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Target Date Funds: Characteristics and Performance
... Using Vanguard funds (exclusively indexed portfolios), Mitchell & Utkus (2022) show that low-cost target date series can raise retirement wealth by 50 percent compared to non-target date investors due to more efficient portfolio choice. Using an earlier dataset from Morningstar (covering the 2004-2012 period), Elton et al. (2015) study the asset allocation and performance of TDFs. They find that additional allocation of the TDFs structured as funds of funds are mostly offset by the low-cost funds they choose. ...
... They find that additional allocation of the TDFs structured as funds of funds are mostly offset by the low-cost funds they choose. Elton et al. (2015) document that the average alpha of the TDFs is negative over the sample period, but similar to funds found in mutual fund studies. ...
There are significant differences in the performance of Target Date Funds (TDFs) with the same target year. Using a unique dataset from Morningstar, we show that within the same target year, funds with lower than the average expense ratio, or higher than average allocation to equities, outperform similar funds. This outperformance exists across all target year groups and is economically meaningful. Furthermore, deviations in the equity allocation have a greater impact on performance than does expense ratio. Using bootstrap simulations to investigate the impact over a longer horizon, we show that deviations from the average allocations or expense ratios have a meaningful impact on the retirement savings of an average investor.
... Overall, our paper contributes to the growing literature on pension fund investments and TDFs (e.g., Sandhya, 2012 ;Elton et al., 2015 ;Balduzzi and Reuter, 2019 ). We extend the work by Balduzzi and Reuter (2019) , who document an increase in TDF return heterogeneity contributed by small and post-Pension Protection Act of 2006 (PPA) TDF families that take high idiosyncratic risk to compete for a higher market share. ...
... Therefore, the tendency to take more idiosyncratic risk is an important agency issue for TDF managers ( Balduzzi and Reuter, 2019 ;Mao and Wong, 2020 ). If TDF managers substitute equity investment with nonstandard asset classes, such as real estate and commodity funds ( Elton et al., 2015 ), this could result in a reduction in equity market risk and an increase in idiosyncratic risk. To test this explanation, we explore whether TDF shifters exhibit different levels of idiosyncratic volatility and return heterogeneity than non-shifters. ...
We document a reduction in both the level and cross-sectional dispersion of systematic risk in the target-date fund (TDF) market after 2008, which resulted in better performance of TDFs during the COVID-19 selloff compared to the 2008 selloff and a reduction in TDF return dispersion. We find that the shift is more pronounced in close-to-retirement funds and driven by the TDF series investing more in equities in the early period, consistent with TDFs catering to the market demand for lower risk exposure after the 2008 crisis. In addition, TDF systematic risk shifters do not exhibit more idiosyncratic risk-taking.
... A study on the pandemic era also found the existence of herding behavior during normal periods in the cryptocurrency market [43] H. 6 Herding bias significantly influence investor's decision making in cryptocurrency market Overconfidence Bias Overconfidence bias is described as a tendency to overestimate, highly self-credit success and tendency to blame external factors when failure happens [44]; [45]. Literature explained that high levels of overconfidence bias in investors, push them to spend more resources in new information and invest in a huge amount to achieve abnormal return [46] despite the high-risk investment [44]. ...
Cryptocurrency, an innovative asset class that is widely adopted by investors around the world. Indonesia is no exception to this, increasing the investor adoption up to 12 million investors in 2022. This number is very significant compared to Indonesia stock market investors that is only around 7 million investors. Various literatures have covered cryptocurrency in terms of pricing strategy and technicalities, so this paper extends the understanding of cryptocurrency dynamics from a behavioral finance perspective that is still less developed in Indonesia. This paper aims to explore the relationship between financial literacy, behavioral bias as well as its implication on the investment decision making process and investment performance from the perspective of investors based on Indonesia’s cryptocurrency investors at online communities. This paper used Structural Equation Modeling (SEM) to predict the relation between variables. Our results show that financial literacy has an impact on each behavioral bias. While the behavioral biases that investigate in this study have different result in term of impact on decision-making process and the investment performance. Overconfidence, herding and anchoring are the biases that significantly influence invertor’s decision making in scope of cryptocurrency market in Indonesia. This study outcome may help investors understand and increase the awareness of investor’s investment behavior and decision-making process, and parallel to that the regulators and other stakeholders may use the insight to improve investor’s protection.
We introduce a risk‐reduction‐based procedure to identify a subset of funds with a resulting opportunity set that is at least as good as the original menu when short‐sales are imposed. Relying on Wald tests for mean‐variance spanning, we show that the better results for the subset can be explained by a higher concentration of covariance entries between its assets, ultimately leading to smaller Frobenius norms of the associated matrices. With data on U.S. defined contribution plans, where participants have limited financial literacy, tend to be overwhelmed and prefer to make decisions among fewer choices, we obtain a 75% average reduction. This article is protected by copyright. All rights reserved.
As a response to unforeseeable market turbulence—such as the 2008 financial crisis and the most recent market drawdown triggered by the COVID‐19 pandemic—we propose a new pension investment strategy that could better protect a long‐term pension plan in volatile market conditions. Over a hypothetical 20‐year pension scheme and various target volatility scenarios, we show that our newly proposed strategy, which attaches a target volatility mechanism to a lifecycle strategy, could provide more effective capital protection and risk control for pension investment vehicles. Our results are robust with a consideration of transaction costs.
Participants in 401(k) retirement plans violate the basic principle of diversification by investing significant fractions of their savings in their employers' equity. This paper characterizes investors' active changes to their company stock investment over time by analyzing new inflows and transfers. The average investor seems to base active changes on salient information, paying attention to past returns, volatility, and business performance. Past returns, over a three-year horizon, predict higher inflow allocations and transfers, whereas volatility and business performance only have a weak effect. The sensitivity to past returns is asymmetric, with investors reacting more strongly to positive and above-S&P500 returns. Copyright 2004, Oxford University Press.