The Determinants of the Performance of Precious Metal Mutual Funds

Abstract

The aim of this paper is to assess the efficiency of a set of 62 precious metal mutual funds (PMMFs) and to explain performance differences between funds using weighted additive data envelopment analysis (DEA) and Tobit regression, respectively. The contribution of this paper is twofold: to provide for the first-time metrics of the relative performance of PMMFs using a particular weighted additive model, namely the range-adjusted measure (RAM), and to explain the performance of the funds by the use of a Tobit model. Results do not suggest positive linkages between RAM-based and standard fund performance metrics (Sharpe ratio and Jensen’s alpha). Moreover, for the sample inefficient funds the mean–variance performance hypothesis does not hold. In addition, fund performance based on RAM can be explained by the persistence of the fund and the beta coefficient.

Performance Evaluation of Utility Exchange-Traded Funds: A Super-Efficiency Approach

“…Depending on the weights chosen, the model ( 1) is related with various measurements (Tsolas 2020). The most known additive weighted model under VRS is the RAM of inefficiency (Cooper et al 1999) that stems from model (1) using the weights…”

“…Expense ratio (ER) (Chu et al 2010;Tsolas 2011Tsolas , 2019, tracking error (TE), beta (BETA) coefficient (Choi and Min 2017), total assets (TA), and (1-year) return (Chu et al 2010;Choi and Min 2017) are the variables used for the current study. Tsolas and Charles (2015) suggested using the TE for DEA-based ETF performance measurement, while the TA was used by Tsolas (2020) for reflecting fund size. The interested reader is directed to Henriques et al (2022) for a recent review on DEA research on the performance evaluation of ETFs.…”

“…The current study proposes the RAM of inefficiency, a non-oriented slacks-based model that accounts for the weighted slacks of both inputs and output in order to generate the performance metric. As a result of the slacks of inputs and outputs being divided by the range of their observed values, RAM of inefficiency is units-invariant, meaning that the objective function of the model is dimensionless (Tsolas 2020).…”

Performance measuring of wood-processing microenterprises through Data Envelopment Analysis: A case study of Slovakia, Poland, and Bulgaria

“…Ammirato et al (2022) developed the performance measurement, further proposing innovative composite indicators to measure and control the performance of production processes. Tsolas (2020) measured performance differences between 62 precious metal mutual funds using weighted additive data envelopment analysis (DEA). Other authors like Dia et al (2020), Neves et al (2020), and Qayyum and Riaz (2018) used DEA for benchmarking and performance measurement in the banking sector in various ways, which proved the applicability of this approach.…”

The performance of metal-related funds before and during covid-19: Evidence from Brazil

“…There are relatively few studies addressing commodity markets and metal-related funds (Kanuri et al 2016;French and Li 2021;Rehman and Vinh 2021), but investments in precious metal funds are gaining attention from investors who want to diversify and seek exposure to variations in precious metals (Otero and Reboredo 2018;Tsolas 2020). The low number of studies reinforces the relevance of a research taking into account investors decisions related to an eventual selection of refuge in the face of various economic, business or social factors, especially considering that these factors can affect the profitability or returns during the health crisis.…”