Asset Allocation Recommendations Research Articles

Research on Portfolio Investment in the Risky U.S. Stock Market Based on the Mean-Variance Model

Abstract: Against the backdrop of a thriving securities market and economy, there is a growing demand among investors for stock portfolio models in risky markets. This paper explores the application of the mean-variance model in this context, aiming to assist investors in making more informed investment decisions in an uncertain market. Through an in-depth analysis of the mean-variance model, the main conclusion of this paper is that certain stocks, such as AAPL, receive a higher investment proportion due to their lower risk and stable expected returns. In contrast, some high-risk or low-expected-return stocks, such as SBUX, MSFT, and QCOM, hold a smaller or zero proportion in the portfolio. This conclusion highlights the importance of balancing risk and return when constructing a portfolio and provides investors with specific asset allocation recommendations. It can help them effectively control risk while pursuing maximum returns, thereby achieving long-term stable investment gains.

Editor’s Letter

1. Hossein Kazemi 1. Editor-in-Chief In March 2021, Goldman Sachs announced that Bitcoin is a new asset class, and, for the first time, it was “officially” included in the firm’s asset allocation recommendations. If one agrees with this statement, the next step is to examine Bitcoin’s

Combining Reinforcement Learning and Inverse Reinforcement Learning for Asset Allocation Recommendations

We suggest a simple practical method to combine the human and artificial intelligence to both learn best investment practices of fund managers, and provide recommendations to improve them. Our approach is based on a combination of Inverse Reinforcement Learning (IRL) and RL. First, the IRL component learns the intent of fund managers as suggested by their trading history, and recovers their implied reward function. At the second step, this reward function is used by a direct RL algorithm to optimize asset allocation decisions. We show that our method is able to improve over the performance of individual fund managers.

Practical Applications of Allocation of Wealth Both Within and Across Goals: A Practitioner’s Guide

In <b><i>Allocation of Wealth Both Within and Across Goals: A Practitioner’s Guide</i></b>, from the Summer 2020 issue of <b><i>The Journal of Wealth Management</i></b>, author <b>Franklin J. Parker</b> (of <b>Bright Wealth Management</b> in <b>Dallas, TX</b>) presents a goals-based system for helping clients allocate assets. Many financial advisors make asset allocation recommendations based on mean–variance analysis, which seeks to determine how much volatility clients are willing to tolerate in exchange for higher returns. But in the author’s approach, advisors ask clients to rank their financial goals in order of importance and value them relative to one another. Advisors then can recommend a wealth allocation to each goal and a different investment allocation within each goal. The aim is to maximize the probability of achieving the given goals, based on the available funds, funding requirements, time horizons, and value of the goals relative to one another. Parker says this approach allows for a wider range of investment options, since investors with aspirational goals (financial dreams that will not harm clients if not achieved) can invest small amounts in riskier things, such as venture capital, which often fail but have the potential to reap huge profits. This improves investors’ chances of reaching all their goals, from high-priority to aspirational. <b>TOPICS:</b>Wealth management, performance measurement

Practical Applications of Allocation of Wealth Both Within and Across Goals:A Practitioner’s Guide

In <b><i>Allocation of Wealth Both Within and Across Goals: A Practitioner’s Guide</i></b>, from the Summer 2020 issue of <b><i>The Journal of Wealth Management</i></b>, author <b>Franklin J. Parker</b> (of <b>Bright Wealth Management</b> in <b>Dallas, TX</b>) presents a goals-based system for helping clients allocate assets. Many financial advisors make asset allocation recommendations based on mean–variance analysis, which seeks to determine how much volatility clients are willing to tolerate in exchange for higher returns. But in the author’s approach, advisors ask clients to rank their financial goals in order of importance and value them relative to one another. Advisors then can recommend a wealth allocation to each goal and a different investment allocation within each goal. The aim is to maximize the probability of achieving the given goals, based on the available funds, funding requirements, time horizons, and value of the goals relative to one another. Parker says this approach allows for a wider range of investment options, since investors with aspirational goals (financial dreams that will not harm clients if not achieved) can invest small amounts in riskier things, such as venture capital, which often fail but have the potential to reap huge profits. This improves investors’ chances of reaching all their goals, from high-priority to aspirational. <b>TOPICS:</b>Wealth management, performance measurement

Asset Pricing, Asset Allocation and Risk-Adjusted Performance with Multiple Goals and Agency: The Goals and Risk-based Asset Pricing Model

