Research and Development Risk in Projects Selection

2017 ◽  
pp. 66-91
Keyword(s):  
Standard Deviation ◽  
Six Sigma ◽  
Efficient Frontier ◽  
Sharpe Ratio ◽  
Management Approach ◽  
Portfolio Optimisation ◽  
Expected Return ◽  
Return Variance ◽  
Stochastic Techniques ◽  
Development Risk

Elaborated method is applied to R&D for project 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 projects in the portfolio are individual projects. So, Project Management approach is used for control.

Petroleum Exploration Risk in Prospect Portfolio Selection

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.

Investment Management Risk

2017 ◽  
pp. 13-39
Keyword(s):  
Portfolio Management ◽  
Value At Risk ◽  
Efficient Frontier ◽  
Sharpe Ratio ◽  
Portfolio Performance ◽  
Investment Management ◽  
Expected Return ◽  
Return Variance ◽  
Asset Portfolio ◽  
Investment Process

Proposed method is applied to Investment Management for portfolio selection to achieve investment objectives controlling risk. DMAIC framework applies stochastic techniques to investment risk management. Optimisation constructs Efficient Frontier of optimal portfolios with expected return in a predefined range with a determined increment. Simulation calculates and measures the portfolio return, Variance, Standard Deviation, Value at Risk (VAR), Sharpe Ratio and Beta of Efficient Frontier portfolios; Six Sigma capability metrics of investment process are calculated versus specified limits. Analysis allows for selection of the best Efficient Frontier portfolio with maximum Sharpe Ratio. Simulation sensitivity analysis identifies the riskiest asset. Portfolio revision considers options to improve the portfolio and replaces the asset with an option to reduce risk. Portfolio execution implements the revised portfolio. Ongoing portfolio management evaluates portfolio performance on regular basis and if required, revises the portfolio considering changes in the market and investor's position.

EFFECTS OF MULTIPLE CRITERIA ON PORTFOLIO OPTIMIZATION

2014 ◽  
Vol 13 (01) ◽  
pp. 77-99 ◽  
Author(s):  
TUNCER ŞAKAR CEREN ◽  
MURAT KÖKSALAN
Keyword(s):  
At Risk ◽  
Portfolio Optimization ◽  
Decision Maker ◽  
Value At Risk ◽  
Efficient Frontier ◽  
Conditional Value At Risk ◽  
Computational Studies ◽  
Expected Return ◽  
Risk Criteria ◽  
Return Variance

We study the effects of considering different criteria simultaneously on portfolio optimization. Using a single-period optimization setting, we use various combinations of expected return, variance, liquidity and Conditional Value at Risk criteria. With stocks from Borsa Istanbul, we make computational studies to show the effects of these criteria on objective and decision spaces. We also consider cardinality and weight constraints and study their effects on the results. In general, we observe that considering alternative criteria results in enlarged regions in the efficient frontier that may be of interest to the decision maker. We discuss the results of our experiments and provide insights.

scholarly journals Unit trusts and portfolio selection on the Johannesburg Stock Exchange

1982 ◽  
Vol 13 (4) ◽  
pp. 169-175
Author(s):  
K. J. Carter ◽  
J. F. Affleck-Graves ◽  
A. H. Money
Keyword(s):  
Standard Deviation ◽  
Portfolio Selection ◽  
Stock Exchange ◽  
Efficient Frontier ◽  
Expected Return ◽  
Earnings Volatility ◽  
Johannesburg Stock Exchange ◽  
Market Index ◽  
Efficient Portfolios ◽  
Unit Trust

The application of the standard techniques of portfolio selection on the 34 sectors comprising the JSE All Share index is undertaken for the three equal non-overlapping five-year periods between February 1965 and January 1980. Efficient portfolios in each period which carry the same risk as the market index are seen to outperform the market substantially. Portfolios chosen at random to span the efficient frontier in each period reveal the consistent inefficiency of 10 sectors over the 15-year period. Three of these sectors, namely Mining Holding, Mining Houses and Industrial Holding are shown to be favoured in the Association of Unit Trusts portfolio relative to these sectors' proportion of the market. On the presumption that unit trust managers attempt to act efficiently, holding these sectors is only justified if the measure of risk used in the portfolio selection algorithm, namely standard deviation of expected return, is less appropriate than other measures of risk such as earnings volatility. If standard deviation of expected return is a more appropriate measure of risk in the selection of efficient portfolios, it must be concluded that the large sophisticated investors managing the unit trusts act inefficiently.

