Journal of Asset Management最新文献

We investigate portfolio pumping around quarter-ends by ESG equity mutual funds domiciled in the largest European markets in sustainable investments, i.e., the UK, France and Germany, for the period from January 2010 to December 2022. We find strong evidence that the UK funds inflate quarter-end returns, with price spikes being stronger at year-ends; nevertheless, the magnitude of price inflation is less than that of their conventional counterparts. On the contrary, results indicate that German and French funds do not engage in portfolio pumping. The COVID-19 pandemic strengthened the propensity of fund managers to cause a profound artificial enhancement to the performance of the investment portfolio. Further analysis shows that portfolio pumping is more prominent among the worst-performing funds, funds that charge investors with lower fees and achieve a poor ESG rating. However, managers that pump fund returns do not attract significantly more flows. Our results have produced valuable insights for regulators and investors participating in ESG markets, highlighting the necessity for a rigorous surveillance of the UK ESG equity market.

This paper generalizes the concept of capacity from the portfolio level to the investment process for systematic equity strategies. Capacity is often understood as the maximum asset under management, above which additional inflows would have too great a negative impact on performance. The concept of capacity is often limited to the study of a given portfolio. However, setting up a capacity management framework must consider what the portfolio might look like in the future. This is obviously complicated for discretionary portfolios but theoretically conceivable for portfolios implementing systematic strategies, if we can simulate all possible scenarios. In our framework, we extend the traditional definition of capacity from a number to a random variable, allowing portfolio managers to integrate it into their risk considerations. We provide examples of how portfolio managers can approach this problem, with full-search or modelling methods. Our framework includes several capacity metrics that can be used jointly or selected to align better with the features of each strategy.

Utilities with hard coal and lignite power plants, manufacturers, and aviation companies in the EU that emit greenhouse gases must invest in emission allowances to run their operations. The buy side of the capital market (e.g. hedge funds, insurers, and pension plans) can invest in these allowances to realise an investment asset which is uncorrelated to traditional market-risk investments. Given the high volatility of the price of emission allowances, all investors in emission allowances face a challenging risk-return situation that requires a thorough risk analysis. We show that this analysis can be undertaken using extreme value theory. For the analysed extreme emission allowance price returns, we identified saliently good fits between the empirical and theoretical Pareto distributions. We further show that emission allowances present an interesting investment case.

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.

This paper studies a model proposed by Baaquie (Phys A Stat Mech Appl 541:123367, 2020b) for which a corporate bond pays coupons that are stochastic—depending on the valuation of the issuer. It is shown that by considering bonds with stochastic coupons that are ‘equivalent’ to fixed coupon bonds (defined in the paper), the price of the fixed coupon bond can be accurately explained. The proposed model of stochastic coupons has a built-in hedge for the issuer—and has the feature of profit and loss sharing between investor and issuer making it a viable instrument for Islamic finance.

Abstract

This research delves into the empirical performance of deterministic option pricing models in the dynamic financial landscape of India. The primary focus is on uncovering pricing discrepancies and discerning whether these disparities arise from inherent limitations in the theoretical foundations of the models or are influenced by the trading behaviors of market participants. The investigation centers on the analysis of call and put option contracts for the Nifty Index and Bank Nifty Index, both extensively traded on the National Stock Exchange (NSE) of India. The study’s findings highlight that models developed to address the theoretical constraints of the benchmark Black–Scholes model demonstrate noteworthy performance. However, the complexity of these models does not consistently translate into enhanced pricing efficiency. Notably, the Black–Scholes and Practitioner Black–Scholes models exhibit superior performance across various moneyness-maturity categories. Furthermore, the research underscores the substantial impact of option contract liquidity on the efficiency of the pricing models. Specifically, highly traded at-the-money and out-of-the-money option contracts exhibit a higher level of pricing accuracy.

This study presents network risk parity, a graph theory-based portfolio construction methodology that arises from a thoughtful critique of the clustering-based approach used by hierarchical risk parity. Advantages of network risk parity include: the ability to capture one-to-many relationships between securities, overcoming the one-to-one limitation; the capacity to leverage the mathematics of graph theory, which enables us, among other things, to demonstrate that the resulting portfolios is less concentrated than those obtained with mean-variance; and the ability to simplify the model specification by eliminating the dependency on the selection of a distance and linkage function. Performance-wise, due to a better representation of systematic risk within the minimum spanning tree, network risk parity outperforms hierarchical risk parity and other competing methods, especially as the number of portfolio constituents increases.

While stocks with high dividends have historically outperformed those with low dividends, we show that the difference can be completely explained by a set of well-known factors including value, quality and defensive. Applying a dividend filter to a portfolio of strategies having high exposure to these factors yields sub-optimal results. To test whether incorporating dividend yields can improve the performance of long-only factor portfolios, we construct a set of dividend-favored long-only factor portfolios with a heuristic rebalance algorithm and find that their after-tax net returns are lower than the dividend-agnostic counterparts. Collectively our results indicate that long-only active investors are better off loading directly on value, quality and defensive factors than purposely tilting their portfolios toward high-dividend stocks.

In this paper, we quantify the economic gain from accounting for departures from normality for the mean-variance (MV) investor. We provide two models that account for the key empirical regularities of financial returns: stochastic volatility, asymmetric returns, heavy tails and tail dependence. We show that accounting for departures from normality leads to significant gains in expected utility commensurate with or exceeding typical active management fees. The majority of the uplift in expected utility derives from accounting for stochastic volatility.

Investment committees are widespread across asset management firms, private and public institutional investors or family offices. Poorly designed boards can potentially destroy substantial value in the investment management industry, yet little research has been undertaken on their optimal design. From my 30-year experience as an investor, CIO for various firms and academic researcher, I believe that typical investment committees come with unaddressed challenges. Using qualitative group discussions to create a consensus view results in biases (group shift bias), incentive problems (free-rider) and aggregation problems. How can we ensure that all investment views enter the investment committee equally? In my opinion, we can learn from evidence gathered in social psychology how committees can make better investment decisions. I suggest creating an algorithmic consensus by averaging anonymous member portfolios instead of informal qualitative discussions towards the end of an investment committee meeting.