Proceedings of 11th International Business and Social Science Research Conference
8 - 9 January, 2015, Crowne Plaza Hotel, Dubai, UAE. ISBN: 978-1-922069-70-2
Stock Market Reaction to Surprised Removal of a Central Bank
Governor: Evidence from Nigeria
Osuala, A.E1, Nwansi, G.U2, Osuji J.I and Njoku, B.O1
The study examined the signalling effect of the unexpected removal of Mallam Sanusi Lamido
Sanusi, the erstwhile governor of Central bank of Nigeria on the Nigerian stock market. Data for
nd
the study was collected from the Nigerian Stock Exchange daily official list from January 2 to
February 28, 2014 covering both the estimation period and the event window. The standard
event study method was used for analyzing data for the study while the market return approach
was used for characterizing the normal returns. The results of the study showed that the sudden
ouster of Sanusi L. Sanusi as the governor of CBN had statistically significant negative impact on
the stock market as negative abnormal returns were recorded in the market following the
unexpected ouster from the event day up to four days after the event. The study therefore
recommends that care should be taken in appointing central bank governors to avoid
unexpected termination of their appointments as such action make huge negative publicity and
send negative signals to participants in the market, be they indigenous or external stock market
investors.
Keywords: Stock Market, Event Study, Market Return, Cumulative Abnormal Returns,
Sudden Ouster
1. Introduction
It is a common belief that the stock market acts a barometer for measuring the state of health
and the direction of economic activities of any given economy. In today’s information oriented
world, news travel very fast and hence any event of economic interest is quickly incorporated
into stock prices. Thus, stock prices may react positively, negatively or indifferently depending
on how the market perceives the event (Osuala, et al, 2013) and the prevailing economic
environment at the time of the event.
The appointment of a new chief executive officer (CEO) of a Central Bank, or sudden ouster of
an incumbent one, often makes headlines in the financial press and usually, the reports of the
press attribute the subsequent reaction in the financial markets to the appointment or the
ouster. For example, Kuttner and Posen (2007) observed that the appointment of Ben S.
Bernanke as the chairman of the Federal Reserve Bank of the United States of America on
October 24, 2005 stock prices taking an upward leap while bond prices had a nose dive.
Arguing that the financial markets care about who chairs the central Bank and using a sample
of 15 advanced economies, they reported that changing a central bank governor conveys
important signals about the future course of monetary policy, thereby affecting exchange rates
and financial market returns.
______________________________________________________________________
1
1
Osuala, A.E (PhD), Department of Banking and Finance, College of Management Sciences, Michael Okpara
University of Agriculture, Umudike, Abia State, Nigeria. Email: osuala.alex@mouau.edu.ng;
TeL:+2348030606878.
2 2
Nwansi, G.U , Department of Banking and Finance, Federal Polytechnic, Nekede, Owerri, Imo state, Nigeria.
Email: nwansigraceuloego@gmail.com@yahoo.co.uk; TeL: +2348032782854.
1
Osuji J.I, Department of Banking and Finance, College of Management Sciences, Michael Okpara University of
Agriculture, Umudike, Abia State, Nigeria.
1 1
Njoku, B.O , Department of Banking and Finance, College of Management Sciences, Michael Okpara University
of Agriculture, Umudike, Abia State, Nigeria.
Proceedings of 11th International Business and Social Science Research Conference
8 - 9 January, 2015, Crowne Plaza Hotel, Dubai, UAE. ISBN: 978-1-922069-70-2
Ennser-Jedenastik (2013) observed that the appointments and the turnover of central bank governors
matter and have become a prominent area of research as there is ample evidence to show that scholarly
attention has increasingly been devoted to this area of research especially in the recent times. While
agreeing with Ennser that there is a plethora of studies or increasing studies so to speak on the
appointments of central bankers and its consequences on the financial markets, there is however a
dearth of empirical research on the impact of surprised removal of the monetary policy maker on the
financial markets.
On February 20, 2014, the president of the Federal Republic of Nigeria, Dr. Goodluck E. Jonathan, in a
fiat, announced the “unexpected suspension” of the CBN governor, Mallam Sanusi Lamido Sanusi
(SLS). He was accused of financial recklessness and embezzlement. This event becomes one of interest
and worthy of empirical investigation for the following reasons:
 This is the first time the governor of CBN would be sacked suddenly. Rewane(2014),
commenting on the surprised ouster stated, “it is a very unusual development, there is no
precedent of it”.
