Static Asset Allocation Research Articles

Practical Applications of An Empirical Examination of Lifestyle Mutual Funds

In <b><i>An Empirical Examination of Lifestyle Mutual Funds</i></b>, from the Winter 2022 issue of <b><i>The Journal of Retirement</i></b>, author <b>Srinidhi Kanuri</b> of the <b>University of Southern Mississippi</b> analyzes the performance of lifestyle mutual funds. Unlike target-date funds—which reallocate to more conservative investments as the investor’s retirement date approaches—lifestyle funds maintain a static asset allocation that is aggressive (mostly equities), moderate, or conservative (mostly fixed income). Lifestyle funds are increasingly popular, so it is important to know how they perform relative to lower-cost investments like index funds. The study measured the performance of all lifestyle funds in the Morningstar database from January 1994 through August 2017. It found that lifestyle funds underperformed US stock indexes and comparable balanced portfolios of stock and bond index funds (net of fees). Lifestyle funds had lower risk-adjusted performance and negative net alpha, but their gross alpha was statistically no different from zero. Therefore, they selected well-performing investments but charged high fees that cut into performance. Financial advisors may wish to consider alternatives to lifestyle funds—such as allocating to a mix of low-cost index funds—when constructing balanced retirement portfolios for clients.

Asset allocation efficiency from dynamic and static strategies in underfunded pension funds

This study attempts to conduct a comparative analysis between dynamic and static asset allocation to achieve the long-term target return on asset liability management (ALM). This study conducts asset allocation using the ex ante expected rate of return through the outlook of future economic indicators because past economic indicators or realized rate of returns which are used as input data for expected rate of returns in the “building block” method, most adopted by domestic pension funds, does not fully reflect the future economic situation. Vector autoregression is used to estimate and forecast long-term interest rates. Furthermore, it is applied to gross domestic product and consumer price index estimation because it is widely used in financial time series data. Based on asset allocation simulations, this study derived the following insights: first, economic indicator filtering and upper-lower bound computation is needed to reduce the expected return volatility. Second, to reach the ALM goal, more stocks should be allocated than low-yielding assets. Finally, dynamic asset allocation which has been mirroring economic changes actively has a higher annual yield and risk-adjusted return than static asset allocation.

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

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.

A dynamic target volatility strategy for asset allocation using artificial neural networks

ABSTRACTA challenge to developing data-driven approaches in finance and trading is the limited availability of data because periods of instability, such as during financial market crises, are relatively rare. This study applies a stability-oriented approach (SOA) based on statistical tests to compare data for the current period to a past set of data for a stable period, providing higher reliability due to a more abundant source of data. Based on an SOA, this study uses an artificial neural network (ANN), which is one of the commonly applied machine learning algorithms, for simultaneously forecasting the volatility and classifying the level of market stability. In addition, this study develops a dynamic target volatility strategy for asset allocation using an ANN to enhance the ability of a target volatility strategy that is established for automatically allocating capital between a risky asset and a risk-free cash position. In order to examine the impact of the proposed strategy, the results are compared to the buy-and-hold strategy, the static asset allocation strategy, and the conventional target volatility strategy using different volatility forecasting methodologies. An empirical case study of the proposed strategy is simulated in both the Korean and U.S. stock markets.

Measuring Risk with COGARCH(p,q) Models

In this paper we introduce a multivariate Independent Component COGARCH(p,q) model for financial time series. We determine optimal portfolio weights obtained as a solution of different static asset allocation problems. Empirical analysis is conducted on two datasets. The first is composed by 154 European hedge funds tracking the performance of the FTSE100 Index while the second contains the members of FTSE100. The performances of different strategies are investigated from an out-of-sample perspective.

Practical Applications of Regime-Based versus Static Asset Allocation:Letting the Data Speak

Regime shifts present a big challenge to traditional strategic asset allocation. This article investigates whether regimebased asset allocation can effectively respond to changes in financial regimes at the portfolio level, in an effort to provide better long-term results than more static approaches can offer. The authors center their regime-based approach around a regime-switching model with time-varying parameters that can match financial markets’ tendency to change behavior abruptly and the fact that the new behavior often persists for several periods after a change. In an asset universe consisting of a global stock index and a global government bond index, they show that, even without any level of forecasting skill, holding a static portfolio may not be optimal. (Less)

Return Chasing and Trend Following: Superficial Similarities Mask Fundamental Differences

Return chasing is often cited as one of the primary behavioral foibles of investors, resulting in sub-par returns. Surprisingly, the literature does not provide a generally accepted and testable description of return chasing. This paper proposes a simple definition. It then describes how return chasing so defined differs from trend following and how return chasing explains the shortfall of the returns of active, market timing investors compared to static asset allocation strategies. Finally, it shows that if the trading flows of return chasers are large enough to impact prices, then return chasing provides a powerful explanation of the positive returns earned by trend following strategies, which alternative descriptions of return chasing, such as it is trend following but with too long of a horizon, do not provide.

