# American Institute of Mathematical Sciences

November  2019, 24(11): 6209-6238. doi: 10.3934/dcdsb.2019136

## Portfolio optimization and model predictive control: A kinetic approach

 1 Institut für Geometrie und Praktische Mathematik, RWTH Aachen, Templergraben 55, 52056 Aachen, Germany 2 Department of Mathematics and Computer Science, University of Ferrara, Via Machiavelli 30, I-44121 Ferrara, Italy 3 Karlsruhe Institute of Technology, Steinbuch Center for Computing, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany

Corresponding author: trimborn@igpm.rwth-aachen.de

Received  June 2018 Revised  January 2019 Published  November 2019 Early access  July 2019

In this paper, we introduce a large system of interacting financial agents in which all agents are faced with the decision of how to allocate their capital between a risky stock or a risk-less bond. The investment decision of investors, derived through an optimization, drives the stock price. The model has been inspired by the econophysical Levy-Levy-Solomon model [30]. The goal of this work is to gain insights into the stock price and wealth distribution. We especially want to discover the causes for the appearance of power-laws in financial data. We follow a kinetic approach similar to [33] and derive the mean field limit of the microscopic agent dynamics. The novelty in our approach is that the financial agents apply model predictive control (MPC) to approximate and solve the optimization of their utility function. Interestingly, the MPC approach gives a mathematical connection between the two opposing economic concepts of modeling financial agents to be rational or boundedly rational. Furthermore, this is to our knowledge the first kinetic portfolio model which considers a wealth and stock price distribution simultaneously. Due to the kinetic approach, we can study the wealth and price distribution on a mesoscopic level. The wealth distribution is characterized by a log-normal law. For the stock price distribution, we can either observe a log-normal behavior in the case of long-term investors or a power-law in the case of high-frequency trader. Furthermore, the stock return data exhibit a fat-tail, which is a well known characteristic of real financial data.

Citation: Torsten Trimborn, Lorenzo Pareschi, Martin Frank. Portfolio optimization and model predictive control: A kinetic approach. Discrete and Continuous Dynamical Systems - B, 2019, 24 (11) : 6209-6238. doi: 10.3934/dcdsb.2019136
##### References:
 [1] G. Albi, M. Herty and L. Pareschi, Kinetic description of optimal control problems and applications to opinion consensus, Communications in Mathematical Sciences, 13 (2015), 1407-1429.  doi: 10.4310/CMS.2015.v13.n6.a3. [2] G. Albi, L. Pareschi and M. Zanella, Boltzmann-type control of opinion consensus through leaders, Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 372 (2014), 20140138, 18pp. doi: 10.1098/rsta.2014.0138. [3] A. Beja and M. B. Goldman, On the dynamic behavior of prices in disequilibrium, The Journal of Finance, 35 (1980), 235-248. [4] D. Bertsimas and D. Pachamanova, Robust multiperiod portfolio management in the presence of transaction costs, Computers & Operations Research, 35 (2008), 3-17.  doi: 10.1016/j.cor.2006.02.011. [5] M. Bisi, G. Spiga and G. Toscani, Kinetic models of conservative economies with wealth redistribution, Communications in Mathematical Sciences, 7 (2009), 901-916.  doi: 10.4310/CMS.2009.v7.n4.a5. [6] J.-P. Bouchaud and M. Mézard, Wealth condensation in a simple model of economy, Physica A: Statistical Mechanics and its Applications, 282 (2000), 536-545.  