THE IMPACT OF THE RUSSIA-UKRAINE CONFLICT ON
PROFITABILITY AND VALUATION OF INDONESIAN
COAL STOCKS
Erman Sumirat, Dzikri Firmansyah Hakam and
Irfan Sihab Budin F
Institut Teknologi
Bandung
Email: erman.sumirat@sbm-itb.ac.id,
dzikri.hakam@sbm-itb.ac.id, irfan_sihab@sbm-itb.ac.id
Abstract
This research
investigates the impact of the 2022 Russian invasion of Ukraine on the
profitability and valuation of publicly listed Indonesian coal companies.
Leveraging panel regression, the study offers empirically grounded perspectives
on how an acute geopolitical conflict disrupted global energy trade flows to
influence key financial metrics of a major exported commodity. Specifically,
the analysis focuses on five leading Indonesian coal miners over 2019-2022.
Profitability dynamics are examined through gross profit margins while
valuations reliance on price-to-earnings ratios. Control variables capture coal
prices, a conflict indicator, inflation, interest rates, and exchange rate
fluctuations. Results reveal the global coal price spike had a statistically
significant positive impact on profit margins, affirming the turmoil's
commodity super-cycle upside. However, valuations diverged from earnings
trends, suggesting more complex reassessments of long-term prospects. The
conflict itself directly affected profitability but not valuations. This research
contributes timely empirical insights on an understudied intersection of
geopolitics, commodity markets, and emerging market equities. Evidence-based
quantification of financial linkages and risk transmission inform both theory
and practice. Strategic decision makers obtain granular clarity regarding
exposures and opportunities during energy market turmoil.
Keywords: geopolitical
conflict; profitability; valuation; panel regression
Introduction
The Russian invasion of
Ukraine on February 24, 2022, triggered heightened volatility in global
commodity markets, including the coal sector (Avis, 2022). As a major exporter, the circumstances presented
both opportunities and critical risks for Indonesian coal producers. The issue
lies in the uncertainty surrounding whether Indonesian coal companies can
sustain higher profitability and how
stock valuations and market movements are reflecting the conflict's risks and
prospects.
%203646-3659_files/image002.jpg)
Figure 1
Global coal price around Russian-Ukraine
conflict
Multiple academic studies have
analysed associations between geopolitical conflicts and fluctuations in
profitability, pricing multiples, and systematic risk exposure of related
industries like oil and gas (Zhang et al., 2023) and (Devadoss & Ridley, 2024). However, literature focusing on emerging coal
markets remains sparse. This poses strategic dilemmas for Indonesian coal firms
attempting to translate short-term performance windfalls into lasting market
share and competitive advantages (Kim, 2021).
The Russian-Ukrainian conflict
sparked turmoil in global commodity markets, presenting strategic opportunities
and risks for major Indonesian coal exporters (Avis, 2022). While research on emerging coal markets is sparse
(Kim, 2021), past geopolitical conflicts have destabilized
related industries like oil and gas) and airlines. Initially, top Indonesian
coal companies saw surging profitability, with gross margins jumping 50-250%
over the last five years, indicating strengthened market positions. However,
with profits relying on conflict-induced price spikes, a resolution could
prompt sharp declines. The lack of research on correlations between
geopolitical events and Indonesian coal profitability makes it difficult to
predict future performance. Though low 2022 Price-to-Earnings ratios signal
undervaluation despite rising profits, more analysis is needed to determine
whether this stems from the conflict itself or other factors. Overall,
Indonesian coal firms face strategic dilemmas leveraging short-term windfalls
into lasting competitive advantages without clarity on how geopolitical
uncertainties could reshape market dynamics.
The research questions for this research are “how
does the Russian-Ukraine conflict influence the profitability of coal
companies?” and “how does the Russian-Ukraine conflict influence the valuation
of coal companies?”. This research questions are devolopes
into six hypotheses that will be tested using panel data regression.
The first part of hypotheses for this research that
are related to the first research question are as follows:
H1:
The increase in coal prices due to the conflict has a significant positive
impact on the gross profit of coal companies.
H2:
The presence of conflict, represented by a dummy variable, has a significant
impact on the profitability of coal companies.
