Transformation of the Ukrainian Stock Market: A Data Properties View

Round 1

Reviewer 1 Report

The paper titled "Transformation of the Ukrainian Stock Market: A Data Properties View" provides an analysis of the Ukrainian stock market's evolution over time. The study examines various data properties, including persistence, volatility, normality, and resistance to anomalies, to test the hypothesis that the market's efficiency has grown over the years.

the paper require improvement to enhance the paper's overall quality: 

- Lack of Coherence: The paper lacks coherence and clarity in its structure and presentation of ideas. The flow of the content is disjointed, making it difficult for readers to follow the logical progression of the study. The authors should consider revising the organization of the paper to improve its readability and coherence. for example in the abstract how "Normality tests support a normal distribution of daily 24 returns throughout sub-periods." an other example in line 50: "Stock markets are evolving and are moving from less efficient to more efficient state" 

- why author used the period before Russia invasion ? "This paper using the whole data available for the Ukrainian stock market (since the 80 start of trades in 1995 till 2022)

- the paper fails to provide a comprehensive explanation of the statistical techniques and methods used in the analysis. 

- the model used is very simple. (line 152)

- in line 270 authors state that "Their presence within the range 270 of [-1..1] is an indication of data normality" which is not true according to kurtosis and skewness

- the same thing in line 279

I'am not sure for the english tense the author used. 

Author Response

We thank this reviewer for all his (her) comments:

- Lack of Coherence: The paper lacks coherence and clarity in its structure and presentation of ideas. The flow of the content is disjointed, making it difficult for readers to follow the logical progression of the study. The authors should consider revising the organization of the paper to improve its readability and coherence. for example in the abstract how "Normality tests support a normal distribution of daily 24 returns throughout sub-periods." an other example in line 50: "Stock markets are evolving and are moving from less efficient to more efficient state" 

We want to thank the referee for this comment. We have revised the paper to increase its readability and not to confuse readers.

- why author used the period before Russia invasion ? "This paper using the whole data available for the Ukrainian stock market (since the start of trades in 1995 till 2022)

We want to thank the referee for this comment. We have incorporated additional discussion related to this aspect into the Discussion section. A fragment of this discussion is below:

“The full-scale invasion of Russia in 2022 caused the largest war in Europe since WWII, resulting in a one-third loss of Ukrainian GDP and the actual disappearance of the Ukrainian stock market. In 2024, it was only nominally present”.

A long story short: after the Russian invasion, The Ukrainian stock market disappeared (in 2022, there was some momentum, but afterward, it was exhausted). This means that it is available nominally, but the only function it performs nowadays is to operate with government bonds. That is why 2022 is the last year of analysis.

- the paper fails to provide a comprehensive explanation of the statistical techniques and methods used in the analysis. 

We want to thank the referee for this comment. As the referee mentioned in the next comment of the report, we used rather simple methods, which is why we decided to skip detailed descriptions of some of the simplest methods and instead concentrate on more complicated ones like R/S analysis. Still, we agree with the referee and expanded the Materials and Methods section with additional explanations of the statistical techniques and methods used in the analysis. The following paragraphs are incorporated in the revised version of the paper:

“In this paper, various statistical tests are employed to assess the statistical significance of differences between specific periods and the overall dataset. Depending on the distribution of the data (whether it adheres to a normal distribution or not), both parametric and non-parametric tests are used. Given the diverse nature of the data periods under examination, differences in normality distribution are highly probable. To account for this variability and circumvent the need for extensive preliminary checks (such as data normality tests), a combination of parametric tests (t-test and ANOVA analysis) and non-parametric tests (including the Mann-Whitney test for two groups and the Kruskal-Wallis test for datasets with more than two groups) are applied.

The null hypothesis (H0) in each case is that the data belong to the same general population, and rejecting this hypothesis suggests that the data originate from different populations. This serves as evidence supporting the presence of statistically significant differences between specific period data and the overall dataset”.