Investment managers require a consistent asset pricing model, asset allocation recommendations and risk-adjusted performance measures (or the “three facets of investing”) to be effective in managing portfolios. Incorporating three critical realities of investing into these models (i.e., that investors have many stochastic goals, seek to delegate to skillful agents, and maximize risk-adjusted returns as opposed to expected utility) provides recommendations on the three facets that are markedly different from the foundational papers of Modern Portfolio Theory (MPT). This is important as Goals-based Investing (GBI) and delegation are now the norm, and investors globally are not meeting their goals by adopting traditional MPT. The paper briefly surveys the literature on MPT, GBI, and agency before providing a normative Goals- and Risk-Based Asset Pricing Model (GRAPM) that includes these three realities of investing and articulates the three new facets. GRAPM exploits a simple idea that a relatively risk-free asset for one stochastic goal is a risky asset for another, and vice versa. These two assets, plus the traditional absolute risk-free rate of MPT, allow us to triangulate to establish returns for all other assets based on the return of any goal-replicating asset and multiple correlations (as opposed to a single relationship with the unobservable “market” portfolio). This approach creates a “pair-wise equilibrium” for all assets – very different from MPT - and also lends itself easily to a new asset pricing model with heterogeneous investors (i.e., each investor has a unique goal). GRAPM incorporates a “risk-aversion” parameter that is also easily observable, unlike MPT, and appears to explain why seemingly similar investors can have markedly different asset allocations or expected returns.

Do as I Say, Not as I Do: An Analysis of Portfolio Development Recommendations Made by Financial Advisors

A survey of over 200 financial professionals was used to determine how financial advisors assess, rank, and use client characteristics and risk-profiling inputs when developing asset allocation recommendations. Findings from this study suggest that in a scenario-free context, financial advisors rank a client’s time horizon as the most important risk-profiling input. However, when viewed in the context of a specific client scenario, financial advisors appear to alter the importance of certain risk-profiling inputs, becoming overly reliant upon a client’s age and employment status. Results from this study also show that financial advisors are somewhat inconsistent in their use of risk-profiling inputs across client scenarios. Results do show that older financial advisors with more experience are more apt to consistently recommend portfolios with higher equity ratios than their younger counterparts. TOPICS:Portfolio construction, risk management, wealth management Key Findings • In a client-neutral context, financial advisors rank time horizon, liquidity need, risk capacity, risk need, and risk tolerance as important risk-profiling inputs. • However, when faced with client asset allocation choices, financial advisors appear to use other risk-profiling inputs to shape portfolio recommendations. • Although not a recommended practice, financial advisors seem to be overly reliant on the use of client age and employment status when developing asset allocation recommendations.

Gender, Risk Tolerance, and False Consensus in Asset Allocation Recommendations

Gender, Risk Tolerance, and False Consensus in Asset Allocation Recommendations

Gender, risk tolerance, and false consensus in asset allocation recommendations

Abstract We study the impact of gender on asset allocation recommendations. Graduate business students and professional wealth managers are randomly assigned a male or female client. Participants recommend an allocation and choose an allocation for themselves. Male students choose a riskier allocation than female students, consistent with existing evidence of a gender difference in risk tolerance, and recommend a riskier allocation. In contrast, male and female wealth managers choose and recommend the same allocation, indicating that male and female finance professionals feature similar risk preferences. In both samples, a subject's allocation choice is the strongest predictor of the recommendation provided.

Gender, Risk Tolerance, and False Consensus in Asset Allocation Recommendations

We study the impact of gender on asset allocation recommendations. Graduate business students and professional wealth managers are randomly assigned a male or female client. Participants recommend an allocation and choose an allocation for themselves. Male students choose a riskier allocation than female students, consistent with existing evidence of a gender difference in risk tolerance, and recommend a riskier allocation. In contrast, male and female wealth managers choose and recommend the same allocation, indicating that male and female finance professionals feature similar risk preferences. In both samples, a subject’s allocation choice is the strongest predictor of the recommendation provided.

