2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
Crisis! So what? - The immediate and long run effects of subprime crisis in Brazilian stock
market
Héber Pessoa da Silveira
Banco Central do Brasil
Fundação Escola de Comércio Álvares Penteado, São Paulo – FECAP.
PHD Professor
heber@fecap.br
(+55 11 99106-8060)
Luciana Maia Campos Machado (corresponding author)
Logistics/ Unilever Brazil
Fundação Escola de Comércio Álvares Penteado, São Paulo – FECAP.
MSC Candidate
luciana.maia@unilever.com
(+55 11 97664-1414)
Abstract
This study analyzed the effects of the subprime financial crisis, which had its strongest effects from July,
2008 to Dec, 2009, in Brazilian stock market. We analyzed financial ratios of the largest publicly traded Brazilian
companies from all branches of activity present in the database Economática prior, during and post the event. It
was expected to identify what changed and what is perceptible evidence about the behavior of the Brazilian stock
market in moments known as “crisis”, since a large number of recent studies have shown that returns frequently
exhibit no clear relationship with other indicators of business performance. So, we estimated a series of logistic
regressions with panel data in which the binary dependent variable "crisis" took value 1 in the above mentioned
period (July, 2008 to Dec, 2009) of time and 0 in the remaining moments, between early 2005 and late 2010. This
dummy “crisis” variable was regressed against the return of each stock, its volatility, market share of the company
on its activity branch and in the whole stock market, leverage, equity value and total revenue. The results suggest
that in the time period adopted as the crisis in the stock market in Brazil, stock returns and volatility were strongly
affected, while other variables were in general not directly affected. This suggests that behavioral effects such as
those mentioned in modern studies of behavioral finance, notably panic and overreaction, may be cited as the best
– if not the only statistically relevant – variables in determining the crisis, while other variables related to business
performance seem irrelevant. Briefly saying, it was found that during the crisis the return of stocks drops
dramatically while stock return volatility increases dramatically, independently of other indicators. These results
seem to be apart from more traditional financial theories – which relate risk and return as independent of human
bounded rationality – and indicate the need for a better comprehension of the spillover effects of crisis in stock
markets.
July 2-3, 2013
Cambridge, UK
1
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
1. Introduction
Looking at the historical development of financial theories, works as “Portfolio Selection” by Markowitz
(1952) or “The cost of capital, corporation finance and the theory of investment” by Modigliani & Miller (1958) –
considered the first milestones of "modern finance" – usually assumed classical principles of market efficiency and
investor rationality. In latter findings, the growing importance of human behavior influences can be noticed, as in
Kahnemann and Tversky (1979) or DeBondt and Thaller (1985). Nowadays it is generally accepted that human
decisions are distorted by perceptions of individual investors and, consequently, for heuristics and biases in
judgment. In practice, what often happens is not consistent with the most traditional theories, and empirical
observations usually shows violations to the hypothesis of market efficiency and to the assumptions of utility
theory. In this sense, recent economic crises, that effected capital structure, return and volatility of companies,
contribute to a better understanding of nonstandard behavior in such periods, dealing with the real reasons for
investment decisions and showing whether they are either the result of rational thinking or the result of behavioral
biases.
The subprime crisis that occurred in the U.S. market and extended to other countries’ capital market in
recent period has not yet been depth addressed with regard to its effects on the behavior of investors, and prior
works only restricted their focus to other variables and to specific sectors of business activity. Fama (2011)
highlighted three Brazilian researches on the subject: (i) the work of Silva, Cardoso and Toledo (2010), who
studied the relationship between the subprime crisis and indebtedness of twenty-seven Brazilian companies,
concluding that there was significant influence between crisis and debt capital, and that this did not ended in 2008
(the end of the sample period used by the authors); (ii) the work of Santos et al. (2010) that evaluated the impacts
of the crisis in the common shares of two Brazilian and two American companies of construction industry, and (iii)
Toledo Filho, Sothe and Kroenke (2009), that analyzed from 2005 to 2007 the balance sheets of the largest
Brazilian banks, and concluded that there was minimal impact on the provision of credit risk for the sample. Using
now a more extensive temporal sampling universe and broader observations, which now covers a larger number of
companies, more financial indicators and the entire period of rise and decline of the crisis, we expect to show new
findings, deepen conclusions already obtained and inspire new opportunities for future studies.
