The use of financial statement information to evaluate stock returns
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STATEMENT OF SUBSTANTIVE CHARACTER OF THE TERM PAPER 1
Chapter 1. Introduction 3
1.1 Introduction 3
1.2 Research Relevance and Problem 4
1.3 Research Goal 4
1.4 Research objectives 4
1.5 Research Object and Subject 5
1.6 Characteristics of Research Methodology 5
1.7 Characteristics of research data 5
Chapter 2. Overview of financial indicators 6
2.1 Literature review 6
2.2 Financial performance signals used to differentiate firms 8
2.2.1 Profitability performance signals 9
2.2.2 Leverage, liquidity, and source of funds performance signals 11
2.2.3 Operating efficiency performance signals 14
2.2.4 Calculation of financial performance signals 16
2.3 Composition score 19
2.4 Financial indicators 20
Chapter 3. Results of an empirical study of the use of financial statement information to
evaluate stock returns 23
3.1 Selection 23
3.2 Research methodology 23
3.3 Formulation of hypotheses 24
3.4 Descriptive evidence about firms 30
3.5 Correlation analysis 31
3.6 Analysis of one-year earnings of shares divided by F-score values 33
3.7 Analysis of earnings of shares, divided by year and F-score categories 35
3.8 Analysis of earnings of shares, divided by firm’s size categories 37
3.9 Cross-sectional regression 39
3.10 Panel regression model 41
3.11 Conclusions of empirical research 44
3.12 Practical recommendations 45
Conclusion 47
List of references 49
Chapter 1. Introduction 3
1.1 Introduction 3
1.2 Research Relevance and Problem 4
1.3 Research Goal 4
1.4 Research objectives 4
1.5 Research Object and Subject 5
1.6 Characteristics of Research Methodology 5
1.7 Characteristics of research data 5
Chapter 2. Overview of financial indicators 6
2.1 Literature review 6
2.2 Financial performance signals used to differentiate firms 8
2.2.1 Profitability performance signals 9
2.2.2 Leverage, liquidity, and source of funds performance signals 11
2.2.3 Operating efficiency performance signals 14
2.2.4 Calculation of financial performance signals 16
2.3 Composition score 19
2.4 Financial indicators 20
Chapter 3. Results of an empirical study of the use of financial statement information to
evaluate stock returns 23
3.1 Selection 23
3.2 Research methodology 23
3.3 Formulation of hypotheses 24
3.4 Descriptive evidence about firms 30
3.5 Correlation analysis 31
3.6 Analysis of one-year earnings of shares divided by F-score values 33
3.7 Analysis of earnings of shares, divided by year and F-score categories 35
3.8 Analysis of earnings of shares, divided by firm’s size categories 37
3.9 Cross-sectional regression 39
3.10 Panel regression model 41
3.11 Conclusions of empirical research 44
3.12 Practical recommendations 45
Conclusion 47
List of references 49
The aim of this study is to look at how financial ratios affect stock returns. Specifically, it will take a look at whether or not investing in groups with higher financial performance will bring about better returns than investing in groups with worse financial performance. Taking cues from the frame of preceding literature on fundamental analysis strategies—especially the accountingbased totally strategies—this study aims to decide how financial measures affect funding results. The effectiveness of basic analysis techniques has been the subject of much research in the past, with particular attention paid to the role accounting-based indicators play in determining investor returns. High book-to-market (BM) ratios have been linked to investment returns in the past, as evidenced by research by Lakonishok, Shleifer, and Vishny (1994), Fama and French (1992), and Rosenberg, Reid, and Lanstein (1984). Nevertheless, the success of these tactics frequently depends on the capacity to distinguish between businesses with bright futures and those with less optimistic ones.
By investigating whether simple financial ratio analysis may be used as a prediction tool for investment overall performance, this study aims to fill this gap. By setting apart companies with good financial conditions from those with fewer good measures, this look at targets to clarify if investors may expand their income through choosing better financial signals.
One interesting way to investigate the effectiveness of fundamental analysis techniques is to look at companies with weak financial numbers. These businesses frequently receive less attention from analysts and investor interest, which leads to a dearth of stock recommendations and less publicity. Furthermore, the fact that they rely only on official financial disclosures emphasizes how important it is to use previous financial statements when assessing their performance.
This study's main goal is to show that adding basic screening criteria based on financial measures may help investors enhance the performance of their portfolios. Investors can increase their profits by identifying which companies, based on stronger or weaker financial indicators, are preferable to invest in.
Additionally, this research aims to add to the corpus of financial literature already in existence. This study attempts to shed light on how properly the market carries historic financial indicators into modern-day price of stock by analyzing the link between financial measures and stock returns.
To sum up, this study aims to shed light on the relationship between financial measures and stock returns, which has outcomes for investors who need to maximize their use of fundamental analysis to inform their investing techniques.
