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This paper aims to examine the weak form of efficiency for price series of four precious metals, i.e. gold, silver, platinum and palladium, using a generalized spectral method.

The method has the advantage of detecting both linear and non-linear serial dependence in the conditional mean, and it is robust to various forms of conditional heteroscedasticity. The authors use three different rolling windows for the purpose of robustness.

The authors report weak form of efficiency across metals series for almost all rolling windows. The optimum efficiency for Gold and Palladium is achieved through 250 days rolling window estimates whereas it is 500 days rolling window for silver. Platinum has similar efficiency levels across rolling windows. The degree of efficiency for metal prices is observed to be varying over time with silver market possessing highest levels of efficiency. The efficiency synchronization also varies across rolling windows and metals.

The results reveal that metal markets are efficient for most times implying the low predictability and the low likelihood of earning abnormal returns by speculating in these markets.

The study uses a relatively new statistical technique, the generalized spectral test, to capture linear and non-linear serial dependence. Therefore, the results possess adequate power against departure from market efficiency.

This paper examines the time-varying efficiency of nine major Middle East and North Africa (MENA) stock markets namely Egypt, Bahrain, UAE, Jordan, Saudi Arabia, Oman, Qatar, Morocco and Tunisia during times of COVID-19 pandemic outbreak and vaccines.

The authors use two econometric approaches: (1) autocorrelation tests including the wild bootstrap automatic variance ratio test, the automatic portmanteau test and the Generalized spectral test, and (2) a non-Bayesian generalized least squares-based time-varying model with statistical inferences.

The results show that the degree of stock market efficiency of Egyptian, Bahraini, Saudi, Moroccan and Tunisian stock markets is influenced by the COVID-19 pandemic crisis. Furthermore, the authors find a tendency toward efficiency in most of the MENA markets after the announcement of the COVID-19's vaccine approval. Finally, the Jordanian, Omani, Qatari and UAE stock markets remain globally efficient during the three sub-periods of the COVID-19 pandemic outbreak.

The results have important implications for asset allocations and financial risk management. Portfolio managers may maximize the benefit of arbitrage opportunities by taking strategic long and short positions in these markets during downward trend periods. Policymakers should implement the action plans and reforms to protect the stock markets from global shocks and ensure the stability of the stock markets.

We investigate predictive abilities of nonlinear models for stock returns when density forecasts are evaluated and compared instead of the conditional mean point forecasts. The aim of this paper is to show whether the in-sample evidence of strong nonlinearity in mean may be exploited for out-of-sample prediction and whether a nonlinear model may beat the martingale model in out-of-sample prediction. We use the Kullback–Leibler Information Criterion (KLIC) divergence measure to characterize the extent of misspecification of a forecast model. The reality check test of White (2000) using the KLIC as a loss function is conducted to compare the out-of-sample performance of competing conditional mean models. In this framework, the KLIC measures not only model specification error but also parameter estimation error, and thus we treat both types of errors as loss. The conditional mean models we use for the daily closing S&P 500 index returns include the martingale difference, ARMA, STAR, SETAR, artificial neural network, and polynomial models. Our empirical findings suggest the out-of-sample predictive abilities of nonlinear models for stock returns are asymmetric in the sense that the right tails of the return series are predictable via many of the nonlinear models, while we find no such evidence for the left tails or the entire distribution.

The purpose of this study to evaluate the evolving market efficiency of the housing market under the framework of adaptive market hypothesis and martingale difference hypothesis taking a case of India.

The study used a wild bootstrap version of the generalized spectral (GS) test in the rolling window framework to measure possible time-varying linear and non-linear dependence in the housing market.

The study finds that the Indian housing market, in general, is not efficient, and this efficiency is dynamic, which changes with time lending support to the adaptive market hypothesis. The study confirms that the evolutionary model of individuals adapting to a changing environment via behavioural biases affects the efficiency of the housing market, which leads to the evolving efficiency of the housing market prices.

