This paper advocates a reverse from of event studies that is data-dependent to determine endogeneously the events that trigger non-linear market behavior. Using the Malaysian stock market as our case study, coupled with the ‘windowing' approach proposed by Hinich and Patterson (1995), the present study is able to identify major political and economic events that contributed to the short bursts of non-linear behavior. The present framework can be extended to individual firm to examine the adjustment of its stock price to firm-specific events, which will provide deeper insight into issues on corporate finance.

Kian-Ping Lim

Melvin J. Hinich

English

Research Papers in Economics

Non−linear Market Behavior: Events Detection in the
Malaysian Stock Market 
Kian−Ping Lim Melvin J. Hinich
Labuan School of International Business and Finance,
Universiti Malaysia Sabah
Applied Research Laboratories, University of Texas at
Austin
Abstract
This paper advocates a reverse from of event studies that is data−dependent to determine
endogeneously the events that trigger non−linear market behavior. Using the Malaysian stock
market as our case study, coupled with the ‘windowing’ approach proposed by Hinich and
Patterson (1995), the present study is able to identify major political and economic events
that contributed to the short bursts of non−linear behavior. The present framework can be
extended to individual firm to examine the adjustment of its stock price to firm−specific
events, which will provide deeper insight into issues on corporate finance.
Citation: Lim, Kian−Ping and Melvin J. Hinich, (2005) "Non−linear Market Behavior: Events Detection in the Malaysian Stock
Market." Economics Bulletin, Vol. 7, No. 6 pp. 1−5
Submitted: May 12, 2005.  Accepted: July 22, 2005.
URL: http://www.economicsbulletin.com/2005/volume7/EB−05G10005A.pdf 
  1
1.  Introduction 
 
Over the past three decades, there has been growing interest among researchers in exploring 
the non-linear behavior of financial time series data. This line of research inquiry has 
produced encouraging results, with more and more empirical evidence emerged to suggest 
that non-linearity is a universal phenomenon (for a review of the literature, refer to Lim and 
Hinich, 2005). Though evidence of non-linearity abounds, Hinich and Patterson (1995) 
conjectured that the behavior of the non-linear dependency structures is at best episodic in 
nature. Their conjecture received wide empirical support across different financial markets. 
Among them are Brooks and Hinich (1998) on 10 major foreign exchange rates, Ammermann 
and Patterson (2003) on 6 major stock market indices and 247 individual stocks traded on the 
Taiwan Stock Exchange, Lim et al. (2003) on 4 Southeast Asian foreign exchange rates, Lim 
and Hinich (2005) on 13 Asian stock market indices and Romero-Meza et al. (2005) on 7 
Latin American stock market indices. In all these aforementioned studies, the detected non-
linear behavior is episodic in that there were long periods of pure noise process, only to be 
interspersed with relatively few brief episodes of highly significant non-linearity. In a parallel 
literature, Ramsey and Zhang (1997) found that activities in financial markets are relatively 
short-lived and surrounded by longer periods of apparent randomness.  
 
This paper attempts to shed further light into the widely observed episodic features of 
financial time series. In this context, those long periods of randomness in asset price series 
are consistent with the weak form efficient markets hypothesis, resulting from instantaneous 
market response to the arrival of new information. However, when surprises or unexpected 
shocks hit the market, the adjustment process generally generates a pattern of non-linear price 
movements relative to previous movements since investors are cautious and unsure of how to 
react, resulted in a delay or slow response (Antoniou  et al., 1997;  Brooks  et al., 2000). 
Hence, as highlighted by Ramsey and Zhang (1997: 370), one can view the dominant market 
reaction to isolated shocks as a sequence of ‘short bursts of intense activity that are 
represented by narrow bands of frequencies’. In fact, the authors are optimistic that tying 
those localized frequency bursts to news events might be enlightening, which is the main 
objective of the present study. 
 
To accomplish that, this paper utilizes the ‘windowing’ approach proposed by Hinich and 
Patterson (1995) to detect major political and economic events that have contributed to the 
short burst of non-linear dependencies in the Malaysian stock market, our case study. In those 
event studies pioneered by Fama et al. (1969) that are now an important part of corporate 
finance, researchers hypothesize about an event a priori and gauge the resulting market 
response as reflected in stock prices. The present study, in contrast, advocates a reverse from 
of event studies that is data-dependent to determine endogeneously those events that trigger 
non-linear market reactions. This can be achieved in the ‘windowing’ approach of Hinich and 
Patterson (1995) since the procedure breaks the full sample period into shorter windows of 
time to allow closer examination of the precise time periods during which non-linear 
dependencies are occurring, detected via the bicorrelation test statistic (denoted as H 
statistic). 
 