 SLS was adjudged one of the most successful and pragmatic governors the CBN ever had.
According to Vanguard (2014), “governments rarely fire their central bank governors. No matter
the reasons, controversies would trail the decision, more so if it involves Mallam Sanusi Lamido
Sanusi, a man who hugs the headlines”.
 The ouster has already drawn the irk of some international investors on the Nigerian financial
markets. Samir Gadio, an emerging market strategist at the Standard Bank Group Ltd in London
said “this is a disruptive move which indicates that the CBN has de facto lost much of its
independence”. He stated further that “foreign investors are likely to be active sellers of
Nigerian assets in coming days subject to market liquidity constraints”.
 The event took place at a time the Nigerian economy was seen or regarded as pulling back to
life and good health, and also near the time Sanusi would have retired naturally, or normally.
 The impact of this event on the economy definitely is expected to have some important policy
implication, even if not for the present administration, certainly for the incoming ones; and it is
likely to have a contagion effect across the African continent because of the strategic position
of Nigeria in Africa.
Hence, the primary objective of this is to examine the signalling effect of the unexpected suspension
on the Nigerian stock market, of Mallam SLS, the erstwhile governor of CBN. The hypothesis to be
tested in the study in its null form is stated thus: the sudden removal of CBN governor does not have
significant information effect on the Nigerian Stock Market.
The study appears unique in the sense that, as earlier mentioned, there is no study known to the
authors to date that has empirically investigated the information effect of the sudden removal of
Mallam SLS as governor of CBN.
The paper is organized into five sections. Coming immediately after the introduction is section two
which offers a short sketch of the literature related to appointment and removals of central bank
chief executives. Section three deals with the research methodology and analytical techniques used
while section four is concerned with analysis of data and interpretation of results. Section five
covers the summary of findings, conclusion and recommendation.
Proceedings of 11th International Business and Social Science Research Conference
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2. Literature Review
2.1 Introduction
Quoting New York Times (2001), Moser and Dreher (2007) said “After arguing behind the scene with
his central bank governor over the direction of interest rates, Prime minister Thaksin Shinawatra of
Thailand dismissed the banker. This brought a sharp reaction of financial markets”. The markets
reaction to the ouster of the central banker as depicted by Moser and Dreher in the above quote is in
consonance with the findings of Kuttner and Posen (2007) who, drawing on a sample of industrialized
countries concluded that financial markets do care about who is at the helm of affairs in monetary
policy decision-making. But it should be noted however that the duo confined their study to advanced
markets where central bankers’ turnover are mostly predictable, and there is no “surprise” element in
the removal of such central bank governors.
On the contrary, Santiso (2003) observed that the appointments of central bankers are among the most
sensitive decisions for emerging market governments because these policymakers play a crucial role in
communicating with international markets. According to Moser and Dreher (2007), change in the
governor of the central bank especially in emerging market economies is of great importance to the
stock market operators because such changes may convey very essential signals about future monetary
policy.
Following the suspension of the erstwhile CBN governor, Mallam SLS on the basis of the report of The
Financial Reporting Council of Nigeria (FRCN) alleging financial infractions and acts of financial
recklessness against him, the financial markets reacted promptly: the naira fell more than one percent
against the dollar while most of the stocks in the banking sector also dipped. Consequently, the acting
head of the CBN, Sarah Alade, had to reassure operators at the Nigeria Capital market on Monday,
February 24, 2014, that the removal of the former governor would not change policy trust of the CBN.
But how true is that assurance considering the fact that when CEOs are ousted from office, their
successors, more often than not, would want to shift away from their policy trust, projects and
programmes to prove their allegiance to the power that appointed them.