The study believes that the potential implications go beyond evolutionary forces and the adaptive market hypothesis , which, does not only depend on an individual's decision-making process but also on social psychology. Thus, a further attempt in this line, taking into account the social psychology and quantitative rigour towards drivers of evolving efficiency is suggested for future research.

The study suggests that there is a possibility of extra returns for market players, but not always. The Indian housing market has witnessed several landmark reforms in recent years, so it is believed that these reforms would decrease the inefficiency level of this market. Contrary to this, the study’s findings reveal an increase in the inefficiency level in recent years. As the Indian housing market shows evolving efficiency, it is believed that the increased inefficiency is temporary. The increased inefficiency can be regarded as the settlement stage of the various policy and technical reforms.

Confirming the presence or absence of adaptive efficiency in the housing market under possible non-linear dependence will be a significant addition to the existing literature.

This study aims to provide one of the first empirical investigations of market efficiency for developed markets during the recent global financial crisis.

Using the Morgan Stanley Capital International (MSCI) country indices as proxies for national stock markets, the study conducts a battery of econometric tests in assessing weak-form market efficiency for the developed markets.

The inferential outcomes are consistent among the different tests. Specifically, the study finds that the majority of developed markets are weak-form efficient while the USA is the sole equity market to be commonly diagnosed as weak-form inefficient across the different tests when using full period data spanning the January 2008-November 2011 period. However, when basing the analysis on one-year subsamples over the identical time period, this study fails to reject weak-form market efficiency for all of the developed markets and presents evidence consistent with the Adaptive Market Hypothesis as described by Urquhart and Hudson (2013). When applying technical analysis for the case of the USA over the full study period, the results indicate that the return predictabilities can be exploited for some horizon of variable length moving average (VMA) trading rules.

This study provides one of the first empirical investigations of market efficiency for developed markets during the recent global financial crisis using an extended set of econometric tests. The study contributes to the existing body of empirical research that formally assesses the impact of a financial crisis on stock market efficiency and underlines the significance and relevance of examining market efficiency through subsample analysis.

The purpose of this paper is to discuss the relevance and evolution of adaptive markets hypothesis (AMH) that has gained traction in the recent years, as it provides a dynamic perspective to the concept of informational efficiency.

This paper discusses several issues related to the concept of informationally efficient markets that have indicated efficient market hypothesis to be an incomplete portrayal of stock market behavior.

The authors find that a strict and perpetual adherence to informational efficiency is highly unlikely, and AMH provides a much more plausible description of the behavior of stock markets.

The authors provide a description of studies that examine the testable implications of AMH.

The relationship between returns and trading volume is central in financial economics because it has both a theoretical interest and important practical implications with regard to the structure of financial markets and the level of speculation activity. The aim of this study is to provide new insights into the association between returns and trading volume by investigating their kernel (instantaneous) causality. The empirical analysis relies on time series data from 22 commodities futures markets (agricultural, energy and metals) in the USA.

Non-parametric (local linear) regressions are applied to daily data on returns and on trading activity; generalized correlation measures are computed and their differences are subjected to formal statistical testing.

The results suggest that raw returns are likely to kernel-cause volume and volume is likely to kernel-cause price volatility. The patterns of causal order are generally in line with what is stipulated by the relevant theory, they provide guidance for model specification and they appear to explain the empirical evidence on temporal (lag-lead) causality between the same pairs of variables obtained in earlier works.

The concept of kernel causality has very recently become a part of the toolkit for econometric/statistical analysis. To the best of the author’s knowledge, this is the first study that relies on the notion of kernel (instantaneous) causality to provide new evidence on a relationship that is of keen interest to investors, professional economists and policymakers.

The purpose of this study is to investigate the adaptive market hypothesis (AMH) for 21 major global market indices for the period 1998–2018. These market indices cover the 16 largest global financial markets.

Quantile-regression methodology is employed to examine the market efficiency of a large number of financial markets from America, Europe and the Asia–Pacific region.

