American Institute of Mathematical Sciences

Advanced
  • Previous Article
    Bargaining equilibrium in a two-echelon supply chain with a capital-constrained retailer
  • JIMO Home
  • This Issue
  • Next Article
    An adaptively regularized sequential quadratic programming method for equality constrained optimization
November  2020, 16(6): 2703-2721. doi: 10.3934/jimo.2019076

How's the performance of the optimized portfolios by safety-first rules: Theory with empirical comparisons

Yuanyao Ding 1, and  Zudi Lu 2,

1. 

Faculty of Business, Ningbo University, Ningbo 315211, China
2. 

Southampton Statistical Sciences Research Institute, and Mathematical Sciences, University of Southampton, SO17 1BJ, UK

Received  July 2018 Revised  February 2019 Published  July 2019

Safety-first (SF) rules have been increasingly useful in particular for construction of optimal portfolios related to pension and other social insurance funds. How's the performance of the optimal portfolios constructed by different SF rules is an interesting practical question but yet less investigated theoretically. In this paper, we therefore analytically investigate the properties of the risky portfolios constructed by the three popular SF rules, denoted by the RSF, TSF and KSF, which are suggested and developed by A. D. Roy, L. G. Telser and S. Kataoka, respectively. Using Sharpe ratio as a measure of portfolio performance, we theoretically derive that the performance of an optimal portfolio constructed by the KSF approach depends on an acceptable level of extreme risk tolerance. The unique solution where the performance of the KSF portfolio is the same as that of the other two SF portfolios is found. By this we interestingly find that except this special case, under the finite optimal portfolios existent, the KSF portfolio always dominates the TSF portfolio in terms of the Sharpe ratio. In addition, in some market scenarios, even when the RSF and TSF portfolios do not exist in finite forms, the KSF rule can still apply to get a finite optimal portfolio. Moreover, in comparison with the RSF rule, a series of finite KSF portfolios can be interestingly constructed with their Sharpe ratios approaching to the maximum Sharpe ratio, which however cannot be reached by any corresponding finite RSF portfolio. Numerical comparisons of these rules by using a set of real data are further empirically demonstrated.

Keywords: Optimal portfolio, safety-first rule, Sharpe ratio, portfolio choice, extreme loss risk.
Mathematics Subject Classification: Primary: 91B28; Secondary: 90C90.
Citation: Yuanyao Ding, Zudi Lu. How's the performance of the optimized portfolios by safety-first rules: Theory with empirical comparisons. Journal of Industrial & Management Optimization, 2020, 16 (6) : 2703-2721. doi: 10.3934/jimo.2019076
References:
[1]

V. S. Bawa, Safety-first, stochastic dominance, and optimal portfolio choice, Journal of Financial and Quantitative Analysis, 13 (1978), 255-271.  doi: 10.2307/2330386.  Google Scholar

[2]

T. Bodnar and T. Zabolotskyy, How risky is the optimal portfolio which maximizes the sharpe ratio?, Asta Advances in Statistical Analysis, 101 (2017), 1-28.  doi: 10.1007/s10182-016-0270-3.  Google Scholar

[3]

Y. Ding and B. Zhang, Risky asset pricing based on safety first fund management, Quantitative Finance, 9 (2009a), 353-361.  doi: 10.1080/14697680802392488.  Google Scholar

[4]

Y. Ding and B. Zhang, Optimal portfolio of safety-first models, Journal of Statistical Planning and Inference, 139 (2009b), 2952-2962.  doi: 10.1016/j.jspi.2009.01.018.  Google Scholar

[5]

Y. Ding and Z. Lu, The optimal portfolios based on a modified safety-first rule with risk-free saving, Journal of Industrial and Management Optimization, 12 (2016), 83-102.  doi: 10.3934/jimo.2016.12.83.  Google Scholar

[6]

R. B. Durand, H. Jafarpour, C. Kluppelberg and R. Maller, Maximize the Sharpe Ratio and Minimize a VaR, The Journal of Wealth Management, 13 (2010), 91-102.   Google Scholar

[7]