H3:
Macroeconomic factors altered by the conflict have a significant impact on the
Rationale: The Russia-Ukraine conflict disrupted
energy markets, triggering a spike in global coal prices as countries sought
cheaper alternatives to Russian oil and gas (Colgan et al., 2023). Economic theory suggests
higher commodity selling prices directly translate to increased revenues and
profitability for producers (de Gorter et al., 2021). Empirically, oil and gas
companies exhibited positive gross profit movements in response to
geopolitics-driven oil price shocks (Yakovleva & Nickless, 2022). This precedent
establishes the plausibility of a similar impact on coal company profitability.
The second part of hypotheses for this research
that are related to the first research question are as follows:
H4:
The increase in coal prices due to the conflict has a significant impact on the
valuation of coal companies.
H5:
The presence of conflict, represented by a dummy variable, has a significant
impact on the valuation of coal companies.
H6:
Macroeconomic factors altered by the conflict have a significant impact on the
valuation of coal companies.
Rationale: Theoretical and empirical research shows
commodity price booms directly impact relevant companies' valuations through
cashflow effects on earnings multiples like the price-to-earnings (P/E) ratio (Odiero, 2013). As the coal price spiked
due to shifted energy dynamics during the conflict, corresponding revaluation
likely occurred.
Furthermore, investor risk perceptions tend to
increase amidst geopolitical conflicts, raising systematic risk and equity risk
premiums - key inputs into valuation models (Stoupos et al., 2023). This distinct conflict
effect gets captured through a dummy indicator. Moreover, fluctuations in macro
variables like inflation and interest rates impacted the discount rates applied
in valuation, driving changes separate from coal prices.
Specifically in Indonesia, the coal sector
represents a large weighting in the overall stock index at over 7% (Purwantara et al., 2023). Significant foreign
investor ownership of key coal equities also saw perceptions of Indonesia
country risk factor into valuations. As emerging markets grew sensitive to
global instability from the conflict, valuations adjusted downwards (David & Veronesi, 2022).
In summary, theory and evidence on geopolitics
swinging commodity valuations, altering risk metrics, shifting macroconditions,
and amplifying country risk supports hypotheses of the conflict distinctly
impacting Indonesian coal stock valuations through prices, uncertainty, and
macro financial channels. Panel regression analysis will empirically assess
significance.
This research will analyze the relationship between
Russian-Ukraine conflict on Indonesian coal stocks’ profitability and valuation
using panel data regression and analyze the possible cause and the implication
of the results.
Methode
This study employed a quantitative research design to
objectively measure the impact of the Russia-Ukraine conflict on Indonesia's
coal stock market.
The data came from secondary data such as financial statement, historical stock
price, coal price, and macroeconomic indicators that is publicly listed on www.idx.com, www.barchart.com, and www.bi.go.id.
The research used panel data regression as an analytic tool
following previous research that had been about the impact of Covid-19 on
dividend policy and the impact of political instability on certain commodities (Ahmed
et al., 2021). All simulations were performed
using R application. The panel data regression used two proxy variables for
dependent variables to represent the profitability and valuation of the
company. For independent variables, five proxy variables were used to represent
the impact of the conflict on macroeconomic and industrial level. The samples
consisted of five biggest Indonesian coal companies (PTBA, ADRO, ITMG, BYAN,
and GEMS) to represent the market capitalization of all Indonesian coal stocks.
The timeframe for the analysis was five years from 2018 to 2022.
The proxy variables used for this research were as follows:
Table 1 Proxy variables
for panel data regression
|
Variable |
Proxy Variable |
Formula |
|
Profitability |
Gross Profit |
Revenue - COGS |
|
Valuation |
P/E ratio |
Price / EPS |
|
The impact of conflict |
Coal price |
Newcastle Coal Price |
|
Conflict dummy |
1 = conflict 0 = no conflict |
|
|
Inflation rate |
Indonesian Inflation rate |
|
|
Interest rate |
BI-7day RR |
|
|
Exchange rate |
USDIDR |
The
using of proxy variables was caused by the impact of the conflict could not be
quantified directly, so intermediary variables were chosen to quantify the
impact of the conflict (Dworschak,
2021).
