•Weak-form efficiency of high frequency BTCUSD and BTCEUR is analyzed.•Pricing efficiency has been improving in the last few years at the intraday level.•BTCUSD is slightly more efficient that ...BTCEUR•Higher the return frequency, lower the informational efficiency is.•Strong positive (negative) relation between liquidity (volatility) and high frequency Bitcoin efficiency exists.
We compare the time-varying weak-form efficiency of Bitcoin prices in terms of US dollars (BTCUSD) and euro (BTCEUR) at a high-frequency level by using permutation entropy. We find that BTCUSD and BTCEUR markets have become more informationally efficient at the intraday level since the beginning of 2016, and BTCUSD market is slightly more efficient than BTCEUR market in the sample period. We also find that higher the frequency, lower the pricing efficiency is. Finally, liquidity (volatility) has a significant positive (negative) effect on the informational efficiency of Bitcoin prices.
In this study, we test the efficient market hypothesis for a number of sectors in the US stock market during the COVID-19 pandemic to identify its effects on individual sectors. To test this ...hypothesis, we define the average price for 11 sectors within the S&P 500 and apply multifractal detrended fluctuation analysis to the average return series. Furthermore, we also investigate these sectors’ efficiency and multifractality during the global financial crisis (GFC) to analyze the features of the COVID-19 pandemic. Our findings can be summarized as follows. First, the average return series show non-persistent and persistent features during the GFC and the COVID-19 pandemic, respectively. Second, during each of these crisis periods, the consumer discretionary and utilities sectors had the highest and lowest levels of efficiency, respectively. Third, while both long-range correlations and fat-tailed distribution contributed to the multifractal properties, the latter of these was the chief contributor. Furthermore, the lower the efficiency ranking, the greater the impact of the fat-tailed distribution on multifractality. Finally, we classify sectors with low market efficiency for the two crisis periods. These findings have several important implications for asset allocations by investors in US stock markets.
•We test the EMH for a number of industries in the US stock market during COVID-19.•We also investigate their efficiency and multifractality during the GFC.•The CD and UTI sectors have the highest and lowest efficiency, respectively.•The fat-tailed distribution had a greater impact than on the long-range correlation.•We classify sectors with low market efficiency for the two crisis periods.
•We examine the multifractality properties of Bitcoin compared to gold, stock and currency markets.•We use the MF-DFA approach.•The results show evidence of the long-memory feature and ...multifractality for all markets.•Multifractality is more important in small fluctuations than in large ones for all markets.•The Bitcoin market is the most inefficient among the four markets.
This study assesses the efficiency of Bitcoin market compared to gold, stock and foreign exchange markets. By applying a MF-DFA approach, the study found that the long-memory feature and multifractality of the Bitcoin market was stronger and Bitcoin was therefore more inefficient than the gold, stock and currency markets.
We study efficiency of two Bitcoin markets (with respect to the US dollar and Chinese yuan) and its evolution in time. As inefficiency can manifest through various channels, we utilize the Efficiency ...Index of Kristoufek & Vosvrda (2013) which can cover different types of (in)efficiency measures. We find strong evidence of both Bitcoin markets remaining mostly inefficient between 2010 and 2017 with exceptions of several periods directly connected to cooling down after the bubble-like price surges.
•Efficiency of the USD and CNY Bitcoin markets is studied.•Efficiency index based on long-range dependence, fractal dimension, and entropy is used.•We find strong evidence of both Bitcoin markets remaining mostly inefficient between 2010 and 2017.•Markets are efficient only during cooling-downs after bubble-like price surges.
•We analyse various technical trading rules in the form of the moving average-oscillator and trading range break-out strategies.•We test resistance and support levels and their trading performance ...using high-frequency Bitcoin returns.•Our results provide significant support for the moving average strategies.•The variable-length moving average rule performs the best.•Buy signals are found to generate higher returns than sell signals.
We analyse various technical trading rules in the form of the moving average-oscillator and trading range break-out strategies to specifically test resistance and support levels and their trading performance using high-frequency Bitcoin returns. Overall, our results provide significant support for the moving average strategies. In particular, variable-length moving average rule performs the best with buy signals generating higher returns than sell signals.
This study aims to obtain empirical evidence regarding investor overreaction in winner stocks and investor overreaction in loser stocks after the announcement of the first COVID-19 case in Indonesia. ...Overreaction analysis was carried out in 11 IDX-IC stock sectors. Winner shares and loser shares were selected from one third stock with the highest CAR value and one third stock with the lowest CAR value for each sector. The analysis technique used is the Dependent Paired Sample t-Test by comparing the AAR values of winner or loser stocks on the first 30 trading days after the announcement of the COVID-19 case with the AAR of winner or loser stocks on the next 30 trading days. The results showed that there was investor overreaction after the announcement of the first COVID-19 case in Indonesia, both in loser stocks and in winner stocks. Overreaction in loser stocks occurred in the infrastructure sector, the financial sector and the technology sector. Overreaction in winner stocks occurred in the consumer non-cyclical sector, the energy sector, the basic materials sector, the consumer cyclical sector and the property & real estate sector.