Regime-Based Versus Static Asset Allocation: Letting the Data Speak

Regime shifts present a big challenge to traditional strategic asset allocation. This article investigates whether regimebased asset allocation can effectively respond to changes in financial regimes at the portfolio level, in an effort to provide better long-term results than more static approaches can offer. The authors center their regime-based approach around a regime-switching model with time-varying parameters that can match financial markets’ tendency to change behavior abruptly and the fact that the new behavior often persists for several periods after a change. In an asset universe consisting of a global stock index and a global government bond index, they show that, even without any level of forecasting skill, holding a static portfolio may not be optimal.

Practical Considerations for Factor-Based Asset Allocation

Much has been written about the shortcomings of the traditional approach to asset allocation. Traditional asset allocation policies can be characterized by relatively static asset allocation and by diversification across asset class building blocks. Exactly how risk factors should be included in the portfolio construction process is still a nascent area of research and is fiercely debated among practitioners. Although numerous research papers explore this topic, they tend to be theoretical, and for this reason, this article has a stronger focus on the practical aspects of implementation. Rather than provide definitive answers here, the authors aim to share their reflections on this topic, following feedback from practitioners and discussions that took place in client roundtable events organized by S&P Dow Jones Indices to promote dialogue with industry experts.

Learning From Disruptive Market Events: A Study Of Financial Advisor Beliefs and Behavior

In the wake of the 2008 financial crisis, most financial managers still rely on static asset allocation models to construct client portfolios – proven to be stunningly successful during decades of ...

Regime Shifts: Implications for Dynamic Strategies (corrected)

Regime shifts present significant challenges for investors because they cause performance to depart significantly from the ranges implied by long-term averages of means and covariances. But regime shifts also present opportunities for gain. The authors show how to apply Markov-switching models to forecast regimes in market turbulence, inflation, and economic growth. They found that a dynamic process outperformed static asset allocation in backtests, especially for investors who seek to avoid large losses.

Sustainable Withdrawal Rates from Retirement Portfolios: The Historical Evidence on Buffer Zone Strategies

This paper evaluates the performance of portfolios that dynamically adjust one’s mix of risky and risk-free assets during retirement based on the performance of risky assets. We analyze these “buffer zone” portfolio decumulation strategies over various time periods from 1926 to 2009. We find that they are generally inferior to a simpler static asset allocation at minimizing longevity risk.

Dynamic Versus Static Asset Allocation: From Theory (Halfway) to Practice

In a static optimisation process, the structure of the portfolio in which the investor’s wealth is invested is chosen once for all at the beginning of the period. In a dynamic optimisation process, the structure of the portfolio is continuously (at least theoretically) adjusted. There is no doubt that the dynamic optimisation process is superior to the static one, as one of the dynamic strategies among many is to choose not to adjust the portfolio structure, i.e. to choose a static strategy. The aim of this paper is to assess, with parameter values as reasonable as possible, the order of magnitude of the extent by which the dynamic optimisation process dominates the static optimisation process; and to gauge whether the existence of transaction costs changes significantly the assessment. That assessment is made considering that the values of the relevant parameters are known at the beginning of the investment period. The paper thus does not to try to assess the benefits that dynamic optimisation could reap by incorporating new information on those parameters. The main results are that for high risk adverse investors, the improvement provided by dynamic versus static optimisation is moderate; it may be huge for low risk adverse investors, but this comes principally from the fact that dynamic optimisation leads to a huge leverage, which is normally prohibited in a static framework. Reasonable values for transaction costs do not jeopardise the superiority of dynamic asset allocation.

Market Timing with Option-Implied Distributions: A Forward-Looking Approach

We address the empirical implementation of the static asset allocation problem by developing a forward-looking approach that uses information from market option prices. To this end, constant maturity S&P 500 implied distributions are extracted and subsequently transformed to the corresponding risk-adjusted ones. Then, we form optimal portfolios consisting of a risky and a risk-free asset and evaluate their out-of-sample performance. We find that the use of risk-adjusted implied distributions times the market and makes the investor better off compared with the case where she uses historical returns’ distributions to calculate her optimal strategy. The results hold under a number of evaluation metrics and utility functions and carry through even when transaction costs are taken into account. Not surprisingly, the reported market timing ability deteriorated during the recent subprime crisis. An extension of the approach to a dynamic asset allocation setting is also presented.

