Buhalterinės apskaitos teorija ir praktika
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Evaluation of the Applicability of Artificial Intelligence Tools for Scientific Literature Search and Analysis
In the modern world, the rapidly growing volume of scientific information encourages researchers to seek more effective methods for literature search and analysis. This article addresses the application of artificial intelligence (AI) tools in scientific literature search and analysis, as well as the evaluation of their potential, advantages, and limitations. The relevance of the study stems from the lack of research comparing traditional and AI-based approaches to scientific literature search and analysis. The aim of the study is to assess the applicability of AI tools to scientific literature search and analysis by testing them on the topic of non-financial corporate performance indicators published between 2020 and 2025. The study employed scientific literature analysis, bibliometric analysis, systematic comparative analysis, and experiment methods. During the study, based on the analysis of scientific literature, AI tools such as "Semantic Scholar", "Elicit", "Scite", "Litmaps", "Connected Papers", "Research Rabbit", "MySLR", "Rayyan", "Scispace”, "Petal", and "Scholarcy" were identified as tools capable of automating and/or visualizing literature search, screening, and analysis. The AI tools were evaluated and systematised based on their quality and comprehensiveness, functionality, applicability, ease of use, required user involvement, and time requirements. The findings showed that AI tools accelerate scientific literature search and analysis, but still require researcher involvement in assessing the relevance and quality of the retrieved publications. Comparing traditional scientific literature search and analysis with the capabilities of AI tools revealed the potential of AI tools to process large volumes of scientific literature quickly and systematically.
Copyright (c) 2026 Vanesa Zaicaitė https://creativecommons.org/licenses/by/4.0
Assessing the Quality of Sustainability Disclosures in Lithuania’s Largest Electronic Communications Companies
This study examines the importance and regulatory framework of sustainability information disclosure, with particular emphasis on the requirements established by the European Union. Based on the European Sustainability Reporting Standards (ESRS), a methodology for assessing the scope and quality of sustainability information disclosure was developed. The study presents the results of an analysis of sustainability reports published by the largest electronic communications companies in Lithuania during the period 2022–2024. The findings reveal that the companies disclosed 91% of the assessed sustainability information, indicating a consistently high level of disclosure throughout the study period. Across the ESG dimensions, social responsibility topics were disclosed most extensively, particularly those related to own workforce, workers in the value chain, consumers, and end-users. Within the environmental dimension, climate change disclosures showed the most consistent improvement, reaching the maximum disclosure level in 2024, while resource use and circular economy disclosures remained at a high level throughout the period. Disclosures concerning pollution, water and marine resources, and biodiversity were provided only by UAB „Tele2“. In the governance dimension, the business conduct topic was analysed, and its disclosure level increased steadily between 2022 and 2024. The quality of sustainability information improved across all companies during the study period and was assessed as sufficient. Relevance and understandability received the highest evaluations, whereas comparability and verifiability remained the weakest areas. These findings indicate that companies provide relevant, clear, and faithful representation on sustainability. However, there are still gaps in terms of comparable performance indicators, methodological explanations, and longitudinal data.
Copyright (c) 2026 Sabina Račaitė https://creativecommons.org/licenses/by/4.0
Heterogeneous Internal Financial Drivers of Operating Cash Flow: Evidence from Quantile Regression in an Emerging Market
This study aims to examine the determinants of operating cash flow (cash flow from operating activities, OCF) in dividend-paying firms in Pakistan. Using firm-level data and applying quantile regression alongside ordinary least squares (OLS), the analysis captures variations across the conditional distribution of OCF beyond mean-based estimates. The results indicate that total assets are negatively associated with OCF across most quantiles, suggesting lower efficiency in cash generation among larger firms. In contrast, sales and fixed assets exhibit consistently positive relationships, highlighting their importance for operational performance. Risk shows a positive association, particularly at higher quantiles, indicating stronger cash generation among higher-risk firms. Dividend payout is significant only at the upper quantile, implying a limited role relative to operational factors. Firm age is significant at lower and median quantiles but not at higher levels. Overall, the findings emphasize the role of operational scale, asset utilization, and risk in shaping OCF, with implications for financial decision-making in emerging markets.
Copyright (c) 2026 Muhammad Aamir Ali | Ms | Mr https://creativecommons.org/licenses/by/4.0
Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
Momentum Trading in Cryptocurrencies: A Comparative Study of Time-Series and Cross-Sectional Strategies
Momentum-based trading strategies are widely employed in financial markets and have become increasingly relevant within the cryptocurrency ecosystem. This study examines the profitability of momentum-based trading strategies in cryptocurrency markets using a multi-horizon exponential moving average (EMA) framework. The analysis covers eight major cryptocurrencies, Bitcoin, Ethereum, Litecoin, Ripple, Binance Coin, Cardano, Dogecoin, and Solana over the period 1 January 2020 to 31 October 2025. Momentum signals are constructed using short- and long-term Exponential Moving Average (EMAs) combined with volatility normalization to ensure comparability across assets. Two portfolio structures are evaluated: time-series momentum, which adjusts exposure for each asset individually, and cross-sectional momentum, which ranks assets by relative strength. Empirical results show that momentum effects remain economically meaningful in digital assets. Time-series momentum delivers superior performance, achieving an annual return of 31.96% and outperforming cross-sectional momentum on a risk-adjusted basis. Cross-sectional momentum exhibits higher max drawdowns of 55.0% but lower overall profitability in terms of annual returns, partly due to high correlations among cryptocurrencies. The findings confirm that trend persistence and volatility remain key drivers of momentum profitability in crypto markets.