July 2-3, 2013
Cambridge, UK
2
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
2. Theoretical Foundation
The efficient market theory (FAMA, 1970), even in its weak form, assumes that bond prices fully
incorporate all the information available, enabling businesses and investors to precisely support their financial
decisions. Markets are then classified into three different levels, according to the gradually absorbed information
complexity: (i) weak form – the information reflected is characterized solely on historical prices; (ii) semi-strong
form – also consider all public information as impacting the market and prices determination, and finally (iii)
strong form - occurs when all investors or groups of investors have full access to information, whether public or
not, that may influence the behavior of prices (FAMA, op cit). Other assumptions relevant to a market
characterized as “efficient”, according to Neves and Amaral (2002), is that investors are risk averse and rationally
choose the maximization of return, considering the degree of (undesirable) risk incurred, besides searching
anticipation to future conduct in securities trading.
Assuming the existence of an efficient market, composed by investors that fully reflect available
information on the rational pursuit of profit maximization and risk minimization, it would be possible to define the
basis of rational behavior of investors, which should react rationally to changes in the perceived risk of the market.
The Capital Asset Pricing Model, developed by William Sharpe (1964), separates the risk into two distinct
components, namely, the systematic part, i.e., a part due to changes in general market, which cannot be avoided
through diversification of portfolios and would be captured by the beta of the company, and an ‘unsystematic’ or
idiosyncratic part, that can be avoided through rational diversification. The evolution of research in the area has
brought some questions about this model. One of the most present questions in the current literature deals with the
behavior of investors, once empirical studies conducted since the late 1970s have shown consistently that investors
tend, among other things, to overreact to positive and negative information, with stronger overreaction in the latter.
Works like DeBondt and Thaller, (op. cit.) showed that overestimating past events, investors generate an
exaggerated increase in stock prices on the rise, and an excessive decrease for declining stock prices. According to
Kahneman et al. (1982) apud DeBondt and Thaller (op. cit.), people tend to give more weight to recent information
while analyzing historical information as irrelevant, and also tend to believe that recent events will continue to
occur. The authors call this set of behavioral tendencies of ‘representativeness bias’.
July 2-3, 2013
Cambridge, UK
3
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
Classical financial theories usually assume that investors are economically rational, and employ rationality
when making investment decisions, so uninfluenced by any behavioral bias or irrational judgment. In other words,
investors possess full insight and rationality in the analysis of relevant information for financial decision-making,
as discussed by Thaler; Halfeld (2001) and Torres (2001) apud Fama (2011). Contrary to these assumptions, since
the end of the 1970s – especially with the work of Kahneman and Tversky (1979) – has arisen the hypothesis that
markets actually are not exactly efficient, and some inconsistencies may be justified not only by scarcity of
available information but also by biases in individual decisions and by the bounded rationality of investors.
Kahneman and Tversky (op. cit.) reported that in risky situations behavioral aspects lead investors to consider
differently the gains and losses possibility. The ‘negative feeling’ of losses, although the actual losses being
smaller than the possible gains, would be stronger than the ‘positive feeling’ of gains - contradicting the perfectly
rational choice model adopted in Utility Theory. The ‘disposition effect’ studied by Shefrin and Statman (1985)
noted the tendency of investors to quickly get rid of stocks whose returns are positive, resulting in short term profit,
while noted the tendency of investors to be conservative in selling shares with losses, retaining them for longer
periods. In other words, the ‘negative feeling’ of a loss leads investors to hold shares with bad performance for
longer periods of time in an (sometimes irrational) effort not to realize losses. Other topic in this theory which also
diverges from the efficient markets: not only the investors are risk averse in case of gains, but they show
willingness to take greater risk in face of losses already incurred. The intensity with which the market is affected
by small individual investors - who tend to exhibit the same behavioral biases - also depends on other stakeholders,
such as traders and professional investors, who, despite their position, also tend to exhibit the same biases. If the
disposition effect, as cited by Lakonishokanda and Smidt (1986 apud Odean, 1998) takes part in the behavior of
those agents, this contributes for a positive correlation effect between price changes and trading volume. Black
(1986) suggests that there are many non-causal events often misused to justify what happens in the world. Market
noises on one hand often ensure the liquidity of assets, but on the other hand are usually confounded with
information and may - even if coming from small events - have larger proportions than major events. This would
happen as investors try to maximize the utility of information available. When it is assumed the existence of noise
in the market, noise that we cannot measure or specify, again we move away from the idea of an efficient market
based solely on information.