doi: 10.1016/S0378-4371(00)00205-3. [7] W. Braun and K. Hepp, The Vlasov dynamics and its fluctuations in the 1/N limit of interacting classical particles, Communications in Mathematical Physics, 56 (1977), 101-113.  doi: 10.1007/BF01611497. [8] W. A. Brock and C. H. Hommes, Heterogeneous beliefs and routes to chaos in a simple asset pricing model, Journal of Economic Dynamics and Control, 22 (1998), 1235-1274.  doi: 10.1016/S0165-1889(98)00011-6. [9] M. Burger, L. Caffarelli, P. A. Markowich and M.-T. Wolfram, On a Boltzmann-type price formation model, Proc. R. Soc. A, 469 (2013), 20130126, 20pp. doi: 10.1098/rspa.2013.0126. [10] E. Camacho and C. Bordons, Model Predictive Control, Springer, USA, 2004. [11] A. Chatterjee and B. K. Chakrabarti, Kinetic exchange models for income and wealth distributions, The European Physical Journal B-Condensed Matter and Complex Systems, 60 (2007), 135-149.  doi: 10.1140/epjb/e2007-00343-8. [12] L. Chayes, M. del Mar González, M. P. Gualdani and I. Kim, Global existence and uniqueness of solutions to a model of price formation, SIAM Journal on Mathematical Analysis, 41 (2009), 2107-2135.  doi: 10.1137/090753346. [13] J. Che, A kinetic model on portfolio in finance, Communications in Mathematical Sciences, 9 (2011), 1073-1096.  doi: 10.4310/CMS.2011.v9.n4.a7. [14] C. Chiarella, R. Dieci and X.-Z. He, Heterogeneous expectations and speculative behavior in a dynamic multi-asset framework, Journal of Economic Behavior & Organization, 62 (2007), 408-427. [15] D. Colander, H. Föllmer, A. Haas, M. D. Goldberg, K. Juselius, A. Kirman, T. Lux and B. Sloth, The financial crisis and the systemic failure of academic economics, 2009. [16] R. Cont, Empirical properties of asset returns: Stylized facts and statistical issues, 2001. [17] S. Cordier, L. Pareschi and C. Piatecki, Mesoscopic modelling of financial markets, Journal of Statistical Physics, 134 (2009), 161-184.  doi: 10.1007/s10955-008-9667-z. [18] S. Cordier, L. Pareschi and G. Toscani, On a kinetic model for a simple market economy, Journal of Statistical Physics, 120 (2005), 253-277.  doi: 10.1007/s10955-005-5456-0. [19] R. Cross, M. Grinfeld, H. Lamba and T. Seaman, A threshold model of investor psychology, Physica A: Statistical Mechanics and its Applications, 354 (2005), 463-478.  doi: 10.1016/j.physa.2005.02.029. [20] M. Delitala and T. Lorenzi, A mathematical model for value estimation with public information and herding, Kinetic & Related Models, 7 (2014), 29-44.  doi: 10.3934/krm.2014.7.29. [21] R. L. Dobrushin, Vlasov equations, Functional Analysis and Its Applications, 13 (1979), 115-123. [22] B. Düring, D. Matthes and G. Toscani, Kinetic equations modelling wealth redistribution: A comparison of approaches, Physical Review E, 78 (2008), 056103, 12pp. doi: 10.1103/PhysRevE.78.056103. [23] E. Egenter, T. Lux and D. Stauffer, Finite-size effects in Monte Carlo simulations of two stock market models, Physica A: Statistical Mechanics and its Applications, 268 (1999), 250-256.  doi: 10.1016/S0378-4371(99)00059-X. [24] J. D. Farmer and D. Foley, The economy needs agent-based modelling, Nature, 460 (2009), 685-686.  doi: 10.1038/460685a. [25] T. Hellthaler, The influence of investor number on a microscopic market model, International Journal of Modern Physics C, 6 (1996), 845-852.  doi: 10.1142/S0129183195000691. [26] D. Kahneman and A. Tversky, Prospect theory: An analysis of decision under risk, Econometrica: Journal of the Econometric Society, 47 (1979), 263-291.  doi: 10.2307/1914185. [27] K. Kanazawa, T. Sueshige, H. Takayasu and M. Takayasu, Derivation of the boltzmann equation for financial brownian motion: direct observation of the collective motion of high-frequency traders, Physical Review Letters, 120 (2018), 138301. doi: 10.1103/PhysRevLett.120.138301. [28] K. Kanazawa, T. Sueshige, H. Takayasu and M. Takayasu, Kinetic theory for finance brownian motion from microscopic dynamics, arXiv: 1802.05993, 2018. [29] R. Kohl, The influence of the number of different stocks on the Levy–Levy–Solomon model, International Journal of Modern Physics C, 8 (1997), 1309-1316.  