Increasing market efficiency in the stock markets

Yang, Jae‐Suk; Kwak, Wooseop; Kaizoji, Taisei; Kim, In-mook

doi:10.1140/epjb/e2008-00050-0

Cited by 36 publications

(29 citation statements)

References 29 publications

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“…However, information flow is currently faster and more even because of the rapid development of communication through high speed internet, mobile technologies, and worldwide broadcasting systems. The expectation is of the present stock markets to become more efficient than past markets, confirming the EMH (Yang et al, 2008).…”

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confidence: 55%

“…This has been attributed to the advancement in technology that has enabled information to quickly reflect on the share prices. In a study conducted by Yang, Kwak, Kaizoji and Kim (2008) that analyzed the time series of the Standard and Poor's 500 Index (S & P 500), the Korean Composite Stock Price Index (KOSPI) and the Nikkei 225 Stock Average (NIKKEI), it was observed that, before the year 2000, information used to get by slowly, hence, resulting in the markets being less efficient. However, information flow is currently faster and more even because of the rapid development of communication through high speed internet, mobile technologies, and worldwide broadcasting systems.…”

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confidence: 99%

“…Cognizant of the observation by Yang et al (2008) that, as a result of technology, market efficiency increased significantly from the year 2000 and that by Lim (2009) on using both linear and non-linear tests to determine market efficiency, it is only proper to re-visit the issue for the NSE.…”

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confidence: 99%

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Testing the efficient market hypothesis on the Nairobi Securities Exchange

Njuguna

2016

Investment Management and Financial Innovations

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This paper tests the weak-form of the efficient market hypothesis (EMH) of the Nairobi Securities Exchange (NSE) using daily and weekly index data from the NSE 20 share index over the period, January 2001 to January 2015 and the NSE All Share Index (ASI) from its initiation, in February 2008 to January 2015. To test weak-form efficiency in this market, this study uses the serial correlation test, unit root tests (ADF and Phillips-Perron) and runs test. Results indicate that we cannot accept the EMH for the NSE using the serial correlation test, unit root tests and the runs test. Overall, the Kenyan market is found to not be weak-form efficient.

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confidence: 55%

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confidence: 99%

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Testing the efficient market hypothesis on the Nairobi Securities Exchange

Njuguna

2016

Investment Management and Financial Innovations

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“…The authors in [48] used inter-day data from the Standard & Poor's 500 index to construct causal state models. The authors in [49] constructed causal state models using high-frequency, single minute resolution data from the Standard & Poor's 500 index, the Korean Stock Exchange (KOSPI) and the Nikkei index. Both papers used first-order differencing of either the price or log-price to detrend the time series before binarizing.…”

Section: Macroscale Dynamics Of the Marketmentioning

confidence: 99%

“…Figure 10. The residualsη t computed using first-order differencing for each double-decade period, similar to the methods used in [48,49]. Note that the residuals exhibit strong non-stationarity, even after differencing.…”

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confidence: 99%

Complexity-Regularized Regression for Serially-Correlated Residuals with Applications to Stock Market Data

Darmon

Girvan

2014

Entropy

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A popular approach in the investigation of the short-term behavior of a non-stationary time series is to assume that the time series decomposes additively into a long-term trend and short-term fluctuations. A first step towards investigating the short-term behavior requires estimation of the trend, typically via smoothing in the time domain.We propose a method for time-domain smoothing, called complexity-regularized regression (CRR). This method extends recent work, which infers a regression function that makes residuals from a model "look random". Our approach operationalizes non-randomness in the residuals by applying ideas from computational mechanics, in particular the statistical complexity of the residual process. The method is compared to generalized cross-validation (GCV), a standard approach for inferring regression functions, and shown to outperform GCV when the error terms are serially correlated. Regression under serially-correlated residuals has applications to time series analysis, where the residuals may represent short timescale activity. We apply CRR to a time series drawn from the Dow Jones Industrial Average and examine how both the long-term and short-term behavior of the market have changed over time.

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Application of Thermodynamics Entropy Concept in Financial Markets

Prabakaran

2018

Advances in Panel Data Analysis in Applied Economic Research

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Increasing market efficiency in the stock markets

Cited by 36 publications

References 29 publications

Testing the efficient market hypothesis on the Nairobi Securities Exchange

Testing the efficient market hypothesis on the Nairobi Securities Exchange

Complexity-Regularized Regression for Serially-Correlated Residuals with Applications to Stock Market Data

Application of Thermodynamics Entropy Concept in Financial Markets

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