A Very Simple Goals - and Risk-Based Asset Pricing Model (or Asset Pricing with Heterogeneous Investors)

Prof, Andre Perold has shown how the Capital Asset Pricing Model (with implications for asset pricing and risk-adjusted performance) can be derived from maximizing the Sharpe ratio as opposed to the traditional approach of assuming that investors maximize the utility of wealth. More recently, Goals-based Investing (GBI) is becoming the norm for investors and Prof. Robert Merton has suggested that theory needs to accommodate the fact that investors maximize relative (wealth divided by the present value of the goal) and not absolute wealth. Individuals save for a range of goals (e.g., retirement, a child’s college expenses) and each has a unique set of cash flows. The paper first argues for the creation of a new class of relative risk-free fixed income securities for each goal as proposed for a number of countries by Prof. Merton. These instruments will typically be interest-only real bonds (linked to the appropriate inflation index), with a forward start date, and pay coupons for the period required for the respective goal. The second contribution of this paper is to use the Perold approach and show that with two such goals/instruments and the traditional risk-free asset, all risky assets can be priced and optimal asset allocation recommendations derived, without requiring a formal utility function, risk aversion parameter or market portfolio. All that is needed is the assumption that investors maximize risk-adjusted returns relative to the goal-replicating asset and specify risk goals much like traditional investors would specify. The asset pricing model is derived from the simple idea that a relatively risk-free asset for one goal is a risky asset for another, and hence these two assets, plus the absolute risk-free rate allow us to triangulate to establish returns for all other assets. This approach also lends itself easily to an asset pricing model with heterogeneous investors and effective risk-adjusted performance measures.

Annuitized Income and Optimal Asset Allocation

An investor who either buys an income annuity at retirement, or who has a higher level of guaranteed income through a pension or Social Security, should hold a different asset allocation than an investor who holds little guaranteed income. We use current annuity and bond prices to estimate optimal equity allocation for retirees with varying levels of guaranteed income who have higher and lower preference for income stability and bequests. We find that increasing annuitized income has a strong impact on optimal equity allocation. The average retiree will see their optimal equity allocation increase by roughly one percentage point for each percentage point increase in annuitized total wealth. Our results provide insight into prudent asset allocation recommendations for clients who haver higher levels of annuitized income.

Comments by Deborah Swenson, on A Multiple-Goal Investment Strategy for Sovereign Wealth Funds: An Application to China

The primary question in this paper is how should SWF managers allocate their assets, when their SWF is tasked with investing on behalf of multiple objectives? To answer this question the authors develop a new technical approach to provide guidelines that could be used by SWF managers. Then, to illustrate how their approach affects asset allocation decisions, the authors apply their model to the case of China. They also explore how their asset allocation recommendations are affected by the timeframe for the SWF goal structure (short-, medium-, and long-run) and how their asset allocation recommendations depend on the degree of risk aversion. I appreciate the clarity of the question, and the directness of the answer. Because the application of the problem is clean and the model and application matches the authors’ intended goals, the paper cannot be criticized for lacking internal consistency, or for failing to match the question and its analysis.

KNOW YOUR CLIENT! INVESTOR PROFILE AND TAILOR‐MADE ASSET ALLOCATION RECOMMENDATIONS

AbstractWe study how the investor profile influences the asset allocation recommendations of professional advisors. We find the investor's perceived risk attitude influences more the mix of risky assets, whereas the socioeconomic variables influence more the cash percentage. The recommendations are consistent with a diversification behavior driven by actual asset correlations. These findings support the utility of investor advisory that may help enhance the risk and return trade‐off. The main drawback of the recommendations may consist in the degree of customization that is limited by the small number of investor characteristics actually influencing the asset allocation.

Invest As I Say, Not As I Do? Gender Dif-ferences In Financial Risk Preferences

This paper examines gender differences in stated versus observed financial risk preferences. The responses of women versus men to a question regarding financial risk preferences are compared to the proportion of risky assets held in their portfolios using data from the 1995 Survey of Consumer Finances. The data show that women are more likely to express an unwillingness to take financial risks. Stated financial risk preferences are found to be consistent with observed risk preferences at the ordinal, but not the quantitative, level. Contradicting their stated risk preferences, risky assets constitute, on average, one-third of the financial assets of households that indicate they are unwilling to take any financial risks. Financial planners and advisers frequently use a clients expressed willingness to take on risk as an important determinant in asset allocation recommendations. Consistent gender differences in these responses, in addition to inconsistencies between the clients stated risk preferences and observed portfolio allocation, may lead advisers to make inappropriate recommendations.

