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This study evaluates the success of the monthly withdrawal of funds from hypothetical retirement portfolios for the period January 1930 to December 2001. The objective of this research is to provide an empirical examination of the historical effect of global diversification on the withdrawal of funds from a retirement portfolio. We compare portfolios consisting of U.S. stocks and U.S. corporate bonds, and portfolios consisting of global stocks and U.S. corporate bonds. We examine both portfolio compositions using a variety of portfolio weights, fund withdrawal rates, and fund withdrawal periods. The results of the study indicate that, in general, portfolios with a higher equity portion had a greater likelihood of sustaining a given number of withdrawals over this time. Additionally, for much of the 1930 to 2001 period, including international stocks in a withdrawal portfolio decreased the likelihood the withdrawals lasted for a given period. However, the inclusion of international stocks does increase the terminal value of retirement portfolios after withdrawals during the latter part of the period under study. The results of this study can be used for retirement planning since it provides a historical perspective on the success of various withdrawal rates. The results can also be used to determine the value of the portfolio an individual needs at retirement to fund a given level of withdrawals. This can assist in the retirement timing decision.

The purpose of this paper is to compare simple dynamic withdrawal strategies with the static withdrawal method, examining not only failure rates and ending wealth but also spending. All withdrawal strategies are adjusted for the Internal Revenue Service’s (IRS) required minimum distribution (RMD). In addition, this study investigates the use of small company stocks (SCS) in place of large company stocks (LCS). Results indicate SCS portfolios are superior to large. When returns are poor, some dynamic strategies will not ensure income for life. This study demonstrates that the simplest dynamic strategy is superior to two popular dynamic strategies.

Using historical overlapping periods, different withdrawal strategies are examined. Previous studies focused on failure rates and ending wealth. As discussed in Milevsky (2016) different statistical distributions can have similar tail properties (prob of failure) but dissimilar risk and return profile. The detailed examination of both spending and use of small stocks advances the literature in this area.

Results indicate that use of small stocks is superior to using large stocks in the portfolios. When US historical stock returns are adjusted downward, there is the potential that some dynamic strategies will not ensure income for life. This study demonstrates that the simplest dynamic strategy is superior to two popular dynamic strategies.

This paper is the first to examine, in detail, annual spending results for the retiree. Second, it is shown that, overall, SCS are superior to LCS for all stock/bond allocations. Even though absolute downside risk increases slightly, this increase in downside risk is dominated by the upside potential. In other words, the positive skewness of small stock returns along with the cumulative effects of compounding at a higher rate increases both the available wealth for spending and ending wealth. Third, IRS’s RMDs are taken into account for every withdrawal strategy examined. Lastly, it demonstrates that the simplest dynamic strategy is superior to two popular dynamic strategies.

Outlines some research on the effects of risk on portfolios for retirement planning and puts forward a method to help individuals “increase their chances of not outliving their retirement portfolios”. Uses numerical examples to show how calculations of the savings needed to achieve specific retirement incomes may prove inaccurate, and how regular portfolio assessment can be used to make any necessary adjustments during the accumulation and/or the retirement stage.

The purpose of this paper is to develop a prescriptive portfolio selection (PPS) model based on a compromise between the idea of “fast” and “slow” thinking proposed by Kahneman.

“Fast” thinking is effortless and comfortable for investors, while “slow” thinking may result in better performance. These two systems are related to the first two types of analysis in the decision theory: descriptive, normative and prescriptive analysis. However, to compromise between “fast” and “slow” thinking, “overconfidence” is used as a weighting parameter. A case study including a sample of 161 active investors in Tehran Stock Exchange (TSE) is provided. Moreover, the feasibility and optimality of the model are discussed.

Results show that the PPS recommendations are efficient with a shift from the mean-variance efficient frontier; investors prefer PPS portfolios over the advisor recommendations; and investors have no significant preference between PPS and their own expectations.

Two assumptions of this study include: first, investors follow their “fast” system of thinking by themselves. Second, the investors’ “slow” system of thinking is represented by advisor recommendations which are simple expected value of risk and return. Therefore, considering these two assumptions for any application is the main limitation of this study. Moreover, the authors did not have access to more investors in TSE or other financial markets.

This is the first study that includes overconfidence in modeling portfolio selection for the purpose of achieving a portfolio that has a reasonable performance and one that investors are comfortable with.

