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Papers by Anand Radhakrishnan

Research paper thumbnail of A New Approach to Goals-Based Wealth Management

SSRN Electronic Journal, 2018

Research paper thumbnail of Dynamic optimization for multi-goals wealth management

Journal of Banking and Finance, 2021

We develop a dynamic programming methodology that seeks to maximize investor outcomes over multip... more We develop a dynamic programming methodology that seeks to maximize investor outcomes over multiple, potentially competing goals (such as upgrading a home, paying college tuition, or maintaining an income stream in retirement), even when financial resources are limited. Unlike Monte Carlo approaches currently in wide use in the wealth management industry, our approach uses investor preferences to dynamically make the optimal determination for fulfilling or not fulfilling each goal and for selecting the investor’s investment portfolio. This can be computed quickly, even for numerous investor goals spread over different or concurrent time periods, where each goal may be all-or-nothing or may allow for partial fulfillment. The probabilities of attaining each (full or partial) goal under the optimal scenario are also computed, so the investor can ensure the algorithm accurately reflects their preference for the relative importance of each of their goals. This approach vastly outperforms...

Research paper thumbnail of Combining Investment and Tax Strategies for Optimizing Lifetime Solvency under Uncertain Returns and Mortality

SSRN Electronic Journal

We consider an investor who is looking to maximize their probability of remaining solvent through... more We consider an investor who is looking to maximize their probability of remaining solvent throughout their lifetime by using an algorithm that aims to optimize their investment allocation strategy and optimize their tax strategy for withdrawal allocations between tax deferred accounts (TDAs), Roth accounts, and taxable stock and bond accounts. Our optimization works with stochastic investment returns and stochastic mortality. We find that optimizing the investment strategy (via dynamic programming) has a much larger impact on the investor remaining solvent than optimizing the tax strategy (via Monte Carlo and numerical optimization). This result is key to effectively optimizing both strategies simultaneously. We show that our optimized investment strategy soundly beats a standard target date fund strategy, while our novel optimized tax strategy displays the optimal desired properties suggested by non-stochastic tax optimization research.

Research paper thumbnail of Combining Investment and Tax Strategies for Optimizing Lifetime Solvency under Uncertain Returns and Mortality

Journal of Risk and Financial Management, 2021

This paper considers investors who are looking to maximize their probability of remaining solvent... more This paper considers investors who are looking to maximize their probability of remaining solvent throughout their lifetime by using an algorithm that aims to optimize their investment allocation strategy and optimize their tax strategy for withdrawal allocations between tax deferred accounts (TDAs), Roth accounts, and taxable stock and bond accounts. This optimization works with stochastic investment returns and stochastic mortality, extending and combining different investment and tax-efficiency paradigms. We find that optimizing the investment strategy has a much larger impact on the investor remaining solvent than optimizing the tax strategy. This result is key to effectively optimizing both strategies simultaneously. This optimized investment strategy soundly beats a standard target date fund strategy, and the novel optimized tax strategy displays optimal desired properties suggested by non-stochastic tax optimization research.

Research paper thumbnail of Dynamic Optimization for Goals-Based Wealth Management with Multiple Goals

Research paper thumbnail of Dynamic Portfolio Allocation in Goals-Based Wealth Management

SSRN Electronic Journal

We report a dynamic programming algorithm which, given a set of efficient (or even inefficient) p... more We report a dynamic programming algorithm which, given a set of efficient (or even inefficient) portfolios, constructs an optimal portfolio trading strategy that maximizes the probability of attaining an investor’s specified target wealth at the end of a designated time horizon. Our algorithm also accommodates periodic infusions or withdrawals of cash with no degradation to the dynamic portfolio’s performance or runtime. We explore the sensitivity of the terminal wealth distribution to restricting the segment of the efficient frontier available to the investor. Since our algorithm’s optimal strategy can be on the efficient frontier and is driven by an investor’s wealth and goals, it soundly beats the performance of target date funds in attaining investors’ goals. These optimal goals-based wealth management strategies are useful for independent financial advisors to implement behavioral-based FinTech offerings and for robo-advisors.

Research paper thumbnail of Optimal Goals-Based Investment Strategies for Switching between Bull and Bear Markets

The Journal of Wealth Management

We apply dynamic programming to solve a long-horizon fund choice problem, given that the underlyi... more We apply dynamic programming to solve a long-horizon fund choice problem, given that the underlying market can switch between different regimes. The objective function is based on reaching a target level of wealth, following the paradigm of goal-based investing. In a world with a good regime (e.g., a bull market) and a bad regime (e.g., a bear market), we find that an investor who is cognizant of regime switching can potentially do much better over time than an investor who assumes there is only one regime. However, there is a caveat---an investor must be able to predict the regime they are in with reasonable levels of confidence, and if not, they are in fact worse off than an investor who assumes just one regime. Using data from recent history, we find that investors may be better off not switching from existing single-regime models to more complex multiple-regime models.

Research paper thumbnail of A New Approach to Goals-Based Wealth Management

Journal of Investment Management, 2018

We introduce a novel framework for goals-based wealth management (GBWM), where risk is understood... more We introduce a novel framework for goals-based wealth management (GBWM), where risk is understood as the probability of investors not attaining their goals, not just the standard deviation of investors' portfolios. Our framework is based on a foundation of developments in behavioral economics and finance and is consistent with modern portfolio theory. Using a simple geometric analysis, we determine a specifi c portfolio that matches each individual investor's stated goals. Our approach requires information from the investor about their goals, elicited in a clear manner that market research shows is superior to common current practices. This new approach can improve the communication between advisors and clients and produce better advice for enabling clients to attain their goals with high probability through the use of efficient portfolios.

