16 © 2024 by the author; licensee Eastern Centre of Science and Education, USA Asian Business Research Journal Vol. 9, 16-23, 2024 ISSN: 2576-6759 DOI: 10.55220/25766759.168 © 2024 by the author; licensee Eastern Centre of Science and Education, USA The impact of intangible assets on stock prices on Bulgarian stock exchange: A panel data analysis Ani Stoykova South-West University “Neofit Rilski”-Blagoevgrad, Bulgaria. Email: ani_qankova_st@abv.bg Abstract This study examines the factors influencing stock prices of Bulgarian non-financial listed companies, employing descriptive statistics, correlation model, panel regression model, and Granger causality test for the period 2015-2022. The research investigates the impact of computer software assets, intellectual property rights, goodwill, earnings, book value, and dividend on stock prices. The results indicate that software products per share (SFS), book value per share (BVS), dividend per share (DPS), and earnings per share (EPS) have statistically significant impacts on the stock prices of non-financial publicly traded companies in Bulgaria. Intellectual property rights per share (IPS) and goodwill per share (GPS) do not significantly influence stock prices. Additionally, the study highlights the dynamic relationships and causality between these variables and stock prices over the examined period. This research not only contributes to the academic discourse on financial market dynamics but also offers pragmatic insights for investors and policymakers. As financial markets continue to evolve, this research provides a timely and relevant contribution to the ongoing dialogue surrounding stock price determinants and their implications for market participants. Keywords: Bulgarian stock exchange, Intangible assets, Panel data, Panel regression analysis, Stock prices. JEL Classification: E22. 1. Introduction The main purpose of financial reporting is to assist investors in making informed economic decisions and assessing the value of a company. Exploring and understanding the main factors that influence stock prices is paramount for investors, policymakers, and stakeholders. Certainly, the correlation between accounting data and the financial markets is among the most extensively analyzed and debated subjects in the realms of financial and accounting literature. In particular, intangible assets such as computer software and intellectual property have gained unprecedented significance, especially in their contribution to the computerization of inventory management and production processes within the manufacturing sector. Intangible assets play a pivotal role in businesses' future growth and success. Companies with more intangible assets tend to exhibit greater innovation and competitiveness, resulting in augmented market values. Furthermore, the size of intangibles is positively correlated with the market value of a firm. Consequently, both companies and investors need to consider the extent of intangible assets when evaluating the market value of a company. Basheva (2017) suggests that the adoption of consistent and universal accounting standards will improve the effectiveness of capital markets by promoting the comparability of accounting information in financial statements. The main goal of this research is to examine the effect of intangible assets on the share prices of non-financial public enterprises listed on BSE (Bulgarian Stock Exchange). The following hypotheses are developed: H1: Computer software assets have a significant influence on the share prices of listed companies in Bulgaria. H2: Intellectual property rights have a significant influence on the share prices of listed companies in Bulgaria. H3: Goodwill have a significant influence on the share prices of listed companies in Bulgaria. H4: Earnings per share, book value per share, and dividend for share have a significant influence on the share prices of listed companies in Bulgaria. The research underscores the critical role of specific intangible assets and traditional financial metrics in shaping stock prices. These insights offer valuable implications for investors, policymakers, and stakeholders in understanding and predicting stock market behavior in emerging markets like Bulgaria. 