Alice Monroe was an admissions officer at the Kellogg School of Management at Northwestern University. It was early January 2017 and Alice had enrolled in Northwestern's 403(b) retirement plan two months earlier. After spending a considerable amount of time examining the mutual funds available through the university's retirement plan, Alice had picked two to invest in: a large-cap equity growth fund and a mid-cap equity fund. (See the related case "Selecting Mutual Funds for Retirement Accounts (A).") Her initial allocations were 50% of her investment dollars in each fund.

Upon further reflection, however, she realized these initial allocations were somewhat simplistic. She recalled, from an investments class she had taken at college, the topic of modern portfolio theory, which held that by adding more funds to her portfolio she might be able to achieve greater diversification and thereby reduce the overall risk of her portfolio and/or achieve a higher expected return. Alice now was considering adding an intermediate-term bond fund and a real estate fund to her retirement account.

She hoped to use modern portfolio theory to prove that these new funds would indeed help her diversify her portfolio. If they did, she would also reassess her portfolio weights to determine the optimal allocation.

As the assets of public employee retirement systems grow (to $1 trillion by 1994), so does the interest in targeting these assets to specific goals, primarily housing and job creation, in a system's geographic area. If properly structured, these investments, often called economically targeted investments, or ETIs, can be a legitimate part of a public retirement system's portfolio. This article clarifies several essential characteristics of ETIs. Previous studies, national surveys and actual ETI portfolios are examined. The article argues that further analysis, especially involving evaluation techniques, is essential as this investment strategy continues to gain steam, especially with the encouragement of the Clinton Administration.

This paper evaluates potential methods for reducing ambiguity surrounding returns on equity to improve long-term savings decisions.

We evaluate 221 undergraduate students in the US and first assess the degree of ambiguity aversion exhibited by individuals in the sample population as they decide between a risky (known probability) option and ambiguous (unknown probability) option pertaining to their chances of winning $0 or $1 in a hypothetical lottery. Similarly, we test whether sampling historical return data through learning modules influences long-term decision making regarding asset allocation within a retirement portfolio.

Allowing participants to experience the underlying probability through sampling significantly influences behavior, as participants were more likely to select the ambiguous option after sampling. Here, we also find that participants who receive interactive learning modules – which require users to manually alter the asset allocation to produce a sample of historical return data based on the specific allocation entered in the model – increase their post-learning equity allocations by 10.1% more than individuals receiving static modules. Interestingly, we find no significant evidence of ambiguity aversion playing a role in the asset allocation decision.

We find that decision-making related to ambiguous and risky options can be substantially influenced by experiential learning. Our study supplements previous literature, providing a link between research on the effect of ambiguity on stock market participation and implementation of educational programs to improve the asset allocation decision for young adults.

Alice Monroe, a 30-year-old married mother of two, was an admissions officer at the Kellogg School of Management at Northwestern University. She was just completing her first year of service at Northwestern and qualified for the university's 403(b) retirement plan. It was early October 2017, and she had until the end of the month to decide if and to what extent she would participate in Northwestern's retirement plan–that is, how much of her salary should she put into the retirement plan, and into which mutual fund or funds should she allocate her savings?

The case includes background on defined contribution and benefit plans as well as mutual funds. It goes into detail about Northwestern's retirement plan, including data on the performance of 15 of the plan's core mutual funds. The case also provides each fund's strategy, Morningstar Rating and Morningstar Category, expense ratio, assets under management, turnover rate, and historical performance for the last 10 years.

Using modern portfolio theory (diversification and risk-return trade-off) and with an understanding of mutual fund fees and the tax advantages of retirement savings, students will decide how much Alice should invest and in which mutual funds.

Off‐farm investment decisions of farm households are analyzed. Farm‐level data for a sample of Kansas farms observed from 1994 through 2000 are utilized. A system of censored dependent variable models is estimated to investigate the factors that influence the composition of farm households’ portfolios. The central question underlying the analysis is whether farm income variability influences off‐farm investment decisions. Previous analyses on the determinants of non‐farm investments have failed to consider the role of income variability. Results of this study indicate that higher farm income fluctuations increase the relevance of non‐farm assets in the farm household portfolio, thus suggesting these assets are used as farm household income risk management tools.