1.2 Research Relevance and Problem
Offering insights into the possibility of changing investor returns, the paper discusses the practical use of accounting-based fundamental analysis of companies. Investors looking to improve their value portfolio by differentiating between firms with strong and bad financial performance should find this information valuable. The study advances knowledge of the mechanics of market efficiency as well as the significance of financial statement data in stock valuation.
The study aims to investigate the effectiveness of a simple fundamental analysis approach that relies on past financial performance in distinguishing companies with promising future prospects from those that are likely to perform poorly. The performance of the companies in this portfolio varies greatly, even though high book-to-market investment techniques have been shown to yield returns. Given these out-of-favor firms' low attention, lack of analyst coverage, and credibility issues with voluntary disclosures, the study attempts to determine if financial statement analysis might help investors anticipate winners and losers among these stocks.
...
By investigating whether simple financial ratio analysis may be used as a prediction tool for investment overall performance, this study aims to fill this gap. By setting apart companies with good financial conditions from those with fewer good measures, this look at targets to clarify if investors may expand their income through choosing better financial signals.
One interesting way to investigate the effectiveness of fundamental analysis techniques is to look at companies with weak financial numbers. These businesses frequently receive less attention from analysts and investor interest, which leads to a dearth of stock recommendations and less publicity. Furthermore, the fact that they rely only on official financial disclosures emphasizes how important it is to use previous financial statements when assessing their performance.
This study's main goal is to show that adding basic screening criteria based on financial measures may help investors enhance the performance of their portfolios. Investors can increase their profits by identifying which companies, based on stronger or weaker financial indicators, are preferable to invest in.
Additionally, this research aims to add to the corpus of financial literature already in existence. This study attempts to shed light on how properly the market carries historic financial indicators into modern-day price of stock by analyzing the link between financial measures and stock returns.
To sum up, this study aims to shed light on the relationship between financial measures and stock returns, which has outcomes for investors who need to maximize their use of fundamental analysis to inform their investing techniques.
1.2 Research Relevance and Problem
Offering insights into the possibility of changing investor returns, the paper discusses the practical use of accounting-based fundamental analysis of companies. Investors looking to improve their value portfolio by differentiating between firms with strong and bad financial performance should find this information valuable. The study advances knowledge of the mechanics of market efficiency as well as the significance of financial statement data in stock valuation.
The study aims to investigate the effectiveness of a simple fundamental analysis approach that relies on past financial performance in distinguishing companies with promising future prospects from those that are likely to perform poorly. The performance of the companies in this portfolio varies greatly, even though high book-to-market investment techniques have been shown to yield returns. Given these out-of-favor firms' low attention, lack of analyst coverage, and credibility issues with voluntary disclosures, the study attempts to determine if financial statement analysis might help investors anticipate winners and losers among these stocks.
...
After collecting data, conducting numerous analyses and testing hypotheses, the results described in paragraph 3.10 were finally obtained and recommendations based on the results of this study were given. Returning to the main purpose of the study, it was necessary to determine whether the stock returns would be higher when investing in firms with better financial performance than when investing in firms with worse financial performance. And for this purpose, the F-score variable was used, which evaluated the financial performance of firms. According to the results of the first panel linear model with a fixed effect, the independent variable F-score was not statistically significant and had no effect on stock returns.
While the results of the second panel linear model with a fixed effect were slightly different from the first. Firstly, the coefficient of determination of the second model is higher than that of the first (52%>41%), which in turn makes it clear that the second model is better than the first, since there the independent variables better explain the variations of the dependent variable. As for the F-score indicator in the second panel linear model, this indicator has become statistically significant at the 5% significance level, but it also has a negative impact on the change in the share price. Accordingly, with an increase in this indicator by 1 unit of measurement, the dependent variable Share price will decrease by 0.79. Moreover, in the second model there is a variable interaction of the independent variable F-score with Small firms, which tells us that there is a confirmed statistical influence between these variables on the change in the share price. It should also be noted that such an F-score relationship is observed only with Small firms, while the relationship between F-score and Medium and Large firms does not have any confirmed statistical effect on the change in stock prices.
As for other independent variables that were also used in the panel linear model with a fixed effect, the variables Market value of equity and Book value of Equity showed confirmed statistical significance in two models. Moreover, in two models, these variables had similar forces of influence on the dependent variable Share price. The Market value of equity variable is positive and statistically significant, which means that this variable has a strong positive effect on the stock returns, therefore, if the Market value of equity variable is increased by 1 unit, the share price will also increase by a coefficient with this variable by about 12. While the Book value of Equity variable has the opposite effect. The Book value of Equity variable is negative and statistically significant, which means that with an increase in the Book value of the equity variable by 1 unit of measurement, the Share price variable will decrease by about 3.5. Moreover, in the second panel linear model, the relationship of these two variables was additionally considered and this relationship showed statistical significance for changes in the stock returns. Accordingly, when analyzing companies, attention should be paid not only to these two variables separately, but also to compare them together to extract greater benefits.