- the model used is very simple. (line 152)

We agree with the referee. But this is a standard approach for purposes like this. Plus, the only purpose of this model is to find additional evidence in favor/against the differences between specific data sets and general data set. That is why it is very simple.

- in line 270 authors state that "Their presence within the range 270 of [-1..1] is an indication of data normality" which is not true according to kurtosis and skewness.

- the same thing in line 279

We want to thank the referee for this comment. We have corrected the information in the revised version of the paper:

“Preliminary conclusions about data normality can be made based on the analysis of descriptive statistics parameters kurtosis and skewness. For a perfectly symmetrical distribution (i.e., normal distribution), the skewness is zero. While there is no strict threshold, skewness values between -1 and 1 (in some sources -0.5 to 0.5) are often considered acceptable for assuming normality. For a normal distribution, the kurtosis is typically around 3. Excess kurtosis values between -2 and 2 are commonly considered acceptable for assuming normality (George and Mallery, 2010; Field, 2009). Going beyond these ranges raises doubts about the normality of data distribution”.

And

“Skewness across all periods is within the range [-1..1], indicating data normality. However, kurtosis significantly exceeds 3 in all cases, which, in turn, is a sign of data non-normality”.

The following references are incorporated into the Reference section

George, D., & Mallery, M. (2010). SPSS for Windows Step by Step: A Simple Guide and Reference, 17.0 update (10a ed.) Boston: Pearson.

Field, A. (2009). Discovering statistics using SPSS. London: SAGE.

Author Response File: Author Response.docx

Reviewer 2 Report

Article: TRANSFORMATION OF THE UKRAINIAN STOCK MARKET: A DATA PROPERTIES VIEW

 This article examines the change in the stock index PFTS of the stock market of Ukraine for the period 1995-2022. The authors have studied various properties of the data, including stability, volatility, normality and resistance to anomalies using various statistical methods.

There are some comments and suggestions on the proposed article:

1. It is necessary to add several keywords (2-3 words) on the topic under study.

2. The literature review does not fully disclose the research topic.

3. Summing up the evolution of the Ukrainian stock market, it can be concluded that the level of its efficiency did not demonstrate a clear growth trend (line 394-395). Why has the level of the Ukrainian stock market not shown an upward trend? What factors influenced this?

4. What factors would influence the development of the Ukrainian stock market?

5. What measures does the stock market of Ukraine take to monetary policy?

6. What variables are selected in the regression analysis? The article does not show the regression equation between variables.

7. What are the ways to improve the efficiency of the stock market in Ukraine?

8. At what level is the transparency of the Ukrainian stock market?

It is recommended for publication after correcting the comments.

Comments for author File: Comments.pdf

Author Response

We thank this reviewer for all his (her) comments:

It is necessary to add several keywords (2-3 words) on the topic under study.

We want to thank for this suggestion. The following keywords are added in the Keywords section:

“Adaptive market hypothesis; R/S analysis; Efficient market hypothesis”

The literature review does not fully disclose the research topic.

We have significantly revised the reference section with a related discussion of the added sources in the paper.

Ball, R. (2009), The Global Financial Crisis and the Efficient Market Hypothesis: What Have We Learned?. Journal of Applied Corporate Finance, 21: 8-16. https://doi.org/10.1111/j.1745-6622.2009.00246.x

Barkoulas, J. T., Labys, W. C., and Onochie, J. I. (1997), Fractional dynamics in international commodity prices, Journal of Futures Markets, 17 (2), 737–745

Batten, J. A., Ellis, C., Fetherston, T. A. (2003), Return Anomalies on the Nikkei: Are they Statistical Illusions?, Available at SSRN: http://ssrn.com/abstract=396680

Corazza, M., Malliaris, A. G. (2002). Multifractality in Foreign Currency Markets, Multinational Finance Journal, 6, 387-401

Crato, N. and Ray, B.K. (2000), Memory in returns and volatilities of futures' contracts. J. Fut. Mark., 20: 525-543.