scholarly journals Introducing REITs (Real Estate Investment Trusts) to Enhance the Risk Adjusted Returns of the Risky Direct Real Estate Portfolio

2016 ◽  
Vol 2 (2) ◽  
pp. 323
Author(s):  
David HO Kim Hin ◽  
Justin WONG Chia Chern
Keyword(s):  
Standard Deviation ◽  
Real Estate ◽  
Asset Allocation ◽  
Efficient Frontier ◽  
Investment Strategy ◽  
Sharpe Ratio ◽  
Expert Opinions ◽  
Allocation Process ◽  
Asian Cities ◽  
Ex Ante

<p><strong><em>Purpose</em></strong><strong><em>:</em></strong><em> </em><em>The paper has several objectives in mind: to examine whether or not </em><em>a dynamic, ex ante AHP-SAA model and a dynamic Markowitz QP TAA model that utilizes de-smoothed data, produces an investment strategy, which further optimizes the risk-adjusted return of the pan-Asian real estate portfolio. It examines the required de-smoothing and Modern Portfolio Theory (MPT) for the TAA. </em><em></em></p><p><strong><em>Design/Methodology/Approach</em></strong><strong><em>:</em></strong><em> </em><em>This paper reveals that the efficient frontier of risk-adjusted returns for direct real estate portfolio is enhanced by introducing REITS. The portfolio comprises the Pan-Asian office and industrial real estate markets for 13 major Asian cities, to which Asian REITS are added. Direct real estate total return data is in its </em><em>“</em><em>smooth</em><em>”</em><em> form while the REIT data is </em><em>“</em><em>de-smoothed</em><em>”</em><em> under the 1<sup>st</sup> and 4<sup>th</sup> order autoregressive model. The efficient frontier is constructed under a dynamic Strategic Asset Allocation (SAA) model, incorporating the Analytic Hierarchy Process (AHP) approach. Secondly, the dynamic Markowitz quadratic-programming Tactical Asset Allocation (TAA) model is adopted to obtain a geographically and real estate sector diversified portfolio.</em><em></em></p><p><strong><em>Findings</em></strong><strong><em>:</em></strong><em> </em><em>The resulting efficient frontier with the de-smoothed data reveals a higher overall TR for every corresponding standard deviation as compared to the smoothed data. TAA for the de-smoothed returns would lie on the efficient frontier at the maximum Sharpe ratio of 1.44 with a TR on 15.30% and a standard deviation of 7.31%. Conversely, TAA for the smoothed returns would lie on the efficient frontier at the maximum Sharpe ratio of 1.31 with a lower TR of 14.2% and a standard deviation of 7.18%.</em><em></em></p><p><strong><em>Practical implications</em></strong><strong><em>: </em></strong><em>This paper should serve as a meaningful guide to look at </em><em>an alternative asset allocation process that can be effectively adopted and refined by practitioners and researchers. It enables asset managers/or investors to deploy expert opinions on an ex ante basis for a longer term dynamic SAA model and a short term dynamic Markowitz QP TAA model. </em><em></em></p><p><strong><em>Originality/Value</em></strong><strong><em>:</em></strong><em> The paper offers insightful information for </em><em>in adopting the AHP to develop a dynamic SAA and the dynamic Markowitz QP TAA model in utilizing de-smoothed direct real estate TR data. This paper is specific to a Pan Asian direct real estate portfolio of 13 Asian cities together with the introduction of Asian REITS, to provide greater diversification and risk-return benefits.</em><em></em></p>

Managing Market Risk and Controlling VAR

2018 ◽  
pp. 113-131
Keyword(s):  
Efficient Frontier ◽  
Market Risk ◽  
Sharpe Ratio ◽  
Optimal Portfolio ◽  
Return Variance ◽  
Simulation Results ◽  
Investment Portfolios ◽  
Mean Variance ◽  
Portfolio Return ◽  
Correlated Assets

The market risk management in a portfolio selection of correlated assets is considered in this chapter. The chapter elaborates how to construct and select an optimal portfolio of correlated assets in order to control VAR considering the risk associated limits. Stochastic optimisation is used to construct the efficient frontier of minimal mean variance investment portfolios with maximal return and a minimal acceptable risk. Monte Carlo simulation is utilised to stochastically calculate and measure the portfolio return, Variance, Standard Deviation, VAR and Sharpe Ratio of the efficient frontier portfolios. Six Sigma process capability metrics are also stochastically calculated against desired specified target limits for VAR and Sharpe Ratio of the Efficient Frontier portfolios. Simulation results are analysed and the optimal portfolio is selected from the Efficient Frontier based on the criteria of maximum Sharpe Ratio.

