Enterprise AI Analysis: Analysis of financial distress risk in public listed agrifood companies in Lithuania
Resilient Lithuanian Agrifood Sector Navigates Economic Headwinds with Mixed Financial Health
Publicly listed Lithuanian agrifood companies maintain a relatively strong financial position despite challenges from Covid-19, soaring energy prices, and inflation.
The sector has experienced significant growth in total assets and sales (150% increase from 2015-2022) but also a notable rise in indebtedness.
Bankruptcy prediction models (Altman Z, Springate S, Zmijewski J, Grover) reveal varied financial health across companies.
The Springate S score model particularly highlighted early signs of financial distress for some firms with weak repayment performance.
Zmijewski J score model was identified as the most reliable predictor of financial stability in the context of the Portuguese agricultural sector, and its results for Lithuanian companies generally indicate low risk.
Policy recommendations include promoting risk management strategies, targeted support for struggling companies (e.g., Auga Group), and enhanced financial stability monitoring.
Quantifying the Impact
Our analysis reveals the critical financial dynamics within the Lithuanian agrifood sector, highlighting both growth and areas of vulnerability.
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Sector Sales Growth (2015-2022)
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Agrifood Share of GDP (2022)
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Largest Company Liability Growth (2021-2022)
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Publicly Listed Agrifood Companies Analyzed
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Altman Z-Score
The original Altman Z score model (1968) is a linear combination of five financial ratios to foresee financial failure, categorizing firms into safe, gray, or distress zones. While effective, its context-specificity (US-based, mid-20th century data) limits generalizability.
Formula: Z = 1.2X1 + 1.4X2 + 3.3X3 + 0.6X4 + 0.999X5
X1: Working Capital/Total Assets
X2: Retained Earnings/Total Assets
X3: EBIT/Total Assets
X4: Market Value Equity/Book Value of Total Debt
X5: Sales/Total Assets
Interpretation: Z > 2.99 (Safe Zone), 1.81 < Z ≤ 2.99 (Gray Zone), Z ≤ 1.81 (Distress Zone). Higher scores indicate lower bankruptcy risk, but caution is advised due to context and static nature.
Springate S-Score
Developed by Gordon Springate (1978), this model also uses multiple discriminant analysis with four financial ratios to determine bankruptcy likelihood. It's known for showing early signs of distress, though results can be volatile.
Formula: Z = 1.03X1 + 3.07X2 + 0.66X3 + 0.4X4
X1: Working Capital/Total Assets
X2: EBIT/Total Assets
X3: Earnings Before Taxes/Current Liabilities
X4: Sales/Total Assets
Interpretation: Z > 0.862 (Safe Zone), Z ≤ 0.862 (Distress Zone). Values below 0.862 suggest a possibility of financial failure. The model's volatility implies sensitivity to market conditions and company-specific factors.
Zmijewski J-Score
Mark and Zmijewski (1984) developed this probit model based on firm performance, leverage, and liquidity. It is often cited as a reliable predictor, with consistently negative scores indicating a low level of financial risk.
Formula: Z = -4.3 - 4.5X1 + 5.7X2 + 0.004X3
X1: Net Profit/Total Assets
X2: Total Debt/Total Assets
X3: Current Asset/Short-Term Liabilities
Interpretation: X > 0 (Financial Distress/Bankruptcy), X < 0 (Healthy Category). Consistently negative J-scores generally indicate a strong financial position and lower risk. Cited as most reliable.
Grover Model
Designed by Grover (2003) as a reassessment of the Altman Z-score, incorporating profitability (ROA) and revising proportions. It categorizes companies based on a G-Score threshold.
Formula: Z = 1.650X1 + 3.404X2 - 0.016X3 + 0.057
X1: Working Capital/Total Assets
X2: EBIT/Total Assets
X3: Net Income/Total Assets (ROA)
Interpretation: Z > 0.01 (Safe Zone), -0.02 < Z ≤ 0.01 (Gray Zone), Z ≤ -0.02 (Distress Zone). Scores equal to or less than -0.02 indicate bankruptcy.
Early Distress
Identified by Springate S-Score
The Springate S-score model was notably effective in highlighting early signs of financial distress among Lithuanian agrifood companies that later demonstrated weak debt repayment performance. This underscores its utility as a precautionary tool.
Methodology for Financial Distress Analysis
Identification of companies
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Analysis of annual audited financial reports
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Bankruptcy Prediction Models Based on Discriminant Analysis: Altman, Springate, Zmijewski, Grover
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Discussion of results
| Model | Strengths | Weaknesses/Nuances | Best Use Cases |
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| Altman Z-Score |
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| Springate S-Score |
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| Zmijewski J-Score |
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| Grover Model |
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Lithuanian Agrifood Sector: Key Players & Dynamics
The Lithuanian agrifood sector, though declining in GDP share, remains significant. Publicly listed companies showcase diverse strategies and financial trajectories.
Akola Group (formerly Linas Agro Group): Largest agrifood entity, significant acquisitions (KG Group in 2021) led to asset/liability growth and sales spike to nearly €1.9 billion in 2022. Consistent net profit, strong financial position.
Auga Group: Focus on organic farming. Experienced negative working capital in 2022 and financial losses in 2018, 2019, 2021, and 2022, indicating instability despite asset growth.
Dairy Companies (Pieno žvaigždės, Rokiškio sūris, Vilvi Group, Žemaitijos pienas): A group of historically significant firms. Rokiškio sūris shows strong growth in capital and sales, consistent profit. Pieno žvaigždės and Vilvi Group show fluctuating performance with periods of negative working capital, but overall sales growth. Žemaitijos pienas has a strong balance sheet and consistent profit but lowest net profit in 2022.
INVL Baltic Farmland: Smallest publicly listed agrifood company focused on agricultural land investment. Marginally positive working capital but consistent net profit and asset growth, capitalization has risen significantly.
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