Research paper thumbnail of A New Approach to Goals-Based Wealth Management

SSRN Electronic Journal, 2018

Research paper thumbnail of Dynamic optimization for multi-goals wealth management

Journal of Banking and Finance, 2021

We develop a dynamic programming methodology that seeks to maximize investor outcomes over multip... more We develop a dynamic programming methodology that seeks to maximize investor outcomes over multiple, potentially competing goals (such as upgrading a home, paying college tuition, or maintaining an income stream in retirement), even when financial resources are limited. Unlike Monte Carlo approaches currently in wide use in the wealth management industry, our approach uses investor preferences to dynamically make the optimal determination for fulfilling or not fulfilling each goal and for selecting the investor’s investment portfolio. This can be computed quickly, even for numerous investor goals spread over different or concurrent time periods, where each goal may be all-or-nothing or may allow for partial fulfillment. The probabilities of attaining each (full or partial) goal under the optimal scenario are also computed, so the investor can ensure the algorithm accurately reflects their preference for the relative importance of each of their goals. This approach vastly outperforms...

Research paper thumbnail of Combining Investment and Tax Strategies for Optimizing Lifetime Solvency under Uncertain Returns and Mortality

SSRN Electronic Journal

We consider an investor who is looking to maximize their probability of remaining solvent through... more We consider an investor who is looking to maximize their probability of remaining solvent throughout their lifetime by using an algorithm that aims to optimize their investment allocation strategy and optimize their tax strategy for withdrawal allocations between tax deferred accounts (TDAs), Roth accounts, and taxable stock and bond accounts. Our optimization works with stochastic investment returns and stochastic mortality. We find that optimizing the investment strategy (via dynamic programming) has a much larger impact on the investor remaining solvent than optimizing the tax strategy (via Monte Carlo and numerical optimization). This result is key to effectively optimizing both strategies simultaneously. We show that our optimized investment strategy soundly beats a standard target date fund strategy, while our novel optimized tax strategy displays the optimal desired properties suggested by non-stochastic tax optimization research.

Research paper thumbnail of Combining Investment and Tax Strategies for Optimizing Lifetime Solvency under Uncertain Returns and Mortality

Journal of Risk and Financial Management, 2021

This paper considers investors who are looking to maximize their probability of remaining solvent... more This paper considers investors who are looking to maximize their probability of remaining solvent throughout their lifetime by using an algorithm that aims to optimize their investment allocation strategy and optimize their tax strategy for withdrawal allocations between tax deferred accounts (TDAs), Roth accounts, and taxable stock and bond accounts. This optimization works with stochastic investment returns and stochastic mortality, extending and combining different investment and tax-efficiency paradigms. We find that optimizing the investment strategy has a much larger impact on the investor remaining solvent than optimizing the tax strategy. This result is key to effectively optimizing both strategies simultaneously. This optimized investment strategy soundly beats a standard target date fund strategy, and the novel optimized tax strategy displays optimal desired properties suggested by non-stochastic tax optimization research.

Research paper thumbnail of Dynamic Optimization for Goals-Based Wealth Management with Multiple Goals

Research paper thumbnail of Dynamic Portfolio Allocation in Goals-Based Wealth Management

SSRN Electronic Journal

We report a dynamic programming algorithm which, given a set of efficient (or even inefficient) p... more We report a dynamic programming algorithm which, given a set of efficient (or even inefficient) portfolios, constructs an optimal portfolio trading strategy that maximizes the probability of attaining an investor’s specified target wealth at the end of a designated time horizon. Our algorithm also accommodates periodic infusions or withdrawals of cash with no degradation to the dynamic portfolio’s performance or runtime. We explore the sensitivity of the terminal wealth distribution to restricting the segment of the efficient frontier available to the investor. Since our algorithm’s optimal strategy can be on the efficient frontier and is driven by an investor’s wealth and goals, it soundly beats the performance of target date funds in attaining investors’ goals. These optimal goals-based wealth management strategies are useful for independent financial advisors to implement behavioral-based FinTech offerings and for robo-advisors.

Research paper thumbnail of Optimal Goals-Based Investment Strategies for Switching between Bull and Bear Markets

The Journal of Wealth Management

We apply dynamic programming to solve a long-horizon fund choice problem, given that the underlyi... more We apply dynamic programming to solve a long-horizon fund choice problem, given that the underlying market can switch between different regimes. The objective function is based on reaching a target level of wealth, following the paradigm of goal-based investing. In a world with a good regime (e.g., a bull market) and a bad regime (e.g., a bear market), we find that an investor who is cognizant of regime switching can potentially do much better over time than an investor who assumes there is only one regime. However, there is a caveat---an investor must be able to predict the regime they are in with reasonable levels of confidence, and if not, they are in fact worse off than an investor who assumes just one regime. Using data from recent history, we find that investors may be better off not switching from existing single-regime models to more complex multiple-regime models.

Research paper thumbnail of A New Approach to Goals-Based Wealth Management

Journal of Investment Management, 2018

We introduce a novel framework for goals-based wealth management (GBWM), where risk is understood... more We introduce a novel framework for goals-based wealth management (GBWM), where risk is understood as the probability of investors not attaining their goals, not just the standard deviation of investors' portfolios. Our framework is based on a foundation of developments in behavioral economics and finance and is consistent with modern portfolio theory. Using a simple geometric analysis, we determine a specifi c portfolio that matches each individual investor's stated goals. Our approach requires information from the investor about their goals, elicited in a clear manner that market research shows is superior to common current practices. This new approach can improve the communication between advisors and clients and produce better advice for enabling clients to attain their goals with high probability through the use of efficient portfolios.