Danylchuk, H., Kovtun, O., Kibalnyk, L., Sysoiev O. (2020). Monitoring and modelling of cryptocurrency trend resistance by recurrent and R/S-analysis. E3S Web Conf. 166 13030 DOI: 10.1051/e3sconf/202016613030

Greene, M.T., Fielitz, B.D. (1977), Long-term dependence in common stock returns, Journal of Financial Economics, 4, 339-349

Hja, S., Lin, Y. (2003), R/ S Analysis of China Securities Markets, Tsinghua Soence and Technology, Vol. 8 No.5, pp. 537 – 540

Lennart Berg & Johan Lyhagen (1998) Short and long-run dependence in Swedish stock returns, Applied Financial Economics, 8:4, 435-443, DOI: 10.1080/096031098332961

Lo, A. and C. Mackinlay, (1988), Stock Market Do Not follow Random Walks: Evidence From a Simple Specification Test, Review of Financial Studies, 1, 41 – 66

Lo, A. W. (1991), Long-term memory in stock market prices, Econometrica 59 (5), 1279-1313

Metescu, Ana-Maria (2022). Fractal market hypothesis vs. Efficient market hypothesis: applying the r/s analysis on the Romanian capital market. Journal of Public Administration, Finance and Law. 11. 199-209. 10.47743/jopafl-2022-23-17.

Peters, E. E. (1994), Fractal Market Analysis: Applying Chaos Theory to Investment and Economics,John Wiley and Sons, New York.

Raimundo, M., & Okamoto Jr, Jun. (2018). Application of Hurst Exponent (H) and the R/S Analysis in the Classification of FOREX Securities. International Journal of Modeling and Optimization. 8. 116-124. 10.7763/IJMO.2018.V8.635.

Scherbina A., Schlusche B. (2014). Asset price bubbles: a survey, Quantitative Finance, 14:4, 589-604.

Shiller, R. (2003). From Efficient Markets to Behavioral Finance, Journal of Economic Perspectives 17, 1, 83–104

Taqqu, M., Teverovsky, V. and Willinger W. (1995). Estimators for long-range dependence: an empirical study, Fractals 3 785–798

Zhi-Qiang Jiang, Wen-Jie Xie, Wei-Xing Zhou and Didier Sornette, (2019). Multifractal analysis of financial markets: A review, Reports on Progress in Physics, 82 (12), 125901

Summing up the evolution of the Ukrainian stock market, it can be concluded that the level of its efficiency did not demonstrate a clear growth trend (line 394-395). Why has the level of the Ukrainian stock market not shown an upward trend? What factors influenced this?

We want to thank for this comment. To address it we have expanded the Discussion section with the following explanation:

“Partially, this can be attributed to the extremely unstable situation in Ukraine, both economically and politically. The 1990s witnessed a transition from the Soviet economic model to a market-oriented one, during which neither citizens nor companies were familiar with the stock market. The early 2000s saw rapid development in the Ukrainian stock market, but the 2004 revolution marked a significant shift in mentality and development trajectory, impacting the stock market as well. Subsequently, the global financial crisis of 2007-2009, with some time lags, further disrupted the Ukrainian stock market, which has yet to recover fully. Additionally, the revolution of 2013-2014, accompanied by the annexation of Crimea and the onset of war with Russia, led to the physical loss of many industrial companies, plunging the Ukrainian stock market into a new phase of degradation and depression. The full-scale invasion of Russia in 2022 caused the largest war in Europe since WWII, resulting in a one-third loss of Ukrainian GDP and the actual disappearance of the Ukrainian stock market. In 2024, it was only nominally present. Given this tumultuous history, the results of this paper may not be as confusing as they initially appear, and it cannot be conclusively argued that they contradict the adaptive market hypothesis (AMH)”.

What factors would influence the development of the Ukrainian stock market?

