Predictability of precious metals and adaptive market hypothesis

Abstract

Purpose The purpose of this paper is to boost the existing literature on adaptive market hypothesis (AMH) as it first time links predictability of gold, silver and metal returns with AMH which permits the predictability of returns to vary over time. Design/methodology/approach To know whether commodity (gold, silver and metal) market is efficient or not, the commodity returns are observed by using appropriate linear time series tests (variance ratio test, runs test and auto-correlation test). To capture the varying efficiency of three commodities, the study employs subsamples of five years and all sub-samples are exposed to linear econometric tests to reveal how market efficiency (independency of returns) has behaved over time. Findings It is found that the commodity market (gold, silver and metal) is adaptive because fluctuation is observed in the market efficiency. Returns of all three commodities go under the periods of efficiency and inefficiency. Thus, AMH is the better description of behavior of commodity markets than traditional efficient market hypothesis. Research limitations/implications Choice of sub-sample in the study is the first limitation as the authors employ a sub-sample comprising five years. Second, commission, fee and taxes (transection cost) are ignored in the study. Finally, the results are reported on the basis of linear econometric tests. In future, longer time period sub-sample analysis is suggested by the study to explore the varying nature of the commodities. Moreover, rolling window analysis may be a more appropriate method to elucidate the idea of AMH in further research. It is further suggested that the method used in the study could be helpful and adapted to examine other commodities (metal and agriculture), bonds and equity markets around the world. Practical implications The study will provide a better investment model which can enable the investors to seek more returns in future. Moreover, this research can be extended to explore multiple issues like adaptive behavior of returns from crypto currencies, bonds, stocks and real estate investment trusts. Social implications As all the linear tests reveal that almost all the commodities show inefficient behavior in full sample period, it is clear that past prices widely would be helpful to predict the future prices at NYSE; furthermore, investors can use the time-varying information to reduce the risk of investment at NYSE. The study is helpful for individual investors as well as portfolio managers and brokers to forecast the prices on the bases of findings. Originality/value The paper identifies the need to study why behavior of commodity returns varies over time.

Adaptive market hypothesis: An empirical analysis of the Wine Market

“…AMH have been verified in Equity markets [3][4][5][6] Forex markets [2,7], Commodity markets [8,9] and cryptocurrency markets [10,11]. Here, our purpose is to test the validity of AMH in the fine wine market.…”

Global islami pay piyasalarinda adaptif piyasa hipotezinin test edilmesi: markov-switching adf testi

“…Using a time-varying approach to examine bond market efficiency in the U.S., the U.K., South Africa, and India, Charfeddine et al (2018) found evidence in favor of the AMH in characterizing the behavior of government bond returns. Likewise, Shahid et al (2019a) hold a similar view for the behavior of commodity markets after they examine commodity (gold, silver, and metal) market efficiency from 1992 to 2016 via linear time series models (variance ratio test, runs test, and autocorrelation test). Charles et al (2012) evaluate time-varying return predictability of major foreign exchange rates over the period from 1974 to 2009 by applying the wild bootstrap automatic variance ratio test, generalized spectral test, and Dominguez-Lobato consistent tests.…”

“…Numerous previous studies have focused on market efficiency testing several versions of the EMH. Some focus on market efficiency in the energy markets (Khediri & Charfeddine, 2015;Ghazani & Ebrahimi, 2019), bond markets (Charfeddine et al, 2018), stock markets (Alvarez-Ramirez et al, 2012;Urquhart & Hudson, 2013;Hiremath & Kumari, 2014;Rodriguez et al, 2014;Xiong et al, 2019), commodities markets (Shahid et al, 2019a), cryptocurrencies (Khursheed et al, 2020) and foreign exchange markets (Charles et al, 2012) within the framework of AMH.…”

Stock prices' long memory in China and the United States

“…Kumar (2014), Maciel (2018), and Pernagallo and Torrisi (2019) found evidence of long-range dependence in most Indian sectoral indices, the Brazilian stock market, and emerging economies respectively. Beyond that, Shahid et al (2020) concluded that past prices of metals would be helpful to predict future prices in the New York Stock Exchange, implying the possible long memory in future markets.…”