International Journal of Management, Knowledge and Learning, 9(1), 75–94
Investment Behaviour and Firms’
Financial Performance:
A Comparative Analysis Using
Firm-Level Data from the Wine
Industry
Claudiu Tiberiu Albulescu
Politehnica University of Timisoara, Romania
University of Poitiers, France
This paper assesses the role of financial performance in explaining firms’ in-
vestment dynamics in the wine industry from the three European Union (EU)
largest producers. The wine sector deserves special attention to investigate
firms’ investment behaviour given the high competition imposed by the late-
comers. More precisely, we investigate how the capitalisation, liquidity and
profitability influence the investment dynamics using firm-level data from the
wine industry from France (331 firms), Italy (335) firms and Spain (442) firms.
We use data from 2007 to 2014, drawing a comparison between these coun-
tries, and relying on difference- and system-GMM estimators. Specifically, the
impact of profitability is positive and significant, while the capitalisation has
a significant and negative impact on the investment dynamics only in France
and Spain. The influence of the liquidity ratio is negative and significant only
in the case of Spain. Therefore, we notice different investment strategies for
wine companies located in the largest producer countries. It appears that
these findings are in general robust to different specifications of liquidity and
profitability ratios, and to the different estimators we use.
Keywords: firm investment, financial performance, wine industry, comparative
analysis
Introduction
One of the key challenges the corporate finance literature has to cope with
is the identification of determinants of firms’ investment behaviour. Under-
standing the factors influencing firms’ investment is important from the
perspective of financial management optimisation and investors’ wealth.
For this purpose, prior literature investigates the role of a large set of ex-
ternal and internal determinants, and reports mixed empirical evidence.
However, the interest for studding the investment behaviour of wine compa-
nies is scarce. This paper fills in this gap and adds to the menu of studies
addressing the role of internal factors in supporting the firms’ investment
www.issbs.si/press/ISSN/2232-5697/9_75-94.pdf
76 Claudiu Tiberiu Albulescu
behaviour, by focusing on the role of financial performance and using wine
industry firm-level data from the largest wine producing countries, namely
France, Italy and Spain. We posit that the investment behaviour of the wine
companies located in these countries is not only influenced by the economic
context and competition policies (Rizzo, 2019), but also by their financial
performances.
The external determinants of firms’ investment behaviour are related to
business cycle (Gertler & Gilchrist, 1994; Jeon & Nishihara, 2014; Pérez-
Orive, 2016), taxation (Hall & Jorgenson 1967; Morck, 2003; Jugurnath
et al., 2008), monetary policy (Vithessonthi et al., 2017), quality of insti-
tutions (Ajide, 2017), and even to the behaviour of other firms from the
same industry (Lyandres, 2006; Leary & Roberts, 2014; Park et al., 2017).
Noteworthy studies (Abel, 1983; Bernanke, 1983; Hartman, 1972; Pindyck,
1988; Calcagnini & Iacobucci, 1997; Baum et al., 2008; Glover & Levine,
2015) investigate the controversial role of uncertainty in influencing firms’
investment behaviour.1
Two main categories of internal factors explain firms’ investment behav-
iour.2 On the one hand, building upon Modigliani and Miller (1958), the lit-
erature underlines the role of financial constraints, leverage and cash flow
(Fazzari et al., 1988; Gilchrist & Himmelberg, 1995; Lang et al., 1996; Chen
et al., 2001; Suto, 2003; Aivazian et al., 2005; Ahn et al., 2006; Baum
et al., 2010; Almeida et al., 2011; Maçãs Nunes et al., 2012; Colombo
et al., 2013; Vermoesen et al., 2013; Ameer, 2014). On the other hand,
agency costs, information asymmetry and ownership structure are put for-
ward (Jensen & Meckling, 1976; Koo & Maeng, 2006; Danielson & Scott,
2007; Alex et al., 2013; Farla, 2014; Mavruk & Carlsson, 2015). Several
papers (e.g. Shen & Wang, 2005) show that both financial constraints and
ownership structure influence the investment decision, while other papers
(e.g. Bokpin & Onumah, 2009) underline the role of firms’ size in explaining
the investment behaviour.
The financial constraints and firms’ leverage have important implications
on the investment behaviour (Suto, 2003; Ahn et al., 2006), at the same
time influencing the structure of investment (Almeida et al., 2011). A se-
ries of studies shows that financial constraints have a negative impact on
firm-level investment. In this line, Vermoesen et al. (2013) report that high
leveraged Belgian firms experienced a larger investment contraction during
crisis times, compared to less leveraged firms. Opposite findings are re-
ported by Baum et al. (2010) for a set of manufacturing United States (US)
firms, who show that leverage stimulates the investment under the effects
of uncertainty. However, most of existing empirical works focus on the role
of financial constraints in explaining the investment – cash flow sensitivi-
ties. The financial friction theory mentions that the impact of cash flow on
International Journal of Management, Knowledge and Learning
Investment Behaviour and Firms’ Financial Performance 77
investment increases in the presence of credit constraints. While Aidogan
(2003) shows that the sensitivity of firm’s investment to its own cash flow
increases for growing firms, Kim (2014) states that the investment – cash
flow sensitivity is explained by the level of external financing. Using a Panel
Smooth Transition Regression model for 519 Asian listed firms over the
period 1991–2004, Ameer (2014) reports that investment – cash flow sen-
sitivity varies across different categories of firms. Mulier et al. (2016) also
point out that the highest investment – cash flow sensitivity characterises
financially constrained firms. Another set of works (e.g. Gamba and Triantis
2008; Arslan-Ayaydin et al., 2014) underlines the role of financial flexibil-
ity in fostering firm-level investment. Using a sample of 1,068 Asian firms,
Arslan-Ayaydin et al. (2014) report that financial flexibility achieved through
conservative leverage policies has significant influence on investment, in
particular in crisis periods.