doi: 10.1142/S0129183197001168. [30] M. Levy, H. Levy and S. Solomon, A microscopic model of the stock market: Cycles, booms, and crashes, Economics Letters, 45 (1994), 103-111. [31] T. Lux et al., Stochastic Behavioral Asset Pricing Models and the Stylized Facts, Technical report, Economics working paper/Christian-Albrechts-Universität Kiel, Department of Economics, 2008. [32] T. Lux and M. Marchesi, Scaling and criticality in a stochastic multi-agent model of a financial market, Nature, 397 (1999), 498-500.  doi: 10.1038/17290. [33] D. Maldarella and L. Pareschi, Kinetic models for socio-economic dynamics of speculative markets, Physica A: Statistical Mechanics and its Applications, 391 (2012), 715-730.  doi: 10.1016/j.physa.2011.08.013. [34] H. Markowitz, Portfolio selection, The Journal of Finance, 7 (1952), 77-91. [35] D. Matthes and G. Toscani, Analysis of a model for wealth redistribution, Kinetic and Related Models, 1 (2008), 1-22.  doi: 10.3934/krm.2008.1.1. [36] D. Q. Mayne and H. Michalska, Receding horizon control of nonlinear systems, IEEE Trans. Automat. Control, 35 (1990), 814-824.  doi: 10.1109/9.57020. [37] R. C. Merton, Lifetime portfolio selection under uncertainty: The continuous-time case, The review of Economics and Statistics, 51 (1969), 247-257.  doi: 10.2307/1926560. [38] J. E. Mitchell and S. Braun, Rebalancing an investment portfolio in the presence of convex transaction costs, including market impact costs, Optimization Methods and Software, 28 (2013), 523-542.  doi: 10.1080/10556788.2012.717940. [39] H. Neunzert, The Vlasov equation as a limit of Hamiltonian classical mechanical systems of interacting particles, Trans. Fluid Dynamics, 18 (1977), 663-678. [40] A. Pagan, The econometrics of financial markets, Journal of Empirical Finance, 3 (1996), 15-102.  doi: 10.1016/0927-5398(95)00020-8. [41] L. Pareschi and G. Toscani, Self-similarity and power-like tails in nonconservative kinetic models, Journal of statistical physics, 124 (2006), 747-779.  doi: 10.1007/s10955-006-9025-y. [42] L. Pareschi and G. Toscani, Interacting Multiagent Systems: Kinetic Equations and Monte Carlo Methods, Oxford University Press, 2013. [43] H. A. Simon, A behavioral model of rational choice, The Quarterly Journal of Economics, 69 (1955), 99-118.  doi: 10.2307/1884852. [44] D. Sornette, Physics and financial economics (1776–2014): Puzzles, Ising and agent-based models, Reports on Progress in Physics, 77 (2014), 062001, 28pp. doi: 10.1088/0034-4885/77/6/062001. [45] A.-S. Sznitman, Topics in propagation of chaos, In Ecole d'Eté de Probabilités de Saint-Flour XIX-1989, 165–251, Lecture Notes in Math., 1464, Springer, Berlin, 1991. doi: 10.1007/BFb0085169. [46] T. Trimborn, M. Frank and S. Martin, Mean field limit of a behavioral financial market model, Physica A: Statistical Mechanics and its Applications, 505 (2018), 613-631.  doi: 10.1016/j.physa.2018.03.079. [47] C. Villani, On a new class of weak solutions to the spatially homogeneous Boltzmann and Landau equations, Archive for Rational Mechanics and Analysis, 143 (1998), 273-307.  doi: 10.1007/s002050050106. [48] L. Walras., Études D'économie Politique Appliquée: (Théorie de la Production de la Richesse Sociale), F. Rouge, 1898. [49] E. Zschischang and T. Lux, Some new results on the Levy, Levy and Solomon microscopic stock market model, Physica A: Statistical Mechanics and its Applications, 291 (2001), 563-573.  doi: 10.1016/S0378-4371(00)00609-9.

