Multi-asset allocation of exchange traded funds: Application of Black–Litterman model

The volatility of asset returns can be classified into market and firm-specific volatility, otherwise known as idiosyncratic volatility. Idiosyncratic volatility is increasing over time with some literature attributing this to the IT revolution. An understanding of the relationship between idiosyncratic risk and return is indeed relevant for idiosyncratic risk pricing and asset allocation, in a context of emerging technologies. The case of high-tech exchange traded funds (ETFs) is especially interesting, since ETFs introduce new noise to the market due to arbitrage activities and high frequency trading. This article examines the relevance of idiosyncratic risk in explaining the return of nine high-tech ETFs. The Markov regime-switching (MRS) methodology for heteroscedastic regimes has been applied. We found that high-tech ETF returns are negatively related to idiosyncratic risk during the high volatility regime and positively related to idiosyncratic risk during the low volatility regime. These results suggest that idiosyncratic volatility matters in high-tech ETF pricing, and that the effects are driven by volatility regimes, leading to changes across them.

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RPI’s James Fund: ETFs, decision making, & manager transitions

Managerial Finance ◽

10.1108/mf-08-2018-0397 ◽

2019 ◽

Vol 46 (5) ◽

pp. 662-674

Author(s):

Thomas Shohfi

Keyword(s):

Asset Allocation ◽

Individual Student ◽

Portfolio Performance ◽

Policy Statement ◽

Exchange Traded Funds ◽

Investment Fund ◽

Fixed Income ◽

Dormant Period ◽

Content Type ◽

Managed Funds

Purpose The James Fund at Rensselaer Polytechnic Institute’s Lally School of Management is a small, recently established, course-driven student-managed investment fund (SMIF). The purpose of this paper is to provide insight to new and existing funds in improving individual fund operation and structure. Design/methodology/approach The James Fund seeks to outperform an 80/20 equity/fixed income benchmark by investing exclusively in exchange traded funds and to move primary emphasis away from idiosyncratic risk and individual equity valuation back toward asset allocation, the most significant driver of portfolio performance. Buy and sell decisions must receive a three-fifths majority in voting among students and adhere with the investment policy statement. Findings Groupthink, a common problem in student-managed funds, is observed in trade proposal and manager voting patterns. Originality/value Groupthink is partially addressed through the use of instructor feedback on individual student trade diaries. Student managers transition each semester; therefore, the portfolio must meet dormant period criteria limited to a specific list of broadly diversified ETFs, mitigating potential problems from knowledge transfer between management teams that are largely unexamined in the context of SMIFs.

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Behavioral Aspects of Portfolio Investments

10.1093/acprof:oso/9780190269999.003.0021 ◽

2017 ◽

Author(s):

Nathan Mauck

Keyword(s):

Mutual Funds ◽

Behavioral Finance ◽

Asset Allocation ◽

Hedge Funds ◽

Pension Funds ◽

Exchange Traded Funds ◽

Behavioral Biases ◽

Wide Range ◽

Money Managers ◽

Selection Of

Investors are inextricably linked to financial institutions, money managers, and the products they market. Mutual funds, exchange-traded funds (ETFs), hedge funds, and pension funds manage or hold roughly $55 trillion in combined wealth. This chapter examines these topics with a behavioral finance approach, focusing on two main ideas: the performance and rationality of each group, and the behavioral biases that relate to individuals’ selection of particular investments within each group. Research indicates that actively managed mutual funds and hedge funds underperform passive investments. Pension funds generate alpha of roughly zero on a risk-adjusted basis. The fees involved in investing in such funds exacerbate the observed underperformance in mutual funds and hedge funds. Behavioral biases provide one perspective on sources of underperformance. Further, individuals exhibit a wide range of behavioral biases that may lead to suboptimal asset allocation, including the selection of mutual funds, ETFs, and hedge funds.

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Asset Allocation Model for a Robo-Advisor Using the Financial Market Instability Index and Genetic Algorithms

Sustainability ◽

10.3390/su12030849 ◽

2020 ◽

Vol 12 (3) ◽

pp. 849 ◽

Cited By ~ 2

Author(s):

Wonbin Ahn ◽

Hee Soo Lee ◽

Hosun Ryou ◽

Kyong Joo Oh

Keyword(s):

Genetic Algorithms ◽

Portfolio Management ◽

Asset Allocation ◽

Financial Time Series ◽

Threshold Value ◽

Exchange Traded Funds ◽

Instability Index ◽

Allocation Model ◽

Risk Parity ◽

One Year

There has been a growing demand for portfolio management using robo-advisors, and hence, research on the automation of portfolio composition has been increasing. In this study, we propose a model that automates the portfolio structure by using the instability index of the financial time series and genetic algorithms (GAs). We use the instability index to filter the investment assets and optimize the threshold value used as a filtering criterion by applying a GA. For an empirical analysis, we use stocks, bonds, commodities exchange traded funds (ETFs), and exchange rate. We compare the performance of our model with that of risk parity and mean-variance models and find our model has better performance. Several additional experiments with our model using various internal parameters are conducted, and the proposed model with a one-month test period after one year of learning is found to provide the highest Sharpe ratio.

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