Emerging Market Equity Prices and Chaos: Evidence from Indonesia and Malaysia

This paper is an attempt to test for nonlinear structure and chaos indicators on the returns of bank stocks listed in Athens Exchange (ATHEX) as well as the indices: ATHEX Composite index, FTSE/ASE 20 and FTSE/ASE mid 40.

Ssrn Electronic Journal, 2006

The chaos theory assumes that the returns dynamics are not normally distributed and more complex approaches have to be used to study these time series. In fact, the Fractal Market Hypothesis assumes that the returns dynamics are not independent of the investors' attitudes and represent the result of the interaction of traders who, frequently, adopt different investment styles.

THE 4TH INNOVATION AND ANALYTICS CONFERENCE & EXHIBITION (IACE 2019)

This project is focused on existing of chaos in stock market price. Stock market price is frequently changing in a particular time period. The pattern of stock market price is hard to predict as it is complex and exhibit nonlinear behavior. Due to insufficient chaotic findings for local company in Malaysia, existence of chaos in stock market price of Gamuda Berhad is determined in this project. There are two tests involved in this project which BDS Test and Close Returns Test are mainly used in this project for examining chaos in the stock market price. As for the result, BDS Test results show the rejection of the null hypothesis which means stock market price of Gamuda Berhad is not independent and identically distributed (IID) and Close Returns Test shows the non-chaotic behavior for the data. Therefore, the stock market price of Gamuda Berhad has non-chaotic nonlinearity behavior.

This research finds evidence of noisy chaotic properties in the returns of four Dow Jones indices, based on three tests of non-linearity and chaos. The study uses an average of 24,815 data points to correctly simulate chaos in financial time-series. The data consists of the Dow Jones Industrial Average (29,229 observations); Dow Jones Transportation Average (29,121 observations); Dow Jones Utility Average (21,150 observations) and the Dow Jones Composite Average (19,906 observations). The a) Brock, Dechert, and Scheinkman (BDS) test indicates that most of the Dow Jones indices are not iid series, except for the filtered residuals from the GARCH of the Dow Jones Utility Average. The b) rescaled range analysis shows that after scrambling the data, all Hurst exponents are above 0.5, and a trend-reinforcing property, which helps in the conclusion of having a chaotic process. Lastly, the c) correlation dimension analysis complements the initial findings and concludes the presence of a high dimensional noisy chaotic structure in the four Dow Jones indices.

ABAC Journal, 2017

Understanding stock market price fluctuations plays an important role in economic policy and in corporate investment and financing strategies. In recent years, Khantavit and others have investigated the proposition that nonlinear processes studied in Chaos theory play an important role in these fluctuations. This study provides a detailed examination of this hypothesis using data from the Stock Exchange of Thailand (SET) from 1975 to 1999. The study finds that the distribution of the daily return on the SET index is nonnormal and leptokurtic. The results of the study also suggest that non-linear processes play a significant role in stock market behavior

2015

Understanding stock market price fluctuations plays an important role in economic policy and in corporate investment and financing strategies. In recent years, Khantavit and others have investigated the proposition that nonlinear processes studied in Chaos theory play an important role in these fluctuations. This study provides a detailed examination of this hypothesis using data from the Stock Exchange of Thailand (SET) from 1975 to 1999. The study finds that the distribution of the daily return on the SET index is non-normal and leptokurtic. The results of the study also suggest that non-linear processes play a significant role in stock market behavior I.

Journal of Economic Surveys, 1988

2011

Financial Time Series often exhibit either chaotic or persistent or mean reversing behaviour. This behaviour could be quantified through Chaotic Exponent which ranges from zero to one. The rescaled range technique developed for hydrology by Hurst is applied in financial time series to estimate chaotic exponents which determines whether financial time series behaviour is purely chaotic white noise or any pattern exists. We have computed Chaotic Exponent coefficient for nine shares traded in Kula Lumpur Stock Exchange, nine popular stock market indices and nine selected exchange rates. As per theory pure random time series which behaves in a chaotic form cannot be forecasted, but somewhat skewed or non-normal financial time series could be forecasted. The forecasts could be used in several financial decisions like pricing of derivatives and very useful in hedging decisions. Our results indicate that the chaotic exponent of the selected financial time series is not consistent. They are...

Brazilian Review of Econometrics, 2001

In this paper we examine certain properties of the Dow Jones and the Nikkey indices, investigating the existence of stochastic and deterministic non linear structures. Using the detrended fluctuation analysis, we construct a local measurement of randomness which identifies some extreme events and their im pact on the randomness of the systems. Our results suggest no evidence of chaos in the data. In fact, GARCH processes explain most of the nonlinear dependence in the Dow Jones daily returns and the estimated Kolmogorov entropy for the Nikkey index diverges, conversely to what one would expect if the data fo llowed a chaotic dynamics. Resurno o artigo investiga algumas propriedades dos indices de a�oes Nikkey e Dow Jones, tais como a existencia de estruturas nao-lineares deterministicas e es tocasticas. Usando 0 metoda "detrended fluctuation analysis" , construimos uma medida local de aleatoriedade que identifica alguns eventos extremos e seus im pactos na aleatoriedade dos sistemas em estudo. Os resultados nao sugerem evidencia de caos nos cl ades. De fato, processes GARCH explicam a maior parte da dependencia nao-linear nes retornos diaries do Dow Jenes enquanto que a entropia de Kolmogorov estimada para ° indice Nikkey diverge, evidenciando assirn urn sistema estocastico para este indice.