The second strand of literature investigates the role of agency costs, in-
formation asymmetry and ownership structure in influencing the investment
behaviour. In their pioneering paper, Jensen and Meckling (1976) show that
agency conflicts might distort firms’ investment decision in the presence of
multiple owners. Performing an empirical investigation for a panel of 115
listed firms in Taiwan for the period 1991–1997, Shen and Wang (2005)
highlight that investment behaviour is financially constrained in a cross-
ownership system. At the same time, Koo and Maeng (2006) find that the
presence of foreign ownership in Korean firms decreases the investment
– cash flow sensitivity. More recently, Farla (2014) discovers that firms’ in-
vestment behaviour has little dependency on a country’s macroeconomic
setting, while foreign-owned firms have lower investment dynamics.
Only few papers, however, focus on the role of profitability and liquidity
on the investment behaviour (Perić & Ðurkin, 2015; Yu et al., 2017). While
some studies (Stickney & McGee, 1982; Gilchrist & Himmelberg, 1995;
Black et al. 2000) use financial performance indicators as control variables
in their empirical specifications, several papers put accent on the role of liq-
uidity in influencing the investment behaviour. As Baum et al. (2008) show,
the impact of liquidity on investment is not straightforward. While in cri-
sis periods characterised by credit contractions and financial frictions it is
expected that liquidity positively influence the investment decision, an op-
posite effect appears if investment projects are delayed. On the one side,
Acharya et al. (2007) state that the liquidity level sustains firms’ future
investment and offers protection against market risks. On the other side,
Hirth and Viswanatha (2011) find that in the case of financially constrained
firms, the relationship between liquidity and investment is U-shaped.
We extend the existing literature by examining not only the role of liq-
uidity, but also the impact of capitalisation and profitability on investment
Volume 9, Issue 1, 2020
78 Claudiu Tiberiu Albulescu
behaviour. All these variables characterise the firms’ financial performance,
offering at the same time information about risk protection and incentive
to develop the business. The level of cash holdings and thus the level of
liquidity is considered the cheapest cost of investment. Therefore, for a
specific period, if firms decide to increase their liquidity for risk protection
reasons (i.e. during crisis periods), a trade-off is expected between liquidity
and investment. The increase of capitalisation level might also be done in
the detriment of investment. It is surprising that previous literature does
not debate the role of capitalisation in the investment behaviour. However,
the level of capitalisation provides, on the one hand, information about the
debt level and, on the other hand, information about the way sharehold-
ers interact with managers in the investment decision. When investment
becomes risky, shareholders might prefer to increase capitalisation. At the
same time, shareholders’ equity represents an investment resource. In this
context, during a fiscal year, it is expected that an increase in capitalisation
negatively influence the investment dynamics. Finally, the level of profitabil-
ity positively affects the investment behaviour. First, profitability increases
the level of internal funds available for investment and has a negative in-
fluence on leverage (Datta & Agarwal, 2014). Second, high profits provide
information about market dynamics and recommend future investments.
Another contribution of this paper to the bulk of literature investigating
the determinants of firm-level investment consists of the empirical approach
we use. Investment dynamics affects the firms’ financial performance in
its turn (Gatchev et al., 2009). Therefore, in line with other studies, we
address the endogeneity issues resorting to a Generalised Method of Mo-
ments (GMM) panel approach. Nevertheless, different form previous works,
we address different econometric issues as residual autocorrelation or in-
struments’ over-identification, which may introduce a bias in the reported
results, if the models are not correctly specified. Comparing a difference-
GMM (Arellano & Bond, 1991) and a system-GMM estimator (Blundell &
Bond, 1998), we show that the results are sensitive to different econo-
metric specifications, although they are robust to alternative measures of
liquidity and profitability.
Finally, we investigate the role of financial performance on the invest-
ment behaviour using wine industry firm-level data from France, Italy and
Spain, the largest European Union (EU) and worldwide producers. As far as
we know, the study by Outreville and Hanni (2013) is the only one address-
ing the determinants of investment in the wine industry. However, the au-
thors focus on the foreign investment, investigating the case of the largest
multinational enterprises, and underline the role of location for the inward
investment. Different from this work, we analyse the case of domestic and
foreign firms acting in the wine industry from the largest producing coun-
International Journal of Management, Knowledge and Learning
Investment Behaviour and Firms’ Financial Performance 79
tries. France and Italy dominated the international wine market before the
1980s (Morrison & Rabellotti, 2017). Spain recorded a considerable devel-
opment of the wine industry since then. Therefore, even after the increas-
ing importance of newcomers in the industry (i.e. US, Chile, South Africa
or Australia), the three EU countries continued to dominate the wine in-
dustry at global level.3 Has the financial performance of firms located in
these countries a similar impact on their investment behaviour in the con-
text of an increased competition on the wine market? We try to respond
to this question analysing firm-level data for 331 firms located in France,
335 firms located in Italy and 442 firms from Spain, over the period 2007
to 2014.
The rest of the paper is structured as follows. The second section
presents some general statistics about the wine industry, with a focus on
the EU. The third section describes the data and the methodology. The
fourth section highlights the empirical results and presents the robustness
checks. In the fifth section we present the summary of results and dis-
cuss in a comparative manner the role of financial performance on firms’
investment behaviour in the three analysed countries, generating policy rec-
ommendations. The last section concludes.