 
 
 
  
  2
2.  Methodology 
 
This section provides a brief description of the ‘windowing’ approach and the bicorrelation 
test statistic (denoted as H statistic). Let the sequence {y(t)} denote the sampled data process, 
where the time unit, t, is an integer. The test procedure employs non-overlapped data 
window, thus if n is the window length, then k-th window is {y(tk), y(tk+1),….., y(tk+n-1)}. 
The next non-overlapped window is {y(tk+1), y(tk+1+1),….. y(tk+1+n-1)}, where tk+1 = tk+n. The 
null hypothesis for each window is that y{t} are realizations of a stationary pure noise process 
that has zero bicovariance. The alternative hypothesis is that the process in the window is 
random with some non-zero bicorrelations Cyyy(r, s) = E[y(t)y(t+r)y(t+s)] in the set 0 < r < s < 
L, where L is the number of lags. 
 
We state without proof and derivation that the H statistic
1 is defined as:  
 
1
2
21
(,)
Ls
sr
HG r s
−
==
=∑∑  ~    χ
2
 (L-1) (L/2)                                   (1)       
 
where 
1
2 (,) ( ) (,) ZZZ Grs n s C rs =− ,  and 
1
1
( ,) ( ) ( )( )( )
ns
ZZZ
t
Cr sn s Z t Z t r Z t s
−
−
=
= −+ + ∑  for   0 < r 
< s. Z(t) are the standardized observations, obtained by subtracting the sample mean of the 
window and dividing by its standard deviation. The number of lags L is specified as L= n
b 
with 0 < b < 0.5, where b is a parameter under the choice of the user. Based on the results of 
Monte Carlo simulations, Hinich and Patterson (1995) recommended the use of b=0.4 in 
order to maximize the power of the test while ensuring a valid approximation to the 
asymptotic theory even when n is small. In this test procedure, a window is significant if the 
H statistic rejects the null of pure noise at the specified threshold level.  
 
 
 
3.  The Data 
 
In this study, the reaction of the Malaysian stock market
2 as a whole towards major events is 
proxied by the behavior of the Kuala Lumpur Composite Index (KLCI) returns series. In 
particular, daily closing prices for this index collected from Datastream are transformed into 
a series of continuously compounded percentage returns, using the following relationship: 
 
rt = 100* ln(pt/pt-1)                                       (2) 
 
where pt is the closing price of the index on day t, and  pt-1 the price on the previous trading 
day.  
                                                 
1 Interested readers can refer Hinich and Patterson (1995) and Hinich (1996) for a full theoretical derivation of 
the H statistic and some Monte Carlo evidence on the good small sample properties of the test. The analysis in 
this paper is conducted using the T23 program, which is available from the personal website of Melvin J. Hinich 
at http://www.gov.utexas.edu/hinich/. Instead of reporting the H statistic as chi-square variates, this program 
transforms the computed statistic to a p-value based on the appropriate chi square cumulative distribution value. 
2 The Malaysian stock market, formerly known as Kuala Lumpur Stock Exchange was officially renamed as 
Bursa Malaysia on 20 April 2004.   
  3
On the other hand, annual reports of Securities Commission
3 from years 1995 through 2004 
are our important historical sources with good documentation of the major events affecting 
the performance of the KLCI over the years. In tandem with the availability of annual reports, 
the sample period for the KLCI is limited to 1 January 1995 through 31 December 2004. 
 
To apply the bicorrelation test in conjunction with the windowed testing procedure, all the 
returns series are split into a set of non-overlapping windows of 25 observations in length 
(approximately 5 trading weeks), so as to reduce the loss of observations at end sample. 
According to Brooks and Hinich (1998), the window length should be sufficiently long to 
provide adequate statistical power and yet short enough for the test to be able to detect the 
events that trigger the nonlinear behavior. In fact, it was found that the choice of the window 
length does not alter much the results of the significant H statistics in this study.  
 
 
 
4.  Empirical Results 
 
Before proceeding with the bicorrelation test, we first remove linear dependencies from the 
returns series by fitting an autoregressive model. The bicorrelation test is then applied to the 
residuals of the fitted AR(p) model, so that a rejection of the null of pure noise at the 
specified threshold level is due to significant non-linearity. Table 1 presents the results of the 
bicorrelation test using the ‘windowing’ approach for the KLCI returns series. Clearly, the 
null of pure noise is rejected by the H statistic in only 7 windows, which is equivalent to 
6.73%.
4 Consistent with earlier studies, the KLCI returns series are characterized by 
relatively few brief episodes of highly significant non-linearity surrounded by long periods of 
pure noise. The interesting question to be answered here is: what events trigger these short 
bursts of non-linear market behavior? The last column of Table 1 identifies those key events 
that represent shocks to the Malaysian stock market in which most investors were slow to 
response as they took time to work out the impact of the events. Uninformed traders were 
also delaying their response to see how informed market participants behave because they do 
not have the resources to fully analyze the information (Antoniou et al., 1997). Some of the 
contributing events in this paper are consistent with the findings of Lai et al. (2001), who 
conducted a survey on 77 institutional investors to gauge their responses and reactions to a 
list of external factors and events in the questionnaire. 
 