2. Literature Review Intangible assets have a significant impact on the market value of firms (Qureshi and Siddiqui, 2021; Pan, 2022; Lim and Jones, 2023). Ionita and Dinu (2021) find that intangibles like R&D and patents do not have a positive impact on firm value, but IT programs do have a positive and significant impact on firm value. mailto:ani_qankova_st@abv.bg https://www.doi.org/10.55220/25766759.168 Asian Business Research Journal, 2024, 9: 16-23 17 © 2024 by the author; licensee Eastern Centre of Science and Education, USA Oryina and Suleiman (2020) investigate market value relevance of intangible assets in Nigeria and prove that computer software is positively related to market value. Also, intellectual property has a significant effect on the market price per share. Aulia et al. (2020) confirm that most investors use and pay attention to the earning value and more to intangible value as a basis for consideration of their investment decision making on the Indonesian stock exchange. Paolone et al. (2020) find that intangibles are positively related to security prices of Italian stock market for the period 2010-2018. The results show that the role of intangibles increase. Kimouche and Rouabhi (2016) establish that goodwill and book values are more correlated with market values compared to intangible assets and earnings for French companies. The authors conclude that intangibles enhance the value relevance of accounting data. Similarly, Al-Ani and Tawfik (2021) confirm that intangible assets improve the value relevance of accounting data positively in UAE and negatively in Kuwait. The results suggest that intangible assets can improve the value relevance in emerging markets, such as GCC. Dancaková et al. (2022) examine the impact of intangible assets on a firm’s market valuation in France, Germany, and Switzerland. The results suggest that intangible assets have a positive effect on companies' market value although investors still prioritize profitability over intangible assets. Abdulhadi et al. (2022) prove that the effect of intangible assets on the stock prices of active firms in Iran is positive and significant, while it is negative and significant in the Iraq. Andersson and Saiz (2018) establish that that the significance of investment in intangible assets has grown in the euro area. Vasconcelos et al. (2019) examine the correlation between intangible assets, market value and the macroeconomic environment of German, Portuguese and English public firms spanning the period from 1999 to 2016. Their results indicate that the comprehension value is positively and significantly correlated to the market value of these companies. Furthermore, the researchers find that the intangible capital and the intangibles-driven-earnings exhibit positive correlations with research and development expenditures as well as general administrative procedures. Owiredu et al. (2014) investigate whether the concentration of intangible assets on a firm's balance sheet influences the volatility of its stock price. The results indicate that in the basic pharmaceuticals sector, there is a positive correlation between the concentration of intangible assets and stock price volatility. Pereira da Silva (2019) analyses value relevance of accounting indicators in European market. Results show strong evidence supporting value relevance of book value and earnings. Jaba et al. (2016) conduct a statistical evaluation of the value relevance of accounting data for companies traded on the Bucharest Stock Exchange. Their findings indicate that accounting information has value for investors. Glezakos et al. (2012) prove that earnings per share and book value per share have an influence on stock prices in the Athens Stock Exchange. 3. Bibliometric Analysis Bibliometric analysis is a popular and reliable method for examining and interpreting vast volumes of scientific data. To construct and visually represent bibliometric networks, the VOSviewer software tool is used. Keywords co- occurrence analysis is frequently employed to assess the strength of links between various keywords across numerous documents. Applying VOSviewer, we generate a map that illustrates the relationship between the keywords: „stock price“ and „ intangible assets“. A total of 1 372 documents were retrieved from Scopus database in March 2024. The selection is limited to the following subject areas: Business, Management, and Accounting (1,080), as well as Economics, Econometrics, and Finance (770). The final number of documents found is 1 250. In all the literature collected, a total of 3 120 keywords emerged. VOSviewer software identified 176 keywords that appeared more than 5 times. Figure 1. The visualization of the co-occurrence of keywords in VOSviewer. Asian Business Research Journal, 2024, 9: 16-23 18 © 2024 by the author; licensee Eastern Centre of Science and Education, USA Figure 1 presents the