The variables were chosen
based on these justifications:
- Global coal price: As one of the primary commodities impacted by
geopolitical conflict, coal prices can serve as a direct indicator of
market sentiment and supply chain disruptions. Fluctuations in coal prices
can significantly impact a company's profitability and valuation (Mayliza
et al., 2020).
- Conflict Time Dummy: The inclusion of a conflict time dummy is based
on the event-study methodology, widely used in finance to capture the
impact of a significant event (like geopolitical conflict) on a dependent
variable. In this case, the dummy variable helps isolate the effects of
the Russia-Ukraine conflict on the coal companies' profitability and
valuation.
- Gross Profit: Gross profit is a fundamental measure of a company's
operational efficiency and its ability to turn raw inputs into earnings.
It reflects the difference between sales and the cost of goods sold.
Higher gross profit can indicate better cost management and productive
efficiency, which is a direct contributor to the financial health of a
company. Furthermore, the gross profit is the metric that can help in
giving business solutions to the coal companies by focusing on the core
business.
- P/E ratio: The P/E ratio was chosen as a proxy variable to represent
valuation of Indonesian coal companies because it is the familiar
parameter that is used to value companies by investors. The P/E ratio is
commonly used as a valuation metric in financial markets. It is the
market's assessment of the earning capability of a firm relative to its
industry peers. The ratio encapsulates the market's expectation of future
earnings growth, risk, and the required rate of return (Aswath Damodaran,
"Investment Valuation"). It also can be analysed
using time-series and cross-section which absolute valuation such as
Discounted Cash Flow cannot achieve.
- Interest Rates: Interest rates directly affect the cost of capital
for businesses and can significantly impact investment decisions and
profitability within the coal industry. Interest rates can have both
direct and indirect effects on a company's profitability. Higher interest
rates increase the cost of borrowing and affect the financial expenses (Tinungki
et al., 2022).
- Inflation: Inflation rates can influence operational costs, coal
prices, and ultimately the profitability of coal companies. Inflation can
erode purchasing power and affect both costs and revenues. Companies might
be unable to pass increased costs onto consumers, affecting their
profitability. It serves as a control variable to isolate such
macroeconomic effects from the impact of the conflict.
- Exchange Rates: As coal is a globally traded commodity, exchange
rates play a pivotal role in determining export competitiveness and
revenue in local currency terms.
Results and Discussions
There were two models being used for
this research, with gross profit and PE ratio as dependent variables. The
dependent variables were the result from macroeconomic and industry analysis.
The models and explanations of each variable are as follows:
GP=α + α
COAL + β2CONFLICT_Dummy + β3INTEREST + β4INFLATION
+ β5INFLATION + u)
PER=α + β1COAL
+ β2CONFLICT_Dummy + β3INTEREST + β4INFLATION
+ β5INFLATION + u)
Where
GP: Gross profit
PER: P/E ratio
α: intercept
β: coefficient of each independent variables
u: residual
Table 2 Statistic descriptive
|
|
GP |
PER |
Coal |
Conflict dummy |
Interest |
Inflation |
Exchange rate |
|
Min |
5,58 |
1,15 |
3,94 |
0 |
3,5% |
1,4% |
9,53 |
|
1st
Quar |
7,22 |
1,73 |
4,23 |
0 |
3,5% |
1,7% |
9,57 |
|
Median |
7,78 |
2,01 |
4,64 |
0 |
4,3% |
2,9% |
9,58 |
|
Mean |
7,89 |
2,07 |
4,77 |
0,2 |
4,5% |
2,8% |
9,58 |
|
3rd Qua |
8,42 |
2,28 |
5,14 |
0 |
5,3% |
3,3% |
9,60 |
|
Max |
9,98 |
3,23 |
6,04 |
1 |
6,0% |
5,5% |
9,66 |
Table 2 shows the statistical descriptive of each variable in
the model. Gross profit, coal price, and exchange rate were using logarithmic
transformations to help stabilise the variance of the
error term, especially when the variance of the residuals increases with the
level of the dependent variable (heteroscedasticity). By compressing larger
values and expanding smaller values, the transformation would make the spread
of the residuals more consistent across different levels of the dependent
variables. Table 2 shows that the gross profit and coal prices showed
substantial variation, Interest rates and inflation demonstrated moderate
variability, and exchange rates remained relatively stable.