Copyright (c) 2026 Adedeji Daniel Gbadebo https://creativecommons.org/licenses/by/4.0
Comparative Analysis of Deep Learning Models for Cryptocurrency Price Predictions: Evidence Based on Bitcoin (BTC), Ethereum (ETH), Ripple (XRP) and Solana (SOL)
This research examines deep-learning and machine-learning models for cryptocurrency price prediction, with a keen focus on Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Solana (SOL). Cryptocurrencies exhibit high volatility, non-linear behavior and are able to react strongly to exogenous events, making their prediction and forecasting challenging. The primary aim of this research is to determine which predictive models yield optimal performance in characterizing these complexities and to provide empirical guidance on real-life investment and risk-management applications. Four approaches were used for this forecasting: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), a combination of LSTM-GRU models, and Stochastic Gradient Descent (SGD) regression. The daily historical data were used to train and test each model on different forecast horizons, and performance was measured accordingly by Mean Squared Error (MSE) and Mean Absolute Error (MAE) values. As shown in the results, it can be observed that GRU exhibited the lowest error rates in the majority of the assets, particularly in short-term predictions. LSTM demonstrated a promising ability to capture long dependencies, whereas the hybrid LSTM-GRU system showed a similar performance proficiency by combining the relative superiorities of the two respective models. On the other hand, the conventional SGD regression was the worst among all the deep-learning algorithms, thereby demonstrating the extreme capability of these algorithms in modelling non-linear time sequences. The results confirm GRU as the most viable model for AI-powered crypto prediction and demonstrate the potential of hybrid architecture, at least in certain situations. This study will contribute to the existing debates about the role of deep learning in predicting financial outcomes and provide valuable insights to traders, analysts, and researchers navigating the uncertainties of the digital asset world.
Unifying CSR and Responsibility Accounting: Towards an Integrated Accountability Framework
Traditional responsibility accounting and accountability frameworks often lack integration of corporate social responsibility, despite corporate social responsibility being considered a paramount of modern business practice. Business managers increasingly face challenges when balancing various often competing stakeholder interests in complex and dynamic environments, which complicates high-quality decision-making. This study reviews and synthesizes academic literature on responsibility accounting and CSR to identify the strengths and limitations of conventional accountability systems and to explore the underlying motivations behind socially responsible management. The aim of this study is to propose an integrated accountability framework which includes not only financial goal setting and tracking, but also socially responsible management evaluation and decisions. Societal expectations from businesses have significantly increased, but corporate social responsibility is often perceived as a company’s promotional asset or image-enhancement strategy aimed at boosting profitability, which fundamentally contradicts the principles of socially responsible business. Corporate social responsibility should not be evaluated in isolation from overall business performance. To ensure that CSR serves as a genuine driver of sustainable economic development rather than a symbolic gesture, it must be embedded within the broader performance management system. The proposed framework emphasizes the need to integrate CSR throughout all key management processes, including organizational structure analysis, responsibility center goal-setting, performance evaluation, and accountability control.
Copyright (c) 2025 Gintarė Špogienė | Bohdan Holovash | Liliia Holovash https://creativecommons.org/licenses/by/4.0
Bankruptcy Prediction in Lithuania’s Food Service Sector: Application of Traditional, Machine Learning, and Hybrid Models and the Impact of Macroeconomic Factors
This study examines bankruptcy prediction models specifically for Lithuania's food service sector, which is known for its high economic sensitivity and significant bankruptcy risk. The primary issue addressed is the limited accuracy of traditional bankruptcy prediction models when applied to this industry—an important concern for business management and investors. The aim of the study is to evaluate the effectiveness of different types of bankruptcy prediction models for companies in Lithuania’s food service sector and to develop a hybrid model based on advanced technologies, tailored to the specific characteristics of the sector. The models investigated include the Altman Z-score, Ohlson O-score, Support Vector Machine (SVM), and Gradient Boosting Machine (GBM), with macroeconomic indicators incorporated into the advanced models. The empirical analysis was conducted using a dataset comprising 96 Lithuanian food service companies. Model performance was assessed through metrics such as AUC (ROC), F1 score, Brier score, and other key indicators. The findings reveal that the developed hybrid model with integrated macroeconomic factors achieved the highest prediction accuracy at 93.06%, along with the best overall balance of sensitivity, precision, and calibration, highlighting its potential as an effective tool for practical bankruptcy risk assessment in this sector.
Copyright (c) 2025 Ramunė Budrionytė | Miglė Martusevičiūtė https://creativecommons.org/licenses/by/4.0
The Implementation of Accrual Accounting in Lithuania’s Public Sector: Retrospective Assessment of the Challenges
The article analyses the reform of the Lithuanian public sector accounting system, with an emphasis on the transition from cash-based to accrual-based accounting based on International Public Sector Accounting Standards (IPSAS). The study examines the stages of the reform and the challenges encountered, including the lack of qualified personnel, complex legal systems and insufficient technological infrastructure. Based on a literature review and qualitative expert interviews, the study finds that external pressures - EU policy, IPSAS recommendations, and internal motivation - led to the initiation of the reform. Although the long-term benefits of the reform are evident, the study highlights persistent systemic challenges, including gaps in legal alignment with the accrual basis, uneven technological readiness, and varying levels of accounting professionals’ competence. Therefore, to ensure the sustainability and effectiveness of accrual-based public sector accounting in Lithuania, it is essential to further develop the legal and institutional framework, continuously enhance professional development, and strengthen inter-institutional cooperation. The article contributes to the international discourse on public sector accounting harmonisation by providing a detailed, context-specific case study of the Lithuanian experience.
Copyright (c) 2025 Asta Šalienė | Greta Timukaitė https://creativecommons.org/licenses/by/4.0