Adducing reasons as to why a central bank chief executive officer may be given the booth, Adolph
(2013:280-303) opined that shifts in government ideology increase (or exacerbate) the risk of monetary
policy maker ouster. Said he, the strongest determinant of tenures of central bankers is change in the
partisan composition of governments. This assertion may be a truism because if the president that
appointed SLS to the office of a governor had remained (i.e., president Shehu Umaru Yaradua), perhaps
he, SLS would have served out his term. Kuttner and Posen (2007) theorizing on the impact of partisan
ties on the survival of the apex bank governors predicted that central bank governors affiliated with a
party in the executive will have longer tenures whereas those affiliated with the opposition will be
removed more quickly. This must have been a very important issue in the removal of SLS since he was
seen as playing around the corridors of APC, a major opposition party to the ruling PDP party.
Most of the earlier studies on the reaction of the financial markets to changes in central bank governors
focussed mainly on the impact of new appointments. Only very few studies investigated the impact of
the removal of the monetary policy makers on the financial markets. There are yet fewer known studies
that empirically evaluated the impact of the “surprised ouster” of chief executive officers of the apex
bank, or corporate firms, for that matter. One of such related and important study is the one by Osuala,
Proceedings of 11th International Business and Social Science Research Conference
8 - 9 January, 2015, Crowne Plaza Hotel, Dubai, UAE. ISBN: 978-1-922069-70-2
et al. (2013). They examined the information content of the sudden ouster of corporate chief executives
of five Nigerian commercial banks using event study methodology and concluded that the sudden
removal did not have significant information effect due to the prompt injection of intervention fund by
the CBN into the affected banks.
Another important and closely related work is that credited to Kuttner and Posen (2007) which assessed
the effects of central bank scheduled and unscheduled appointments involving a sample of 15 countries,
using also event study methodology. Their findings revealed a significant reaction of exchange rates
and bond yields to unscheduled appointments. They observed that the Federal Reserve Bank chairman’s
appointment stand out in terms of their unusually pronounced effects on the financial markets.
2.2 Theoretical Framework
The major theoretical underpinning for the signalling or information effect of surprise events in the
capital market is the efficient market hypothesis developed by Fama (1965). In the past three decades,
the efficient markets hypothesis (EMH) developed by Fama (1965) has been the most venerable tenet of
financial economics and a staple of academic analysis (Erzurumlu, 2011). The EMH suggests that
security prices reflect all currently available information in the market, granted that stock market
investors are rational. Under the EMH, a natural metric for assessing the information content of a new
event in the market is the fraction of the variance of stock returns that is associated with the
announcement of such events (Aga and Kocaman, 2008). Osuala (2007) opined that stock price
performance around the announcement of sudden removal of CEOs is a measure of the information
conveyed by the change.
2.3 Empirical Methods of Evaluating Information Effect of Surprise Events
The change in selected stock prices following the announcement of a surprise or unexpected event in
the stock market constitutes the information effect of the action. Most of the earlier empirical studies on
information effect adopted different evaluation techniques. But one method that has received wide
usage in most of these studies is the event study methodology. According to Henderson (1989), the
event study is one of the most popular statistical designs in finance. He identified three types of event
studies, namely, market efficiency studies, information usefulness studies and metric explanation
studies.
Henderson stated that market efficiency studies assess how quickly and correctly the market reacts to a
particular type of new information, while information usefulness studies assess the degree to which
company returns react to the release a particular bit of news. In metric explanation studies, the metrics
(extra returns) is used as dependent variable in cross-sectional regressions to explain the source of the
extra returns.
Sorokina, Booth and Thornton (2013) observed that event study is an important tool in the financial
economist’s toolkit that can be traced back to the 1930s and which is continuously evolving. They
opined that a commonly used estimation technique in event studies is Ordinary Least Squares (OLS)
regression method which many authors have observed that inferences drawn from the regressions are
Proceedings of 11th International Business and Social Science Research Conference
8 - 9 January, 2015, Crowne Plaza Hotel, Dubai, UAE. ISBN: 978-1-922069-70-2
sensitive to the presence of outliers and high leverage data points. They therefore suggested the use of
Robust Regression method which combines weighted regression (also called M-estimation) method
credited to Huber (1973) and MM-estimation approach due to Yohai (1987), rather than OLS method.
Kramer (2001) stated that there are two broad goals in conducting financial event studies: testing for a
significant information effect in stock returns at the time of the event announcement and identifying
factors which determine the information effect. The first of these- testing for an information effect- is
however the focus of this study.