show all references

##### References:
 [1] G. Albi, M. Herty and L. Pareschi, Kinetic description of optimal control problems and applications to opinion consensus, Communications in Mathematical Sciences, 13 (2015), 1407-1429.  doi: 10.4310/CMS.2015.v13.n6.a3. [2] G. Albi, L. Pareschi and M. Zanella, Boltzmann-type control of opinion consensus through leaders, Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 372 (2014), 20140138, 18pp. doi: 10.1098/rsta.2014.0138. [3] A. Beja and M. B. Goldman, On the dynamic behavior of prices in disequilibrium, The Journal of Finance, 35 (1980), 235-248. [4] D. Bertsimas and D. Pachamanova, Robust multiperiod portfolio management in the presence of transaction costs, Computers & Operations Research, 35 (2008), 3-17.  doi: 10.1016/j.cor.2006.02.011. [5] M. Bisi, G. Spiga and G. Toscani, Kinetic models of conservative economies with wealth redistribution, Communications in Mathematical Sciences, 7 (2009), 901-916.  doi: 10.4310/CMS.2009.v7.n4.a5. [6] J.-P. Bouchaud and M. Mézard, Wealth condensation in a simple model of economy, Physica A: Statistical Mechanics and its Applications, 282 (2000), 536-545.  doi: 10.1016/S0378-4371(00)00205-3. [7] W. Braun and K. Hepp, The Vlasov dynamics and its fluctuations in the 1/N limit of interacting classical particles, Communications in Mathematical Physics, 56 (1977), 101-113.  doi: 10.1007/BF01611497. [8] W. A. Brock and C. H. Hommes, Heterogeneous beliefs and routes to chaos in a simple asset pricing model, Journal of Economic Dynamics and Control, 22 (1998), 1235-1274.  doi: 10.1016/S0165-1889(98)00011-6. [9] M. Burger, L. Caffarelli, P. A. Markowich and M.-T. Wolfram, On a Boltzmann-type price formation model, Proc. R. Soc. A, 469 (2013), 20130126, 20pp. doi: 10.1098/rspa.2013.0126. [10] E. Camacho and C. Bordons, Model Predictive Control, Springer, USA, 2004. [11] A. Chatterjee and B. K. Chakrabarti, Kinetic exchange models for income and wealth distributions, The European Physical Journal B-Condensed Matter and Complex Systems, 60 (2007), 135-149.  doi: 10.1140/epjb/e2007-00343-8. [12] L. Chayes, M. del Mar González, M. P. Gualdani and I. Kim, Global existence and uniqueness of solutions to a model of price formation, SIAM Journal on Mathematical Analysis, 41 (2009), 2107-2135.  doi: 10.1137/090753346. [13] J. Che, A kinetic model on portfolio in finance, Communications in Mathematical Sciences, 9 (2011), 1073-1096.  doi: 10.4310/CMS.2011.v9.n4.a7. [14] C. Chiarella, R. Dieci and X.-Z. He, Heterogeneous expectations and speculative behavior in a dynamic multi-asset framework, Journal of Economic Behavior & Organization, 62 (2007), 408-427. [15] D. Colander, H. Föllmer, A. Haas, M. D. Goldberg, K. Juselius, A. Kirman, T. Lux and B. Sloth, The financial crisis and the systemic failure of academic economics, 2009. [16] R. Cont, Empirical properties of asset returns: Stylized facts and statistical issues, 2001. [17] S. Cordier, L. Pareschi and C. Piatecki, Mesoscopic modelling of financial markets, Journal of Statistical Physics, 134 (2009), 161-184.  doi: 10.1007/s10955-008-9667-z. [18] S. Cordier, L. Pareschi and G. Toscani, On a kinetic model for a simple market economy, Journal of Statistical Physics, 120 (2005), 253-277.  doi: 10.1007/s10955-005-5456-0. [19] R. Cross, M. Grinfeld, H. Lamba and T. Seaman, A threshold model of investor psychology, Physica A: Statistical Mechanics and its Applications, 354 (2005), 463-478.  doi: 10.1016/j.physa.2005.02.029. [20] M. Delitala and T. Lorenzi, A mathematical model for value estimation with public information and herding, Kinetic & Related Models, 7 (2014), 29-44.  doi: 10.3934/krm.2014.7.29. [21] R. L. Dobrushin, Vlasov equations, Functional Analysis and Its Applications, 13 (1979), 115-123. [22] B. Düring, D. Matthes and G. Toscani, Kinetic equations modelling wealth redistribution: A comparison of approaches, Physical Review E, 78 (2008), 056103, 12pp. doi: 10.1103/PhysRevE.78.056103. [23] E. Egenter, T. Lux and D. Stauffer, Finite-size effects in Monte Carlo simulations of two stock market models, Physica A: Statistical Mechanics and its Applications, 268 (1999), 250-256.  doi: 10.1016/S0378-4371(99)00059-X. [24] J. D. Farmer and D. Foley, The economy needs agent-based modelling, Nature, 460 (2009), 685-686.  