visualization map of the keyword network. The co-occurrence of keywords formed eight distinct clusters. There is a significant correlation between the keywords in each cluster. The main elements within these eight clusters are as follows: intangible assets; investments; value relevance; disclosure; research and development; intellectual capital; innovation; and financial performance. The distance between the two circles illustrates the proximity of the two keywords. The lines connecting them in the figure represent the relationships between the elements. Additionally, a thicker line indicates a stronger relationship between the items. The link strength of the keyword "intangible assets" is 148 points. 4. Methodology and Data This research presents an empirical analysis of the effect of intangible assets on the share prices of the capital market in Bulgaria. It uses annual panel data spanning from January 1, 2015, to December 31, 2022. Employing panel data enables the observation of a particular variable at one specific moment and simultaneously observes another variable at another moment, facilitating the exploration of various relationships, interactions, and patterns (Kapitanov, 2021). Table 1 lists the accounting and financial variables used in the empirical analysis. Table 1. Variables used in the empirical analysis. Variable Type of variable Abbreviation Formula Stock price Dependent SP Stock price at the end of the year (December 31st) Earnings per share Independent EPS Profit after taxes Weighted average number of shares Book value per share Independent BVS Shareholders′ equity Number of shares outstanding Dividend per share Independent DPS Total amount of dividends paid Number of shares outstanding Intellectual property rights per share Independent IPS Total Intellectual Property Rights Number of shares outstanding Software products per share Independent SFS Total Software products Number of shares outstanding Goodwill per share Independent GPS Goodwill Number of shares outstanding This study uses several accounting metrics, including intellectual property rights per share, software products per share, Goodwill per share, earnings per share, book value per share, and dividend per share. These variables were chosen based on previous research and data availability for computation (Stoykova, 2022). The accounting data were gathered from the individual annual financial statements of the examined companies. Moreover, the Bulgarian capital market mandates listed companies to disclose detailed quarterly and annual financial statements. Consequently, all accounting information was sourced from the Bulgarian Stock Exchange (BSE). Table 2 outlines the explanatory variables utilized in the regression equation along with the anticipated sign for each of their coefficients. Table 2. Independent variables and the anticipated sign for each of their coefficients. Independent variables Expected sign Earnings per share (EPS) Positive + Book value per share (BVS) Positive + Dividend per share (DPS) Positive + Intellectual property rights per share (IPS) Positive + Software products per share (SFS) Positive + Goodwill per share (GPS) Positive + The sample for this study includes 20 individual joint-stock companies across various sectors in Bulgaria. Consistent with Hellström's (2006) methodology, financial firms are excluded due to notable differences in structure and accounting compared to non-financial entities (Hellström, 2006; Stoykova, 2021). All included companies are publicly listed on BSE. Market indices such as SOFIX, BGBX40, BGTR30, and BGREIT are based on the market capitalization of the common stock issued by chosen Bulgarian companies. These companies must meet specific criteria for liquidity, market capitalization, and shareholder count to be included in these indices. This study focuses on companies from diverse sectors of the Bulgarian economy, including manufacturing, professional activities and research, transport, storage and post, real estate operations, hotels and restaurants, extractive industry, and construction. These 20 companies were chosen because they provide complete accounting data spanning the analysis period from 2015 to 2022. Before conducting the empirical analysis regarding the influence of accounting data on share prices, it is necessary to apply a Panel Unit Root Test. This test is used to assess the stationarity of the analysed panel data, determining whether the panel is stationary or non-stationary. Correlation model and Granger causality test are employed to test the relationship between the