General Statistics about the Wine Industry in the Selected EU Countries
During the last decades, in the context of new EU regulations, wine-
producing regions of Europe struggled to adapt to changing market con-
ditions and to fight against the competition of newcomers in this industry
(Outreville & Hanni, 2013). Table 1 indicates that France, Italy and Spain
together represented more than 55% from the total wine production, and
more than 25% of total wine exports during the 1960s. However, the total
production of these countries dropped to 45% out of the world produc-
tion during the 2010s, while the total exports represent nowadays more
than 50%. These figures show that world-level production and consumption
increased with the newcomers on the wine market, but the consecrated pro-
ducers became more and more competitive. This happened in the context
of an intensive process of international acquisitions, driven by competitive
prices and the opportunity to acquire key brands (Anderson et al., 2003).
Given that wine is considered a typical cultural commodity, these producers
readapted their market strategy, underlining the intangible characteristics
of their product (e.g. the notion of ‘terroir’ in France). Nevertheless, while
Italy and Spain continued to increase their quotas in the world exports,
France encountered a severe contraction during the last decade.
As compared to other EU countries, France, Italy and Spain are consid-
ered by far the largest producers, representing according to the Eurostat
statistics, more than 80% of the total wine production in the EU. Table 2
Volume 9, Issue 1, 2020
80 Claudiu Tiberiu Albulescu
Table 1 Wine Production and Exports
1961 1970 1980 1990 2000 2007 2008 2009 2010 2011 2012 2013
(a) FR 22.59 24.97 19.79 22.98 20.32 17.80 15.69 17.47 16.77 18.69 16.17 14.67
IT 24.42 22.81 24.57 19.24 19.10 15.47 16.15 16.22 16.54 14.87 14.70 15.39
SP 9.39 8.48 12.03 13.92 14.54 13.30 13.73 12.14 13.36 12.33 11.95 15.75
(b) FR 14.72 11.26 19.58 28.19 22.07 16.34 15.17 13.66 14.12 14.30 14.87 14.52
IT 6.87 15.25 33.49 29.55 23.20 21.12 20.91 22.79 23.26 23.70 21.08 20.31
SP 5.48 9.03 12.22 10.80 12.01 16.32 17.66 16.98 18.37 21.81 20.31 17.96
Notes Rpw headings are as follows: (a) wine production, (b) wine exports. Percentages of world total
volumes. Based on data from Faostat database (http://www.fao.org/faostat/en/#home).
Table 2 Opening Stocks by Vintage Year in the EU Countries
2007–8 2008–9 2009–10 2010–1 2011–2 2012–3 2013–4 2014–5 2015–6 2016–7
FR 57,062 57,459 53,901 54,061 54,518 59,958 53,238 47,830 50,318 51,514
IT 41,120 41,719 44,746 41,360 41,502 40,632 36,500 45,250 41,276 42,692
SP 33,817 34,168 36,962 36,446 34,169 28,677 29,311 36,619 33,730 30,701
EU 165,624 167,871 174,182 170,454 164,921 160,483 150,868 164,249 162,908 163,586
Notes 1,000 Hl. Based on data from Eurostat database (http://www.fao.org/faostat/en/#home).
presents the dynamics of the wine industry in terms of opening stocks in
the selected EU countries.
Data and Methodology
Data
We use firm-level annual data from AMADEUS database to investigate the
impact of firms’ financial performance on the investment dynamics over the
period 2007 to 2014. To avoid the broken panel bias, we have included in
our analysis only firms without missing values for a specific indicator. Fur-
ther, we have dropped from our sample the companies where data indicate
a capitalisation ratio (capital to total assets) over 100%. Finally, our sample
includes 331 firms out of 367 firms registered in France (90%), 335 firms
out of 410 recorded in Italy (82%), and 442 firms out of 531 registered in
Spain (83%). The focus on firms with complete data may only introduce a
sample bias, because firms with specific characteristics are more likely to
enter in our sample. However, in our case, this bias is marginal given the
high percentage of retained companies from each country. Moreover, as An-
drén and Jankensgård (2015) state, balancing the panel has an important
benefit as it allows the possibility to perform different robustness checks.
The investment dynamics (inv) is calculated as the growth rate of fixed
assets. The liquidity ratios (general liquidity ratio – lr and current ratio – cr),
as well as the profitability ratios (Return on Equity – roe and Return on As-
sets – roa) are extracted from AMADEUS database, while the capitalisation
ratio (cap) is equivalent with the capital to total assets ratio.
Table 3 presents the results of panel unit root tests for all variables
International Journal of Management, Knowledge and Learning
Investment Behaviour and Firms’ Financial Performance 81
Table 3 Panel Unit Root Tests
Country Variable (1) (2) (3) (4)
France inv –178.48‡ –26.687‡ 1283.5‡ 1832.4‡
cap –59.872‡ –4.8567‡ 826.21‡ 1139.2‡
lr –29.625‡ –4.2284‡ 938.34‡ 1266.6‡
cr –136.49‡ –8.8148‡ 875.02‡ 1255.3‡
roe –95.209‡ –13.785‡ 1162.3‡ 1672.9‡
roa –93.703‡ –14.462‡ 1112.0‡ 1577.2‡
Italy inv –10523‡ –3696.9‡ 1830.4‡ 1708.2‡
cap –633.61‡ –40.561‡ 860.73‡ 1455.6‡
lr –34.530‡ –3.5804‡ 871.94‡ 1191.2‡
cr –25.908‡ –2.1644† 872.10‡ 1042.6‡
roe –55.071‡ –11.468‡ 1051.9‡ 1635.9‡
roa –43.487‡ –8.1827‡ 971.91‡ 1396.2‡
Spain inv 504.00 –33.357‡ 1882.1‡ 2807.2‡
cap 0.2664 –11.625‡ 1053.3‡ 1270.6‡
lr –38.522‡ –3.9996‡ 1179.6‡ 1581.3‡
cr –33.441‡ –3.9028‡ 1226.2‡ 1498.1‡
roe –254.89‡ –19.882‡ 1409.3‡ 2367.1‡
roa –214.84‡ –14.507‡ 1327.7‡ 2044.8‡
Notes Column headings are as follows: (1) Levin-Lin-Chu t*, (2) Im-Pesaran-Shin W-stat, (3) ADF-Fisher
Chi-square, (4) PP-Fisher Chi-square. *, †, and ‡ mean stationarity significant at 10%, 5%, and 1%. For
all the tests, the null hypothesis is that the panel contains a unit root. Probabilities for Fisher tests are
computed using an asymptotic Chi-square distribution, while the other tests assume asymptotic normality.
