US stock market efficiency over weekly, monthly, quarterly and yearly time scales

Rodríguez, Eduardo; Aguilar-Cornejo, M.; Femat, R.; Alvarez-Ramı́rez, José

doi:10.1016/j.physa.2014.07.036

Cited by 36 publications

(17 citation statements)

References 38 publications

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“…Zapart shows that more regularities are to be found on smaller scales (which is consistent with our results), but he uses Approximate Entropy, which cannot be directly linked to predictability [19]. Finally, Rodriguez et al show differences at scales ranging from a week to a year, while we are interested in intraday scales [18]. This paper can be thought of as an application of the multiscale entropy analysis framework to testing the efficiency of financial markets [20,21].…”

Section: Introductionsupporting

confidence: 86%

“…However, we do not know how this predictability changes with varying time scales, which is the subject of this paper. Some papers have partially explored this question, but their methodology leaves something to be desired [17][18][19]. Aghamohammadi et al show complexity at different sampling rates, which are not equivalent to different scales as understood in this study, and we find this approach less interesting, as it ignores a lot of market activity.…”

Section: Introductionmentioning

confidence: 77%

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Multiscale Analysis of the Predictability of Stock Returns

Fiedor¹

2015

Risks

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Due to the strong complexity of financial markets, economics does not have a unified theory of price formation in financial markets. The most common assumption is the Efficient-Market Hypothesis, which has been attacked by a number of researchers, using different tools. There were varying degrees to which these tools complied with the formal definitions of efficiency and predictability. In our earlier work, we analysed the predictability of stock returns at two time scales using the entropy rate, which can be directly linked to the mathematical definition of predictability. Nonetheless, none of the above-mentioned studies allow any general understanding of how the financial markets work, beyond disproving the Efficient-Market Hypothesis. In our previous study, we proposed the Maximum Entropy Production Principle, which uses the entropy rate to create a general principle underlying the price formation processes. Both of these studies show that the predictability of price changes is higher at the transaction level intraday scale than the scale of daily returns, but ignore all scales in between. In this study we extend these ideas using the multiscale entropy analysis framework to enhance our understanding of the predictability of price formation processes at various time scales.

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Section: Introductionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 77%

Multiscale Analysis of the Predictability of Stock Returns

Fiedor¹

2015

Risks

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“…Rizvi and Arshad (2017) using MFDFA and MGARCH technique found that Japan improved efficiency over the period. Rodriguez et al (2014) employed the DFA technique and found the US market efficiency varies over time and time scales.…”

Section: Testing Efficiency Using Both Modified-standard and Mf-dfa Mmentioning

confidence: 99%

Time-Varying Price–Volume Relationship and Adaptive Market Efficiency: A Survey of the Empirical Literature

Patil¹,

Rastogi

2019

JRFM

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This paper conducts a review of the literature on the price–volume relationship and its relation with the implications of the adaptive market hypothesis. The literature on market efficiency is classified as efficient market hypothesis (EMH) studies or adaptive market hypothesis (AMH) studies. Under each class, studies are categorized either as return predictability studies or price–volume relationship studies. Finally, review in each category is analyzed based on the methodology used. Our review shows that the literature on return predictability and price–volume relationship in classical EMH approach is extensive while studies in return predictability in the AMH approach have gained increased attention in the last decade. However, the studies in price–volume relationship under adaptive approach are limited, and there is a scope for studies in this area. Authors did not find any literature review on time-varying price–volume relationship. Authors find that there is a scope to study the nonlinear cross–correlation between price and volume using detrended fluctuation analysis (DFA)-detrended cross–correlational analysis (DXA) in the AMH domain. Further, it would be interesting to investigate whether the same cross–correlation holds across different measures of stock indices within a country and across different time scales.

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“…The Clark-West (CW) test was proposed to adjust Diebold Mariano (DM) statistics since the originally proposed test statistics were upward biased heavily if models tested were nested. The test statistics is defined in Equation (14).…”

Section: Data Collectionmentioning

confidence: 99%

“…It becomes increasingly clear that the incorporation of the nonlinear and complex dynamics during the modeling process provide the promising alternative to deeper understanding of the market dynamics and higher level of forecasting accuracy, especially when the modeling and forecasting exercises are conducted at the daily frequency level, higher than three weekly and monthly frequency in the literature. Rodriguez et al [14] showed that the level of market efficiency varies across different time scales.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Entropy Based Analysis and Forecasting of Crude Oil Price Dynamics

Zou

2015

Entropy

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Abstract:For the modeling of complex and nonlinear crude oil price dynamics and movement, wavelet analysis can decompose the time series and produce multiple economically meaningful decomposition structures based on different assumptions of wavelet families and decomposition scale. However, the determination of the optimal model specification will critically affect the forecasting accuracy. In this paper, we propose a new wavelet entropy based approach to identify the optimal model specification and construct the effective wavelet entropy based forecasting models. The wavelet entropy algorithm is introduced to determine the optimal wavelet families and decomposition scale, that will produce the improved forecasting performance. Empirical studies conducted in the crude oil markets show that the proposed algorithm outperforms the benchmark model, in terms of conventional performance evaluation criteria for the model forecasting accuracy.

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US stock market efficiency over weekly, monthly, quarterly and yearly time scales

Cited by 36 publications

References 38 publications

Multiscale Analysis of the Predictability of Stock Returns

Multiscale Analysis of the Predictability of Stock Returns

Time-Varying Price–Volume Relationship and Adaptive Market Efficiency: A Survey of the Empirical Literature

Wavelet Entropy Based Analysis and Forecasting of Crude Oil Price Dynamics

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