examined variables. Regression analysis is commonly used to determine the presence and direction of relationships between two or more variables. For empirical analysis purposes, Ordinary Panel Least Squares Model is applied. The simple panel regression can be expressed by the following formula: 𝑦𝑖𝑡 = 𝛼 + 𝛽𝑋𝑖𝑡 + 𝜀𝑖𝑡 (1) The following explanatory legend applies to Equations (4): For i = 1, 2, ...., N и t = 1, 2, ...., T. Asian Business Research Journal, 2024, 9: 16-23 19 © 2024 by the author; licensee Eastern Centre of Science and Education, USA Where: 𝑦 – dependent variable; х – independent variables; 𝑡 - reflects the time range; T - the number of periods; 𝑖 - reflects the spatial range; N - the number of individual or spatial data; 𝑎 - intercept of the model; β - unknown parameter subject to calculation; ε - error. In panel regression, the model error ε𝑖𝑡 combines the error associated with temporal data and the error associated with spatial data (Nenova, 2020). 5. Empirical Analysis First, the panel unit root test was applied. The results of Levin, Lin & Chu t panel unit root test show that all the panel time series are non-stationary at level (zero order), thus requiring acceptance of the null hypothesis indicating the presence of non-stationarity in the variables under study at level (0) (p-value > 0.05). Consequently, transformation into the first difference is necessary. The first difference of all examined variables is stationary, so the empirical analysis can be proceeded. Figure 2 presents graphically the mean values by date for each of the examined variables. The trend observed for software products is upward for the period 2018-2022, suggesting that enterprises are allocating increasing funds towards the implementation of digitalization and innovation (Figure 2). On the other hand, the mean values of intellectual property per share and goodwill per share are relatively stable and unchanged for the period 2018-2022. Moreover, the highest mean value was registered for book value per share (24.2 for 2022), whereas the lowest was observed for IPS (0.003 for 2020, 2021, 2022). The highest mean value of stock prices is observed in 2022 (17.1), while the lowest mean value of SP is registered in 2019 and 2020. This could be attributed to the COVID-19 pandemic that has started in March 2020, which negatively affected capital markets. Table 3 presents the descriptive statistics for the whole sample. Asian Business Research Journal, 2024, 9: 16-23 20 © 2024 by the author; licensee Eastern Centre of Science and Education, USA Figure 2. Graphic presentation of the descriptive statistics results for mean by date. Asian Business Research Journal, 2024, 9: 16-23 21 © 2024 by the author; licensee Eastern Centre of Science and Education, USA The descriptive statistics for each variable that shows the mean, median, maximum, minimum, standard deviation, skewness, kurtosis, and Jargue-Bera are presented in Table 3. Table 3. Descriptive statistics. SP SFS IPS GPS BVS DPS EPS Mean 14.02913 0.043079 0.010320 0.036985 13.21526 0.275750 1.209532 Median 6.200000 0.000968 0.000000 0.000000 3.188523 0.000000 0.211219 Maximum 112.0000 0.618675 0.482796 0.721946 243.7282 5.250000 57.87126 Minimum 0.135000 0.000000 0.000000 0.000000 0.004040 0.000000 -7.794385 Std. dev. 19.14543 0.117860 0.053256 0.134889 28.38166 0.885507 5.445477 Skewness 2.307171 3.203110 8.256242 3.853410 4.758727 4.687908 8.003638 Kurtosis 9.206820 12.25544 71.82785 16.61302 32.34882 24.97632 78.22793 Jarque-Bera 398.7784 844.6851 33399.57 1631.396 6346.234 3805.765 39436.49 Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 Sum 2244.661 6.892657 1.651262 5.917587 2114.442 44.12000 193.5251 Sum sq. dev. 58281.06 2.208681 0.450959 2.893000 128077.4 124.6755 4714.862 Observations 160 160 160 160 160 160 160 The mean stock price (SP) is 14.02913, indicating a relatively high average. The skewness value of 2.31 suggests a moderate positive skewness, implying a distribution that is slightly skewed to the right. The kurtosis value of 9.21 indicates a distribution with heavier tails compared to a normal distribution. The median value is 6.20, indicating that half of the observations fall below this value. The maximum value is 112.00 and the minimum value is 0.135. The mean software product per share (SFS) is 0.0431, with a relatively low standard deviation of 0.1179. The skewness and kurtosis values are both high, indicating a distribution that is highly skewed to the right and has heavy tails. The standard deviation is 0.1179, suggesting a relatively low degree of variability. The