and countries. With a small exception (the t* test indicates the absence
of stationarity for investment and capitalisation in the case of Italy), all
variables are stationary and GMM models may be tested.
Methodology
Classical panel data analyses investigating the role of firms’ financial per-
formance on their investment behaviour usually use fixed effects models to
avoid the omitted variables bias. Therefore, along with previous studies, we
draw first on a panel fixed effects model (Equation 1).
Yi,t =α0 +α1Xi,t +βi + εi,t, (1)
where Yit is the dependent variable (inv), α0 is the intercept, βi represents
all the stable characteristics of firms from each country, Xit represents
the vector of independent financial performance variables, α are the co-
efficients, and εi,t is the error term.
Given the fact that our sample has a N > T structure (the number of
companies is much higher than the number of periods), we also test a
random model (Equation 2), which controls for all stable covariates (Allison
& Waterman, 2002). To select between these two static models, a Hausman
test is performed.
Volume 9, Issue 1, 2020
82 Claudiu Tiberiu Albulescu
Yi,t =α0 +α1Xi,t +βi +μi,t + εi,t, (2)
where μ represents between-entity errors and εi,t are the within-entity errors.
The results of the classic static models might be affected by an endo-
geneity bias. While the firms’ financial performance influences the invest-
ment behaviour in the wine industry, we can also expect that an increase
in investment will have a negative impact on liquidity and profitability in the
short-run, and an opposite effect in the long run. Further, static models do
not account for dynamics, where changes in explicative variables influence
the dependent variables after a time adjustment, that is, in the long run.
Therefore, we address the endogeneity issue applying a GMM approach. We
first resort to the dynamic-GMM estimator of Arellano and Bond (1991):
Δinvestmenti,t =
t−1∑
j=t−p
ϑjΔinvestment +α1Δcapitalisationi,t
+ α2Δliquidityi,t +α3Δprofitabilityi,t +Δμi,t +Δνi,t, (3)
where ϑ is the first lag of investment dynamics, μi,t and νi,t are the error
terms which vary over both firms and time, α are the coefficients of the
explanatory variables.
However, for large N and small T samples, the system-GMM might have
better properties (Blundell & Bond, 1998), since in the case of difference-
GMM estimator, lagged levels of regressors are considered poor instru-
ments and Δinvestmenti,t might be still correlated with Δνi,t. The system-
GMM estimator implies a system of two simultaneous equations, one in
level and one in first difference. In this case, both lagged first differences
and lagged levels of variables act as instruments.
Both GMM estimators might suffer from the proliferation of instruments
and a Sargan test is used for over-identifying restrictions related to instru-
ments. However, the Sargan test is not powerful enough in the presence of
too many instruments. Therefore, a Hansen test statistic should be used if
nonsphericity is suspected in the errors, which requires robust error correc-
tion (Roodman, 2009).
In conclusion, the two GMM estimators we use (difference- and system-
GMM) serve as different tools for testing the robustness of our findings. In
addition, we also check the robustness by using a two-step estimator in-
stead of the default one-step. The two-step estimator requires robust errors
and, in this case, the standard covariance matrix is robust to panel-specific
autocorrelation and heteroscedasticity. Further, in the two-step approach
the number of parameters does not grow with the number of estimated re-
gressors in the nonlinear GMM step. The autocorrelation issue is checked
with the Arellano-Bond tests (AR(1) and AR(2)) for autocorrelation, applied
to differenced residuals. While the AR(1) process usually rejects the null hy-
International Journal of Management, Knowledge and Learning
Investment Behaviour and Firms’ Financial Performance 83
Table 4 GMM Results for France (One-Step Results, GMM Errors)
Difference-GMM System-GMM
Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4
c 20.31‡ 20.49‡ 21.49‡ 21.73‡ 14.26‡ 14.24‡ 16.16‡ 16.05‡
lag(1) 0.000* 0.000 0.000* 0.000 0.001* 0.000 0.001* 0.000
cap –2.462‡ –2.268‡ –2.447‡ –2.254‡ –1.846‡ –1.692‡ –1.884‡ –1.731‡
lr –0.533 –0.364 –0.138 –0.284
cr –0.725 –0.659 –0.687 –0.730
roe 0.666‡ 0.671‡ 1.174‡ 1.196‡
roa 0.539 0.556 2.205‡ 2.261‡
Observations 1,986 2,317
Groups 331 331
Instruments 94 59
Sargan over-
identification
721.4
[0.00]
724.3
[0.00]
719.8
[0.00]
722.5
[0.00]
885.4
[0.00]
896.9
[0.00]
886.4
[0.00]
898.5
[0.00]
Notes lag(1) is the first lag of the dependent variable; capitalisation is considered strictly exogenous
while liquidity and profitability are endogenous variables; *, †, and ‡ means significance at 10%, 5% and
1%; inv – investment dynamics, cap – capitalisation ratio, lr – liquidity ratio, cr – current ratio, roe – return
on equity, roa – return on assets.
pothesis of no autocorrelation, the AR(2) test is more important as it helps
detecting the autocorrelation in levels.