Using a dataset of appointments announcements of central bank governors from 15 countries, Kuttner
and Posen (2007) conducted an event study to determine the information effect on the financial markets
of such appointments and found that the Federal Reserve Chairman appointments stood out in terms of
their unusual pronounced effects on financial market. Ennser-Jedenastik (2013) equally used the event
study method to investigate the impact of party politics on the survival of central bank governors using
a sample of 195 governors. He concluded that affiliation with a party represented in the executive (the
Presidency) has a large and significant positive effect on governor’s survival while affiliation with an
opposition party only increases governor’s hazards during the first four years of their term. This finding
may significantly be true in Sanusi Lamido Sanusi’s ouster.
3.
Research Methodology
3.0 Introduction
This section deals with the methods and procedures employed in the collection and analysis of the data
needed for the study. It also discusses the source of data and the period over which it was collected.
3.1 Source, Type and Method of Data Collection
The dataset for the study was collected from the Nigerian Stock Exchange (NSE). Daily official list for
all commercial banks listed on the exchange was collected for the period covering January 2, 2014 to
February 28, 2014. NSE all-share index was equally collected for all the trading days over the same
period. Since the study aimed at evaluating the information effect of the ouster on Bank stocks, the data
collected was limited to banks whose stocks traded on the NSE during the study period. On the basis of
the above criterion, 15 commercial banks were selected. Two commercial banks out of the 15 were
however dropped out because their stock prices remained static throughout the two months period
leaving us with only 13 banks.
3.2 Data Estimation Technique
The standard event study method was used for analyzing data for the study. An event study measures
the impact of new information on the return of financial assets.
The basic steps in an event study are as follow:
1. Identification of the event date. This is the date on which the event occurred, that is, when the
market first learnt of the event.
Proceedings of 11th International Business and Social Science Research Conference
8 - 9 January, 2015, Crowne Plaza Hotel, Dubai, UAE. ISBN: 978-1-922069-70-2
2. Definition of the event window. This refers to the number of trading days preceding and
following the event date that are considered necessary to capture both the leakage, if any, and
the time needed for the data to effectively reach the marketplace.
3. Definition of the estimation period. The estimation period is the period of time over which no
event has occurred. It is used to establish how the returns should behave normally (i.e., in the
absence of the event).
4. Selection of the sample of firms. This entails definition of a criterion to screen the firms.
5. Calculation of “normal” returns (the returns that would have occurred in the absence of the
event). There are several approaches for characterizing the normal returns, namely, the mean
return, the market return, proxy portfolio return and risk-adjusted return. Each of the methods
has its own pros and cons.
6. Calculation of abnormal returns (that is the excess return arising from the occurrence of the
event of interest. To calculate the abnormal returns (ARs) you take the actual return for the
sample firms for each day in the event window and you subtract the estimated normal return for
each day in the event window. When the abnormal returns are cumulated, what emerges is the
cumulative abnormal returns (CARs).
7. Determination of the statistical significance of ARs and CARs. By determining the statistical
significance of the AR and CARs, you are then determining the significance of the event, which
is the punch line of an event study.
In the present study, the event date is 20th day of February, 2014, the day on which SLS was
unexpectedly removed. The event window is 8 days, defined as -1, to +6, (i.e., as -1, 0 +1, +2, +3, +4,
+5, +6. This is the number of trading days preceding and following the event date that are considered
necessary to capture both the leakage and the time needed for the data to effectively reach the
marketplace. The estimation period is defined as -36 to -20, effectively avoiding an overlap. The idea
behind making sure that the event window and the estimation window do not overlap is to ensure to
have an unbiased estimate of how the firm’s stock prices would have behaved normally without the
event’s occurrence; otherwise the normal return is contaminated by the event. Among the 13
commercial banks listed on the NSE as at the time of the study, only 10 were selected as two of the
other three banks’ prices were rather static both in the prevent and event windows and the third had a
wide swing from the rest in terms of its daily prices. The market return approach was used for
characterizing the normal returns, and it is given as:
Rit  i  i Rmt  eit
where:
Rit :
is realized rate of return of the i-th security during period t,
Rmt : is rate of return on the equally-weighted market index(m) at period t,
eit :
is a random variable that is expected to have a mean value of zero.