Jarque-Bera statistic is 844.69 with a p-value of 0.000, indicating a departure from normality. The mean intellectual property per share (IPS) is 0.0103, with a low standard deviation of 0.0533. The skewness and kurtosis values are extremely high, indicating a highly skewed and leptokurtic distribution. The maximum value is 0.48280. The mean value for goodwill per share (GPS) is 0.037, with a moderate standard deviation of 0.1349. The skewness and kurtosis values suggest a distribution that is moderately skewed to the right and has moderate tails. The Jarque-Bera statistic is 1631.40 with a p-value of 0.000, indicating a departure from normality. The mean book value per share (BVS) is 13.22, with a substantial standard deviation of 28.38. The mean dividend per share (DPS) is 0.2758, with a moderate standard deviation of 0.8855. The skewness and kurtosis values of DPS show a distribution that is moderately skewed to the right and has moderate tails. The mean earnings per share (EPS) is 1.2095 and the maximum value is 57.87126. The Jarque-Bera statistic of EPS is 39436.49, indicating a departure from normality. Table 4 presents the correlation matrix. A strongest positive correlation is observed between stock price and dividend per share (0.572148). Also, a positive moderate correlation is revealed between stock price and software product per share (0.501062). The correlation coefficient between stock price and book value per share is 0.400182, suggesting that the relationship between these two variables is moderate and positive. Moreover, as the earnings per share rise, there is a corresponding increase in the stock price. The strongest correlations observed involve DPS, BVS, and SP. Table 4. Correlation matrix. SP SFS IPS GPS BVS DPS EPS SP 1.000000 0.501062 -0.068537 -0.153580 0.400182 0.572148 0.368951 SFS 0.501062 1.000000 -0.063865 -0.099904 0.217786 0.553162 0.105884 IPS -0.068537 -0.063865 1.000000 0.244818 -0.008781 -0.039126 -0.005738 GPS -0.15358 -0.099904 0.244818 1.000000 -0.126507 -0.085297 -0.059756 BVS 0.400182 0.217786 -0.008781 -0.126507 1.000000 0.593100 0.571932 DPS 0.572148 0.553162 -0.039126 -0.085297 0.593100 1.000000 0.401969 EPS 0.368951 0.105884 -0.005738 -0.059756 0.571932 0.401969 1.000000 Table 5. Ordinary panel least squares model. Dependent variable: D(SP) Method: Panel least squares Sample (Adjusted): 2016 2022 Periods included: 7 Cross-sections included: 20 Total panel (Balanced) observations: 140 Variable Coefficient Std. error t-statistic Prob. C 0.310009 0.504711 0.614231 0.5401 D(SFS) 44.96278 19.19743 2.342125 0.0207 D(IPS) 16.50182 26.39834 0.625108 0.5330 D(GPS) -18.00604 31.36429 -0.574094 0.5669 D(BVS) 0.130089 0.049427 2.631920 0.0095 D(EPS) 0.262823 0.070880 3.708009 0.0003 D(DPS) -2.829344 0.997535 -2.836335 0.0053 Root MSE 5.647378 R-squared 0.210661 Mean dependent var 0.639557 Adjusted R-squared 0.175052 S.D. dependent var 6.379282 S.E. of regression 5.794088 Akaike info criterion 6.400260 Sum squared resid 4465.004 Schwarz criterion 6.547342 Log likelihood -441.0182 Hannan-Quinn criter. 6.460030 F-statistic 5.915909 Durbin-Watson stat 1.865914 Prob(F-statistic) 0.000017 Asian Business Research Journal, 2024, 9: 16-23 22 © 2024 by the author; licensee Eastern Centre of Science and Education, USA The Ordinary Panel Least Squares model is applied and the results are presented in Table 5. The results of the panel regression show that the accounting variables can have a statistically significant impact on the stock prices (SP) of the Bulgarian Stock Exchange for the period 2015-2022. The highest statistically significant coefficient in the model is for software products per share (SFS) and it is 44.96278. These results show that a one-unit increase in the SFS is associated with an increase in the dependent variable SP by approximately 44.963 units. The coefficient for this variable is positive, aligning with the expected sign (+). On the other hand, the coefficients for the other two intangible assets (IPS and GPS) are not statistically significant. This suggests that intellectual property rights per share (IPS) and goodwill per share (GPS) are not significantly related to the dependent variable (SP). The coefficient for BVS is 0.130089, and it is statistically significant at the 1% level (p-value = 0.0095). These findings suggest that book value per share is a significant accounting variable for determining stock prices alongside other accounting variables incorporated in the model. The sign of this coefficient is positive and it matches the expected sign. This implies that