Managing Credit Risk in Bank Loan Portfolio

2018 ◽  
pp. 10-36
Keyword(s):  
Credit Risk ◽  
Six Sigma ◽  
Efficient Frontier ◽  
Sharpe Ratio ◽  
Bank Loan ◽  
Credit Risk Management ◽  
Loan Portfolio ◽  
Maximal Profit ◽  
Investment Grade ◽  
Loan Losses

In this chapter, the Six Sigma DMAIC approach is applied to improve credit risk management in banking loan portfolio selection. The objective is to select the optimal loan portfolio which achieves the bank's investment objectives with an acceptable credit risk according to their predefined limits. Stochastic optimisation constructs an efficient frontier of optimal loan portfolios in banking with maximal profit and minimising loan losses, i.e. credit risk. Simulation stochastically calculates and measures mean gross profit, loan losses, variance, standard deviation and the Sharpe ratio. The Six Sigma capability metrics determines if the loan portfolio complies with the bank's limits regarding the gross profit; loan losses, which quantifies the credit risk; and Sharpe ratio, i.e. a risk adjusted measure. Also, the bank regulation limits are applied based on the bank's capital to control the maximum loan amount per loan investment grade. Analysis allows for selection of the best Efficient Frontier loan portfolio with the maximum Sharpe ratio.

Risk-Relevance of Fair-Value Income Measures for Commercial Banks

2006 ◽  
Vol 81 (2) ◽  
pp. 337-375 ◽  
Author(s):  
Leslie D. Hodder ◽  
Patrick E. Hopkins ◽  
James M. Wahlen
Keyword(s):  
Standard Deviation ◽  
Stock Returns ◽  
Commercial Banks ◽  
Fair Value ◽  
Net Income ◽  
Income Volatility ◽  
Expected Return ◽  
Market Pricing ◽  
Share Prices ◽  
Comprehensive Income

We investigate the risk relevance of the standard deviation of three performance measures: net income, comprehensive income, and a constructed measure of full-fair-value income for a sample of 202 U.S. commercial banks from 1996 to 2004. We find that, for the average sample bank, the volatility of full-fair-value income is more than three times that of comprehensive income and more than five times that of net income. We find that the incremental volatility in full-fair-value income (beyond the volatility of net income and comprehensive income) is positively related to marketmodel beta, the standard deviation in stock returns, and long-term interest-rate beta. Further, we predict and find that the incremental volatility in full-fair-value income (1) negatively moderates the relation between abnormal earnings and banks' share prices and (2) positively affects the expected return implicit in bank share prices. Our findings suggest full-fair-value income volatility reflects elements of risk that are not captured by volatility in net income or comprehensive income, and relates more closely to capital-market pricing of that risk than either net-income volatility or comprehensiveincome volatility.

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.

BUSINESS CYCLE AND OPTIMAL TIMING FOR INVESTMENT

2012 ◽  
Vol 6 (1) ◽  
pp. 31-41
Author(s):  
Joseph Cheng ◽  
Jeffery Lippitt
Keyword(s):  
Standard Deviation ◽  
Business Cycle ◽  
Risk Ratio ◽  
Equity Capital ◽  
Optimal Timing ◽  
Expected Return ◽  
Gdp Growth Rate ◽  
The Business Cycle ◽  
Risk Free Rate ◽  
Free Rate

The objective of this paper is to determine which point of the business cycle offers investors the best reward to risk ratio in the stock market.  Expected reward is defined as expected return in excess of the risk free rate, whereas risk is defined as the standard deviation of return.  Thus, the expected reward to risk ratio is measured by expected return in excess of risk free rate relative to the standard deviation of return.  Expected return in excess of the risk free rate and standard deviation of return are generated on a continuum of time periods and the GDP growth rate.  The point where the expected reward to risk ratio peaks, would signify the best time for investment.  Being able to identify this point could help investors in deciding the best time to invest as well as help firms in choosing a favorable time for raising equity capital.  While most people think that the best time to invest is near the bottom, it is not clear whether the best time for investing is before, at, or after the economic trough.  The interesting finding in our model is that the best time is after the point of the economic trough.

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