αi , βi : are the intercept and slope parameters for the firm i, respectively.
[1]
Proceedings of 11th International Business and Social Science Research Conference
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The abnormal return (AR) for the i-th common stock on day t, is given by:
ARit  Rit -(ˆi  ˆi Rmt )
-----
[2]
where (ˆi  ˆi Rmt ) is the expected rate of return [E(R)]; the coefficients ˆi and ˆi are Ordinary
^
Least Squares estimates of αi and βi, estimated from a regression of daily security returns on daily
market returns from t = –36 to t = -20 (where t = 0 is the event date and t = –36 to t = -20 is the
estimation window). It should be noted however, that in an efficient market (where investors have
rational or unbiased expectations), E(ARit) = 0, where E(ARit) is expected abnormal return.
The individual security’s abnormal returns, ARit, is aggregated and averaged across all the observations
as shown below:
N
ARit  
i 1
ARit
N
---------------------
[3]
where N is the number of events in the sample. The reason for averaging across firms is that stock
returns are noisy, but the noise tends to cancel out when averaged across a large number of firms.
Therefore the more firms in the sample the better the ability to distinguish the effect of an event. The
cumulative abnormal returns (CARs) are then obtained by aggregating the individual abnormal returns
(ARs). Finally, the average abnormal returns and the cumulative abnormal returns are the tested for
their statistical significance.
Before the statistical significance of the abnormal returns can be determined, the standard deviation of
the abnormal returns in the estimation period need first be computed. To do this, the following steps need
to be followed:
a) For each time period t in the ESTIMATION period, we calculate the average abnormal return over
all securities. For example, as the estimation period in this study is 16 days and there are 13 companies in
the sample, after averaging over all companies in the sample there will be 16 average abnormal returns
(one for each day). Algebraically:
---------------------------where
[4]
is the average abnormal return across all companies at time t in the estimation period.
Proceedings of 11th International Business and Social Science Research Conference
8 - 9 January, 2015, Crowne Plaza Hotel, Dubai, UAE. ISBN: 978-1-922069-70-2
b) The average abnormal return over all companies for the whole estimation period must be calculated.
To do this, we calculate the average of the average abnormal returns in the estimation period,
.
Algebraically:
T
ARt
i 1
T
AR  
where
---------------------------------------------
[5]
is the average abnormal return over all companies in the control period and
is the
average abnormal return over all securities in period t.
and
are used in the calculation of the standard deviation of the abnormal returns. Abnormal
returns from the event period are not used so that the standard deviation estimate is protected from
being biased by the uncharacteristic movements in share price returns during this period.
Given our estimates of
and
we then calculate an estimate of the expected abnormal return
standard deviation.
c) The standard deviation of the abnormal returns in the estimation period is:
 ( ARt ) 
 ( ARt  AR)2
T 1
---------------------------------
[6]
d) We then calculate the average abnormal return over all securities in each period in the event period.
This is the same process as carried out in (a) but with event period abnormal returns instead of
estimation period abnormal returns:
------------------------------------
[7]
e) The final step is to test each average abnormal return in the event period for significance. This is
simply done by dividing each average abnormal return in the event period by the standard deviation
estimate calculated in c) above. If we assume that the average abnormal returns over all companies are
independent, identically distributed and come from a normal distribution, the test statistic is distributed
Proceedings of 11th International Business and Social Science Research Conference
8 - 9 January, 2015, Crowne Plaza Hotel, Dubai, UAE. ISBN: 978-1-922069-70-2
as a Student’s t with degrees of freedom equal to (T-1). Since we have averaged the abnormal returns,
data problems such as cross sectional correlation have been taken into account:
----------------------------------------
[8]
For cumulative average abnormal returns, the t-test formula is:
CARt (t  Stat ) 
CARt
 i, pre Nt
---------------------------------
[9]
where Nt = the absolute value of event day, t, plus 1 (e.g. for event day –10, the absolute value is 10 and
Nt = 11).
The procedure for calculating the significance of abnormal returns is the same regardless of the model
one chooses to generate abnormal returns.
4.