high BVS is directly related to stock price; that is, the higher the book value per share, the higher the stock price. These results reconfirm findings obtained by other researchers (Malhotra & Tandon, 2013; Almumani, 2014; Warrad, 2017). The coefficient for earnings per share (EPS) is 0.262823, and it is statistically significant (p-value = 0.0003). A one-unit increase in EPS is associated with an increase in SP by 0.262823 units. The coefficient for EPS is positive, aligning with the expected sign. Other researchers have also established a positive and statistically significant correlation between EPS and SP (Kwon, 2009; Khanna, 2014; Shehzad & Ismail, 2014). The coefficient for dividend per share (DPS) is -2.829, and it is statistically significant. DPS has a negative impact on stock prices, indicating that high values of DPS are inversely correlated to stock prices (i.e. an increase in the dividend amount results in a decline in the stock price). The sign of this coefficient does not align with its anticipated positive sign. These findings align with those of numerous research papers (Srinivasan, 2012; Malhotra & Tandon, 2013; Neupane, 2020). In summary, the results show that software products per share (SFS), book value per share (BVS), earnings per share (EPS), and dividend per share (DPS) are statistically significant predictors of stock price (SP). The regression model employed on the panel data can be expressed by the following equation (in the equation, the statistically significant coefficients for each individual variable are indicated in bold): 𝐷(𝑆𝑃) = 0.310009012703 + 44.962775713 ∗ 𝐷(𝑆𝐹𝑆) + 16.5018171526 ∗ 𝐷(𝐼𝑃𝑆) − 18.0060414184 ∗ 𝐷(𝐺𝑃𝑆) + 0.130089177323 ∗ 𝐷(𝐵𝑉𝑆) + 0.26282260112 ∗ 𝐷(𝐸𝑃𝑆) − 2.82934365585 ∗ 𝐷(𝐷𝑃𝑆) (2) The results from granger causality test for time period of 2 lags are presented in Table 6. Table 6. Granger Causality Test for time period of 2 lags. Pairwise granger causality tests Sample: 2015 2022 Lags: 2 Null hypothesis: Obs F-statistic Prob. SFS does not granger cause SP 120 7.92696 0.0006 SP does not granger cause SFS 0.87892 0.4180 IPS does not granger cause SP 120 0.14600 0.8643 SP does not granger cause IPS 0.43222 0.6501 GPS does not granger cause SP 120 0.02390 0.9764 SP does not granger cause GPS 0.09288 0.9114 BVS does not granger cause SP 120 6.39914 0.0023 SP does not granger cause BVS 3.05442 0.0510 DPS does not granger cause SP 120 46.2584 2.E-15 SP does not granger cause DPS 3.88799 0.0232 EPS does not granger cause SP 120 7.10495 0.0012 SP does not granger cause EPS 6.97371 0.0014 These results validated the findings from the previous tests. Therefore, there is evidence to reject the null hypothesis that changes in software products per share (SFS) do not provide useful information about future changes in SP. In summary, software products per share (SFS), book value per share (BVS), dividend per share (DPS), and earnings per share (EPS) granger cause changes in stock price (SP). Considering the results from correlation matrix, panel regression model and granger causality test, we can except H1 and H4. SFS, EPS, BVS, DPS have an impact on stock prices. Contrarily, we have enough evidences to reject H2 and H3 because GPS and IPS have no statistically significant impact on stock prices. 6. Conclusion The relevance of accounting information describes how investors respond to its disclosure. This reaction demonstrates the importance of accounting information in the investment decision-making process, underscoring its value for investors (Scott, 2015). This empirical analysis assesses how intangible assets and other accounting variables affect the stock prices of non-financial listed companies on the Bulgarian Stock Exchange between 2015 and 2022. The study employs descriptive statistics, correlation analysis, panel regression models, and Granger causality tests, to test the relationships between stock prices and key accounting variables. The results of the descriptive statistics reveal upward trends in software products per share (SFS) from 2018-2022, while intellectual property per share (IPS) and goodwill per share (GPS) remain relatively stable. The highest mean value is observed for book value per share (BVS), indicating its significance in market valuation. Correlation analysis reveals a moderate positive relationship between stock prices (SP) and variables such as dividend per share (DPS), book value per share (BVS), and software products per share (SFS). The results of the panel regression model show that software products per share (SFS), book value per share (BVS), earnings