M. Engels, Portfolio Optimization: Beyond Markowitz, Master's thesis, Leiden University, Netherlands, 2004. Google Scholar

[8]

N. Gressis and W. A. Remaley, Comment: ``Safety first – an expected utility principle", Jounal of Financial and Quantitative Analysis, 9 (1974), 1057-1061.   Google Scholar

[9]

H. Hagigi and B. Kluger, Assessing return and risk of pension funds–portfolios by the Telser safety-first approach, Journal of Business Finance and Accounting, 14 (1987), 241-253.   Google Scholar

[10]

M. R. Haley and M. K. McGee, Tilting safety first and the Sharpe portfolio, Finance Research Letters, 3 (2006), 173-180.   Google Scholar

[11]

S. Kataoka, A stochastic programming model, Econometrica, 31 (1963), 181-196.  doi: 10.2307/1910956.  Google Scholar

[12]

H. Levy and M. Sarnat, Safety first–an expected utility principle, Journal of Financial and Quantitative Analysis, 7 (1972), 1829-1834.   Google Scholar

[13]

Z. F. Li and G. J. Chen, Some discussions on Telser's safety-first model for portfolio selection (in Chinese), Theory and Practice of System Engineering, 36 (2005), 8-14.   Google Scholar

[14]

Z. F. Li, J. Yao and D. Li, Behavior patterns of investment strategies under Roy's safety-first principle, The Quarterly Review of Economics and Finance, 50 (2010), 167-179.  doi: 10.1016/j.qref.2009.11.004.  Google Scholar

[15]

R. A. Maller and D. A. Turkington, New light on the portfolio allocation problem, Mathematical Methods of Operational Research, 56 (2002), 501-511.  doi: 10.1007/s001860200211.  Google Scholar

[16]

H. Markowitz, Portfolio selection, Journal of Finance, 7 (1952), 77-91.   Google Scholar

[17]

V. I. Norkin and S. V. Boyko, Safety-first portfolio selection, Cybernetics and Systems Analysis, 48 (2012), 180-191.  doi: 10.1007/s10559-012-9396-9.  Google Scholar

[18]

Y. Okhrin and W. Schmid, Distributional properties of portfolio weights, Journal of Econometrics, 134 (2006), 235-256.  doi: 10.1016/j.jeconom.2005.06.022.  Google Scholar

[19]

L. S. Ortobelli and S. T. Rachev, Safety-first analysis and stable paretian approach to portfolio choice theory, Mathematical and Computer Modelling, 34 (2001), 1037-1072.  doi: 10.1016/S0895-7177(01)00116-9.  Google Scholar

[20]

D. H. Pyle and S. J. Turnovsky, Safety-first and expected utility maximization in mean-standard deviation portfolio analysis, The Review of Economics and Statistics, 52 (1970), 75-81.   Google Scholar

[21]

A. D. Roy, Safety-first and the holding of assets, Econometrica, 20 (1952), 431-449.  doi: 10.2307/1907413.  Google Scholar

[22]

W. F. Sharpe, The Sharpe Ratio, Journal of Portfolio Management, 21 (1994), 49-58.  doi: 10.3905/jpm.1994.409501.  Google Scholar

[23]

L. G. Telser, Safety first and hedging, Review of Economic Studies, 23 (1955), 1-16.  doi: 10.2307/2296146.  Google Scholar

[24]

S. Wang and Y. Xia, Portfolio Selection and Asset Pricing, Springer-Verlag, Berlin Heidelberg New York, Printed in Germany, 2002. doi: 10.1007/978-3-642-55934-1.  Google Scholar

show all references

References:
[1]

V. S. Bawa, Safety-first, stochastic dominance, and optimal portfolio choice, Journal of Financial and Quantitative Analysis, 13 (1978), 255-271.  doi: 10.2307/2330386.  Google Scholar

[2]

T. Bodnar and T. Zabolotskyy, How risky is the optimal portfolio which maximizes the sharpe ratio?, Asta Advances in Statistical Analysis, 101 (2017), 1-28.  doi: 10.1007/s10182-016-0270-3.  Google Scholar