July 2-3, 2013
Cambridge, UK
4
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
2.1. Subprime crisis
The so-called subprime crisis, which had financial repercussions worldwide, began in the United States due
to the rise of subprime loans - credit category granted to lenders with questionable history and high default risk.
The consequences of this crisis - that exceed the economy where it began - can be explained by contagion, defined
as a significant increase of cross effects on markets, after effective impact in a single country. Contagion is still a
controversial topic and has been discussed and resulted on different conclusions between researchers. Research
areas favorable to contingencies crisis defend how the effects and behavior of investors have high repercussion
after a shock or crisis in a particular country. The so-called crisis-contingent theories explain the effects
incorporated internationally through three possibilities: (i) investor behavior, (ii) changes in the liquidity of assets that generate rebalancing of portfolios - and (iii) economic policies, that affect exchange rate regimes. In contrast,
conflicting theories allege that the mechanisms and impacts between countries and markets are stable and
equivalent, even in times of crisis, and any financial consequences are perceived only as a continuation of already
existing relationships (FORBES; RIGOBON, 2001). Hortas, Mendes and Vieira (2008) and Vidal (2011) showed
strong evidence of contagion in financial markets of Brazil in studies performed after the onset of the sub-prime
crisis. The evidence points out to the fact that investors suffer loss expectations and confidence changes due to a
clash occurred in a particular economy, and this reflects in the financial indicators evaluated. The contagion effects
can be derived from the memory of previous crises and their repercussions, and this fact can leverage the remaining
occurrences, signaling the start of major proportions that, in drastic situations, lead to the collapse of the financial
system.
One of the most important events in the formation of the sub-prime crisis was the collapse of U.S. bank
Lehman Brothers, occurred in the third quarter of 2008. Before that, the United States possessed an economy
considered reliable – which companies from emerging countries commonly used when searching for safe portfolio
diversification – in addition to a monetary policy considered efficient and a highly liquid market. In the years
immediately preceding the crisis, the level of indebtedness in the United States was raised and led the search for
international capital, as it can be reflected on the United States debits and credits balance account, available on
Bureau of Economic Analysis (2012), during this time.
July 2-3, 2013
Cambridge, UK
5
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
Source: Bureau of Economics Analysis, International Transactions section, 2012.
Financial integration and good perception of investments facilitated indebtedness (REIHART 2010; apud
VIDAL; ROGOFF, 2011). Beginning in this point, the third quarter of 2008 with the collapse of Lehman Brothers,
we consider the sub-prime crisis and its impact, until the partial recovery of Brazilian stock market in late 2009.
3. Methodological approach, sample and variable descriptions
The research sample has a transverse dimension formed by 89 companies and longitudinal data within 12
semesters. The data distribution characterizes an unbalanced panel given the existence of periods with missing data
(mainly by non-disclosure of financial results in part of the period analyzed). The configuration used and the
number of companies considerably larger than the number of observed periods allows us to consider the result as
asymptotically valid based on the assumption of a "short panel", ie, where N (the number of individuals in the
sample) is ‘large’ and T (time periods) is ‘small’ (Wooldridge, 2002).
For the research, we observed, besides the variable “Crisis”, the variables (i) stock return, (ii) volatility, (iii)
leverage (long-term liabilities/equity), (iv) representativeness in the stock exchange and (v) in the sector of activity,
and also variables that measure profitability as (vi) equity/total assets and (vii) net income/total assets for each of
the companies in the sample period 2005.1 (first half of 2005) to 2010.2 (second half of 2010, inclusive). Based on
the data we then estimated a panel data logistic regression model, considering that the variable "Crisis" is a binary
variable which seeks to determine the time when the Brazilian stock market was in crisis due to the "spillover
July 2-3, 2013
Cambridge, UK
6
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
effect" of the sub-prime crisis triggered in the U.S. market in early 2007 and which lasted more heavily on the
returns on the stock market until early 2010, and the variables that are correlated with the cited crisis. During this
time (the end of 2007.1 to the end of 2010.2) Ibovespa (the stock exchange index in Brazil) rose to a top of 73.516
basis points in May 20, 2008 to a minimum of 29.435 basis points in Oct 27, 2008 and crossing again the line of
70,000 basis points in Oct 1, 2010, in a V-shape movement as seen below. In the short period of five months in
2008, Brazilian stock market lost about 60% of its market value. Then, in a period of 24 months, recovered almost
all of it. What actually occurred in this strange period? This drastic movement in stock prices is driven by
economic rationality, or stems from panic?