per share (EPS), and dividend per share (DPS) are statistically significant predictors of stock prices. The study affirms that these accounting variables are beneficial for investors and analysts in examining and assessing companies across various sectors, thereby aiding informed economic decision-making. The study results demonstrate the value of accounting information for investors, albeit with considerable variation over time and among different companies. However, the results also reveal that intellectual Asian Business Research Journal, 2024, 9: 16-23 23 © 2024 by the author; licensee Eastern Centre of Science and Education, USA property per share (IPS) and goodwill per share (GPS) lack statistical significance, challenging the hypotheses positing their impact on stock prices. Additionally, the results from the Granger causality test validated the findings from the previous tests (correlation matrix and panel regression model). In summary, based on the results obtained, Hypothesis 1 (H1) stating that computer software assets have a significant influence on the share prices of listed companies in Bulgaria is accepted. Hypothesis 2 (H2) positing that intellectual property rights have a significant influence on the share prices of listed companies in Bulgaria is rejected. Hypothesis 3 (H3) that goodwill has a significant influence on the share prices of listed companies in Bulgaria is rejected. Hypothesis 4 (H4) suggesting that earnings per share, book value per share, and dividend per share have a significant influence on the share prices of listed companies in Bulgaria is accepted. This research gives practical insights into the Bulgarian stock market and the findings help investors, policymakers, and analysts better understand the factors shaping stock prices. The primary focus for future research related to this issue involves exploring the application of artificial intelligence (AI) in financial reporting and forecasting, leveraging AI techniques, such as machine learning algorithms and natural language processing. References Abdulhadi, K., Dashtbayaz, M. & Salehi, M. (2022). Comparing the effect of intangible assets and macroeconomic parameters on the stock prices in firms listed on the stock exchange of Iran, Saudi Arabia and Iraq. CEMJP, Vol.30, No.4, pp. 52–76. Retrieved from https://journals.kozminski.cem-j.org/index.php/pl_cemj/article/view/4 Al-Ani, M. K., & Tawfik, O. I. (2021). Effect of intangible assets on the value relevance of accounting information: Evidence from emerging markets. Journal of Asian Finance, Economics and Business, Vol. 8, No. 2, pp. 0387–0399. https://doi.org/10.13106/jafeb.2021.vol8.no2.0387 Almumani, M. A. (2014). Determinants of equity share prices of the listed banks in Amman stock exchange: Quantitative approach, International Journal of Business and Social Science, Vol.5, No.1, pp. 91-104. https://doi.org/10.3126/pravaha.v26i1.41648 Andersson, M. & Saiz, L. (2018). Investment in intangible assets in the euro area, Economic Bulletin Boxes, European Central Bank, 7. Aulia, F., Koeswayo, P., & Bede, D. (2020). Relevance of intangible asset, equity book, and earning value on stock price in information technology era, Journal of Accounting Auditing and Business, Vol. 3 No. 1, pp. 135-146. Basheva, S. (2017). Advantages, disadvantages and general characteristics of principles-based accounting standards. Issues in the development of principles-based accounting standards. Collection of scientific articles, Department of "Accounting and Analysis" 1920 – 2017, pp. 140-151, http://unicat.nalis.bg/Record/PU.000049542 Dancaková, D, Sopko, J, Glova, J, & Andrejovská, A. (2022). The impact of intangible assets on the market value of companies: Cross-sector evidence, Mathematics. Vol. 10, No. 20, 3819. https://doi.org/10.3390/math10203819 Glezakos., M., Mylonakis., J. & Kafouros, C. (2012). The impact of accounting information on stock prices: Evidence from the athens stock exchange, International Journal of Economics and Finance, Vol. 4, No. 2, pp. 56-68, https://doi.org/10.5539/IJEF.V4N2P56 Hellström, K. (2006). The value relevance of financial accounting information in a transition economy: The case of the czech republic, European Accounting Review, Vol.15, No.3, pp. 325-349, https://doi.org/10.1080/09638180600916242 Ionita, C., & Dinu, E. (2021). The effect of intangible assets on sustainable growth and firm value - Evidence on intellectual capital investment in companies listed on bucharest stock exchange, Kybernetes, Vol. 50, pp. 2823-2849. https://doi.org/10.1108/K-05-2020-0325 