[3]

Y. Ding and B. Zhang, Risky asset pricing based on safety first fund management, Quantitative Finance, 9 (2009a), 353-361.  doi: 10.1080/14697680802392488.  Google Scholar

[4]

Y. Ding and B. Zhang, Optimal portfolio of safety-first models, Journal of Statistical Planning and Inference, 139 (2009b), 2952-2962.  doi: 10.1016/j.jspi.2009.01.018.  Google Scholar

[5]

Y. Ding and Z. Lu, The optimal portfolios based on a modified safety-first rule with risk-free saving, Journal of Industrial and Management Optimization, 12 (2016), 83-102.  doi: 10.3934/jimo.2016.12.83.  Google Scholar

[6]

R. B. Durand, H. Jafarpour, C. Kluppelberg and R. Maller, Maximize the Sharpe Ratio and Minimize a VaR, The Journal of Wealth Management, 13 (2010), 91-102.   Google Scholar

[7]

M. Engels, Portfolio Optimization: Beyond Markowitz, Master's thesis, Leiden University, Netherlands, 2004. Google Scholar

[8]

N. Gressis and W. A. Remaley, Comment: ``Safety first – an expected utility principle", Jounal of Financial and Quantitative Analysis, 9 (1974), 1057-1061.   Google Scholar

[9]

H. Hagigi and B. Kluger, Assessing return and risk of pension funds–portfolios by the Telser safety-first approach, Journal of Business Finance and Accounting, 14 (1987), 241-253.   Google Scholar

[10]

M. R. Haley and M. K. McGee, Tilting safety first and the Sharpe portfolio, Finance Research Letters, 3 (2006), 173-180.   Google Scholar

[11]

S. Kataoka, A stochastic programming model, Econometrica, 31 (1963), 181-196.  doi: 10.2307/1910956.  Google Scholar

[12]

H. Levy and M. Sarnat, Safety first–an expected utility principle, Journal of Financial and Quantitative Analysis, 7 (1972), 1829-1834.   Google Scholar

[13]

Z. F. Li and G. J. Chen, Some discussions on Telser's safety-first model for portfolio selection (in Chinese), Theory and Practice of System Engineering, 36 (2005), 8-14.   Google Scholar

[14]

Z. F. Li, J. Yao and D. Li, Behavior patterns of investment strategies under Roy's safety-first principle, The Quarterly Review of Economics and Finance, 50 (2010), 167-179.  doi: 10.1016/j.qref.2009.11.004.  Google Scholar

[15]

R. A. Maller and D. A. Turkington, New light on the portfolio allocation problem, Mathematical Methods of Operational Research, 56 (2002), 501-511.  doi: 10.1007/s001860200211.  Google Scholar

[16]

H. Markowitz, Portfolio selection, Journal of Finance, 7 (1952), 77-91.   Google Scholar

[17]

V. I. Norkin and S. V. Boyko, Safety-first portfolio selection, Cybernetics and Systems Analysis, 48 (2012), 180-191.  doi: 10.1007/s10559-012-9396-9.  Google Scholar

[18]

Y. Okhrin and W. Schmid, Distributional properties of portfolio weights, Journal of Econometrics, 134 (2006), 235-256.  doi: 10.1016/j.jeconom.2005.06.022.  Google Scholar

[19]

L. S. Ortobelli and S. T. Rachev, Safety-first analysis and stable paretian approach to portfolio choice theory, Mathematical and Computer Modelling, 34 (2001), 1037-1072.  doi: 10.1016/S0895-7177(01)00116-9.  Google Scholar

[20]

D. H. Pyle and S. J. Turnovsky, Safety-first and expected utility maximization in mean-standard deviation portfolio analysis, The Review of Economics and Statistics, 52 (1970), 75-81.   Google Scholar

[21]

A. D. Roy, Safety-first and the holding of assets, Econometrica, 20 (1952), 431-449.  doi: 10.2307/1907413.  Google Scholar

[22]

W. F. Sharpe, The Sharpe Ratio, Journal of Portfolio Management, 21 (1994), 49-58.  doi: 10.3905/jpm.1994.409501.  Google Scholar