Separating Portfolio Performance into Opportunity, Foresight, and Active Management Risk

We introduce a decomposition showing precisely how actively-managed portfolio returns can be separated into three measurable components that we call Opportunity, Foresight, and Active Management Risk. Opportunity reflects the degree to which the investment opportunity set contains exploitable asset returns. Foresight and Active Management Risk reflect how well a manager forecasts relative asset returns for the next period and how aggressively a manager changes portfolio weights at the start of the period. Using simulated weights, we show that our measures have the power to detect even modest levels of predictive ability. They also can disentangle predictive ability and aggression. We apply the decomposition to asset allocation recommendations from a set of Investment Houses. We find little Foresight, but considerable Active Management Risk.

Pravda Asset Management

This case examines the importance of forecasting expected returns in asset allocation decisions. While the case is targeted to MBA students in an investments or portfolio managment course, it is also appropriate for an advanced undergraduate course. It is written from the perspective of a new employee at a small investment management firm that was surprised by the market crash of 2008 and subsequent market rebound in 2009. The CIO of the firm is meeting with an important client to explain his asset allocation recommendations for 2008 and 2009 and to suggest a new allocation going forward. He enlists the help of this new employee in preparing the materials for this presentation. The case requires students to analyze the ability of simple valuation ratios to forecast returns. Students will use a smoothed price-to-earnings ratio to forecast future returns and given step-by-step instructions for the analysis. In addition, students must use their regression results to form a simple (two-asset) tactical asset allocation strategy to better understand the importance of forecasting expected returns for asset allocation decisions and how such forecasts could be used to form a simple tactical asset allocation model. Students also will examine the issues associated with performance measurement for a tactical asset allocation or market-timing investment strategy.

Integrating Asset-Liability Risk Management with Portfolio Optimization for Individual Investors

A majority of private client practitioners rely on mean-variance optimization (MVO), rules of thumb, or model portfolios for making asset allocation recommendations. Considerations for income levels and other constraints figure into the typical approach. However, not enough attention is given to the nature of an investor’s multiple time horizons and implications for cash flows. These are the future demands placed upon the portfolio. The risks that these demands will not be met need to be clearly understood in order to validate any asset allocation decision. This article presents an approach incorporating MVO within a multi-horizon asset-liability management risk model. This approach allows for cash-flow matching of a portion of an investor’s portfolio within the optimization framework. This combined approach allows an individual’s portfolio to provide short-term cash flow, as needed, while also considering the longer-term demands on the portfolio.

Measuring the Effects of Foresight and Commitment on Portfolio Performance

A central question in finance is how to measure portfolio performance. We assert that foresight (i.e., the ability of managers to forecast relative returns) and commitment (i.e., the aggressiveness with which managers act on their foresight) are necessary ingredients to generate portfolio performance not attributable to chance. We create two portfolio performance measures free from the need to pre-specify a benchmark portfolio. Our measures allow for many investment styles because they use benchmarks created from the style implied in the portfolio being tested. To calibrate these new measures, we use a weight-generating process that simulates various combinations of foresight and commitment. We decompose our measures to show how foresight and commitment act jointly to generate statistically measurable excess performance. An outcome of our measures is a scoring mechanism that groups portfolios by commitment, and then ranks portfolio performance by foresight. We apply our measures to a series of actual asset allocation recommendations made by a panel of Investment Houses. Largely, we find little foresight, but we document considerable aggression. The Investment Houses made allocation recommendations to equity, bonds, and cash. However, our measures easily generalize to a wide variety of potentially hard to measure investment styles, like those of hedge funds and other alternative investment managers.

What Moves Money Managers' Portfolios? An Investigation of Preferences and Beliefs

Asset allocation is widely recognized as the most fundamental decision in the investment process. Surprisingly little work has been done on examining what drives the asset allocation recommendations of professional investment advisors. To address this issue, we propose a general framework to identify and estimate the parameters characterizing the preferences and beliefs of money managers. In a mean-variance framework, we provide joint estimates of the preference parameters and the beliefs conditioned on observable information that most closely reproduce the dynamics of the observed portfolio recommendations made by a panel of international money managers for The Economist. Our findings suggest that money managers behave as low risk-averse investors and that heterogeneity in their conditional beliefs is key to explaining differences in the recommended portfolio allocations. The source of heterogeneity lies in the diverse interpretation of publicly available information. Using our general framework, we cannot reject the hypothesis that, in general, money managers use information efficiently.