Presentation and Discussion of Result
The result of the analysis of data is presented in Tables 4.1 and 4.2. Table 4.1 shows how the standard deviation
used for the test of statistical significance of the event window abnormal returns was derived. We present in
Table 4.2 the event widow average abnormal returns and the cumulative average abnormal returns for the sample
of 10 commercial banks along with the t-statistics for each day in the event window.
Proceedings of 11th International Business and Social Science Research Conference
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TABLE 4.1
ACC
DIAM
THE PREVENT WINDOW ABNORMAL RETURNS
ETI
FIDE
SKY
STERL
UBA
UBN
WEMA
ZENITH
AAR
(AAR-AAAR) (AARAAAR)2
5.15E-02
-0.59239
-0.52709
0.045719
0.056911
0.034413
0.04119
42.68495
0.029967
0.978449
4.28E+00
-1.04E-02
0.000109
1.19E-01
-0.53113
0.028969
0.117685
0.094396
0.078145
0.340229
43.00039
0.092811
0.918838
4.43E+00
1.35E-01
0.01825
9.75E-02
-0.49081
0.015162
0.105867
0.031669
0.049338
0.3879
43.50994
0.043777
-0.01202
4.37E+00
8.30E-02
0.006893
-1.42E-02
-0.3891
-0.0576
0.098985
-0.0273
-0.01167
-0.07898
43.63564
0.041567
-1.05731
4.21E+00
-7.68E-02
0.005899
-1.23E-01
-0.41896
-0.06347
0.092464
0.080041
-0.04691
-0.37146
43.6482
0.05623
-1.75468
4.11E+00
-1.81E-01
0.032755
5.13E-02
-0.39163
0.110215
-0.0349
-0.01218
-0.05908
-0.38696
43.40493
-0.04406
-0.9783
4.17E+00
-1.25E-01
0.015594
7.49E-03
-0.34096
0.021645
0.012025
0.183558
0.044082
-0.0304
43.11606
-0.00137
-1.00283
4.20E+00
-8.99E-02
0.008079
-2.68E-01
-0.39118
0.101227
0.066347
0.051572
0.026314
0.05734
43.10556
-0.00898
-0.76448
4.20E+00
-9.32E-02
0.00869
-1.41E-01
-0.14021
-0.01007
-0.02918
0.06324
0.049797
0.160198
43.18394
-0.03618
-0.28668
4.28E+00
-9.45E-03
8.93E-05
-1.70E-01
-0.13947
-0.05157
-0.02051
0.092644
0.055332
0.050509
43.15134
-0.01116
0.610119
4.36E+00
6.60E-02
0.00435
-1.24E-01
-0.16941
0.355809
-0.03896
0.105921
0.057455
-0.01614
43.15695
-0.00908
0.712369
4.40E+00
1.12E-01
0.012616
2.91E-01
-0.25101
0.383915
-0.06932
0.020719
-0.00776
-0.1033
43.41121
-0.03317
0.663883
4.43E+00
1.40E-01
0.019544
1.39E-01
-0.29839
0.27205
0.026971
0.160666
0.112931
0.109867
43.55059
0.035673
0.582891
4.47E+00
1.78E-01
0.03184
-8.85E-02
-0.68102
-0.13576
-0.10951
-0.37969
-0.04802
-0.20372
42.80051
-0.10343
0.154846
4.12E+00
-1.70E-01
0.028983
1.20E-01
-0.77824
-0.12424
-0.0093
-0.04147
-0.02197
0.097913
43.50404
-0.02933
0.95429
4.37E+00
7.63E-02
0.005824
9.45E-02
-0.68847
-0.58453
-0.02506
-0.17361
-0.02984
0.005488
43.94327
-0.06704
0.083015
4.26E+00
-3.50E-02
0.001228
0.002688
-0.41827
-0.01658
0.014332
0.019192
0.017659
0.00373
43.30047
-0.00274
-0.01235
4.29E+00
0.200745
0.013383
0.115685
SDEV/T-1
STD
Proceedings of 11th International Business and Social Science Research Conference
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TABLE 4.2: EVENT WINDOW ABNORMAL RETURNS AND THE t-STATISTICS FOR AVERAGE AND CUMULATIVE ABNORMAL
RETURNS
Access
DIA
ETI
fide
Sky
sterl
uba
UBN
Wema
zenith
AAR
t-stat
CAR
STD
Nt
 pre ( Nt )
SQRT
t-stat
0.404196
-0.08783
-0.32395
-0.05328
-0.35052
1.58E-02
-2.12E-01
-3.71E-01
-6.23E-02
-3.51E-02
-0.1076
-0.93015
-0.93015
0.115685
2
1.414214
0.163603
-5.68539
0.388676
-0.21556
-0.70745
-0.01299
-0.24841
-5.48E-02
-3.05E-01
-3.94E-01
-1.89E-03
-4.86E-01
-0.20375
-1.76121
-2.69136
0.115685
1
1
0.115685
-23.2646
0.036446
-0.12329
-0.15429