 
 
 
 
 
 
 
                                                 
3 The Securities Commission established on 1 March 1993 under the Securities Commission Act 1993, is a self-
funding statutory body in Malaysia with investigative and enforcement powers. Apart from discharging its 
regulatory functions, the Securities Commission is also obliged by statute to encourage and promote the 
development of the securities and futures markets in Malaysia (Further information on Securities Commission 
can be obtained from her official website at http://www.sc.com.my/). 
4 In this study, the threshold level was set at 0.01. The level of significance is the bootstrapped thresholds that 
correspond to 0.01.  
  4
Table 1 
‘Windowing’ Test Results for KLCI Returns Series 
 
 
Fitted 
AR(p) 
model 
 
Total  
number 
of windows 
 
Significant  
H windows 
 
Dates of 
significant H 
windows 
 
 
Key Events 
 
AR(1) 
 
 
104 
 
7 
(6.73%) 
 
7/2/95-13/3/95 
 
14/3/95-17/4/95 
 
 
27/2/96-1/4/96 
 
 
 
 
25/8/98-28/9/98 
 
 
 
 
 
 
 
 
 
 
 
 
12/1/99-15/2/99 
 
 
 
10/8/99-9/9/99 
 
 
 
 
 
 
 
 
27/3/01-30/4//01 
 
Baring debacle on 27/2/95. 
 
Announcement of the general 
election on 5/4/95. 
 
A 3% fall on Dow on 8/3/96; 
Increased tension between 
China and Taiwan on 
23/3/96. 
 
KLSE announced additional 
measures to enhance 
transparency on 31/8/98, 
including no longer 
recognizing deals involving 
KLSE-listed securities traded 
overseas; On 1/9/98, the 
government introduced wide-
ranging foreign-exchange 
controls, including the 
pegging of 3.80 ringgit to 1 
US dollar. 
 
Graduated repatriation levy 
on foreign portfolio profits 
was introduced on 15/2/99. 
 
Announcement of the 
reinstatement of Malaysian 
stocks into the MSCI 
benchmark indices effective 
Feb 2000 was made on 
12/8/99; Lifting of the 
moratorium on foreign 
portfolio capital on 1/9/1999.  
 
Announcement of a drastic 
fall in net international 
reserves was made on 9/4/01; 
Tabling of the Eighth 
Malaysia Plan on 24/4/01. 
 
 
 
  
  5
5.  Conclusion 
 
This paper advocates a reverse from of event studies that is data-dependent to determine 
endogeneously the events that trigger non-linear market behavior. Using the Malaysian stock 
market as our case study, coupled with the ‘windowing’ approach proposed by Hinich and 
Patterson (1995), the present study found that the KLCI returns series are characterized by 
long periods of pure noise, only to be interspersed with relatively few brief episodes of highly 
significant non-linearity. Major political and economic events that contributed to those short 
bursts of non-linear behavior are identified. The present framework can be extended to 
individual firm to examine the adjustment of its stock price to firm-specific events, which 
will provide deeper insight into issues on corporate finance.  
 
 
 
References 
 
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universality of nonlinear serial dependencies: evidence from world stock indices and the 
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Antoniou, A., N. Ergul and P. Holmes (1997) “Market efficiency, thin trading and non-linear 
behaviour: evidence from an emerging market” European Financial Management 3(2), 
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Brooks, C. and M.J. Hinich (1998) “Episodic nonstationarity in exchange rates” Applied 
Economics Letters 5, 719-722. 
Brooks, C., M.J. Hinich and R. Molyneux (2000) “Episodic nonlinear event detection: 
political epochs in exchange rates” in Political complexity: political epochs in exchange 
rates by D. Richards, Ed., Michigan University Press: Ann Arbor, MI, 83-98.  
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Hinich, M.J. (1996) “Testing for dependence in the input to a linear time series model” 
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Hinich, M.J. and D.M. Patterson (1995) “Detecting epochs of transient dependence in white 
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of Psychology and Financial Markets 2(4), 210-215. 
Lim, K.P. and M.J. Hinich (2005) “Cross-temporal universality of non-linear dependencies in 
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Lim, K.P., M.J. Hinich and V.K.S. Liew (2003) “Episodic non-linearity and non-stationarity 
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Romero-Meza, R., C.A. Bonilla and M.J. Hinich (2005) “Episodic nonlinearity in Latin 
American stock market indices” Applied Economics Letters, forthcoming. 
 
 

Are Malaysian investors rational?”

Cross-temporal universality of non-linear dependencies in Asian stock markets”

Detecting epochs of transient dependence in white noise”

Episodic non-linearity and non-stationarity in ASEAN exchange rates returns series”

Episodic nonlinear event detection: political epochs in exchange rates” in Political complexity: political epochs in exchange rates by

Episodic nonlinearity in Latin American stock market indices”

Episodic nonstationarity in exchange rates”

Market efficiency, thin trading and non-linear behaviour: evidence from an emerging market”

Testing for dependence in the input to a linear time series model”

The adjustment of stock prices to new information”

The analysis of foreign exchange data using waveform dictionaries”

The cross-sectional and cross-temporal universality of nonlinear serial dependencies: evidence from world stock indices and the Taiwan Stock Exchange”

Non-linear Market Behavior: Events Detection in the Malaysian Stock Market

http://www.accessecon.com/pubs/EB/2005/Volume7/EB-05G10005A.pdf

Non-linear Market Behavior: Events Detection in the Malaysian Stock Market

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