The Impact of SNAP Vehicle Asset Limits on Household Asset Allocation

2016 ◽  
Vol 83 (1) ◽  
pp. 146-175 ◽  
Author(s):  
Deokrye Baek ◽  
Christian Raschke
Keyword(s):  
Asset Allocation ◽  
Household Asset ◽  
The Impact

Herding and Delegated Portfolio Management: The Impact of Relative Performance Evaluation on Asset Allocation

2011 ◽  
Vol 01 (02) ◽  
pp. 265-292 ◽  
Author(s):  
Ernst Maug ◽  
Narayan Naik
Keyword(s):  
Performance Evaluation ◽  
Portfolio Management ◽  
Asset Allocation ◽  
Relative Performance ◽  
Optimal Contracts ◽  
Fund Managers ◽  
Portfolio Managers ◽  
Delegated Portfolio Management ◽  
The Impact ◽  
Allocation Decisions

This paper investigates the effect of fund managers' performance evaluation on their asset allocation decisions. We derive optimal contracts for delegated portfolio management and show that they always contain relative performance elements. We then show that this biases fund managers to deviate from return-maximizing portfolio allocations and follow those of their benchmark (herding). In many cases, the trustees of the fund who employ the fund manager prefer such a policy. We also show that fund managers in some situations ignore their own superior information and "go with the flow" in order to reduce deviations from their benchmark. We conclude that incentive provisions for portfolio managers are an important factor in their asset allocation decisions.

Impact of volatility and equity market uncertainty on herd behaviour: evidence from UK REITs

2018 ◽  
Vol 11 (2) ◽  
pp. 169-186 ◽  
Author(s):  
Omokolade Akinsomi ◽  
Yener Coskun ◽  
Rangan Gupta ◽  
Chi Keung Marco Lau
Keyword(s):  
Asset Allocation ◽  
Regime Switching ◽  
Stock Exchange ◽  
Equity Market ◽  
Market Uncertainty ◽  
Content Type ◽  
Regime Switching Model ◽  
Switching Model ◽  
Market Sentiment ◽  
The Impact

PurposeThis paper aims to examine herding behaviour among investors and traders in UK-listed Real Estate Investment Trusts (REITs) within three market regimes (low, high and extreme volatility periods) from the period June 2004 to April 2016.Design/methodology/approachObservations of investors in 36 REITs that trade on the London Stock Exchange as at April 2016 were used to analyse herding behaviour among investors and traders of shares of UK REITs, using a Markov regime-switching model.FindingsAlthough a static herding model rejects the existence of herding in REITs markets, estimates from the regime-switching model reveal substantial evidence of herding behaviour within the low volatility regime. Most interestingly, the authors observed a shift from anti-herding behaviour within the high volatility regime to herding behaviour within the low volatility regime, with this having been caused by the FTSE 100 Volatility Index (UK VIX).Originality/valueThe results have various implications for decisions regarding asset allocation, diversification and value management within UK REITs. Market participants and analysts may consider that collective movements and market sentiment/psychology are determinative factors of risk-return in UK REITs. In addition, general uncertainty in the equity market, proxied by the impact of the UK VIX, may also provide a signal for increasing herding-related risks among UK REITs.

scholarly journals The missing link: economic exposure and pension plan risk

2015 ◽  
Vol 9 (2) ◽  
pp. 290-303
Author(s):  
Paul Sweeting ◽  
Alexandre Christie ◽  
Edward Gladwyn
Keyword(s):  
Asset Allocation ◽  
Pension Plan ◽  
Asset Returns ◽  
Oil Price ◽  
Defined Benefit ◽  
Financial Health ◽  
Defined Benefit Pension Plan ◽  
Asset Class ◽  
Normal Shape ◽  
The Impact

AbstractThe funding position of a defined benefit pension plan is often closely linked to the performance of the sponsoring company’s business. For example, a plan sponsor whose financial health is dependent on high oil prices may struggle during periods of oil price weakness. If the pension plan’s assets perform poorly at this time, the ability of the sponsor to address any funding requirement could be restricted precisely when the need for funding is heightened. In this paper, we propose an approach to dealing with joint plan and sponsor risk that can provide protection against extreme adverse events for the sponsor. In particular, adopt a strategy of minimising a portfolio’s expected losses in the event of an assumed drop of x% in the oil price. Our methodology relies on an asset allocation framework that takes into account the impact of serial correlation in asset returns, as well as the negative skewness and leptokurtosis resulting from the non-normal shape of marginal distributions of historical asset returns. We also make use of copulas to measure the dependence between asset class returns.