Table 3 Chow, Haussman
and Lagrange Test
|
|
Chow Test |
Haussman Test |
Lagrange Test |
|||
|
Cross Section X |
p-value |
Cross Section Random |
p-value |
Cross Section Random |
p-value |
|
|
(Statistics) |
(X Statistics) |
(X Statistics) |
||||
|
GP |
7 |
5,25E-05 |
37,724 |
4,29E-07 |
- |
- |
|
PER |
1,427 |
0,23 |
- |
- |
0,0634 |
0,475 |
The Chow test result for Gross Profit model showed p-value
<0.05 that indicates the Fixed Effect Model is a better model compared to
the Common Effect Model. The Hausman Test was conducted next to compare between
Fixed Effect Model (FEM) and Random Effect Model (REM). The result shows
p-value <0.05 that indicates the best model is Fixed Effect Model (FEM). The
Lagrange Multiplier test was not carried out because Chow and Hausman
consistently selected FEM (Tinungki
et al., 2022).
The result of the Chow test for P/E ratio model showed a
p-value >0.05 that indicates Common Effect Model is a better model compared
to the Fixed Effect Model. Lagrange Test was conducted next to compare between
Common Effect Model and Random Effect Model The result shows p-value >0.05
that indicates the best model is Common Effect Model. The Haussman test was not
carried out because Chow and Lagrange tested consistently selected CEM (Tinungki
et al., 2022).
Table 4 Result of
Goodness Fit Test
|
|
GP |
PER |
|
F-statistic |
61,672 |
33,9061 |
|
p-value |
2,22E-16 |
2,22E-16 |
|
R-squared |
0,826 |
0,68627 |
|
Adj. R-Squared |
0,802 |
0,66603 |
|
Total Sum of Squares |
29,353 |
29,76 |
|
Residual Sum of Squares |
5,110 |
9,3365 |
The goodness of fit tests indicates the regression model for
Gross Profit strongly explains the variability in profitability of Indonesian
coal companies. The R-squared of 0.802 shows 80.2% of profitability changes are
accounted for by the independent variables. The high adjusted R-squared of
0.802 confirms this even after considering the number of variables.
Additionally, with a statistically significant F-statistic, the model is valid.
In summary, the tests demonstrate high explanatory power, good model fit and
that the conflict, coal prices and other factors significantly influence
profitability. This supports the robustness of using the model to evaluate the
geopolitical event's impact.
Meanwhile the model evaluating factors impacting the
price-to-earnings ratios of Indonesian coal companies demonstrates strong
explanatory power and statistical validity. An R-squared of 0.686 indicates
almost 69% of changes in the PE ratios are accounted for by the independent
variables like coal prices and conflict events. The high adjusted R-squared of
0.666 further supports the model's robustness even considering the number of
variables. Additionally, with a significant F-statistic, the overall model itself
is valid. In summary, the goodness of fit assessments shows the model strongly
fits the data and significantly explains PE ratio movements, supporting its use
for determining how much the conflict and other factors drive market valuation
changes.
Table 5 Classical
Assumption Test
|
|
Normality |
Multicollinearity |
Autocorrelation |
Heteroscedasticity |
|
p-value of Shapiro |
VIF |
p-value of BG Test |
p-value of BP Test |
|
|
GP |
0,5368 |
No Multicollinearity |
0,06672 |
0,8042 |
|
PER |
0,00006 |
No Multicollinearity |
0,097 |
0,375 |
Tests were conducted to ensure the gross profit and P/E ratio
models did not violate classical assumptions. The gross profit model passed
tests for normality, multicollinearity, autocorrelation, and
heteroscedasticity, indicating no violations. However, the P/E ratio model
failed the normality test with a p-value below 0.05, showing the error is not
normally distributed and this assumption is violated. Still, the P/E model
passed tests for multicollinearity, autocorrelation, and heteroscedasticity. In
summary, while the gross profit model does not violate any classical
assumptions, the normality assumption is violated in the P/E ratio model (Alghifari
et al., 2022). However, the other assumptions
remain valid, though this normality issue should be considered when
interpreting the P/E model results. To address the normality issue for P/E
ratio model, robust standard error was performed ().