doi: 10.1038/460685a. [25] T. Hellthaler, The influence of investor number on a microscopic market model, International Journal of Modern Physics C, 6 (1996), 845-852.  doi: 10.1142/S0129183195000691. [26] D. Kahneman and A. Tversky, Prospect theory: An analysis of decision under risk, Econometrica: Journal of the Econometric Society, 47 (1979), 263-291.  doi: 10.2307/1914185. [27] K. Kanazawa, T. Sueshige, H. Takayasu and M. Takayasu, Derivation of the boltzmann equation for financial brownian motion: direct observation of the collective motion of high-frequency traders, Physical Review Letters, 120 (2018), 138301. doi: 10.1103/PhysRevLett.120.138301. [28] K. Kanazawa, T. Sueshige, H. Takayasu and M. Takayasu, Kinetic theory for finance brownian motion from microscopic dynamics, arXiv: 1802.05993, 2018. [29] R. Kohl, The influence of the number of different stocks on the Levy–Levy–Solomon model, International Journal of Modern Physics C, 8 (1997), 1309-1316.  doi: 10.1142/S0129183197001168. [30] M. Levy, H. Levy and S. Solomon, A microscopic model of the stock market: Cycles, booms, and crashes, Economics Letters, 45 (1994), 103-111. [31] T. Lux et al., Stochastic Behavioral Asset Pricing Models and the Stylized Facts, Technical report, Economics working paper/Christian-Albrechts-Universität Kiel, Department of Economics, 2008. [32] T. Lux and M. Marchesi, Scaling and criticality in a stochastic multi-agent model of a financial market, Nature, 397 (1999), 498-500.  doi: 10.1038/17290. [33] D. Maldarella and L. Pareschi, Kinetic models for socio-economic dynamics of speculative markets, Physica A: Statistical Mechanics and its Applications, 391 (2012), 715-730.  doi: 10.1016/j.physa.2011.08.013. [34] H. Markowitz, Portfolio selection, The Journal of Finance, 7 (1952), 77-91. [35] D. Matthes and G. Toscani, Analysis of a model for wealth redistribution, Kinetic and Related Models, 1 (2008), 1-22.  doi: 10.3934/krm.2008.1.1. [36] D. Q. Mayne and H. Michalska, Receding horizon control of nonlinear systems, IEEE Trans. Automat. Control, 35 (1990), 814-824.  doi: 10.1109/9.57020. [37] R. C. Merton, Lifetime portfolio selection under uncertainty: The continuous-time case, The review of Economics and Statistics, 51 (1969), 247-257.  doi: 10.2307/1926560. [38] J. E. Mitchell and S. Braun, Rebalancing an investment portfolio in the presence of convex transaction costs, including market impact costs, Optimization Methods and Software, 28 (2013), 523-542.  doi: 10.1080/10556788.2012.717940. [39] H. Neunzert, The Vlasov equation as a limit of Hamiltonian classical mechanical systems of interacting particles, Trans. Fluid Dynamics, 18 (1977), 663-678. [40] A. Pagan, The econometrics of financial markets, Journal of Empirical Finance, 3 (1996), 15-102.  doi: 10.1016/0927-5398(95)00020-8. [41] L. Pareschi and G. Toscani, Self-similarity and power-like tails in nonconservative kinetic models, Journal of statistical physics, 124 (2006), 747-779.  doi: 10.1007/s10955-006-9025-y. [42] L. Pareschi and G. Toscani, Interacting Multiagent Systems: Kinetic Equations and Monte Carlo Methods, Oxford University Press, 2013. [43] H. A. Simon, A behavioral model of rational choice, The Quarterly Journal of Economics, 69 (1955), 99-118.  doi: 10.2307/1884852. [44] D. Sornette, Physics and financial economics (1776–2014): Puzzles, Ising and agent-based models, Reports on Progress in Physics, 77 (2014), 062001, 28pp. doi: 10.1088/0034-4885/77/6/062001. [45] A.-S. Sznitman, Topics in propagation of chaos, In Ecole d'Eté de Probabilités de Saint-Flour XIX-1989, 165–251, Lecture Notes in Math., 1464, Springer, Berlin, 1991. doi: 10.1007/BFb0085169. [46] T. Trimborn, M. Frank and S. Martin, Mean field limit of a behavioral financial market model, Physica A: Statistical Mechanics and its Applications, 505 (2018), 613-631.  doi: 10.1016/j.physa.2018.03.079. [47] C. Villani, On a new class of weak solutions to the spatially homogeneous Boltzmann and Landau equations, Archive for Rational Mechanics and Analysis, 143 (1998), 273-307.  doi: 10.1007/s002050050106. [48] L. Walras., Études D'économie Politique Appliquée: (Théorie de la Production de la Richesse Sociale), F. Rouge, 1898. [49] E. Zschischang and T. Lux, Some new results on the Levy, Levy and Solomon microscopic stock market model, Physica A: Statistical Mechanics and its Applications, 291 (2001), 563-573.  doi: 10.1016/S0378-4371(00)00609-9.