What are the ways to improve the efficiency of the stock market in Ukraine?

We want to thank the referee for raising these questions. We decided to combine them because we think the development of the Ukrainian stock market will improve the efficiency of the stock market in Ukraine. The following paragraphs are incorporated in the Discussion section to address this issue:

“Factors influencing the development of the Ukrainian stock market encompass various aspects, including the war's end and post-war economic recovery. Additionally, security guarantees and the alignment of Ukrainian legislation with European standards play pivotal roles. Furthermore, the influx of foreign funds is significant, but the establishment of proper stock market infrastructure and business processes is equally crucial. Moreover, the growth of financial literacy among the population and the fostering of an investment culture are essential for market development. Addressing information asymmetry within the Ukrainian stock market is another vital factor. Lastly, introducing new financial products such as futures, options, ETFs, and ESG indices contributes to market diversification and growth.

In conclusion, following the war, the Ukrainian stock market is poised to embark on a new trajectory, indicating that the market is still far from achieving efficiency, as evidenced by the findings of this paper”.

What measures does the stock market of Ukraine take to monetary policy?

We want to thank for this interesting comment. To address it we have expanded Discussion section with the following paragraph:

“The asset price transmission channel in Ukraine is currently ineffective. The National Bank of Ukraine primarily operates through other transmission channels, notably the Exchange Rate Channel and the Credit Channel. This is largely due to the fact that the Ukrainian stock market wields minimal influence over the country's financial system. Hence, it is imperative to prioritize the development of the Ukrainian stock market”.

What variables are selected in the regression analysis? The article does not show the regression equation between variables.

We want to thank for this comment. A proper explanation was missing. We have corrected this in the revised version of the paper:

“Variable X1 in the regression model is the dummy variable coefficient. Dummy variable = 1 when data belongs to a proper period (1995-1999, 200-2004, etc – see column 1); otherwise, dummy variable = 0. A p-value below 0.05 is evidence of statistical differences between a specific period from column 1 and the overall data set”.

At what level is the transparency of the Ukrainian stock market?

We want to thank for this question. Information asymmetry is one of the factors influencing transparency and in turn, efficiency. Because only an informationally transparent market can be efficient. Still, we didn’t measure the transparency of the Ukrainian stock market in this paper; it was out of the scope of this particular research.

Discussing factors that would influence the development and efficiency of the Ukrainian stock market, we have incorporated a note related to the transparency issue:

“Addressing information asymmetry and transparency within the Ukrainian stock market is another vital factor”.

Author Response File: Author Response.docx

Reviewer 3 Report

The author could have introduced relevant theories, such as the Efficient Market Hypothesis and Adaptive Market Hypothesis, and discussed how their research contributes to these theoretical perspectives in the context of the Ukrainian stock market.
The author could have conducted a more critical evaluation of the existing literature, identifying key debates, contradictions, or limitations that their research aims to address.
Overall, improving the introduction by addressing these weaknesses would provide a stronger foundation for the study, situate the research within a broader academic discourse, and elucidate the potential contributions of the research to theory and practice

Moderate editing of English language required

Author Response

We thank this reviewer for all his (her) comments:

The author could have introduced relevant theories, such as the Efficient Market Hypothesis and Adaptive Market Hypothesis, and discussed how their research contributes to these theoretical perspectives in the context of the Ukrainian stock market.

The author could have conducted a more critical evaluation of the existing literature, identifying key debates, contradictions, or limitations that their research aims to address.

Overall, improving the introduction by addressing these weaknesses would provide a stronger foundation for the study, situate the research within a broader academic discourse, and elucidate the potential contributions of the research to theory and practice

We want to thank the referee for this reasonable comment. We have expanded the discussion of existing theories with the following paragraphs:

“However, there is much empirical evidence against the EMH: persistence of returns and volatility (Caporale et al., 2019), the existence of price bubbles in the financial markets (Scherbina and Schlusche, 2014); various types of market anomalies (Plastun et al., 2019), fat tails in data, non-randomness of data (Lo and Mackinlay, 1988) etc. Ball (2009) showed that during the global financial crisis (2007-2009) the most efficient markets suffered the most significant losses.