Table 6 Panel Data
Regression Test for Gross Profit Model
|
Coefficient |
Estimate |
Std. Error |
t-value |
p-value |
|
COAL |
1,162 |
0,138 |
8,413 |
5,397E-12 |
|
CONFLICT DUMMY |
-0,456 |
0,183 |
-2,48 |
0,016 |
|
INFLATION |
2,0846 |
5,648 |
0,369 |
0,713 |
|
INTEREST |
9,51 |
5,83 |
-1,631 |
0,108 |
|
EXCHANGE RATE |
2,1979 |
1,419 |
1,544 |
0,128 |
Table 6 shows the result of
panel data regression for each independent variable. Coal price has a positive
coefficient and p-value less than 0.05 that means it has a positive and
significant relationship with gross profit. This indicates the profitability of
Indonesian coal companies is highly affected by the movement of global coal
price and hypothesis 1 can be accepted. This result is supported by previous
research where oil price shocks affect the financial performance of U.S. oil
and gas companies significantly. Conflict dummy has p-value less than 0.05 that
means the presence of Russian-Ukraine conflict has significant impact on the
profitability of Indonesian coal companies and hypothesis 2 can be accepted.
Inflation, interest rate, and exchange rate has p-value greater than 0.05 that
means macroeconomic factors do not have significant impact on the profitability
of Indonesian coal companies and hypothesis 3 is rejected.
The positive and significant relationship between coal prices
and profitability aligns with the current market dynamics. Global coal prices
in 2022 reached record highs due to strong post-pandemic demand and supply
constraints. Indonesian coal companies reaped the benefits through increased
profit margins during this super cycle, consistent with hypothesis 1. Past
literature documents similar boosts in oil and gas earnings from price spikes
caused by geopolitical tensions disrupting energy trade flows (Li et
al., 2023).
Confirmation of hypothesis 2 highlights the uniqueness of the
Russia-Ukraine conflict as an epochal geopolitical event driving volatility
beyond just commodity prices. The significant dummy coefficient conforms recent
findings that major conflicts directly increase firms' systematic risk exposure
and cashflow uncertainty from altered consumer behaviours
(Stoupos
et al., 2023). Indonesian coal miners likely
confronted demand fluctuations, shifting contract terms, and inventory
revaluations tied to the conflict itself.
Rejection of hypothesis 3 counterintuitively implies
Indonesian coal profitability had insulation from broader macroeconomic
instability triggered by the conflict, like imported inflation and rising
interest rates which squeezed margins in other sectors. A potential explanation
is the sector's export-orientation and currency hedging allowing relief from
domestic monetary impacts. Furthermore, as a global price-taker, Indonesian
firms possibly passed higher input costs to international coal customers amidst
tight supply conditions.
Table 7 Panel Data
Regression Result for P/E Rasio Model
|
Coefficient |
Estimate |
Std. Error |
t-value |
Pr(>|t|) |
|
COAL |
-0,306 |
0,098 |
-3,113 |
0,002 |
|
CONFLICT DUMMY |
-0,032 |
0,06 |
0,546 |
0,586 |
|
INFLATION |
5,236 |
2,4 |
2,182 |
0,032 |
|
INTEREST |
-0,677 |
6,66 |
-0,1017 |
0,919 |
|
EXCHANGE RATE |
1,041 |
1,113 |
0,935 |
0,352 |
|
LAGGED PER |
0,729 |
0,049 |
14,738 |
2,2E-16 |
Table 7 shows the result of robust standard
error of panel data regression for each independent variable. Coal price has a
negative coefficient and p-value less than 0.05 that means it has a negative and
significant relationship with gross profit. This indicates the valuation of
Indonesian coal companies is highly affected by the movement of global coal
price and hypothesis 4 can be accepted. This result is supported by previous
research where there is significant effect on the role oil price on GCC stock
markets during the Gulf War as an exogenous geopolitical shock (Alqahtani & Klein, 2021). Conflict dummy
has p-value greater than 0.05 that means the presence of Russian-Ukraine
conflict has no significant impact on the valuation of Indonesian coal
companies and hypothesis 5 is rejected. Inflation, interest rate, and exchange
rate has p-value greater than 0.05 that means macroeconomic factors do not have
significant impact on the valuation of Indonesian coal companies and hypothesis
6 is rejected.