[23]

L. G. Telser, Safety first and hedging, Review of Economic Studies, 23 (1955), 1-16.  doi: 10.2307/2296146.  Google Scholar

[24]

S. Wang and Y. Xia, Portfolio Selection and Asset Pricing, Springer-Verlag, Berlin Heidelberg New York, Printed in Germany, 2002. doi: 10.1007/978-3-642-55934-1.  Google Scholar

Figure 1.  RSF portfolio, KSF portfolio and TSF portfolio
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 2.  Characteristics of Sharpe ratio of KSF portfolio vs. $ \alpha $ when $ R_b <\frac{B}{C} $
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 3.  Comparison of $ P(X'R\le R_b) $ among the SF Portfolios
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 4.  Comparison of $ P(X'R\le R_d) $ between KSF and RSF Portfolios
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 5.  Comparison of Sharpe ratios of RSF, KSF and TSF portfolios
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 6.  Extreme risks of KSF and TSF Portfolios with Equal Sharpe Ratios
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 7.  Characteristics of Sharpe ratio of KSF portfolio vs. $ \alpha $ when $ R_b >\frac{B}{C} $
Figure Options

Download full-size image

Download as PowerPoint slide

Table 1.  Expected Returns and Covariance of Three Risky Assets
Covariance 1B0010 1B0011 1B0006 Returns
1B0010 0.00863 0.00657 0.00830 0.00497
1B0011 0.00657 0.00609 0.00648 0.01214
1B0006 0.00830 0.00648 0.01390 0.00613
Table Options

Download as excel

Covariance 1B0010 1B0011 1B0006 Returns
1B0010 0.00863 0.00657 0.00830 0.00497
1B0011 0.00657 0.00609 0.00648 0.01214
1B0006 0.00830 0.00648 0.01390 0.00613
Table 2.  Some results of RSF, TSF and KSF portfolios
Panel Rule $ P(R'X\le R_d) $ $ P(R'X\le R_b) $ $ X_1 $ $ X_2 $ $ X_3 $ $ SR $
RSF 0.40512 0.40512 -2.51889 3.51014 0.00875 0.24011
1 TSF 0.40512 0.40512 -2.51889 3.51014 0.00875 0.24011
KSF 0.40512 0.40512 -2.51889 3.51014 0.00875 0.24011
RSF 0.41019 0.40512 -2.51889 3.51014 0.00875 0.24011
2 TSF 0.41297 0.41000 -4.95741 5.94221 0.01520 0.22754
KSF 0.41000 0.40532 -2.83436 3.82477 0.00959 0.23960
RSF 0.41778 0.40512 -2.51889 3.51014 0.00875 0.24011
3 TSF 0.42074 0.41650 -8.91672 9.89105 0.02567 0.21086
KSF 0.41650 0.40657 -3.51776 4.50637 0.01139 0.23638
RSF 0.42255 0.40512 -2.51889 3.51014 0.00875 0.24011
4 TSF 0.42393 0.42000 -13.26792 14.23075 0.03718 0.20189
KSF 0.42000 0.40813 -4.17332 5.16010 0.01313 0.23235
RSF 0.11226 0.40512 -2.51889 3.51014 0.00875 0.24011
5 TSF N 0.05000 N N N N
KSF 0.05000 0.42963 -0.52073 1.51727 0.00347 0.17730
RSF 0.20922 0.40512 -2.51889 3.51014 0.00875 0.24011
6 TSF N 0.15000 N N N N
KSF 0.15000 0.42565 -0.64983 1.64602 0.00381 0.18745
RSF 0.40016 0.40512 -2.51889 3.51014 0.00875 0.24011
7 TSF N 0.35000 N N N N
KSF 0.35000 0.41119 -1.33611 2.33049 0.00562 0.22449
RSF 0.43132 0.40512 -2.51889 3.51014 0.00875 0.24011
8 TSF 0.42500 0.41307 -6.47843 7.45921 0.01922 0.21966
KSF 0.42500 0.41307 -6.47843 7.45921 0.01922 0.21966
RSF 0.32258 0.40512 -2.51889 3.51014 0.00875 0.24011
9 TSF 0.38805 0.41650 -8.91672 9.89105 0.02567 0.21086
KSF 0.30000 0.41650 -1.01839 2.01361 0.00478 0.21086
RSF 0.24886 0.40512 -2.51889 3.51014 0.00875 0.24011
10 TSF 0.40088 0.42330 -22.61759 23.55569 0.06190 0.19346
KSF 0.20000 0.42330 -0.73281 1.72878 0.00403 0.19346
RSF 0.16529 0.40512 -2.51889 3.51014 0.00875 0.24011
11 TSF 0.42240 0.42768 -160.68449 161.25747 0.42702 0.18229
KSF 0.10000 0.42768 -0.58263 1.57900 0.00363 0.18229
Table Options