As a result, alternative concepts and theories emerged to explain the behavior of financial markets. Among the most commonly known are behavioral finance (Shiller, 2003), adaptive market hypothesis (Lo, 2004), fractal market hypothesis (Peters, 1994), noisy market hypothesis (Black, 1986), overreaction hypothesis (De Bondt and Thaler, 1985) and many others.

These theories contradict each other; they are based on different assumptions and different methodologies of analysis and typically concentrate on a specific aspect of financial markets. Lo (1994) tried to put them all within a single concept, explaining existing differences with the instability of financial markets”.

The reference section is expanded with additional sources.

Zhi-Qiang Jiang, Wen-Jie Xie, Wei-Xing Zhou and Didier Sornette, (2019). Multifractal analysis of financial markets: A review, Reports on Progress in Physics, 82 (12), 125901

Shiller, R. (2003). From Efficient Markets to Behavioral Finance, Journal of Economic Perspectives 17, 1, 83–104

Lo, A. and C. Mackinlay, (1988), Stock Market Do Not follow Random Walks: Evidence From a Simple Specification Test, Review of Financial Studies, 1, 41 – 66

Ball, R. (2009), The Global Financial Crisis and the Efficient Market Hypothesis: What Have We Learned?. Journal of Applied Corporate Finance, 21: 8-16. https://doi.org/10.1111/j.1745-6622.2009.00246.x

Scherbina A., Schlusche B. (2014). Asset price bubbles: a survey, Quantitative Finance, 14:4, 589-604.

Peters, E. E. (1994), Fractal Market Analysis: Applying Chaos Theory to Investment and Economics, John Wiley and Sons, New York.

Author Response File: Author Response.docx

Reviewer 4 Report

Analysis the evolution of an entire stock market is a challenging task, particularly in a country such as Ukraine that has experienced such profound changes in the 1995-2022 period. The authors generate an interesting analysis using (as it should be) formal statistical tests such as the Kolmogorov-smirnov test. I think that the presentation of the article could be improved, which is a bit of a let down after the authors carry out a very interesting analysis. For example the graphs and the tables should be formatted a bit better as some readers might be put off by the presentation.

A few other comments:

In table 10. Rather than saying “not rejected” there should be a reference to the confidence level with which the hypothesis is not rejected. Similar comment for table 11. The information that the authors want to communicate is clear but it should be expressed a bit more precisely.

For this type of article the discussion section is a bit short. I think that the authors have plenty material to expand it.

There are also more relevant references that should be included. Only 15 references for an article of this type is not enough.

I encourage the authors to make this changes. I think that with a bit of polishing this article could be published.

Minor English changes suggested

Author Response

We thank this reviewer for all his (her) comments:

In table 10. Rather than saying “not rejected” there should be a reference to the confidence level with which the hypothesis is not rejected. Similar comment for table 11. The information that the authors want to communicate is clear but it should be expressed a bit more precisely.

We want to thank the referee for this proposition. The following note is incorporated to Tables 10-14

“Null hypothesis status is provided for the case of 95% confidence level”.  

For this type of article the discussion section is a bit short. I think that the authors have plenty material to expand it.

We want to thank the referee for this recommendation. Really, the case of the Ukrainian stock market is rather rare and unique. This should be discussed because it explains the results of the paper. We have revised the discussion section and significantly expanded it.