The negative and significant coal price
coefficient contradicts expectations but aligns with falling price-to-earnings
(P/E) ratios for Indonesian coal stocks in 2022 despite record coal prices.
This inverse relationship implies valuations fell even as commodity earnings
rose, accepting hypothesis 4. One explanation is investors applying higher
discount rates amid economic uncertainty, or perceptions that peak coal demand
is near despite current profitability. Similar trends occurred during the 1970s
oil crisis where petroleum valuations dropped before recovering (Alqahtani & Klein, 2021).
Dismissal of hypothesis 5 reinforces
that the Russia-Ukraine conflict itself did not significantly impact
valuations. This suggests the downturn had more complex origins than just
geopolitical tensions increasing systematic risk (Stoupos et al., 2023). Potential
factors include pre-existing environmental policies accelerating energy
transitions in major markets like China and the EU, tamping Indonesian coal
growth prospects.
Additionally, rejection of hypothesis 6
indicates domestic inflation and interest rate shifts tied to the conflict did
not affect valuations. This contrasts with evidence that monetary conditions
sway equity risk premiums. However, currency hedging and foreign investor
ownership may have cushioned valuations from Indonesian monetary impacts (Purwantara et al., 2023).
The results of this research can be used by stakeholders
related to coal industry to strategize during the conflict. Some business
solution that can be implemented based on for stakeholders are as follows:
Coal Companies
1.
Leveraging
periods of geopolitical supply constraint: Global coal prices tend to spike
during geopolitical conflicts that disrupt exporting regions, as seen with the
Russia-Ukraine war tightening supply (Doshi,
2023). Indonesian coal companies can capitalise on these supply squeezes by locking in long-term
export contracts at inflated spot prices to lock in wider profit margins. This
provides an earnings buffer even as prices normalise
post-conflict. Looking at how global coal price relation with profitability of
Indonesian coal companies, this can maintain the profitability of Indonesian
coal companies especially when there is a correction of global coal price or
when the conflict is ended.
2.
Stock
buyback: Stock buybacks allow companies to repurchase their own shares,
reducing the number of outstanding shares and boosting earnings per share and
shareholder value (Pinto
& Rastogi, 2022). This is an effective way for coal
companies to return cash to shareholders when valuations are low during
geopolitical conflicts, but profitability is high due to supply constraints.
Coal companies can time buybacks to take advantage of dips in price-earnings
ratios.
Investor
1.
Acquire
undervalued coal stocks based on price-earnings ratios: Geopolitical conflicts
that roil coal markets often affect valuations and disconnect stock prices from
fundamentals. The positive and significant relationship between coal price and
presence of conflict on profitability shows a strong fundamental of coal stocks
performance during the conflict. On the other hand, the negative relationship
between coal price and PE ratio and insignificant relationship between the
presence of conflict and PE ratio shows that the coal stocks are undervalued.
The price-earnings ratio for Indonesian coal companies declined around recent
tensions. This creates value investing opportunities to acquire undervalued
stocks based on low P/E multiples despite strong earnings outlooks. Investors
can pick opportune moments to take positions in beaten-down coal stocks based
on depressed valuations rather than negative sentiment. This contrarian
strategy paid off for investors that bought coal stocks during pandemic lows
based on value disconnects (Monteiro
et al., 2023). Similar mispricing arises around
conflicts.
2.
Maintain
diversified portfolio: Diversification is a fundamental investing principle to
reduce portfolio risk by allocating across assets with low correlations.
Overweighting coal stocks can expose investors to high volatility from
unpredictable geopolitical events. Looking at the relationship between the
profitability and valuation to coal price, those metrics volatility are
determined by the movement of global coal price. There is a risk that the coal
price will drop due to other factors outside the conflict or the conflict
itself is ended. This makes investing overweight in coal stocks cause the
portfolio more exposed to greater declines. Indonesian coal stocks saw greater
declines versus the broader market during recent tensions (Kurniawan
et al., 2020). A prudent strategy is holding a
diversified portfolio that limits concentration in coal stocks rather than
making binary bets on geopolitics.
Regulator
1.