Sketch of the modelling process
Example of the value function $U_{\gamma}$ with different reference points
Stock price evolution in the long-term investor case with a constant fundamental price $s^f$ (left figure) and a time varying fundamental price (right figure). In both figures one obtains that the average stock price is above the funcamental value
Quantile-quantile plot of logarithmic stock return distribution (left-hand side) and logarithmic return of fundamental prices (right-hand side). The simulation has been performed in the case of long-term investors and a stochastic fundamental price. The risk tolerance has been set to $\gamma = 0.9$, the scale to $\rho = \frac{5}{8}$ and the random seed is chosen to be $\texttt{rng(767)}$. All further parameters are chosen as reported in section A.4 of the Appendix
Stock price distribution in the long-term investor case. The solid lines are analytical solution, whereas the circles are the numerical result
Distribution of the wealth invested in stocks with a Gaussian fit (solid line). Left figure has a linear scale, whereas the right figure shows the distribution in log-log scale
Distribution of the wealth invested in bonds in the special case $K>0$. The numerical results (circles) are plotted with the corresponding log-normal analytic self-similar solution (solid lines)
Stock price distribution in the high-frequency case (red circles). The fit by the inverse-gamma distribution (solid line) clearly underestimates the tail. This reveals that the full model can create heavier tails than the inverse-gamma distribution
Marginal wealth distributions in the high-frequency investor case. The left hand side illustrates the distribution of investments in stocks and the right-hand side the wealth invested in bonds at $t = 1$
Steady state stock price distribution in the high-frequency investor case (circles) together with the analytically computed steady state of inverse-gamma type (solid line)
 [1] Wawan Hafid Syaifudin, Endah R. M. Putri. The application of model predictive control on stock portfolio optimization with prediction based on Geometric Brownian Motion-Kalman Filter. Journal of Industrial and Management Optimization, 2021  doi: 10.3934/jimo.2021119 [2] Michael Grinfeld, Harbir Lamba, Rod Cross. A mesoscopic stock market model with hysteretic agents. Discrete and Continuous Dynamical Systems - B, 2013, 18 (2) : 403-415. doi: 10.3934/dcdsb.2013.18.403 [3] Dieter Armbruster, Christian Ringhofer, Andrea Thatcher. A kinetic model for an agent based market simulation. Networks and Heterogeneous Media, 2015, 10 (3) : 527-542. doi: 10.3934/nhm.2015.10.527 [4] Lars Grüne, Marleen Stieler. Multiobjective model predictive control for stabilizing cost criteria. Discrete and Continuous Dynamical Systems - B, 2019, 24 (8) : 3905-3928. doi: 10.3934/dcdsb.2018336 [5] Rudy R. Negenborn, Peter-Jules van Overloop, Tamás Keviczky, Bart De Schutter. Distributed model predictive control of irrigation canals. Networks and Heterogeneous Media, 2009, 4 (2) : 359-380. doi: 10.3934/nhm.2009.4.359 [6] Han Yang, Jia Yue, Nan-jing Huang. Multi-objective robust cross-market mixed portfolio optimization under hierarchical risk integration. Journal of Industrial and Management Optimization, 2020, 16 (2) : 759-775. doi: 10.3934/jimo.2018177 [7] Tao Pang, Azmat Hussain. An infinite time horizon portfolio optimization model with delays. Mathematical Control and Related Fields, 2016, 6 (4) : 629-651. doi: 10.3934/mcrf.2016018 [8] W.C. Ip, H. Wong, Jiazhu Pan, Keke Yuan. Estimating value-at-risk for chinese stock market by switching regime ARCH model. Journal of Industrial and Management Optimization, 2006, 2 (2) : 145-163. doi: 10.3934/jimo.2006.2.145 [9] Yuan Tan, Qingyuan Cao, Lan Li, Tianshi Hu, Min Su. A chance-constrained stochastic model predictive control problem with disturbance feedback. Journal of Industrial and Management Optimization, 2021, 17 (1) : 67-79. doi: 10.3934/jimo.2019099 [10] João M. Lemos, Fernando Machado, Nuno Nogueira, Luís Rato, Manuel Rijo. Adaptive and non-adaptive model predictive control of an irrigation channel. Networks and Heterogeneous Media, 2009, 4 (2) : 303-324. doi: 10.3934/nhm.2009.4.303 [11] Duy Nguyen, Jingzhi Tie, Qing Zhang. Stock trading rules under a switchable market. Mathematical Control and Related Fields, 2013, 3 (2) : 209-231. doi: 10.3934/mcrf.2013.3.209 [12] Daewa Kim, Annalisa Quaini. A kinetic theory approach to model pedestrian dynamics in bounded domains with obstacles. Kinetic and Related Models, 2019, 12 (6) : 1273-1296. doi: 10.3934/krm.2019049 [13] Ellina Grigorieva, Evgenii Khailov. Optimal control of pollution stock. Conference Publications, 2011, 2011 (Special) : 578-588. doi: 10.3934/proc.2011.2011.578 [14] Anibal T. Azevedo, Aurelio R. L. Oliveira, Marcos J. Rider, Secundino Soares. How to efficiently incorporate facts devices in optimal active power flow model. Journal of Industrial and Management Optimization, 2010, 6 (2) : 315-331. doi: 10.3934/jimo.2010.6.315 [15] Luís Tiago Paiva, Fernando A. C. C. Fontes. Sampled–data model predictive control: Adaptive time–mesh refinement algorithms and guarantees of stability. Discrete and Continuous Dynamical Systems - B, 2019, 24 (5) : 2335-2364. doi: 10.3934/dcdsb.2019098 [16] Judy Day, Jonathan Rubin, Gilles Clermont. Using nonlinear model predictive control to find optimal therapeutic strategies to modulate inflammation. Mathematical Biosciences & Engineering, 2010, 7 (4) : 739-763. doi: 10.3934/mbe.2010.7.739 [17] Gregory Zitelli, Seddik M. Djouadi, Judy D. Day. Combining robust state estimation with nonlinear model predictive control to regulate the acute inflammatory response to pathogen. Mathematical Biosciences & Engineering, 2015, 12 (5) : 1127-1139. doi: 10.3934/mbe.2015.12.1127 [18] Lars Grüne, Luca Mechelli, Simon Pirkelmann, Stefan Volkwein. Performance estimates for economic model predictive control and their application in proper orthogonal decomposition-based implementations. Mathematical Control and Related Fields, 2021, 11 (3) : 579-599. doi: 10.3934/mcrf.2021013 [19] Yu Liu, Chun-xiang Guo, Hong Zhou, Xin-yi Chen. Pre-sale ordering strategy based on the new retail context considering bounded consumer rationality. Journal of Industrial and Management Optimization, 2021  doi: 10.3934/jimo.2021217 [20] Ning Chen, Yan Xia Zhao, Jia Yang Dai, Yu Qian Guo, Wei Hua Gui, Jun Jie Peng. Hybrid modeling and distributed optimization control method for the iron removal process. Journal of Industrial and Management Optimization, 2022  doi: 10.3934/jimo.2022003

2020 Impact Factor: 1.327