“Partially, this can be attributed to the extremely unstable situation in Ukraine, both economically and politically. The 1990s witnessed a transition from the Soviet economic model to a market-oriented one, during which neither citizens nor companies were familiar with the stock market. The early 2000s saw rapid development in the Ukrainian stock market, but the 2004 revolution marked a significant shift in mentality and development trajectory, impacting the stock market as well. Subsequently, the global financial crisis of 2007-2009, with some time lags, further disrupted the Ukrainian stock market, which has yet to recover fully. Additionally, the revolution of 2013-2014, accompanied by the annexation of Crimea and the onset of war with Russia, led to the physical loss of many industrial companies, plunging the Ukrainian stock market into a new phase of degradation and depression. The full-scale invasion of Russia in 2022 caused the largest war in Europe since WWII, resulting in a one-third loss of Ukrainian GDP and the actual disappearance of the Ukrainian stock market. In 2024, it was only nominally present. Given this tumultuous history, the results of this paper may not be as confusing as they initially appear, and it cannot be conclusively argued that they contradict the adaptive market hypothesis (AMH).

An additional argument in this favor is that Ukraine's asset price transmission channel is not working. The National Bank of Ukraine primarily operates through other transmission channels, notably the Exchange Rate and Credit Channel. This is mainly because the Ukrainian stock market wields minimal influence over the country's financial system. Hence, it is imperative to prioritize the development of the Ukrainian stock market.

Factors influencing the development of the Ukrainian stock market encompass various aspects, including the end of the war and post-war economic recovery. Additionally, security guarantees and the alignment of Ukrainian legislation with European standards play pivotal roles. Furthermore, the influx of foreign funds is significant, but the establishment of proper stock market infrastructure and business processes is equally crucial. Moreover, the growth of financial literacy among the population and the fostering an investment culture are essential for market development. Addressing information asymmetry and transparency issues within the Ukrainian stock market are other vital factors. Lastly, introducing new financial products such as futures, options, ETFs, and ESG indices contributes to market diversification and growth.

In conclusion, following the war, the Ukrainian stock market is poised to embark on a new trajectory, indicating that the market is still far from achieving efficiency, as evidenced by the findings of this paper.

There are also more relevant references that should be included. Only 15 references for an article of this type is not enough.

We want to thank the referee for this suggestion. The reference section was significantly expanded. The following references (as well as related discussion in the paper) are incorporated in the revised versions of the paper:

Ball, R. (2009), The Global Financial Crisis and the Efficient Market Hypothesis: What Have We Learned?. Journal of Applied Corporate Finance, 21: 8-16. https://doi.org/10.1111/j.1745-6622.2009.00246.x

Barkoulas, J. T., Labys, W. C., and Onochie, J. I. (1997), Fractional dynamics in international commodity prices, Journal of Futures Markets, 17 (2), 737–745

Batten, J. A., Ellis, C., Fetherston, T. A. (2003), Return Anomalies on the Nikkei: Are they Statistical Illusions?, Available at SSRN: http://ssrn.com/abstract=396680

Corazza, M., Malliaris, A. G. (2002). Multifractality in Foreign Currency Markets, Multinational Finance Journal, 6, 387-401

Crato, N. and Ray, B.K. (2000), Memory in returns and volatilities of futures' contracts. J. Fut. Mark., 20: 525-543.

Danylchuk, H., Kovtun, O., Kibalnyk, L., Sysoiev O. (2020). Monitoring and modelling of cryptocurrency trend resistance by recurrent and R/S-analysis. E3S Web Conf. 166 13030 DOI: 10.1051/e3sconf/202016613030

Greene, M.T., Fielitz, B.D. (1977), Long-term dependence in common stock returns, Journal of Financial Economics, 4, 339-349

Hja, S., Lin, Y. (2003), R/ S Analysis of China Securities Markets, Tsinghua Soence and Technology, Vol. 8 No.5, pp. 537 – 540

Lennart Berg & Johan Lyhagen (1998) Short and long-run dependence in Swedish stock returns, Applied Financial Economics, 8:4, 435-443, DOI: 10.1080/096031098332961

Lo, A. and C. Mackinlay, (1988), Stock Market Do Not follow Random Walks: Evidence From a Simple Specification Test, Review of Financial Studies, 1, 41 – 66

Lo, A. W. (1991), Long-term memory in stock market prices, Econometrica 59 (5), 1279-1313

Metescu, Ana-Maria (2022). Fractal market hypothesis vs. Efficient market hypothesis: applying the r/s analysis on the Romanian capital market. Journal of Public Administration, Finance and Law. 11. 199-209. 10.47743/jopafl-2022-23-17.