Empirical findings
This section presents the results obtained for each country retained into
analysis. The findings of static estimators are presented in Tables 6, 9,
and 12 and serve as reference for potential comparisons with similar re-
searches. According to the fixed and random effects models, there is no
significant influence of firms’ financial performance on their investment be-
haviour in the case of France and Italy. However, the capitalisation and liquid-
ity negatively affect the investment dynamics in Spain, while the profitability
level has an opposite effect.
In what follows, we focus on the dynamic estimators’ results, and we
present the empirical findings for each country. For each estimator, four dif-
ferent models are tested (Models 1–4), resulting from an alternative use of
liquidity ratios (lr and cr) and profitability ratios (roe and roa). While liquid-
ity and profitability are considered endogenous variables, the capitalisation
ratio is included in estimations strictly as exogenous variable. There is no
theoretical intuition that shows a direct increase or decrease in the level of
capitalisation, following an increase in the level of investment.
Results for France
In the case of France, the first set of estimations (one-step results) shows
generally robust findings between difference- and system-GMM estimators
(Table 4). As expected, in all the cases the capitalisation level negatively in-
Volume 9, Issue 1, 2020
84 Claudiu Tiberiu Albulescu
Table 5 GMM Results for France (Two-Step Results, Robust Errors)
Difference-GMM System-GMM
Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4
c 12.48 11.55 13.52* 12.56 3.621 1.520 7.926 6.261
lag(1) 0.000‡ 0.000‡ 0.000‡ 0.000‡ 0.073 0.073 0.031 0.037
cap –2.123 –1.747 –2.113 –1.729 –0.015 –0.002 –0.026 –0.021
lr –0.397 –0.203 –2.430 –1.722
cr –0.550 –0.453 –1.593 –1.564
roe 0.654 0.668 0.237 –0.007
roa 0.536 0.603 0.880 0.456
Observations 1,986 2,317
Groups 331 331
Instruments 94 32
Arellano-Bond
test AR(1)
–1.339
[0.18]
–1.325
[0.18]
–1.340
[0.18]
–1.326
[0.18]
–1.320
[0.18]
–1.360
[0.17]
–1.330
[0.18]
–1.350
[0.17]
Arellano-Bond
test AR(2)
–0.447
[0.65]
–0.143
[0.88]
–0.474
[0.63]
–0.169
[0.86]
0.310
[0.75]
0.460
[0.64]
–0.020
[0.98]
0.080
[0.93]
Sargan over-
identification
7.170
[1.00]
10.29
[0.99]
19.70
[0.84]
18.40
[0.89]
Hansen over-
identification
27.62
[0.43]
24.52
[0.60]
22.66
[0.70]
21.74
[0.75]
Notes lag(1) is the first lag of the dependent variable; capitalisation is considered strictly exogenous
while liquidity and profitability are endogenous variables; *, †, and ‡ means significance at 10%, 5% and
1%; inv – investment dynamics, cap – capitalisation ratio, lr – liquidity ratio, cr – current ratio, roe – return
on equity, roa – return on assets.
Table 6 Results of Fixed and Random Effect Estimators for France
Variables Model 1 Model 2 Model 3 Model 4
F R F R F R F R
c 109.0
(106)
107.4
(68.73)
92.32
(108)
82.41
(69.39)
107.6
(108)
118.8
(71.34)
90.37
(111)
93.06
(72.18)
cap –2.158
(13.49)
–0.579
(4.287)
–4.123
(13.45)
–0.338
(4.290)
–2.173
(13.49)
–0.535
(4.277)
–4.155
(13.44)
–0.298
(4.281)
lr –1.639
(37.71)
–10.06
(26.49)
–3.861
(37.77)
–9.031
(26.52)
cr –0.315
(20.69)
–9.197
(15.33)
–1.157
(20.71)
–8.406
(15.34)
roe –7.237
(4.716)
–6.188*
(3.473)
–7.242
(4.714)
–6.220*
(3.473)
roa 1.431
(16.96)
–1.416
(10.89)
1.374
(16.95)
–1.520
(72.18)
Hausman test Prob > χ2 = 0.97 Prob > χ2 = 0.98 Prob > χ2 = 0.91 Prob > χ2 = 0.93
(recommended) (random) (random) (random) (random)
Notes F – fixed, R – random. *, †, ‡ means significance at 10%, 5% and 1%. Standard errors are reported
in brackets.