The appropriateness of default investment options in defined contribution plans: Australian evidence

Abstract For participants in defined contribution (DC) plans who refrain from exercising investment choice, plan contributions are invested following the default investment option of their respective plans. Since default investment options of different plans vary widely in terms of their benchmark asset allocation, the most important determinant of investment performance, participants enrolled in these options face significantly different wealth outcomes at retirement. This paper simulates the terminal wealth outcomes under different static asset allocation strategies to evaluate their relative appeal as default investment choice in DC plans. We find that strategies with low or moderate allocation to stocks are consistently outperformed in terms of upside potential of exceeding the participant's wealth accumulation target at retirement as well as downside risk of falling below that target outcome by aggressive strategies whose allocation to stocks approach 100%. The risk of extremely adverse wealth outcomes for plan participants also does not appear to be very sensitive to asset allocation. Our evidence suggests the appropriateness of strategies heavily tilted towards stocks to be nominated as default investment options in DC plans unless plan providers emphasize predictability of wealth outcomes over adequacy of retirement wealth.

Asset Allocation in a Crisis

The market crisis of 2007 and 2008 shattered many investors’ notions of the efficiency and stability of markets. It also likely shattered a lot of investors’ confidence in asset allocation schemes. Some advisors thought they were following sound strategies, only to have severely adverse results. In our view, the financial crisis of 2007 and 2008 also created a crisis in asset allocation. However, we believe that it was the implementation, and not the essence, of asset allocation that failed. In our assessment, there are three main lessons from the market crisis of 2007 and 2008: (1) Strategic asset allocation is not static asset allocation; (2) An awareness of economic and market conditions should inform portfolio allocations; (3) Risk management goes beyond “checking the box” of size and style exposure. Other factors, like sectors, countries, and liquidity, also matter.

Robust Higher-order Moments and Efficient Portfolio Selection

This article proposes a non-parametric portfolio selection criterion for the static asset allocation problem in a robust higher-moment framework. Adopting the Shortage Function approach, we generalize the multi-objective optimization technique in a four-dimensional space using L-moments, and focus on various illustrations of a fourdimensional set of the first four L-moment primal efficient portfolios. Our empirical findings, using a large European stock database, mainly rediscover the earlier works by Jean (1973) and Ingersoll (1975), regarding the shape of the extended higher-order moment efficient frontier, and confirm the seminal prediction by Levy and Markowitz (1979) about the accuracy of the mean-variance criterion.

Dynamic Strategic Asset Allocation: Risk and Return Across Economic Regimes

We propose a practical investment framework for dynamic asset allocation across different economic regimes, which we illustrate using a sample of U.S. data from 1948 to 2007. We identify four regimes in the economic cycle and find that these regimes capture pronounced time-variation in the risk and return properties of asset classes. Time-variation is also observed in the risk of a traditional, static strategic asset allocation portfolio. In order to stabilize risk across the economic cycle we propose a dynamic strategic asset allocation approach, which has the potential to enhance expected return as well. The proposed approach is found to be robust to variations in the variable composition of the regime model and can easily be extended with different economic variables and/or additional assets.

Market Timing with Option-Implied Distributions: A Forward-Looking Approach

We address the empirical implementation of the static asset allocation problem by developing a forward-looking approach that uses information from market option prices. To this end, we extract constant maturity S&amp;P 500 implied distributions and transform them to the corresponding risk-adjusted ones. Then we form optimal portfolios consisting of a risky and a risk-free asset and evaluate their out-of-sample performance. We find that the use of risk-adjusted implied distributions times the market and makes the investor better off than if she uses historical returns' distributions to calculate her optimal strategy. The results hold under a number of evaluation metrics and utility functions and carry through even when transaction costs are taken into account. Not surprisingly, the reported market timing ability deteriorated during the recent subprime crisis. An extension of the approach to a dynamic asset allocation setting is also presented. This paper was accepted by Wei Xiong, finance.

On the equivalence of the static and dynamic asset allocation problems

A classic dynamic asset allocation problem optimizes the expected final-time utility of wealth, for an individual who can invest in a risky stock and a risk-free bond, trading continuously in time. Recently, several authors considered the corresponding static asset allocation problem in which the individual cannot trade but can invest in options as well as the underlying. The optimal static strategy can never do better than the optimal dynamic one. Surprisingly, however, for some market models the two approaches are equivalent. When this happens the static strategy is clearly preferable, since it avoids any impact of market frictions. This paper examines the question: when, exactly, are the static and dynamic approaches equivalent? We give an easily tested necessary and sufficient condition, and many non-trivial examples. Our analysis assumes that the stock follows a scalar diffusion process, and uses the completeness of the resulting market model. A simple special case is when the drift and volatility depend only on time; then the two approaches are equivalent precisely if (μ (t)− r)/σ2(t) is constant. This is not the Sharpe ratio or the market price of risk, but rather a nondimensional ratio of excess return to squared volatility that arises naturally in portfolio optimization problems.