Peters, E. E. (1994), Fractal Market Analysis: Applying Chaos Theory to Investment and Economics,John Wiley and Sons, New York.

George, D., & Mallery, M. (2010). SPSS for Windows Step by Step: A Simple Guide and Reference, 17.0 update (10a ed.) Boston: Pearson.

Field, A. (2009). Discovering statistics using SPSS. London: SAGE.

Raimundo, M., & Okamoto Jr, Jun. (2018). Application of Hurst Exponent (H) and the R/S Analysis in the Classification of FOREX Securities. International Journal of Modeling and Optimization. 8. 116-124. 10.7763/IJMO.2018.V8.635.

Scherbina A., Schlusche B. (2014). Asset price bubbles: a survey, Quantitative Finance, 14:4, 589-604.

Shiller, R. (2003). From Efficient Markets to Behavioral Finance, Journal of Economic Perspectives 17, 1, 83–104

Taqqu, M., Teverovsky, V. and Willinger W. (1995). Estimators for long-range dependence: an empirical study, Fractals 3 785–798

Zhi-Qiang Jiang, Wen-Jie Xie, Wei-Xing Zhou and Didier Sornette, (2019). Multifractal analysis of financial markets: A review, Reports on Progress in Physics, 82 (12), 125901

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The paper addresses an important and relevant topic, as understanding the evolution of stock markets is crucial for investors and policymakers. here my comments: 

- The introduction could be strengthened by providing a more detailed explanation of the motivation of the research and its relevance.

- The conclusion could be expanded to discuss the implications of the findings for investors and policymakers. Additionally, providing recommendations for future research directions would further enhance the paper's contribution to the field.

Good luck

Author Response

We thank the reviewer for dedicating his (her) time and effort to enhancing this paper. We firmly believe that the manuscript has greatly benefited from this input.

All newly added text is highlighted in green in the revised version of the paper.

- The introduction could be strengthened by providing a more detailed explanation of the motivation of the research and its relevance.

We want to thank for this proposition. We have incorporated an additional paragraph into the Introduction section.

“The Ukrainian stock market presents a unique subject of analysis due to its tumultuous history, which includes two revolutions, economic crises, significant reforms, and the largest war in Europe since World War II. Examining its evolution may offer valuable insights into the behavior of stock markets in the aftermath of revolutions and during periods of conflict”.

- The conclusion could be expanded to discuss the implications of the findings for investors and policymakers. Additionally, providing recommendations for future research directions would further enhance the paper's contribution to the field.

We want to thank for this reasonable comment. The conclusions section is expanded with the following discussion:

“Policymakers can leverage the findings of this paper as a roadmap for their regulatory endeavors. Their primary focus should revolve around post-war economic recovery and ensuring security guarantees. Subsequent efforts should entail harmonizing Ukrainian legislation with European standards, attracting foreign investment, bolstering stock market infrastructure, promoting financial literacy, and introducing innovative financial products such as futures, options, ETFs, and ESG indices.

Furthermore, the results of this paper hold implications for investors, particularly in exploiting abnormal negative returns observed on Mondays in the Ukrainian stock market over recent years. Implementing a straightforward trading strategy of selling on Mondays could yield additional profits, albeit subject to further verification through trading simulations. This avenue presents a promising area for future research, along with exploring other potential price anomalies such as momentum and contrarian effects following abnormal returns, the Month of the Year effect, the Halloween effect, and the Holiday effect.

Moreover, future research directions could include using more sophisticated methodologies to investigate data properties. For example, instead of R/S analysis, fractional integration methodology can be applied”.