fluences the investment dynamics. This result states that an increase of the
capitalisation ratio might be made in the detriment of an increase in invest-
ments. While the liquidity is not important for the investment dynamics, the
profitability has a positive influence, as expected. However, this last result
International Journal of Management, Knowledge and Learning
Investment Behaviour and Firms’ Financial Performance 85
Table 7 GMM Results for Italy (One-Step Result, GMM Errors)
Difference-GMM System-GMM
Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4
c 24.24‡ 27.19‡ 16.26† 23.48‡ 7.021 9.288 –1.830 1.887
lag(1) –0.000 –0.000 –0.000 –0.000 –0.000 –0.001 –0.000 –0.001
cap –0.127 –0.177 0.075 –0.034 –0.076 –0.126 0.087 0.005
lr –3.858 –5.958 12.71‡ 10.25‡
cr 1.709 –1.953 –0.044 12.23‡ 9.779‡
roe –0.006 0.013 –0.031
roa –0.617 –0.399 0.655 1.149
Observations 2,010 2,345
Groups 335 335
Instruments 94 59
Sargan over-
identification
615.7
[0.00]
635.3
[0.00]
489.0
[0.00]
546.9
[0.00]
741.1
[0.00]
777.7
[0.00]
601.7
[0.00]
671.5
[0.00]
Notes lag(1) is the first lag of the dependent variable; capitalisation is considered strictly exogenous
while liquidity and profitability are endogenous variables; *, †, and ‡ means significance at 10%, 5% and
1%; inv – investment dynamics, cap – capitalisation ratio, lr – liquidity ratio, cr – current ratio, roe – return
on equity, roa – return on assets.
is influenced by the way the profitability is measured, a significant influence
being reported only in the case of roe.
The Sargan test shows, nevertheless, that these findings might be af-
fected by the proliferation of instruments. Therefore, in the second part we
have performed two-step estimation, where the number of maximum lags
for the dependent variable is set at one and for the explanatory variable
at two. In this case, the results do not indicate a significant influence of
financial performances on the investment dynamics (Table 5). The findings
are similar for both estimators and for all the models, and in agreement
with the static analysis (Table 6). Moreover, in this case, the Arellano-Bond
tests show no autocorrelation problem, while the Sargan and Hansen tests
indicate that the instruments are well identified.
We thus conclude that in the case of France, the capitalisation nega-
tively impacts the investment dynamics, while the profitability has a positive
impact. The liquidity has no significant influence on investment. However,
these findings might be influenced by the over-identification of instruments
and are not confirmed by the two-step estimation, which puts into question
their robustness.
Results for Italy
In the case of the Italian wine industry, the default one-step estimation
shows no significant influence of financial performance on investment dy-
namics, except for the liquidity ratios for the system-GMM approach. Table
7 shows no significant impact of capitalisation and profitability, while the
Sargan over-identification test indicates a proliferation of instruments is-
Volume 9, Issue 1, 2020
86 Claudiu Tiberiu Albulescu
Table 8 GMM Results for Italy (Two-Step Results, Robust Errors)
Difference-GMM System-GMM
Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4
c 13.29‡ 15.69‡ 6.902 13.04‡ 12.98‡ 13.70‡ 12.89‡ 13.60
lag(1) –0.000 –0.000 –0.000 –0.000 0.016 0.003 0.033* 0.007
cap –0.099 –0.111 0.083 –0.027 –0.086 –0.105 –0.089 –0.081
lr –3.952 –6.044† –1.548 –1.127
cr 1.675 –1.969 –0.956 –0.738
roe –0.006 0.008 0.026 0.057
roa –0.745 –0.491 –0.199 –0.115
Observations 2,010 2,345
Groups 335 335
Instruments 94 59
Arellano-Bond
test AR(1)
–1.716
[0.08]
–1.715
[0.08]
–1.717
[0.08]
–1.716
[0.08]
–1.750
[0.08]
–1.720
[0.08]
–1.750
[0.08]
–1.720
[0.08]
Arellano-Bond
test AR(2)
0.321
[0.74]
0.161
[0.87]
0.686
[0.49]
0.454
[0.64]
0.850
[0.39]
0.610
[0.54]
1.150
[0.25]
0.730
[0.46]
Sargan over-
identification
3.260
[1.00]
4.280
[1.00]
2.580
[1.00]
3.690
[1.00]
Hansen over-
identification
30.99
[0.27]
27.55
[0.43]
31.32
[0.25]
29.78
[0.32]
Notes lag(1) is the first lag of the dependent variable; capitalisation is considered strictly exogenous
while liquidity and profitability are endogenous variables; *, †, and ‡ means significance at 10%, 5% and
1%; inv – investment dynamics, cap – capitalisation ratio, lr – liquidity ratio, cr – current ratio, roe – return
on equity, roa – return on assets.
Table 9 Results of Fixed and Random Effect Estimators for Italy
Variables Model 1 Model 2 Model 3 Model 4
F R F R F R F R
c 71.71
(45.56)
45.86
(35.54)
73.77
(46.70)
47.18
(35.85)
73.94
(52.33)
49.99
(38.61)
75.99
(53.30)
50.82
(38.83)
cap –0.388
(3.452)
–0.829
(2.458)
–0.364
(3.454)
–0.839
(2.458)
–0.387
(3.453)
–0.867
(2.463)
–0.363
(3.455)
–0.873
(2.463)
lr –21.44
(27.28)
6.393
(12.51)
–21.40
(27.28)
7.107
(12.74)
cr –14.15
(22.06)
1.522
(11.21)
–14.12
(22.06)
1.885
(11.34)
roe 0.000
(1.043)
0.036
(0.734)
0.009
(1.043)
0.042
(0.734)
roa –2.483
(12.28)
–2.221
(7.782)
–2.491
(12.28)
–1.583
(7.730)
Hausman test Prob > χ2 = 0.71 Prob > χ2 = 0.69 Prob > χ2 = 0.86 Prob > χ2 = 0.85
(recommended) (random) (random) (random) (random)
Notes F – fixed, R – random. *, †, ‡ means significance at 10%, 5% and 1%. Standard errors are reported
in brackets.
sue. These findings are this time confirmed by the two-step estimations
with robust errors and we notice once again the lack of a significant influ-
ence of firms’ financial performance on their investment dynamics in Italy
(Table 8). As in the case of France, the two-step estimations for Italy do not
present autocorrelation or over-identification problems.
