Dynamic portfolio choices by simulation-and-regression: Revisiting the issue of value function vs portfolio weight recursions

2017 ◽  
Vol 79 ◽  
pp. 174-189 ◽  
Author(s):  
Michel Denault ◽  
Jean-Guy Simonato
Keyword(s):  
Value Function ◽  
Portfolio Choices ◽  
Portfolio Weight ◽  
Dynamic Portfolio ◽  
Simulation And Regression

scholarly journals Dynamic Money Doctors under Cumulative Prospect Theory

2019 ◽  
pp. 1-26
Author(s):  
Liurui Deng ◽  
Lan Yang ◽  
Bolin Ma
Keyword(s):  
Prospect Theory ◽  
Portfolio Selection ◽  
Value Function ◽  
Cumulative Prospect Theory ◽  
Risky Asset ◽  
Investment Strategies ◽  
Portfolio Choices ◽  
Portfolio Managers ◽  
Dynamic Portfolio ◽  
Mean Variance

We investigate the interaction between investors and portfolio managers under cumulative prospect theory. We model trust in the manager and the relative anxiety about investing in a risky asset in an original way. Moreover, we study how trust and anxiety affect the manager’s fee and the portfolios of cumulative prospect theory investors. In contrast to previous work using the classical mean-variance preferences, there are two main novelties in our contribution. First, our research relies on cumulative prospect theory (CPT) rather than the classical mean-variance framework. Second, we focus on a dynamic portfolio selection. In other words, we formulate the optimal problem under multi-period setting. Besides, we attain an optimal portfolio choices in multi-period relying on the sub-game perfect investment strategies. Moreover, our research differs from traditional CPT work through an improved value function that accurately characterizes the reduction in anxiety suffered by the CPT investors from bearing risk when assisted by the portfolio managers’ help relative to when they lack such assistance.

scholarly journals Robo-Advising: Learning Investors’ Risk Preferences via Portfolio Choices*

2020 ◽  
Author(s):  
Humoud Alsabah ◽  
Agostino Capponi ◽  
Octavio Ruiz Lacedelli ◽  
Matt Stern
Keyword(s):  
Opportunity Cost ◽  
Value Function ◽  
Risk Preference ◽  
Portfolio Decisions ◽  
Learning Framework ◽  
Portfolio Choices ◽  
Trading Decisions ◽  
Exploration Exploitation ◽  
The Value Function ◽  
Over Time

Abstract We introduce a reinforcement learning framework for retail robo-advising. The robo-advisor does not know the investor’s risk preference but learns it over time by observing her portfolio choices in different market environments. We develop an exploration–exploitation algorithm that trades off costly solicitations of portfolio choices by the investor with autonomous trading decisions based on stale estimates of investor’s risk aversion. We show that the approximate value function constructed by the algorithm converges to the value function of an omniscient robo-advisor over a number of periods that is polynomial in the state and action space. By correcting for the investor’s mistakes, the robo-advisor may outperform a stand-alone investor, regardless of the investor’s opportunity cost for making portfolio decisions.

scholarly journals Dynamic portfolio optimization with liquidity cost and market impact: a simulation-and-regression approach

2018 ◽  
Vol 19 (3) ◽  
pp. 519-532 ◽  
Author(s):  
Rongju Zhang ◽  
Nicolas Langrené ◽  
Yu Tian ◽  
Zili Zhu ◽  
Fima Klebaner ◽  
...  
Keyword(s):  
Portfolio Optimization ◽  
Market Impact ◽  
Regression Approach ◽  
Dynamic Portfolio ◽  
Liquidity Cost ◽  
Simulation And Regression ◽  
Dynamic Portfolio Optimization

scholarly journals Solving High-Dimensional Dynamic Portfolio Choice Models with Hierarchical B-Splines on Sparse Grids

2021 ◽  
Author(s):  
Peter Schober ◽  
Julian Valentin ◽  
Dirk Pflüger
Keyword(s):  
Portfolio Choice ◽  
Value Function ◽  
Choice Models ◽  
Sparse Grids ◽  
Continuous Control ◽  
B Splines ◽  
Gradient Based ◽  
Dynamic Portfolio ◽  
Dynamic Portfolio Choice ◽  
The Value Function

AbstractDiscrete time dynamic programming to solve dynamic portfolio choice models has three immanent issues: firstly, the curse of dimensionality prohibits more than a handful of continuous states. Secondly, in higher dimensions, even regular sparse grid discretizations need too many grid points for sufficiently accurate approximations of the value function. Thirdly, the models usually require continuous control variables, and hence gradient-based optimization with smooth approximations of the value function is necessary to obtain accurate solutions to the optimization problem. For the first time, we enable accurate and fast numerical solutions with gradient-based optimization while still allowing for spatial adaptivity using hierarchical B-splines on sparse grids. When compared to the standard linear bases on sparse grids or finite difference approximations of the gradient, our approach saves an order of magnitude in total computational complexity for a representative dynamic portfolio choice model with varying state space dimensionality, stochastic sample space, and choice variables.

Dynamic portfolio choice: a simulation-and-regression approach

2017 ◽  
Vol 18 (2) ◽  
pp. 369-406 ◽  
Author(s):  
Michel Denault ◽  
Erick Delage ◽  
Jean-Guy Simonato
Keyword(s):  
Portfolio Choice ◽  
Regression Approach ◽  
Dynamic Portfolio ◽  
Dynamic Portfolio Choice ◽  
Simulation And Regression

Retesting the Shape-of-Voice Value Function

2005 ◽  
Author(s):  
Jaewon Ko ◽  
Layne Paddock ◽  
Kees Van den Bos ◽  
Gary J. Greguras ◽  
Kidok Nam ◽  
...  
Keyword(s):  
Value Function
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