Download as excel

Panel Rule $ P(R'X\le R_d) $ $ P(R'X\le R_b) $ $ X_1 $ $ X_2 $ $ X_3 $ $ SR $
RSF 0.40512 0.40512 -2.51889 3.51014 0.00875 0.24011
1 TSF 0.40512 0.40512 -2.51889 3.51014 0.00875 0.24011
KSF 0.40512 0.40512 -2.51889 3.51014 0.00875 0.24011
RSF 0.41019 0.40512 -2.51889 3.51014 0.00875 0.24011
2 TSF 0.41297 0.41000 -4.95741 5.94221 0.01520 0.22754
KSF 0.41000 0.40532 -2.83436 3.82477 0.00959 0.23960
RSF 0.41778 0.40512 -2.51889 3.51014 0.00875 0.24011
3 TSF 0.42074 0.41650 -8.91672 9.89105 0.02567 0.21086
KSF 0.41650 0.40657 -3.51776 4.50637 0.01139 0.23638
RSF 0.42255 0.40512 -2.51889 3.51014 0.00875 0.24011
4 TSF 0.42393 0.42000 -13.26792 14.23075 0.03718 0.20189
KSF 0.42000 0.40813 -4.17332 5.16010 0.01313 0.23235
RSF 0.11226 0.40512 -2.51889 3.51014 0.00875 0.24011
5 TSF N 0.05000 N N N N
KSF 0.05000 0.42963 -0.52073 1.51727 0.00347 0.17730
RSF 0.20922 0.40512 -2.51889 3.51014 0.00875 0.24011
6 TSF N 0.15000 N N N N
KSF 0.15000 0.42565 -0.64983 1.64602 0.00381 0.18745
RSF 0.40016 0.40512 -2.51889 3.51014 0.00875 0.24011
7 TSF N 0.35000 N N N N
KSF 0.35000 0.41119 -1.33611 2.33049 0.00562 0.22449
RSF 0.43132 0.40512 -2.51889 3.51014 0.00875 0.24011
8 TSF 0.42500 0.41307 -6.47843 7.45921 0.01922 0.21966
KSF 0.42500 0.41307 -6.47843 7.45921 0.01922 0.21966
RSF 0.32258 0.40512 -2.51889 3.51014 0.00875 0.24011
9 TSF 0.38805 0.41650 -8.91672 9.89105 0.02567 0.21086
KSF 0.30000 0.41650 -1.01839 2.01361 0.00478 0.21086
RSF 0.24886 0.40512 -2.51889 3.51014 0.00875 0.24011
10 TSF 0.40088 0.42330 -22.61759 23.55569 0.06190 0.19346
KSF 0.20000 0.42330 -0.73281 1.72878 0.00403 0.19346
RSF 0.16529 0.40512 -2.51889 3.51014 0.00875 0.24011
11 TSF 0.42240 0.42768 -160.68449 161.25747 0.42702 0.18229
KSF 0.10000 0.42768 -0.58263 1.57900 0.00363 0.18229
Table 3.  Expected Returns and Covariance of Five Risky Assets
Covariance 1B0012 1B0013 1B0010 1B0011 1B0006 Returns
1B0012 8.62E-05 1.07E-04 -4.22E-05 -5.38E-05 -5.25E-05 0.00246
1B0013 1.07E-04 1.72E-04 -1.62E-04 -1.61E-04 -5.79E-05 0.00351
1B0010 -4.22E-05 -1.62E-04 0.00863 0.00657 0.00830 0.00497
1B0011 -5.38E-05 -1.61E-04 0.00657 0.00609 0.00648 0.01214
1B0006 -5.25E-05 -5.79E-05 0.00830 0.00648 0.01390 0.00613
Table Options