Jabа, Е., Robu, I-B., Istrate, C., Balan, C. B. & Roman, M. (2016). Statistical assessment of the value relevance of financial information reported by romanian listed companies, Journal for Economic Forecasting, Institute for Economic Forecasting, Vol.0, No.2, pp. 27-42 Kapitanov, K. (2021). Influence of foreign direct investments on the investment process of groups of countries from Europe, Abstract, University of Economics – Varna. Khanna, M., (2014). Value relevance of accounting information: An empirical study of selected Indian firms, International Journal of Scientific and Research Publications, Vol.4, No.10, pp. 1-6. https://www.ijsrp.org/research-paper-1014/ijsrp-p3462.pdf Kimouche, B., & Rouabhi, A. (2016). The impact of intangibles on the value relevance of accounting information: Evidence from French companies, Intangible Capital, Vol. 12, No. 2, pp. 506–529. https://doi.org/10.3926/ic.653 Kwon, G. J., (2009). The value relevance of book values, earnings and cash flows: Evidence from Korea, International Journal of Business and Management, Vol.4, No.10, pp. 28-42. https://doi.org/10.5539/ijbm.v4n10p28 Lim, K., & Jones, W. (2023). The effect of regional differences on the value-relevance proposition for intangible assets, International Journal of Finance & Banking Studies (2147-4486), Vol. 11, No. 4, pp. 89–94. https://doi.org/10.20525/ijfbs.v11i4.2348 Malhotra, B. & Tandon, K. (2013). The relevance of accounting information on investor decision. Accounting and Financing, Vol.18, No.3, pp. 1-49. Nenova, R. (2020). Influence of rent on the price of agricultural land in Bulgaria. Yearbook, volume CXXIII, Tsenov Academic Publishing House, pp. 123-158 https://dlib.uni- svishtov.bg/bitstream/handle/10610/4451/f777931ecf92b334eef385450d317c17.pdf?sequence=1&isAllowed=y Neupane, B. (2020). Impact of Earnings, Book value and dividend on market price of stock of nepalese manufacturing firms listed in NEPSE, Pravaha, Vol.26, No.1, pp. 21-30. https://doi.org/10.3126/pravaha.v26i1.41648 Owiredu, A., Oppong, M. & Churchill, R. (2014). The effects of the intensity of firm’s intangible assets on the volatility of their stock prices, Research Journal of Finance and Accounting, Vol. 5, No. 14, pp. 179- 185. Oryina, E., & Suleiman, S. (2020). Effect of intangible assets on value relevance: Evidence from listed manufacturing firms in Nigeria, Journal of Economics and Sustainability, Vol. 2, No. 2, 13. Retrieved from https://www.e- journal.uum.edu.my/index.php/jes/article/view/12561 Pan, S. (2022). Effects of the growth of intangible capital value on firms, American Journal of Industrial and Business Management, Vol. 12, pp. 1552-1563. https://doi.org/10.4236/ajibm.2022.1210085. Paolone, F., Tiscini, R., & Martiniello, L. (2020). Journal of Modern Accounting and Auditing, Vol. 16, No. 5, pp. 201-210. https://doi.org/10.17265/1548-6583/2020.05.001 Pereira da Silva, J. F. (2019). Value relevance of accounting indicators in Europe: The case of intangible items. Thesis, https://repositorio- aberto.up.pt/bitstream/10216/141221/2/362712.pdf Qureshi, M. & Siddiqui, D. (2021). The effect of intangible assets on financial performance, financial policies, and market value of technology firms: A global comparative analysis, Asian Journal of Finance and Accounting, Vol. 12, No. 1, pp. 26-57, https://doi.org/10.5296/AJFA.V12I1.16655. Scott, W. R. (2015). Financial accounting theory: Seventh edition. Pearson Canada Inc. ISBN 978-0-13-298466-9, 2015. https://uuwaterloohome.files.wordpress.com/2020/08/afm-401-scott-financial-accounting-theory-7ed.pdf Shehzad, K. & Ismail, A. (2014). Value relevance of accounting information and its impact on stock prices: Case study of listed banks at Karachi stock exchange, Journal of Economic Info, Vol. 3, No.1, pp. 40-48. Srinivasan, P. (2012). Determinants of equity share prices in India: A panel data approach, The Romanian Economic Journal, Vol. 15, pp.205- 228. Stoykova, A. (2022). Accounting aspects of the functions of capital markets. University Publishing House "Neofit Rilski", Blagoevgrad, (In original: Stoykova, A. (2022). "Accounting aspects of the functions of capital markets", UI "Neofit Rilski", Blagoevgrad) Vasconcelos, T., Forte, D., & Basso, L. F. C. (2019). The impact of intangibles of German, English and Portuguese companies: From 1999 to 2016, Revista de Administração Mackenzie, Vol. 20, No. 4. Warrad, L.H. (2017). The Effect of market valuation measures on stock price: An empirical investigation on Jordanian banks, International Journal of Business and Social Science, Vol.8, No.3, pp. 67-74.