Taking into account all of the above, we can conclude that in practice only one hypothesis of this study has been confirmed, which is related to the Market value of equity indicator and its positive impact on stock price changes. The hypotheses related to the Book value of Equity indicator were not only not confirmed, but instead of a positive effect on the change in the share price, it was found that this variable has a significant negative impact. The main hypothesis related to the F-score indicator was also not confirmed, since according to the results of the first model, the F-score had no statistical significance at all, and in the second model, the F-score indicator had a negative significant impact on stock changes. However, the hypothesis related to F-score cannot be considered completely unconfirmed, since one of the results of the study was also a statistically significant interaction of F-score with the category of Small firms, but more in-depth research is needed to clarify the nature of such interaction.
This study, like many others, has its limitations, so these limitations need to be identified. Firstly, the time period that was considered in the framework of this study is not that long and is only 5 years (from 2018 to 2022). Therefore, to improve the results and get a more accurate picture of the impact, you need to use a longer time interval. Secondly, the selection of companies itself. The study included companies that are traded on the Moscow Stock Exchange and operate in different sectors, except for companies that operate in the banking and financial sectors. Such companies have a different type of financial statements, unlike firms operating in other sectors, so in order not to harm the results of the study, it was decided not to include these companies in the sample. Accordingly, the limitation of the study is that the results obtained cannot be applied to firms operating in the banking and financial sectors, as well as to firms that operate outside the territory of the Russian Federation. Another limitation is the use of variable interaction. It is difficult to unambiguously assess the interaction of variables, therefore, as an improvement in the results of this study, additional analyses can be carried out to study the impact of such interactions of variables.
While the results of the second panel linear model with a fixed effect were slightly different from the first. Firstly, the coefficient of determination of the second model is higher than that of the first (52%>41%), which in turn makes it clear that the second model is better than the first, since there the independent variables better explain the variations of the dependent variable. As for the F-score indicator in the second panel linear model, this indicator has become statistically significant at the 5% significance level, but it also has a negative impact on the change in the share price. Accordingly, with an increase in this indicator by 1 unit of measurement, the dependent variable Share price will decrease by 0.79. Moreover, in the second model there is a variable interaction of the independent variable F-score with Small firms, which tells us that there is a confirmed statistical influence between these variables on the change in the share price. It should also be noted that such an F-score relationship is observed only with Small firms, while the relationship between F-score and Medium and Large firms does not have any confirmed statistical effect on the change in stock prices.
As for other independent variables that were also used in the panel linear model with a fixed effect, the variables Market value of equity and Book value of Equity showed confirmed statistical significance in two models. Moreover, in two models, these variables had similar forces of influence on the dependent variable Share price. The Market value of equity variable is positive and statistically significant, which means that this variable has a strong positive effect on the stock returns, therefore, if the Market value of equity variable is increased by 1 unit, the share price will also increase by a coefficient with this variable by about 12. While the Book value of Equity variable has the opposite effect. The Book value of Equity variable is negative and statistically significant, which means that with an increase in the Book value of the equity variable by 1 unit of measurement, the Share price variable will decrease by about 3.5. Moreover, in the second panel linear model, the relationship of these two variables was additionally considered and this relationship showed statistical significance for changes in the stock returns. Accordingly, when analyzing companies, attention should be paid not only to these two variables separately, but also to compare them together to extract greater benefits.
Taking into account all of the above, we can conclude that in practice only one hypothesis of this study has been confirmed, which is related to the Market value of equity indicator and its positive impact on stock price changes. The hypotheses related to the Book value of Equity indicator were not only not confirmed, but instead of a positive effect on the change in the share price, it was found that this variable has a significant negative impact. The main hypothesis related to the F-score indicator was also not confirmed, since according to the results of the first model, the F-score had no statistical significance at all, and in the second model, the F-score indicator had a negative significant impact on stock changes. However, the hypothesis related to F-score cannot be considered completely unconfirmed, since one of the results of the study was also a statistically significant interaction of F-score with the category of Small firms, but more in-depth research is needed to clarify the nature of such interaction.
This study, like many others, has its limitations, so these limitations need to be identified. Firstly, the time period that was considered in the framework of this study is not that long and is only 5 years (from 2018 to 2022). Therefore, to improve the results and get a more accurate picture of the impact, you need to use a longer time interval. Secondly, the selection of companies itself. The study included companies that are traded on the Moscow Stock Exchange and operate in different sectors, except for companies that operate in the banking and financial sectors. Such companies have a different type of financial statements, unlike firms operating in other sectors, so in order not to harm the results of the study, it was decided not to include these companies in the sample. Accordingly, the limitation of the study is that the results obtained cannot be applied to firms operating in the banking and financial sectors, as well as to firms that operate outside the territory of the Russian Federation. Another limitation is the use of variable interaction. It is difficult to unambiguously assess the interaction of variables, therefore, as an improvement in the results of this study, additional analyses can be carried out to study the impact of such interactions of variables.
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