Download as excel

Covariance 1B0012 1B0013 1B0010 1B0011 1B0006 Returns
1B0012 8.62E-05 1.07E-04 -4.22E-05 -5.38E-05 -5.25E-05 0.00246
1B0013 1.07E-04 1.72E-04 -1.62E-04 -1.61E-04 -5.79E-05 0.00351
1B0010 -4.22E-05 -1.62E-04 0.00863 0.00657 0.00830 0.00497
1B0011 -5.38E-05 -1.61E-04 0.00657 0.00609 0.00648 0.01214
1B0006 -5.25E-05 -5.79E-05 0.00830 0.00648 0.01390 0.00613
Table 4.  Expected Returns and Covariance of Five Risky Assets
weight $ \alpha $
0.10000 0.20000 0.30000 0.35000 0.36000 0.37000 0.37984
$ X_1 $ 1.21251 1.07523 0.76327 0.20470 -0.11043 -0.81445 -93.92237
$ X_2 $ -0.24424 -0.11734 0.17103 0.68736 0.97865 1.62943 87.69587
$ X_3 $ -0.05544 -0.07397 -0.11610 -0.19151 -0.23406 -0.32912 -12.90054
$ X_4 $ 0.08093 0.11220 0.18327 0.31053 0.38232 0.54272 21.75489
$ X_5 $ 0.00624 0.00388 -0.00148 -0.01107 -0.01648 -0.02858 -1.62785
$ SR $ -0.21947 -0.16942 -0.06622 0.05969 0.10400 0.16515 0.30287
Table Options

Download as excel

weight $ \alpha $
0.10000 0.20000 0.30000 0.35000 0.36000 0.37000 0.37984
$ X_1 $ 1.21251 1.07523 0.76327 0.20470 -0.11043 -0.81445 -93.92237
$ X_2 $ -0.24424 -0.11734 0.17103 0.68736 0.97865 1.62943 87.69587
$ X_3 $ -0.05544 -0.07397 -0.11610 -0.19151 -0.23406 -0.32912 -12.90054
$ X_4 $ 0.08093 0.11220 0.18327 0.31053 0.38232 0.54272 21.75489
$ X_5 $ 0.00624 0.00388 -0.00148 -0.01107 -0.01648 -0.02858 -1.62785
$ SR $ -0.21947 -0.16942 -0.06622 0.05969 0.10400 0.16515 0.30287
[1]

Haixiang Yao, Ping Chen, Miao Zhang, Xun Li. Dynamic discrete-time portfolio selection for defined contribution pension funds with inflation risk. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2020166
[2]

Bing Liu, Ming Zhou. Robust portfolio selection for individuals: Minimizing the probability of lifetime ruin. Journal of Industrial & Management Optimization, 2021, 17 (2) : 937-952. doi: 10.3934/jimo.2020005
[3]

Junkee Jeon. Finite horizon portfolio selection problems with stochastic borrowing constraints. Journal of Industrial & Management Optimization, 2021, 17 (2) : 733-763. doi: 10.3934/jimo.2019132
[4]

Lin Jiang, Song Wang. Robust multi-period and multi-objective portfolio selection. Journal of Industrial & Management Optimization, 2021, 17 (2) : 695-709. doi: 10.3934/jimo.2019130
[5]

Nan Zhang, Linyi Qian, Zhuo Jin, Wei Wang. Optimal stop-loss reinsurance with joint utility constraints. Journal of Industrial & Management Optimization, 2021, 17 (2) : 841-868. doi: 10.3934/jimo.2020001
[6]