International Journal of Management, Knowledge and Learning
Investment Behaviour and Firms’ Financial Performance 87
Table 10 GMM Results for Spain (One-Step Results, GMM Errors)
Difference-GMM System-GMM
Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4
c 14.02‡ 12.51‡ 14.79‡ 13.26‡ 16.10‡ 15.35‡ 16.69‡ 15.91‡
lag(1) 0.052† 0.054‡ 0.050‡ 0.052‡ 0.023 0.020 0.023 0.020
cap –0.236† –0.193* –0.217† –0.174* –0.336‡ –0.320‡ –0.319‡ –0.303‡
lr –1.580‡ –1.565‡ –0.940‡ –0.912‡
cr –1.137‡ –1.128‡ –0.770‡ –0.752‡
roe 0.067* 0.067* 0.075† 0.075†
roa 0.325 0.326 0.400* 0.411*
Observations 2,652 3,094
Groups 442 442
Instruments 94 59
Sargan over-
identification
215.7
[0.00]
202.0
[0.00]
211.2
[0.00]
199.8
[0.00]
190.0
[0.00]
228.2
[0.00]
185.8
[0.00]
222.9
[0.00]
Notes lag(1) is the first lag of the dependent variable; capitalisation is considered strictly exogenous
while liquidity and profitability are endogenous variables; *, †, and ‡ means significance at 10%, 5% and
1%; inv – investment dynamics, cap – capitalisation ratio, lr – liquidity ratio, cr – current ratio, roe – return
on equity, roa – return on assets.
Results for Spain
The first set of results recorded for Spain (Table 10) shows that, in the case
of a one-step classical estimation, the capitalisation ratio has a significant
and negative impact on investment for all tested models, while the prof-
itability has a positive impact, regardless the way profitability is computed.
For firms acting in Spain, we notice that liquidity negatively influences the
investment behaviour. Namely, firms that decide to increase their liquidity
accept a reduction in the investment growth rate and conversely, the in-
crease of investment is made in the detriment of the liquidity level. This
result can be explained by the fact that Spanish wine companies might use
their own funds with predilection, to finance the investment opportunities.
The two-step estimation partially confirms the one-step findings, al-
though the significance of results decreases (Table 11). For the difference-
GMM estimator, for all the models, we notice a negative impact of capital-
isation and liquidity, and a positive influence of profitability on the invest-
ment dynamics. However, for the system-GMM estimator, the significance
of liquidity and profitability’s coefficients is no longer recorded.
If in the case of the one-step estimators the Sargan test indicates an in-
strument over-identification problem, in the case of the two-step estimators,
the Sargan and Hansen tests show that instruments are well identified, and
the autocorrelation test shows no autocorrelation bias, especially for the
system-GMM specification.
Summary of Results, Comparisons and Policy Implications
This section presents a short overview of the empirical findings in a com-
parative manner and discusses different financial management strategies
Volume 9, Issue 1, 2020
88 Claudiu Tiberiu Albulescu
Table 11 GMM Results for Spain (Two-Step Results, Robust Errors)
Difference-GMM System-GMM
Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4
c 6.610‡ 7.872‡ 8.229‡ 8.831‡ 5.624‡ 4.469† 5.614‡ 4.182†
lag(1) 0.065‡ 0.065‡ 0.062‡ 0.062‡ 0.019 –0.095 0.009 –0.068
cap –0.103 –0.149* –0.119* –0.140* –0.079‡ –0.038 –0.077‡ –0.030
lr –1.499* –1.528* –0.174 –0.167
cr –1.036† –1.062† –0.053 –0.105
roe 0.062† 0.057 –0.009 0.023
roa 0.437* 0.361 0.699 0.820
Observations 2,652 3,094
Groups 442 442
Instruments 94 32
Arellano-Bond
test AR(1)
–3.171
[0.00]
–3.179
[0.00]
–3.153
[0.00]
–3.165
[0.00]
–2.080
[0.03]
–2.100
[0.03]
–2.250
[0.02]
–2.440
[0.01]
Arellano-Bond
test AR(2)
1.687
[0.09]
1.628
[0.10]
1.550
[0.12]
1.515
[0.12]
0.210
[0.83]
–0.059
[0.55]
0.170
[0.86]
–0.470
[0.64]
Sargan over-
identification
55.01
[0.00]
59.66
[0.00]
46.77
[0.02]
52.48
[0.00]
Hansen over-
identification
19.92
[0.83]
26.70
[0.48]
21.57
[0.75]
28.62
[0.38]
Notes lag(1) is the first lag of the dependent variable; capitalisation is considered strictly exogenous
while liquidity and profitability are endogenous variables; *, †, and ‡ means significance at 10%, 5% and
1%; inv – investment dynamics, cap – capitalisation ratio, lr – liquidity ratio, cr – current ratio, roe – return
on equity, roa – return on assets.