-0.05002
-0.15796
2.14E-02
-2.08E-01
-3.37E-02
-1.78E-02
-2.55E-01
-0.09414
-0.81378
-3.50514
0.115685
2
1.414214
0.163603
-21.4247
-0.27922
0.040539
-0.04926
-0.0198
-0.08736
7.07E-02
3.44E-02
-2.87E-01
-3.05E-02
1.41E-01
-0.0466
-0.40281
-3.90795
0.115685
3
1.732051
0.200372
-19.5035
0.020502
-0.04939
0.335968
0.015392
0.17522
-2.22E-02
1.33E-01
-2.65E-01
-6.77E-02
6.11E-01
0.088644
0.76625
-3.1417
0.115685
4
2
0.23137
-13.5787
-0.03651
0.288992
0.459082
0.091357
0.242585
-4.72E-02
2.27E-01
3.28E-01
-6.74E-03
4.85E-01
0.203069
1.75536
-1.38634
0.115685
5
2.236068
0.25868
-5.35931
-0.17907
0.181359
0.512548
-0.03143
0.278135
-8.36E-03
3.08E-01
6.53E-01
6.09E-02
3.07E-02
0.180579
1.560957
0.174612
0.115685
6
2.44949
0.283369
0.616199
-0.31376
-0.02139
-0.09996
-0.02286
-0.00175
2.77E-02
-1.05E-01
2.39E-01
4.09E-02
-4.35E-01
-0.06917
-0.59792
-0.42331
0.115685
7
2.645751
0.306074
-1.38304
0.005158
0.001679
-0.00341
-0.01046
-0.01876
0.000373
-0.01606
-0.01626
-0.01065
0.007169
-0.00612
-0.05291
A cursory look at Table 4.2 shows that the highest statistically significant negative cumulative abnormal return (CAR) was recorded on the event day,
February 20, 2014. This implies that the investors in the Nigerian stock market reacted significantly negatively to the ouster of Mallam Sanusi Lamido
Sanusi as the Governor of Central Bank of Nigeria. There was a statistically significant negative cumulative abnormal return on day -1 on the event
window (that is, on February 19, 2014), a day to the actual event day, implying that there was a leakage of information regarding the event. This is
quite possible. Some of the players in the market are highly connected with those that rule in Nigeria; hence it is very likely that they used their
privileged position to get information regarding the planned removal before the 20th February.
Again, the event was of a very significant effect in the Nigerian Stock Market; as a consequence the effect lingered until the fourth day after the event
day as revealed by statistically significant negative abnormal returns up to day +4 in the event window. By the fifth day after the event, the effect of
the ouster had almost fizzled out of the market.
Proceedings of 11th International Business and Social Science Research Conference
8 - 9 January, 2015, Crowne Plaza Hotel, Dubai, UAE. ISBN: 978-1-922069-70-2
Summary and Conclusions
The study examined the signalling effect of the unexpected removal of Mallam Sanusi Lamido Sanusi,
the erstwhile governor of Central bank of Nigeria on the Nigerian stock market using standard event
study methodology. The results of the study showed that the sudden ouster of Sanusi L. Sanusi as the
governor of CBN had statistically significant negative impact on the stock market as negative abnormal
returns were recorded in the market following the unexpected ouster from the event day up to four days
after the event. The study therefore concludes that the sudden ouster of a central bank chief executive
does convey very important information to the stock market participants and hence recommends that
care should be taken in appointing central bank governors to avoid unexpected termination of their
appointments as such action make huge negative publicity and send negative signals to the market
investors, be they indigenous or external investors.
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