Ibovespa
80000,00
70000,00
60000,00
50000,00
40000,00
Ibovespa
30000,00
20000,00
10000,00
0,00
03/01/2005
03/01/2006
03/01/2007
03/01/2008
03/01/2009
03/01/2010
03/01/2011
With the purpose of to analyze the variables that led to the sub-prime crisis effects over Brazilian
companies, we run a set of different regression models relating the dummy variable crisis to a set of explanatory
variables between those frequently used in empirical studies of the area.
3.1. Regression model
The estimated regression model decrypted above initially took the form:
(3.1)
July 2-3, 2013
Cambridge, UK
7
2013 Cambridge Business & Economics Conference
In the above equation,
ISBN : 9780974211428
represents each one of the companies in the sample, and
(semesters). The regression error, represented by
each period of time
, encompasses the heterogeneity not captured by the other
estimators, and we assume it is a white noise not serially correlated with other stochastic regressors in equation
(3.1). The dependent variable “crisis”, as cited before, is a dummy variable that assumes value 0 in periods
“outside of crisis” and 1 in the period “inside crisis”. Doing so, we aim to analyze the relationship between the
"crisis" in Brazilian capital market and some of the economic fundaments of the firms in the sample, namely, the
leverage, profitability, stocks return, stock return volatility, market share of the individual firm in stock market and
in its sector of activity (those measures of representativeness are used to capture any increase or decrease in
monopoly or oligopoly power by individual firms, a very usual problem in Brazilian stock market) and the natural
logarithm of revenue.
The model in 3.1 basically looks forward to capture in its vector of explanatory variables the reasons for
different behaviors of a sample of firms in a specific period of time. Even assuming the use of a high quality
regression model, part of the behavior of firms in this period can be attributed to a myriad of factors not listed in
the model. Trying to capture a greater part of the heterogeneity between firms, we also estimated a set of models in
which fixed and random effects were controlled. Thus, there is a new research model, less parsimonious as that in
(3.1):
(3.2)
Where
is the vector of variables that control for ‘random’ effects (effects that arise from the fact that
the sample was randomly selected of a bigger population). This way, we can control for effects of variables omitted
in model 3.1 that can disturb the real effects of research variables over the “crisis”. Once isolated other potentially
noisy effects, we can observe if the “crisis” for the sample firms (sample that is formed by the biggest firms of all
business sector in Brazil) has any correlation with the perceived economic fundamentals (risk, leverage,
representativeness, size, profitability, etc.) or if it stems of a generalized fall in stock prices caused solely by panic
and negative overreaction by investors.
July 2-3, 2013
Cambridge, UK
8
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
4. Results
The result, with specific control variables and the random effects control, as in equation 3.2 has its results
below:
Random-effects logistic regression
Group variable: cod_emp
Random effects u_i ~ Gaussian
Log likelihood
= -483.26029
Number of obs
Number of groups
Obs per group: min
avg
max
Wald chi2(7)
Prob > chi2
=
=
=
=
=
=
=
734
78
1
9.4
11
34.79
0.0000
-----------------------------------------------------------------------------crise |
Coef.
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------debt_eq |
.0014413
.0008683
1.66
0.097
-.0002605
.003143
prof_eq | -1.029605
.4478142
-2.30
0.021
-1.907305
-.1519058
return | -5.999116
1.367725
-4.39
0.000
-8.679808
-3.318425
volatility |
1.549992
1.002603
1.55
0.122
-.4150729
3.515057
mkt_share | -31.41496
20.98359
-1.50
0.134
-72.54204
9.712122
sector_share |
2.359063
.9834841
2.40
0.016
.4314698
4.286657
ln_revenue |
.1041822
.0474664
2.19
0.028
.0111499
.1972146
_cons | -1.475625
.708443
-2.08
0.037
-2.864148
-.0871024
-------------+---------------------------------------------------------------/lnsig2u | -15.40873
21.13997
-56.84231
26.02485
-------------+---------------------------------------------------------------sigma_u |
.0004509
.0047655
4.54e-13
447944.2
rho |
6.18e-08
1.31e-06
6.26e-26
1
-----------------------------------------------------------------------------Likelihood-ratio test of rho=0: chibar2(01) =
0.00 Prob >= chibar2 = 1.000
The results show a deep and statistically significant (at 1%) drop in stock returns and a increase in volatility
(although not significant). There is also a significant (at 5%) and not expected increase in revenue, and a significant
drop in profitability. Leverage had a very small change (significant at 10%) correlated with the crisis period, idem
for representativeness of the firms in their business sector.