Implementing
fairer Domestic Market Obligation (DMO) pricing: The research found the
Russia-Ukraine conflict and resultant coal price surge significantly increased
Indonesian coal companies' profitability (hypothesis 1), while the geopolitical
tensions themselves also impacted earnings (hypothesis 2). However, these
positive effects may not be sustainable if domestic supply obligations hinder
firms' ability to fully capitalize on favourable
export dynamics.
The DMO scheme forced suppliers to
sell 25-30% of output locally at often unprofitable discounted fixed rates
amidst global prices rising over 200% (Doshi,
2023). This led to export restrictions
temporarily halting overseas sales for many miners. The research confirmed
global coal price as the prime profit driver (hypothesis 1), so export controls
directly reduce income for Indonesian firms.
Reforming DMO to allow fairer
compensation for high-contribution companies, through mechanisms like tradable
supply quotas, can therefore promote resilience. Letting productive firms
optimize export sales during conflict windfalls gives upside to withstand
instability per hypothesis 2. Sustainable DMO progress requires pricing
reflecting global dynamics per hypothesis 1's coal price profit linkage.
2.
Change
the formula of Indonesian coal price reference (HBA): The research found a
significant positive relationship between global coal prices and Indonesian
coal company profitability (hypothesis 1). However, domestic Indonesian Coal
Price (HBA) benchmarks materially diverged from global price signals in 2022.
This disconnects between actual price drivers per hypothesis 1 and local
reference indexes poses strategic challenges.
The current HBA formula averages four
global indexes weighted equally, despite their capture of predominantly
high-calorie coal dynamics unrelated to Indonesia’s lower-calorie export
profile (Doshi,
2023). The ensuing large spread between
HBA and real-world Indonesian Coal Index (ICI) prices faced by firms has fuelled market distortions.
Reforming the formula to accurately
reflect Indonesia-specific supply-demand dynamics and export competitive
factors will better align with the global price profitability linkage found in
hypothesis 1. Tying HBA construction to indexes like ICI that capture thermal
coal markets where Indonesian miners operate will bolster transparency.
Additionally, a segmented benchmark approach using unique formulae tailored for
low, medium, and high calorie coal categories rather than indiscriminate
averaging will promote fairness. This will account for the grade variations
confronting different Indonesian miners.
Making these sensible reforms will
mean domestic price signals sync with global conditions influencing financial
outcomes as per the research (hypothesis 1), supporting efficient markets. The
changes can facilitate better-informed planning and investments by Indonesian
coal companies seeking to ride global commodity cycles amidst volatility,
including from events like the Russia-Ukraine conflict.
Conclusion
This thesis analysed the impact of the 2022 Russian invasion of Ukraine
on profitability, valuation, and market efficiency of Indonesian coal stocks.
The eruption of this major geopolitical conflict disrupted global energy trade
flows, spurring an exponential increase in international coal prices.
Empirical analysis via panel data regressions yielded several key
conclusions regarding the conflict’s repercussions on Indonesian coal miners
listed on the stock exchange. Firstly, the price surge boosted profit margins,
confirming positive significant effects on earnings performance. However,
corresponding upward revaluations did not materialize as valuations fell
despite rising profits. Market efficiency also prevailed throughout the event
window as prices rapidly incorporated new public information without lag.
Diving deeper, the conflict itself only directly impacted profitability
patterns rather than valuations or market movements. Meanwhile, shifts in
domestic monetary conditions tied to the conflict had negligible effects across
all three aspects studied. This suggests isolation from localized risks, likely
afforded by the sector’s export-driven nature and high foreign investor
ownership.
Therefore, Indonesian coal stocks harvested upside from global supply
bottlenecks and the resultant commodity cycle upside. However, persistent
regulatory headwinds surrounding coal partly suppressed valuations amidst peak
profitability. As the conflict evolves, energy transitions, geopolitics, and
climate policies will likely further reshape market dynamics.
In conclusion, this thesis underscores the interconnectedness of
geo-economic events and emerging market sector performance while illuminating
novel nuances regarding Indonesian coal financials. The empirical insights can
guide firms, investors, and policymakers in navigating markets during turbulent
times. Further research can explore adjacent aspects like optimal risk
management and structural shifts in global coal demand.
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