A Case for Dynamic Asset Allocation for Long Term Investors

Petre, Gabriel

doi:10.1016/s2212-5671(15)01113-2

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Asset allocation based on changing market conditions is a sensible method for active portfolio management [AB02,AT11,NHML15,Pet15]. A popular method is to utilize convex optimization control policies to dynamically allocate assets in a portfolio, where the timevarying statistical properties are modeled as a hidden Markov model [NBLM19].…”

Section: Related Workmentioning

confidence: 99%

Portfolio Construction Using Stratified Models

Tuck¹,

Barratt²,

Boyd³

2021

Preprint

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In this paper we develop models of asset return mean and covariance that depend on some observable market conditions, and use these to construct a trading policy that depends on these conditions, and the current portfolio holdings. After discretizing the market conditions, we fit Laplacian regularized stratified models for the return mean and covariance. These models have a different mean and covariance for each market condition, but are regularized so that nearby market conditions have similar models. This technique allows us to fit models for market conditions that have not occured in the training data, by borrowing strength from nearby market conditions for which we do have data. These models are combined with a Markowitz-inspired optimization method to yield a trading policy that is based on market conditions. We illustrate our method on a small universe of 18 ETFs, using four well known and publicly available market variables to construct 10000 market conditions, and show that it performs well out of sample. The method, however, is general, and scales to much larger problems, that presumably would use proprietary data sources and forecasts along with publicly available data.

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Section: Related Workmentioning

confidence: 99%

Portfolio Construction Using Stratified Models

Tuck¹,

Barratt²,

Boyd³

2021

Preprint

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“…Similarly, Dahlquist and Harvey (2001) show that a popular way to make the most out of a time variation in returns is to apply a tactical asset allocation overlay on the portfolio. Petre (2015) defines dynamic asset allocation as the process of applying time varying expected to excess returns and/or expected risk with a medium term time horizon. He assessed the applicability of the dynamic asset allocation strategy for long term institutional investors, reviewed different forms of implementations seen in practice and discussed key requirements and governance challenges in an institutional context.…”

Section: Literature Review Gse Annualmentioning

confidence: 99%

The state of monetary policy and industrial asset allocation: the Ghanaian perspective

Mensah

Aboagye

Akosah

2018

AJEMS

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Purpose The purpose of this paper is to investigate whether asset allocation across various industries listed on the Ghana Stock Exchange (GSE) varies across different monetary policy states. Design/methodology/approach This paper adopts the Markov Chain technique to split monetary policy into three different states. The authors further adopt the Markowitz portfolio optimization technique to find the minimum variance and optimum portfolio for the industries listed on the GSE. Findings The finding reveals a dynamic asset allocation, which varies the industry’s weight mix across the various monetary policy states enhance excess returns compared to the static asset allocation. Specifically, the authors find risk-return trade-off among industries listed on the GSE. Financial and Food and Beverage industries portfolios record high returns relative to the Government of Ghana 91-day Treasury bill. The Food and Beverage portfolio is the only portfolio that records relatively high excess returns across all the monetary policy states. The authors also find that, during expansionary state (high monetary policy rates) of the monetary policy, investors are to allocate about 69 and 30 percent of their investment into food and beverages and financials, respectively. Corner solution is found in the transient state where 100 percent of wealth is allocated to financial to obtain the optimum portfolio. The optimum portfolio in the contraction state assigns 52 percent to financials and 42 percent to manufacturing. In summary, the result supports the dependence of investors’ asset allocation decisions on monetary policy. Practical implications Therefore, the authors propose an investment strategy which is dynamic and takes into consideration the monetary policy states rather than static asset allocation which maintains the same industry weight mix over the investment period. Social implications In sum, the authors interpret the result as support for the dependence of investors’ asset allocation decisions on monetary policy. In Ghana, an increase in the monetary policy appears to support industries listed on the equity market. The result also gives knowledge about investors’ asset allocation decisions on the GSE, which is practical balanced source of information for investors’ risk and return choices. For a prudent monetary policy framework, the monetary policy committee should monitor industries listed on the GSE. The result from the analysis has also an implication for investors, portfolio managers and fund managers to consider the state of the monetary policy in Ghana when making investment decisions. Originality/value The study differs from earlier research on asset allocation by breaking new grounds on two levels. First of all, based on the notion that different industries have different exposures to monetary policy states, the authors extend the portfolios by grouping the equities listed on the GSE into their industrial sectors. Second, the authors examine how investors’ optimal portfolio allocation may change depending on the state of monetary policy.

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Exchange-Traded Funds (ETFs)

Kakushadze¹,

Serur

2018

151 Trading Strategies

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A Case for Dynamic Asset Allocation for Long Term Investors

Cited by 4 publications

References 13 publications

Portfolio Construction Using Stratified Models

Portfolio Construction Using Stratified Models

The state of monetary policy and industrial asset allocation: the Ghanaian perspective

Exchange-Traded Funds (ETFs)

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