Table 12 Results of Fixed and Random Effect Estimators for Spain
Variables Model 1 Model 2 Model 3 Model 4
F R F R F R F R
c 14.50‡
(2.338)
9.399‡
(35.54)
13.82‡
(2.364)
8.747‡
(1.116)
15.17‡
(2.367)
9.845‡
(1.103)
14.49‡
(2.392)
9.176‡
(38.83)
cap –0.241‡
(0.770)
–0.092‡
(0.025)
–0.225‡
(0.077)
–0.075‡
(0.026)
–0.231‡
(0.077)
–0.086‡
(0.025)
–0.214‡
(0.077)
–0.068†
(0.026)
lr –0.629†
(0.270)
–0.095
(0.189)
–0.631†
(0.270)
–0.123
(0.189)
cr –0.597‡
(0.206)
–0.234
(0.144)
–0.599‡
(0.206)
–0.253*
(0.144)
roe 0.052*
(0.029)
0.044*
(0.026)
0.052*
0.029)
0.045*
(0.026)
roa 0.286
(0.180)
0.352†
(0.139)
0.291
(0.179)
0.355†
(0.139)
Hausman test Prob > χ2 = 0.00 Prob > χ2 = 0.01 Prob > χ2 = 0.01 Prob > χ2 = 0.01
(recommended) (fixed) (fixed) (fixed) (fixed)
Notes F – fixed, R – random. *, †, ‡ means significance at 10%, 5% and 1%. Standard errors are reported
in brackets.
that seem to be implemented by the firms acting in the wine industry from
the largest worldwide producers. Table 13 shows that our empirical findings
are in general robust to different estimators and models we have used but
are sensitive to the way we address the proliferation of instrument issue.
International Journal of Management, Knowledge and Learning
Investment Behaviour and Firms’ Financial Performance 89
Table 13 Results’ Centralization
Country Invest. dynamics Difference-GMM System-GMM
One-step Two-step One-step Two-step
France Capitalisation N – N –
Liquidity – – – –
Profitability P – P –
Italy Capitalisation – – – –
Liquidity – – P –
Profitability – – – –
Spain Capitalisation N N N N
Liquidity N N N –
Profitability P P P –
Notes P – means positive infuence, N – negative significant influence, – indicates no signif-
icant influence.
We can notice that, in the case of Italy, the financial performance of wine
industry companies does not influence their investment behaviour. That is,
the investment decision is based on other factors (e.g. market conditions),
and we may suppose these companies extend their production capacity by
accessing external funds, in the detriment of internal sources. This result
might also indicate a lack of inertia regarding the investment dynamics in
the aftermath of the recent global financial crisis. For the French wine com-
panies, the degree of capitalisation and the level of profitability represent
reliable factors that influence their investment dynamics. In general, the
profitability favours the investment decision, while a trade-off is recorded
between investment and capitalisation. It appears that internal funds play
their role in the investment behaviour, although the results in case of France
are not very robust. In the case of Spanish wine companies, we notice an
important role of financial performance in influencing their investment be-
haviour. On the one hand, the capitalisation and liquidity ratios have a neg-
ative influence on the investment dynamics. On the other hand, a higher
profitability represents a prerequisite for increasing the investment level.
These findings are quite robust and show that Spanish managers from the
wine industry prefer the internal funds to extend their business. The results
reported for Spain indicate the existence of a trade-off between capitalisa-
tion and liquidity on the one hand, and investment dynamics on the other
hand. Moreover, these results confirm the potential trade-off between liq-
uidity and profitability underlined by previous researches.
Conclusions
The purpose of this paper was to investigate how firms’ investment be-
haviour is influenced by their financial performance. With a focus on the
Volume 9, Issue 1, 2020
90 Claudiu Tiberiu Albulescu
wine industry from the largest EU producers, namely France, Italy and Spain,
we use firm-level data for a large set of companies to perform this investiga-
tion. Our panel data analysis covers the post-crisis period (2007 to 2014)
and relies on dynamic model specifications.
The findings show different investment strategies for firms located in
these countries. It appears that the investment behaviour of Italian firms
is not influenced by their financial performance. In addition, in the case
of French companies, only the capitalisation and the profitability ratio are
important for the investment decision, while the influence of liquidity is
insignificant. However, these results are partially robust and might be af-
fected by the over-identification of the instruments used in the analysis.
Finally, interesting and robust results are reported for Spanish firms. We
show that the financial performance of wine companies is very important
for their investment behaviour. If a negative impact is recorded in the case
of capitalisation and liquidity, a positive influence is noticed for the prof-
itability level. This means that the profits are usually re-invested by Spanish
companies, and that internal funds are preferred by managers to sustain
their investment decision. These findings support the growing importance
of the Spanish wine industry at global level and have noteworthy policy im-
plications for financial managers acting in these companies, as well as for
the national authorities interested in the development and increased per-
formance of the wine sector.
Acknowledgements
This work was supported by a grant of the Romanian National Authority for
Scientific Research and Innovation, CNCS – UEFISCDI, project number PN-III-
P1-1.1-TE-2016-0142.
Notes
1. Uncertainty is in general associated with the lack of forecast accuracy (Al-
bulescu et al., 2017). A recent paper by Chen et al. (2017) shows that the
quality of analysts’ forecasts significantly increases the efficiency of firms’
investment.
2. A distinct category of internal factors explaining firms’ investment behaviour
might be related to the technological capabilities (for a discussion, please
see the recent paper by Kang et al., 2017).
3. The EU countries do not only represent the largest wine exporters. For exam-
ple, the United Kingdom is considered to be one of the largest wine importers
(Anderson & Wittwer, 2017).
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Dr Claudiu Tiberiu Albulescu is currently full Professor at the Management
Department, Faculty of Management in Production and Transportations, Po-
litehnica University of Timisoara. He is associated researcher at CRIEF, Uni-
versity of Poitiers, and associated professor at the Doctoral School of Eco-
nomics and Business Administration within the West University of Timisoara.
His research interests are financial macroeconomics, energy economics,
banking and finance, corporate finance, entrepreneurship and innovation.
claudiu.albulescu@upt.ro
International Journal of Management, Knowledge and Learning