To test for robustness, we also estimated the same set of variables using “xtprobit” in place of “xtlogit”, and
the results are shown below:
Random-effects probit regression
Group variable: cod_emp
Random effects u_i ~ Gaussian
Log likelihood
= -483.73162
Number of obs
Number of groups
Obs per group: min
avg
max
Wald chi2(7)
Prob > chi2
=
=
=
=
=
=
=
734
78
1
9.4
11
37.61
0.0000
July 2-3, 2013
Cambridge, UK
9
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
-----------------------------------------------------------------------------crise |
Coef.
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------debt_eq |
.0007227
.0004239
1.70
0.088
-.0001082
.0015536
prof_eq | -.6616233
.2731428
-2.42
0.015
-1.196973
-.1262733
return | -3.464505
.7898353
-4.39
0.000
-5.012554
-1.916456
volatility |
.7421906
.5807928
1.28
0.201
-.3961424
1.880524
mkt_share | -19.62457
12.78117
-1.54
0.125
-44.6752
5.426069
sector_share |
1.505819
.6002712
2.51
0.012
.3293087
2.682329
ln_revenue |
.0640198
.0288361
2.22
0.026
.0075021
.1205374
_cons | -.8870636
.4297938
-2.06
0.039
-1.729444
-.0446832
-------------+---------------------------------------------------------------/lnsig2u | -22.39854
628.6175
-1254.466
1209.669
-------------+---------------------------------------------------------------sigma_u |
.0000137
.0043011
3.9e-273
4.7e+262
rho |
1.87e-10
1.18e-07
0
.
-----------------------------------------------------------------------------Likelihood-ratio test of rho=0: chibar2(01) =
0.00 Prob >= chibar2 = 1.000
The results were very similar and thus robust to different estimation processes. Results point to the fact that
the sub-prime crisis for firms in Brazil in this period of time was mainly represented by a deep and highly
significant decrease in stock returns.
4.1. Changing time period
After all the different estimations, different results were found when we choose a more specific and small
time window as representative of the "crisis" in Brazil. Taking into account the fact that the crisis in Brazil had a
very fast drop followed by a very fast recovery (for example when compared to the US market), we met different
magnitudes and significances for the estimators when considering that the crisis in Brazil as beginning in July,
2008 and lasting until Jan, 2010. Results are shown below in this new estimation:
Random-effects logistic regression
Group variable: cod_emp
Random effects u_i ~ Gaussian
Log likelihood
= -314.22372
Number of obs
Number of groups
Obs per group: min
avg
max
Wald chi2(7)
Prob > chi2
=
=
=
=
=
=
=
734
78
1
9.4
11
49.30
0.0000
July 2-3, 2013
Cambridge, UK
10
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
-----------------------------------------------------------------------------crise |
Coef.
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------debt_eq |
.0001531
.0005205
0.29
0.769
-.000867
.0011733
prof_eq | -.6542551
.3924719
-1.67
0.096
-1.423486
.1149756
return | -7.996039
1.588148
-5.03
0.000
-11.10875
-4.883326
volatility |
6.101257
1.151416
5.30
0.000
3.844523
8.35799
mkt_share | -2.878328
27.67529
-0.10
0.917
-57.1209
51.36424
sector_share |
.8698174
1.127386
0.77
0.440
-1.339819
3.079453
ln_revenue |
.092315
.0639981
1.44
0.149
-.0331189
.217749
_cons | -3.579307
.9620082
-3.72
0.000
-5.464809
-1.693806
-------------+---------------------------------------------------------------/lnsig2u | -13.93046
20.96295
-55.01708
27.15617
-------------+---------------------------------------------------------------sigma_u |
.0009441
.0098961
1.13e-12
788655
rho |
2.71e-07
5.68e-06
3.88e-25
1
-----------------------------------------------------------------------------Likelihood-ratio test of rho=0: chibar2(01) = 8.2e-05 Prob >= chibar2 = 0.496
Here we did the same of the previous situation and re-estimated the model with “xtprobit” in place of
“xtlogit”. The results are below:
Random-effects probit regression
Group variable: cod_emp
Random effects u_i ~ Gaussian
Log likelihood
=
-314.9445
Number of obs
Number of groups
Obs per group: min
avg
max
Wald chi2(7)
Prob > chi2
=
=
=
=
=
=
=
734
78
1
9.4
11
53.94
0.0000
-----------------------------------------------------------------------------crise |
Coef.