A Hybrid Parameter Estimation for Multi-asset Modeling and Dynamic Allocation Based on Financial Market Microstructure Model

2020 ◽  
Vol 29 (07n08) ◽  
pp. 2040007
Author(s):  
Yemei Qin ◽  
Yangyu Zhong ◽  
Zhen Lei ◽  
Hui Peng ◽  
Feng Zhou ◽  
...  
Keyword(s):  
Financial Market ◽  
Asset Allocation ◽  
Financial Time Series ◽  
Great Influence ◽  
Optimization Method ◽  
Excess Demand ◽  
Dynamic Allocation ◽  
Dynamic Asset Allocation ◽  
Mutual Correlation ◽  
The Impact

In the previous works, a discrete-time microstructure (DTMS) model for financial market was constructed by using identification technology and was successfully applied to dynamic asset allocation based on the identified excess demand. However, the initial value setting of the parameters has a great influence on the estimated results of the DTMS model, which may make the estimated model to describe the dynamic characteristics of the financial time series poor and also affect the investment results indirectly. To overcome the weakness, this paper proposes a global optimization method which combines particle swarm optimization (PSO) and genetic algorithm (GA) to estimate the initial parameters. In the paper, the multi-asset DTMS model is established, and a multi-asset dynamic allocation strategy based on excess demand obtained from the DTMS model is also designed. Furthermore, the paper also discusses the impact of mutual correlation of assets on portfolio. Case studies show that, when a portfolio is composed of several stocks which are weak correlation, its total return of the portfolio is more than the sum of two-asset allocation for each stock; while the correlation between stocks is high, the obtained total return is not better than those of two-asset allocation.

Preparing a Multi-Asset Class Portfolio for Shocks to Economic Growth

2019 ◽  
Keyword(s):  
Economic Growth ◽  
Asset Allocation ◽  
Global Economy ◽  
Economic Fundamentals ◽  
Funding Sources ◽  
Liability Management ◽  
Asset Class ◽  
Global Economic Growth ◽  
The Impact ◽  
Real Assets

This article explores shocks to global economic growth and how investors can defend against them. The authors examine the impact of such potential shocks on the asset allocation decision, asset-liability management, and funding sources. This article proposes that the global economy could be poised at an inflection point, and if a regime change occurs it would catch many portfolios off guard. Investors have experienced relatively healthy returns for the last decade, with recency bias leading many investors to creep outward on the risk spectrum. The authors remind the reader that, even in portfolios that appear to be diversified, most of the risk typically comes from equities and equity-like securities, which are greatly exposed to global economic growth risk. To address these concerns, they encourage investors to incorporate economic fundamentals and much longer time horizons into the portfolio construction calculus. Specifically, they argue that true diversification across independent sources of return is the only practical way of reducing exposure to economic growth. The asset classes providing returns independent of the equity market are nominal bonds and real assets (the latter including inflation-indexed bonds) and, for some investors, cash (usually implemented using skill-based assets with a cash-like beta). Many assets marketed as alternatives actually provide equity exposure in disguise.

scholarly journals KiwiSaver fund performance and asset allocation policy

2019 ◽  
Vol 31 (2) ◽  
pp. 232-257
Author(s):  
Huong Dieu Dang
Keyword(s):  
Asset Allocation ◽  
Tracking Error ◽  
Excess Return ◽  
Superior Performance ◽  
Data Set ◽  
Content Type ◽  
Allocation Policy ◽  
Holding Period ◽  
Management Fees ◽  
The Impact

Purpose This paper aims to examine the performance and benchmark asset allocation policy of 70 KiwiSaver funds catergorised as growth, balanced or conservative over the period October 2007-June 2016. The study focuses on the sources for returns variability across time and returns variation among funds. Design/methodology/approach Each fund is benchmarked against a portfolio of eight indices representing eight invested asset classes. Three measures were used to examine the after-fee benchmark-adjusted performance of each fund: excess return, cumulative abnormal return and holding period returns difference. Tracking error and active share were used to capture manager’s benchmark deviation. Findings On average, funds underperform their respective benchmarks, with the mean quarterly excess return (after management fees) of −0.15 per cent (growth), −0.63 per cent (balanced) and −0.83 per cent (conservative). Benchmark returns variability, on average, explains 43-78 per cent of fund’s across-time returns variability, and this is primarily driven by fund’s exposures to global capital markets. Differences in benchmark policies, on average, account for 18.8-39.3 per cent of among-fund returns variation, while differences in fees and security selection may explain the rest. About 61 per cent of balanced and 47 per cent of Growth funds’ managers make selection bets against their benchmarks. There is no consistent evidence that more actively managed funds deliver higher after-fee risk-adjusted performance. Superior performance is often due to randomness. Originality/value This study makes use of a unique data set gathered directly from KiwiSaver managers and captures the long-term strategic asset allocation target which underlines the investment management process in reality. The study represents the first attempt to examine the impact of benchmark asset allocation policy on KiwiSaver fund’s returns variability across time and returns variation among funds.

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