Mathematics

After a long transition period, the Central and Eastern European (CEE) capital markets have consolidated their place in the financial systems. However, little is known about the price behavior and efficiency of these markets. In this context, using a battery of tests for nonlinear and chaotic behavior, we look for the presence of nonlinearities and chaos in five CEE stock markets. We document, in general, the presence of nonlinearities and chaos which questions the efficient market hypothesis. However, if all tests highlight a chaotic behavior for the analyzed index returns, there are noteworthy differences between the analyzed stock markets underlined by nonlinearity tests, which question, thus, their level of significance. Moreover, the results of nonlinearity tests partially contrast the previous findings reported in the literature on the same group of stock markets, showing, thus, a change in their recent behavior, compared with the 1990s.

Journal of Macroeconomics, 2006

This short paper is a comment on "Univariate tests for nonlinear structure" by Catherine Kyrtsou and Apostolos Serletis. We summarize their main results and discuss some of their conclusions concerning the role of outliers and noisy chaos. In particular, we include some new simulations to investigate whether economic time series may be characterized by low-dimensional noisy chaos.

The intention of this research is to investigate the aspect of non-linearity and chaotic behaviour of the Cyprus stock market. For this purpose, we use non-linearity and chaos theory. We perform BDS, Hinich-Bispectral tests and compute Lyapunov exponent of the Cyprus General index. The results show that existence of non-linear dependence and chaotic features as the maximum Lyapunov exponent was found to be positive. This study is important because chaos and efficient market hypothesis are mutually exclusive aspects. The efficient-market hypothesis which requires returns to be independent and identically distributed (i.i.d.) cannot be accepted.

This research finds evidence of noisy chaotic properties in the returns of four Dow Jones indices, based on three tests of non-linearity and chaos. The study uses an average of 24,815 data points to correctly simulate chaos in financial time-series. The data consists of the Dow Jones Industrial Average (29,229 observations); Dow Jones Transportation Average (29,121 observations); Dow Jones Utility Average (21,150 observations) and the Dow Jones Composite Average (19,906 observations). The a) Brock, Dechert, and Scheinkman (BDS) test indicates that most of the Dow Jones indices are not iid series, except for the filtered residuals from the GARCH of the Dow Jones Utility Average. The b) rescaled range analysis shows that after scrambling the data, all Hurst exponents are above 0.5, and a trend-reinforcing property, which helps in the conclusion of having a chaotic process. Lastly, the c) correlation dimension analysis complements the initial findings and concludes the presence of a high dimensional noisy chaotic structure in the four Dow Jones indices.

Review of Economic and Business Studies, 2014

It is difficult to distinguish between multiple random shocks and endogenous informational inflow in nonlinear systems which show complex dynamics. For this reason, we run the chaos tests to investigate the presence of chaotic phenomena using: nonlinearity tests, Recurrence Plot (RP) and Recurrence Quantification Analysis (RQA). In this paper, we compute the Hurst Exponent using R/S analysis on Romanian capital market for a time span between 2005 - 2014 daily data. Substantial changes of Hurst Exponent behaviour in the current period compared to the previous one may be seen as structural break points in the series. The goal of this paper is to determine time series chaotic behaviour in order to highlight the efficiency levels of CEE markets. Also, we aim to investigate the changes in drifting dynamical systems, to examine the recurring patterns – the most important features of complex systems and to admire the "simple beauty of the complexity".

Universal Journal of Accounting and Finance, 2021

This study investigates the evidence of market efficiency dynamics and chaotic behavior of the Dhaka Stock Exchange benchmark index (DSEX) over the 2000-2020 period. We employed the newly developed model of mutual informational and global correlation coefficient in addition to the traditional linear and nonlinear techniques. Results suggest there is evidence of serial dependence in the DSEX returns. We attempted the Lyapunov exponent model to evaluate the possibility of chaos and nonlinear dynamics in the market. The results conspicuously represent the existence of chaotic behavior- a nonlinearity-based profitability pattern revealed in the DSEX return series in its short run behavior. By applying two technical trading indicators, we justify the predicting trend of the Bangladesh stock market and conclude that investors active in the Dhaka Stock Exchange can earn abnormal returns. Findings have practical implications for general investors and professional fund managers to exploit the profitable opportunities and reshuffle the investment decisions. Results also convey the message to the regulatory body to initiate the strategies for intervening in the operating mechanisms to reduce the market inefficiency.