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------exlp_pl |
.0000534
.0002953
0.18
0.856
-.0005255
.0006323
ll_pl | -.3880521
.2190522
-1.77
0.076
-.8173865
.0412823
retorno | -4.196074
.8423465
-4.98
0.000
-5.847043
-2.545105
volatilidade |
3.382868
.6172119
5.48
0.000
2.173155
4.592581
rep_bolsa | -1.160564
14.78644
-0.08
0.937
-30.14146
27.82033
rep_setor |
.5743098
.6102769
0.94
0.347
-.6218109
1.77043
ln_recinfl |
.0415988
.0338778
1.23
0.219
-.0248006
.1079981
_cons | -1.915592
.5081209
-3.77
0.000
-2.91149
-.9196929
-------------+---------------------------------------------------------------/lnsig2u | -15.84855
21.06167
-57.12865
25.43156
-------------+---------------------------------------------------------------sigma_u |
.0003619
.0038106
3.93e-13
332960.3
rho |
1.31e-07
2.76e-06
1.55e-25
1
-----------------------------------------------------------------------------Likelihood-ratio test of rho=0: chibar2(01) =
0.00 Prob >= chibar2 = 1.000
July 2-3, 2013
Cambridge, UK
11
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
The results are very similar in both ways of estimation. This new set of results points out that the subprime
crisis in Brazil in its most dramatic moments showed steady effect on stock returns and its volatility. Returns
dropped independent of the time window and the estimation model, while its volatility was not affected in
significant terms when we analyze a bigger time window. In the short one it was, as expected, very high and
significant. The only other significant variable that explained the crisis was profitability, that dropped even when
revenue showed a positive correlation with the time crisis. In other words, profitability dropped even while revenue
was growing for Brazilian firms. Other variables proved erratically, with empiric results depending on the model
employed and on the time window. Those effects, principally the deep drop in stock returns in the bigger time
window when volatility was not directly affected, in part contradicts the classical theories of finances related to
"efficient markets", where the risk (measured by the variance of returns of the companies or the beta) would be the
only variable affecting the expected returns.
In lack of a better explanation, the evidence seems to point to a behavioral effect more than to changes in
economic fundamentals of firms, i.e. an ‘exaggerated’ risk aversion and a "herd behavior" seems to arise more than
changes that affects unequivocally the financial fundamentals of the companies analyzed.
5. Summary and conclusions
We analyzed the effects of the subprime financial crisis in different periods of time in Brazil. The cited
crisis had its strongest effects from July, 2008 to Dec, 2009, with the stock market exhibiting a drastic movement
of down and up in prices. Besides stock market prices, we analyzed in the same set of regressions a number of
financial ratios of the largest publicly traded Brazilian companies from all branches of activity, searching for
changes in something else than stock prices. We aimed to identify what actually changed and what was perceptible
evidence about not only the behavior of the Brazilian stock market but also about firms economic fundamentals in
moments known as of “crisis”. We estimated a series of logistic regressions with panel data in which the binary
dependent variable "crisis" took value 1 in different time periods of time (initially from the beginning of Jan, 2008
to the end of Dec, 2010 and latter from the beginning of July, 2008 to the end of Dec, 2009) and 0 in the remaining
moments, between early 2005 and late 2010. This dummy “crisis” variable was regressed against the return of each
stock, its volatility, market share of the company on its activity branch and in the whole stock market, leverage,
equity value and total revenue.
