A search for long‐range dependence and chaotic structure in Indian stock market

Mishra, R. K.; Sehgal, Sanjay; Bhanumurthy, N. R.

doi:10.1016/j.rfe.2011.04.002

Cited by 44 publications

(17 citation statements)

References 49 publications

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“…Rege and Martín (2011) calculated the Hurst exponent for the Portuguese stock market and concluded that it exhibits both long memory and short memory depending on the scale of the time period used. Mishra et al (2011) used R/S analysis on daily returns from the Indian stock market to reveal strong evidence of persistence or temporal dependencies. Mukherjee et al (2011) found no evidence for long memory in the Indian stock market.…”

Section: Long Memorymentioning

confidence: 99%

The Efficient Market Hypothesis: Empirical Evidence

Sewell¹

2012

IJSP

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The efficient market hypothesis (EMH) has been the central proposition of finance since the early 1970s and is one of the most well-studied hypotheses in all the social sciences, yet, surprisingly, there is still no consensus, even among financial economists, as to whether the EMH holds. Five statistical analyses are conducted in an attempt to explicate such apparently contrary convictions. An analysis of daily, weekly, monthly and annual Dow Jones Industrial Average log returns found that first-order autocorrelation is small but positive for all time periods, with the autocorrelations for daily and weekly returns closest to zero, and thus an efficient market. A standard runs test showed that the hypothesis of independence is strongly rejected for daily returns, but accepted for weekly, monthly and annual returns, whilst the results of a more sophisticated runs test showed that daily, weekly and decreasing returns are the least consistent with an efficient market. Rescaled range analysis was conducted on the same data sets, and there was no significant evidence for the existence of long memory in the returns, a result consistent with market efficiency. Finally, from an analysis of investment newsletters it may be concluded that technical analysisas applied by practitioners-fails to outperform the market. I reconcile the fact that daily stock market log returns pass linear statistical tests of efficiency, yet non-linear forecasting methods can still generate above-average riskadjusted returns, whilst discretionary technical analysts fail to make abnormal returns.

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Section: Long Memorymentioning

confidence: 99%

The Efficient Market Hypothesis: Empirical Evidence

Sewell¹

2012

IJSP

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“…The statistical approach is restricted in determining the underlying dynamics of the assets' price evolution. Some recent studies show the presence of nonlinear dependence and deterministic chaos in the real-world financial time series [23,30]. This leads to the increasing interest in an alternative approach when high-frequency financial time series are obtained from the chaotic finance systems [16,21].…”

Section: Introductionmentioning

confidence: 99%

Mixed-stable approach to the management of the portfolio using high-frequency financial data

Belovas

Sakalauskas²,

Starikovičius³

2017

ITC

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This paper considers the problem of portfolio selection using high-frequency financial time series. Such time series often exhibit the stagnation effect when the assets' returns are not changing. This effect causes a lot of unusual difficulties in the analysis and modelling of such series. In classical statistics, when the distributional law has two first moments, i.e. mean and variance, the relationship between the two random variables is described by the covariance or correlation. However, if the financial data follow the stable law, and empirical studies often support this assumption, covariance and especially correlation often cannot be calculated. In this work, alternative relation measures are applied to deal with the portfolio selection problem using the mixed-stable modelling. The modelling is applied to the high-frequency financial time series obtained from the German DAX index intra-daily data. The performance of the mixed-stable model is compared with alternative approaches. The portfolio selection problem is formulated as the optimization problem, with covariances replaced by the generalized power-correlations. The results of the portfolio selection strategy without the relationship coefficients matrix are also presented.

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“…providing weak evidences that stock price series behave similar to a chaotic process and the existence of long term dependences can be confirmed within stock price series [8,22,40,42]. However, the existing statistical tests cannot accurately infer whether a series is indeed chaotic or random.…”

Section: Empirical Analysismentioning

confidence: 99%

Time series analysis of S&P 500 index: A horizontal visibility graph approach

Vamvakaris

Pantelous

Zuev

2018

Physica A: Statistical Mechanics and its Applications

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The behavior of stock prices has been studied extensively throughout the last century, and contradictory results have been reported in the corresponding literature. In this paper, we provide another perspective using a network theoretical approach to investigate how crises affected the behavior of US stock prices. We analyze high frequency data from S&P500 via the Horizontal Visibility Graph method, and find that all major crises that took place in the last twenty years, worldwide, affected significantly the behavior of the price-index. Nevertheless, we observe that each of those crises impacted the index in a different way or with a different magnitude. Interestingly, our results suggest that the predictability of the price-index series increases during the periods of crises.

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A search for long‐range dependence and chaotic structure in Indian stock market

Cited by 44 publications

References 49 publications

The Efficient Market Hypothesis: Empirical Evidence

The Efficient Market Hypothesis: Empirical Evidence

Mixed-stable approach to the management of the portfolio using high-frequency financial data

Time series analysis of S&P 500 index: A horizontal visibility graph approach

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