The results show that the returns in higher quantiles are negatively autocorrelated, and those in lower quantiles are positively autocorrelated. This evidence is stronger for the tails of return distribution. The positive autocorrelation (momentum effect) suggests market underreaction, and the negative autocorrelation (reversal effect) suggests overreaction. Overall, market efficiency appears to be time-varying and conditioned to the state of the market.

This study offers considerable evidence in favor of the AMH, for a large number of financial markets. These markets are substantially different from each other in terms of geography, nature of operation and size of the economy. The results from this study would be helpful to the academics, regulators and practitioners interested in financial markets.

Using vector autoregressive modelling (VAR) and Granger causality tests, this paper attempts to empirically investigate the dynamic relationship between return and volume of transactions of two main cryptocurrencies: Bitcoin and Ethereum.

Based on a generalized autoregressive conditional heteroskedasticity (GARCH) model with a transaction volume parameter in the conditional volatility equation.

The results provide empirical evidence of a positive contemporaneous relationship between the variation in transaction volume and the daily return of Bitcoin and Ethereum. The results also show that the conditional volatility of the returns is affected by the past volatility, which implies weak-form inefficiency for both Bitcoin and Ethereum markets. The results of the VAR model, testing Granger causality, indicate that the volume of transactions Granger-Causes Bitcoin and Ethereum returns. Furthermore, the findings show a Granger causal relation from returns to volume.

This result suggests that cryptocurrency returns can predict transaction volumes and vice versa.

The purpose of this paper is to analyse the adaptive market efficiency in the price–volume (P–V) relationship of the stocks listed in the Mexican Stock Exchange. The period under study goes from 1982 to 2015. In order to detect causality and, thus, determine adaptive efficiency in the market, one linear and two non-linear tests are applied. There are few papers in the literature that study the P–V relationship in Latin American markets; as such, this paper may be of interest and importance to financial academics and practitioners alike.

The Diks and Panchenko (DP) non-parametric Granger causality and the Brooks and Hinich (BH) cross-bicorrelation tests are applied.

Derived from the DP test, the findings show that there exists bi-directional non-linear Granger causality in 25.71 per cent of the firms studied, compared to 8 per cent when applying the linear Granger causality test. Therefore, there is evidence of weak-form efficiency in the market. From the BH test, evidence is shown of the adaptive market efficiency, since 71.42 per cent of firms exhibited some form of non-linear dependence in certain periods of time. With these results, the information process should be better studied for a greater comprehension of regulatory policies in the market and better decision-making tools for the investors.

This paper complements studies on the P–V relationship and efficiency in a Latin American market.

Este documento analiza la eficiencia adaptativa del mercado para la relación precio-volumen de las empresas que cotizan en la Bolsa Mexicana de Valores. El periodo bajo estudio es de 1982 a 2015. Para detectar causalidad y determinar la eficiencia adaptativa del mercado, se aplicó una prueba lineal y dos no-lineales. Existen pocos documentos en la literatura que estudien la relación precio-volumen en mercados latinoamericanos. Como tal, este documento puede ser de interés e importancia tanto para académicos como para profesionales de las finanzas.

Se aplicó la prueba de causalidad no-paramétrica de Diks y Panchenko y la prueba de bicorrelación cruzada de Brooks y Hinich.

Derivado de la prueba DP, los hallazgos muestran que existe causalidad no-lineal bidireccional en 25.71% de las empresas bajo estudio, comparado a un 8% cuando se aplica la prueba de causalidad lineal de Granger. Por lo tanto, existe evidencia de eficiencia en forma débil del mercado. De la pruba BH, se muestra evidencia de eficiencia adaptativa del mercado, dado que el 71.42% de las empresas exhibieron alguna forma de dependencia no-lineal en ciertos periodos de tiempo. Con estos resultados, el proceso de información debe ser mejor estudiado para una mayor comprensión de las políticas regulatorias del mercado y mejores herramientas para la toma de decisiones por los inversionistas.

Este documento complementa los estudios sobre la relación precio-volumen y la eficiencia en un mercado latinoamericano.