolves Efficient Frontier in a narrow interval with smaller increments. Simulation measures Efficient Frontier performance including mean return, variance, standard deviation, Sharpe Ratio, and Six Sigma metrics versus pre-specified target. Analysis identifies the maximal Sharpe Ratio portfolio, i.e. the best portfolio for implementation. Selected prospects in the portfolio are individual projects. So, Project Management approach is used for control.

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Mean-gini and mean-extended gini portfolio selection: An empirical analysis

Risk Governance and Control Financial Markets & Institutions ◽

10.22495/rcgv6i3c1art7 ◽

2016 ◽

Vol 6 (3) ◽

pp. 59-66

Author(s):

Jamal Agouram ◽

Lakhnati Ghizlane

Keyword(s):

Standard Deviation ◽

Empirical Analysis ◽

Portfolio Selection ◽

Financial Market ◽

Sharpe Ratio ◽

Rate Of Return ◽

Individual Investor ◽

Market Data ◽

Crisis Period ◽

Portfolio Strategies

The purpose of this study was to examine Mean-Gini strategy (MG) and Mean-Extended Gini strategy (MEG) for optimum portfolio selection, in terms of the monthly Rate of Return, Standard Deviation, Sharpe Ratio, Treynor Ratio and Jensen’s Alpha. This paper compared different optimum portfolio strategies, based on Moroccan financial market data taken from turbulent market periods between the years 2007 to 2015. Two distinct sub-periods were studied: (1) crisis period: 2007-2009; (2) post-crisis period: 2010-2015. The results show that both strategies were profitable for investors, but that the MEG strategy is the more appropriate and secure strategy for an individual investor.

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Reliability of the Minimum Masking Level as Outcome Variable in Tinnitus Clinical Research

American Journal of Audiology ◽

10.1044/2020_aja-20-00047 ◽

2020 ◽

Vol 29 (3) ◽

pp. 429-435

Author(s):

Patricia C. Mancini ◽

Richard S. Tyler ◽

Hyung Jin Jun ◽

Tang-Chuan Wang ◽

Helena Ji ◽

...

Keyword(s):

Standard Deviation ◽

High Frequency ◽

Pearson Correlation ◽

Correlation Coefficients ◽

Outcome Variable ◽

Reliable Estimate ◽

Moderate Correlation ◽

Trained Subjects ◽

The Relationship ◽

Minimum Intensity

Purpose The minimum masking level (MML) is the minimum intensity of a stimulus required to just totally mask the tinnitus. Treatments aimed at reducing the tinnitus itself should attempt to measure the magnitude of the tinnitus. The objective of this study was to evaluate the reliability of the MML. Method Sample consisted of 59 tinnitus patients who reported stable tinnitus. We obtained MML measures on two visits, separated by about 2–3 weeks. We used two noise types: speech-shaped noise and high-frequency emphasis noise. We also investigated the relationship between the MML and tinnitus loudness estimates and the Tinnitus Handicap Questionnaire (THQ). Results There were differences across the different noise types. The within-session standard deviation averaged across subjects varied between 1.3 and 1.8 dB. Across the two sessions, the Pearson correlation coefficients, range was r = .84. There was a weak relationship between the dB SL MML and loudness, and between the MML and the THQ. A moderate correlation ( r = .44) was found between the THQ and loudness estimates. Conclusions We conclude that the dB SL MML can be a reliable estimate of tinnitus magnitude, with expected standard deviations in trained subjects of about 1.5 dB. It appears that the dB SL MML and loudness estimates are not closely related.

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On the sensibility of the Pairs Trading strategy: the case of the FTS stock market index

Studies of Applied Economics ◽

10.25115/eea.v38i3.3145 ◽

2020 ◽

Vol 38 (3) ◽

Author(s):

Ainhoa Fernández-Pérez ◽

María de las Nieves López-García ◽

José Pedro Ramos Requena

Keyword(s):

Standard Deviation ◽

Stock Market ◽

Trading Strategy ◽

Stock Market Index ◽

Pairs Trading ◽

Empirical Application ◽

Statistical Arbitrage ◽

Market Index ◽

Standard Deviations ◽

Using Data

In this paper we present a non-conventional statistical arbitrage technique based in varying the number of standard deviations used to carry the trading strategy. We will show how values of 1 and 1,2 in the standard deviation provide better results that the classic strategy of Gatev et al (2006). An empirical application is performance using data of the FST100 index during the period 2010 to June 2019.

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Estimation of Human Center of Mass Position through the Inertial Sensors-Based Methods in Postural Tasks: An Accuracy Evaluation

Sensors ◽

10.3390/s21020601 ◽

2021 ◽

Vol 21 (2) ◽

pp. 601 ◽

Cited By ~ 1

Author(s):

Marco Germanotta ◽

Ilaria Mileti ◽

Ilaria Conforti ◽

Zaccaria Del Prete ◽

Irene Aprile ◽

...