July 2-3, 2013
Cambridge, UK
12
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
The results suggest that in the time period adopted as the crisis in the stock market in Brazil, stock returns and
volatility were strongly affected, while other variables of economic fundamentals were affected in some periods of
time and some other were not directly affected at all. Our findings in part support the hypothesis that behavioral
effects such as those mentioned in modern studies of behavioral finance, notably panic and overreaction, may be
cited as the best – if not the only statistically relevant – variables in determining the crisis in its more drastic
moments, while other variables related to business performance seem irrelevant. Briefly saying, it was found that
during the short period where the crisis deepened in Brazil, the return of stocks dropped dramatically while stock
return volatility increased dramatically (as expected), but in a completely independent way of other indicators (not
expected). These results seem to be apart from more traditional financial theories – which relate risk and return as
independent of human bounded rationality – and indicate the need for a better comprehension of the spillover
effects of crisis in stock markets.
References
BUREAU OF ECONOMICS ANALYSIS. U.S. International Transactions. Accessed at: January 20, 2013
[available at: www.bea.gov].
DeBONDT, W.F.M; THALER, R. (1985). Does the stock market overreact? Journal of Finance, v. 40, n. 3. 793805.
FAMA, Rubens et al (2011). Análise do Impacto da Crise Subprime na Geração de Retornos Anormais em uma
carteira de índices do mercado acionário brasileiro divulgados pela BM&FBovespa: uma proposta metodológica:
uma Proposta Metogológica. SEMEAD. São Paulo. 14. 1-15. Accessed October 22, 2012, [available at:
http://www.ead.fea.usp.br/semead/14semead/resultado/trabalhosPDF/275.pdf].
FAMA, E. F. (1970). Efficient capital markets: a review of theory and empirical work, Journal of Finance.
FISCHER, Black (1986). Noise. Journal of Finance. 41. 529–543.
FORBES, K.; RIGOBON, R. (2001). Measuring contagion: conceptual and empirical issues. In: Stijn Claessens
and Kristin Forbes (eds.) International Financial Contagion, Kluwer Academic Publishers.
HORTA, Paulo; MENDES, Carlos; VIEIRA, Isabel (2008). Contagion effects of the US subprime crisis on
developed. Cefage-ue Working Paper, Évora. 1-30.
KAHNEMAN, Daniel.; TVERSKY, Amos. (1979). Prospect theory: An analysis of decision making under risk.
Econometrica.
MARKOWITZ, Harry. Portfolio selection. Journal of Finance, v. 7. 77-91.
July 2-3, 2013
Cambridge, UK
13
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
MODIGLIANI, F; MILLER, M. H (1958). The cost of capital, corporation finance and the theory of investment.
American Economic Review, v. 48, n. 3, 261-297.
NEVES, Alexandre Wernersbach; AMARAL, Hudson Fernandes (2002). A precificação de ativos de renda
variável no mercado de capitais brasileiro: uma visão comparativa entre a Arbitrage Pricing Theory e o Capital
Asset Pricing Model. ENANPAD, 2002, São Paulo, Brazil. Accessed at Septemper 24, 2012, [available at
http://www.anpad.org.br/evento.php?acao=trabalho&cod_edicao_subsecao=49&cod_evento_edicao=6&cod_edica
o_trabalho=2515].
ODEAN, Terrance (1998). Are investors reluctant to realize their losses? Journal of Finance, v. 53. 1775-1798.
SILVA, Julio Orestes; CARDOSO, Rodrigo dos Santos; TOLEDO FILHO, Jorge Ribeiro de. (2010). Impacto da
crise do subprime no endividamento das maiores empresas brasileiras. Pensar Contábil. Rio de Janeiro, Brazil. v.
12, n. 47. 16-25.
SHARPE. W. (1964). Capital Asset prices: A theory of market equilibrium under conditions of risk. Journal of
Finance, v. 19, p. 425-442.
SHEFRIN, Hersh; STATMAN, Meir (1985). The disposition to sell winners too early and ride losers too long:
theory and evidence. The Journal of Finance, v. 40, n. 3. 777-790.
VIDAL, Tatiana Ladeira (2011). Crises financeiras: efeito contágio ou interdependência entre os países?
Evidências utilizando uma abordagem multivariada. Master Degree Dissertation. Faculdade de Economia,
Administração e Contabilidade: Universidade de São Paulo (USP), São Paulo.
July 2-3, 2013
Cambridge, UK
14