Jiannan Zhang, Ping Chen, Zhuo Jin, Shuanming Li. Open-loop equilibrium strategy for mean-variance portfolio selection: A log-return model. Journal of Industrial & Management Optimization, 2021, 17 (2) : 765-777. doi: 10.3934/jimo.2019133
[7]

Yen-Luan Chen, Chin-Chih Chang, Zhe George Zhang, Xiaofeng Chen. Optimal preventive "maintenance-first or -last" policies with generalized imperfect maintenance models. Journal of Industrial & Management Optimization, 2021, 17 (1) : 501-516. doi: 10.3934/jimo.2020149
[8]

Youming Guo, Tingting Li. Optimal control strategies for an online game addiction model with low and high risk exposure. Discrete & Continuous Dynamical Systems - B, 2020  doi: 10.3934/dcdsb.2020347
[9]

Yu Zhou, Xinfeng Dong, Yongzhuang Wei, Fengrong Zhang. A note on the Signal-to-noise ratio of $ (n, m) $-functions. Advances in Mathematics of Communications, 2020  doi: 10.3934/amc.2020117
[10]

Manil T. Mohan. First order necessary conditions of optimality for the two dimensional tidal dynamics system. Mathematical Control & Related Fields, 2020  doi: 10.3934/mcrf.2020045
[11]

Wenyan Zhuo, Honglin Yang, Leopoldo Eduardo Cárdenas-Barrón, Hong Wan. Loss-averse supply chain decisions with a capital constrained retailer. Journal of Industrial & Management Optimization, 2021, 17 (2) : 711-732. doi: 10.3934/jimo.2019131
[12]

Sebastian J. Schreiber. The $ P^* $ rule in the stochastic Holt-Lawton model of apparent competition. Discrete & Continuous Dynamical Systems - B, 2021, 26 (1) : 633-644. doi: 10.3934/dcdsb.2020374
[13]

Kohei Nakamura. An application of interpolation inequalities between the deviation of curvature and the isoperimetric ratio to the length-preserving flow. Discrete & Continuous Dynamical Systems - S, 2021, 14 (3) : 1093-1102. doi: 10.3934/dcdss.2020385
[14]

Ripeng Huang, Shaojian Qu, Xiaoguang Yang, Zhimin Liu. Multi-stage distributionally robust optimization with risk aversion. Journal of Industrial & Management Optimization, 2021, 17 (1) : 233-259. doi: 10.3934/jimo.2019109
[15]

Haodong Yu, Jie Sun. Robust stochastic optimization with convex risk measures: A discretized subgradient scheme. Journal of Industrial & Management Optimization, 2021, 17 (1) : 81-99. doi: 10.3934/jimo.2019100
[16]

Hongguang Ma, Xiang Li. Multi-period hazardous waste collection planning with consideration of risk stability. Journal of Industrial & Management Optimization, 2021, 17 (1) : 393-408. doi: 10.3934/jimo.2019117
[17]

Jia Cai, Guanglong Xu, Zhensheng Hu. Sketch-based image retrieval via CAT loss with elastic net regularization. Mathematical Foundations of Computing, 2020, 3 (4) : 219-227. doi: 10.3934/mfc.2020013
[18]

Yuyuan Ouyang, Trevor Squires. Some worst-case datasets of deterministic first-order methods for solving binary logistic regression. Inverse Problems & Imaging, 2021, 15 (1) : 63-77. doi: 10.3934/ipi.2020047
[19]

Wenyuan Wang, Ran Xu. General drawdown based dividend control with fixed transaction costs for spectrally negative Lévy risk processes. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2020179
[20]

José Madrid, João P. G. Ramos. On optimal autocorrelation inequalities on the real line. Communications on Pure & Applied Analysis, 2021, 20 (1) : 369-388. doi: 10.3934/cpaa.2020271

2019 Impact Factor: 1.366

Tools

Metrics

  • PDF downloads (303)
  • HTML views (600)
  • Cited by (0)

Other articles
by authors

[Back to Top]

Copyright © 2021 American Institute of Mathematical Sciences