This paper examines whether the efficient market hypothesis (EMH) holds in the Chinese carbon trading pilots by employing the Sequential Panel Selection Method (SPSM) which is combined with the Panel ...KSS unit root tests with Fourier function. This approach serves as a highly valid tool in controlling for cross-sectional dependence and heterogeneity as well as structural shifts and nonlinearities. In virtue of the SPSM, the paper could divide the whole panel into two groups and clearly identify which and how many series belong to stationary or non-stationary group. The results show that carbon prices follow mean reversion process, indicating that the EMH does not hold in the Chinese emissions trading scheme (ETS), with the exception of Shanghai. The inefficiency of ETS pilots can be explained by irrational behaviors, poor information transparency, imperfect market mechanism and transaction costs. Accordingly, the failure of EMH implies the existence of profitable arbitrage opportunities for market participants. To establish the nationwide ETS market, policy lessons should be learned from Shanghai as this pilot is demonstrated to be an efficient market. Additionally, policymakers should improve the trading information disclosure system and formulate uniform guidelines, which is propitious to enhance market efficiency of the Chinese ETS.
•Market efficiency of the Chinese ETS pilots and optimal scheme are assessed.•Nonlinearities and structural breaks in ETS markets are taken into account.•Carbon prices follow mean reversion process for 5 of 6 pilots, except for Shanghai.•The inefficiency is due to poor information transparency and market mechanism.•The nationwide ETS market should draw on policy experience of Shanghai pilot.
We examine the interactive link between oil prices and the stock market in the 4 selected South Asian countries using a Nonlinear Autoregressive Distributed Lag (NARDL) model for 1997–2018. We find a ...positive relationship between the world oil price and stock market index, and the response of the stock market index to positive and negative oil price shocks is asymmetric. Our results further reveal that higher oil prices in the world market stimulate stock price, suggesting that the South Asian countries do not follow the Efficient Market Hypothesis (EMH). We recommend that the policymakers take initiatives to make the South Asian stock market more efficient by removing the barriers to stock market development, developing the country’s infrastructure, enhancing the stock market’s capacity, and restoring the confidence of the market participants within the region.
We study the cryptocurrency market with respect to the efficient market hypothesis. Specifically, we are interested in testing whether the examined coins and tokens are efficient or not but we also ...compare the levels of efficiency within the cryptomarket. To do so, we utilize the Efficiency Index comprising the long-range dependence, fractal dimension and entropy components. Focusing on a set of historical currencies – Bitcoin, DASH, Litecoin, Monero, Ripple, and Stellar – as well as popular currencies and tokens of the last year (with market capitalization above $0.5 billion), we uncover some surprising results. First, the historical currencies are unanimously inefficient over the analyzed period. Second, efficiency itself and ranking as well are dependent on the denomination (the US dollar or Bitcoin). Third, most of the coins and tokens were efficient between July 2017 and June 2018. And fourth, the least efficient coins turn out to be Ethereum and Litecoin whereas DASH is the winner as the most efficient cryptocurrency.
•Set of top cryptocurrencies is examined for market efficiency.•Persistence, fractal dimension and entropy are studied.•Historical cryptocurrencies are found inefficient.•Between July 2017 and June 2018, most coins and tokens were efficient.•Ethereum and Litecoin are the least efficient, DASH is the most efficient.
In this paper, we address the problem of probabilistic forecasting using an adaptive volatility method rooted in classical time-varying volatility models and leveraging online stochastic optimization ...algorithms. These principles were successfully applied in the M6 forecasting competition under the team named AdaGaussMC. Our approach takes a unique path by embracing the Efficient Market Hypothesis (EMH) instead of trying to beat the market directly. We focus on evaluating the efficient market and emphasize the importance of online forecasting in adapting to the dynamic nature of financial markets. The three key points of our approach are: (a) apply the univariate time-varying volatility model AdaVol, (b) obtain probabilistic forecasts of future returns, and (c) optimize the competition metrics using stochastic gradient-based algorithms. We contend that the simplicity of our approach contributes to its robustness and consistency. Our performance in the M6 competition resulted in an overall 7th place, with a 5th place in the forecasting task. Considering our approach’s perceived simplicity, this achievement underscores the efficacy of our adaptive volatility method in probabilistic forecasting.
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