Keyword(s):

Inertial Sensors ◽

Center Of Mass ◽

Correlation Coefficients ◽

Biomechanical Model ◽

Lower Limbs ◽

Accuracy Evaluation ◽

Postural Tasks ◽

Single Sensor ◽

Using Data ◽

Displacement Based

The estimation of the body’s center of mass (CoM) trajectory is typically obtained using force platforms, or optoelectronic systems (OS), bounding the assessment inside a laboratory setting. The use of magneto-inertial measurement units (MIMUs) allows for more ecological evaluations, and previous studies proposed methods based on either a single sensor or a sensors’ network. In this study, we compared the accuracy of two methods based on MIMUs. Body CoM was estimated during six postural tasks performed by 15 healthy subjects, using data collected by a single sensor on the pelvis (Strapdown Integration Method, SDI), and seven sensors on the pelvis and lower limbs (Biomechanical Model, BM). The accuracy of the two methods was compared in terms of RMSE and estimation of posturographic parameters, using an OS as reference. The RMSE of the SDI was lower in tasks with little or no oscillations, while the BM outperformed in tasks with greater CoM displacement. Moreover, higher correlation coefficients were obtained between the posturographic parameters obtained with the BM and the OS. Our findings showed that the estimation of CoM displacement based on MIMU was reasonably accurate, and the use of the inertial sensors network methods should be preferred to estimate the kinematic parameters.

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CAPITAL ALLOCATION WITH MULTIVARIATE RISK MEASURES: AN AXIOMATIC APPROACH

Probability in the Engineering and Informational Sciences ◽

10.1017/s0269964819000032 ◽

2019 ◽

Vol 34 (2) ◽

pp. 297-315

Author(s):

Linxiao Wei ◽

Yijun Hu

Keyword(s):

Standard Deviation ◽

Portfolio Management ◽

Risk Measures ◽

Risk Measure ◽

Axiomatic Approach ◽

Capital Allocation ◽

Axiom System ◽

Multivariate Risk ◽

Multivariate Risk Measures ◽

Multivariate Risk Measure

AbstractCapital allocation is of central importance in portfolio management and risk-based performance measurement. Capital allocations for univariate risk measures have been extensively studied in the finance literature. In contrast to this situation, few papers dealt with capital allocations for multivariate risk measures. In this paper, we propose an axiom system for capital allocation with multivariate risk measures. We first recall the class of the positively homogeneous and subadditive multivariate risk measures, and provide the corresponding representation results. Then it is shown that for a given positively homogeneous and subadditive multivariate risk measure, there exists a capital allocation principle. Furthermore, the uniqueness of the capital allocation principe is characterized. Finally, examples are also given to derive the explicit capital allocation principles for the multivariate risk measures based on mean and standard deviation, including the multivariate mean-standard-deviation risk measures.

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An assessment of the quality indicators of operative and non-operative times in a public university hospital

Einstein (São Paulo) ◽

10.1590/s1679-45082015gs3289 ◽

2015 ◽

Vol 13 (4) ◽

pp. 594-599 ◽

Cited By ~ 5

Author(s):

Altair da Silva Costa Jr ◽

Luiz Eduardo Villaça Leão ◽

Maykon Anderson Pires de Novais ◽

Paola Zucchi

Keyword(s):

Standard Deviation ◽

Operating Room ◽

Operative Time ◽

Patient Flow ◽

Cross Sectional Study ◽

Public University ◽

Major Effect ◽

University Hospital ◽

Cross Sectional ◽

Using Data

ABSTRACT Objective To assess the operative time indicators in a public university hospital. Methods A descriptive cross-sectional study was conducted using data from operating room database. The sample was obtained from January 2011 to January 2012. The operations performed in sequence in the same operating room, between 7:00 am and 5:00 pm, elective or emergency, were included. The procedures with incomplete data in the system were excluded, as well as the operations performed after 5:00 pm or on weekends or holidays. Results We measured the operative and non-operative time of 8,420 operations. The operative time (mean and standard deviation) of anesthesias and operations were 177.6±110 and 129.8±97.1 minutes, respectively. The total time of the patient in operative room (mean and standard deviation) was 196.8±113.2. The non-operative time, e.g., between the arrival of the patient and the onset of anesthesia was 14.3±17.3 minutes. The time to set the next patient in operating room was 119.8±79.6 minutes. Our total non-operative time was 155 minutes. Conclusion Delays frequently occurred in our operating room and had a major effect on patient flow and resource utilization. The non-operative time was longer than the operative time. It is possible to increase the operating room capacity by management and training of the professionals involved. The indicators provided a tool to improve operating room efficiency.

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