Drivers of The Initial Public Offering Prospectus’s Accuracy.pptx

Drivers of The Initial Public Offering Prospectus’s Accuracy

1. Drivers of The Initial Public Offering Prospectus’s Accuracy: Evidence from Egyptian Market

2. Introduction The Initial Public Offering's event is considered one of the most important events for any investor, whether it is an individual or a company, as it is considered one of the investment choices with high returns, but at the same time it is also characterized by high risk due to the lack of sufficient information about the company. The companies that will going public should disclose in the IPO prospectus their historical data as well as their future performance plans represented in the forecast of their revenues and profits for the next five years after IPO. The investor relies on the data disclosed in IPO prospectus to make their decision whether to invest in this IPO or not. Therefore, the accuracy of the data mentioned in the prospectus may be a helpful factor for making a successful investment decision and in case the disclosed data is not accurate will mislead the investors and led to inefficient investment decision. This study aimed to investigate the drivers of IPO prospectuses accuracy among the companies listed in the Egyptian stock exchange.

3. The Research Sample and Data Source The analysis in the study based on data of 38 companies, each company will have five observations to investigate the difference between the actual profit that declared in annual financial statement against the forecasted profit that declared in the prospectus, generating an overall 190 observations. The data extracted from the following sources, the prospectuses of IPO, the annual financial statements published in website of Egyptian Stock Exchange and using the subscribed database of Decypha ( https://www.decypha.com/ ).

4. Research Variables

5. Dependent Variable: Absolute Profit Forecast Accuracy (APFA): Most of past studies had used the gross profit as proxy to investigate the accuracy of prospectus by calculating the deviation between the profit forecast and the actual profit . In this study will follow the same method that used in past studies and will use the gross profit to avoid the impact of taxes differentiation according to the business nature and size. Absolute Profit Forecast Accuracy (APFA) =  

6. Independent Variables Company Size (CS) There are various variables used in past studies as a proxy to measure the company size. Studies had used the total assets as a proxy for the company size, while others had used the total shareholders equity. In this study will use the total asset as a proxy to measure the company size and used the log of total asset to eliminate the impact of big variation in total asset figures between the companies of sample. Company size (CS) = Log of total asset

7. Independent Variables Company Age (CA) The company age is the difference between the company establishment date and the IPO date, most of studies measured the difference between the two dates using the number of years and in this study will follow the same method to measure the company age. Company age (CA) = Age of company in years

8. Independent Variables Forecast horizon (FH) The forecast horizon is the time length of forecasted period from the date of IPO, some studies used the number of months and others used the number of years, and because there is no difference between two methods in this study will use the number of years as a proxy to measure the forecast horizon between the IPO year and the forecasted year which are first, second, third, fourth and fifth year. Forecast horizon (FH) = Number of years from the IPO issuance year

9. Independent Variables Leverage Ratio (LR) Leverage ratio is usually measured by the debt amount to the assets or equity amount, in this study will use the ratio between the debt amount and the total asset amount of the same year of IPO.   Leverage ratio (LR) =  

10. Independent Variables Retained Ownership Percentage (ROP) The retained ownership is the total number of stocks owned by the owners or founders of the company and will not be issued for public to the total number of outstanding stocks of the company, and this is mandatory data should be declared in the prospectus of IPO to clarify the number of stocks that will be issued for public. Retained ownership percentage (ROP) =  

11. Independent Variables Industry Type (IND) In this study will classify the companies according to the industry nature into two class non-manufacturing and manufacturing and will use dummy variable by assign (0) for non – manufacturing company and assign (1) for manufacturing company. Industry type (IND) = A dummy variable coded ‘1’ for firms in the services and trading sectors (trading, finance, hotels, etc.), and coded ‘0 for firms in the manufacturing sector from (pharmaceutical, chemical, electrical, etc.).

12. Independent Variables National Economic Output Growth Rate in the IPO Issuance Year (NEOIY) Some studies used one factor as a proxy to measure the external factors that may impact the accuracy of IPO prospectuses such as inflation rate, exchange rate, interest rate, etc. In this study will use the gross domestic growth as proxy because it is representing the overall performance of economic and will get the data of GDP from the IPO prospectus since all prospectuses have part that cover the economic situation and industry situation. National economic output growth rate in the IPO issuance year (NEOIY) = Gross domestic product (GDP) growth rate in the IPO issuance year.

13. The Research Hypotheses H 1 : There is a statistically significant positive relationship between CS and APFA. H 2 : There is a statistically significant positive relationship between CA and APFA. H 3 : There is a statistically significant negative relationship between FH and APFA. H 4 : There is a statistically significant negative relationship between LR and APFA. H 5 : There is a statistically significant positive relationship between ROP and APFA. H 6 : There is a statistically significant negative relationship between IND and APFA. H 7 : There is a statistically significant positive relationship between NEOIY and APFA.

14. The Model APFA = β0 + β1*CS + β2*CA - β3*FH - β4*LR + β5*ROP - β6*IND + β7*NEOIY + ε Where: β0 : Regression intercept (the expected mean value of APFA when all independent variables are all equal to zero) β1 : Regression coefficient of CS as a predictor of APFA (the amount by which APFA is expected to change for every one unit increase in CS) Β 2: Regression coefficient of CA as a predictor of APFA (the amount by which APFA is expected to change for every one unit increase in CA) β3 : Regression coefficient of FH as a predictor of APFA (the amount by which APFA is expected to change for every one unit increase in FH) Β 4: Regression coefficient of LR as a predictor of APFA (the amount by which APFA is expected to change for every one unit increase in LR) β5 : Regression coefficient of ROP as a predictor of APFA (the amount by which APFA is expected to change for every one unit increase in ROP) β6 : Regression coefficient of IND as a predictor of APFA (the amount by which APFA is expected to change for every one unit increase in IND) β7 : Regression coefficient of NEOIY as a predictor of APFA (the amount by which APFA is expected to change for every one unit increase in NEOIY) ε : Error term

15. Multiple linear regression analysis Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .759 .577 .560 .18 ANOVA Model Sum of Squares Df Mean Square F Sig. 1 Regression 8.109 7 1.453 35.420 .00** Residual 5.952 182 .043     Total 14.061 189      

16. Multiple linear regression analysis Coefficients Model Unstandardized Coefficients Standardized Coefficients T Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 1 (Constant) 0.77 0.27   2.91 0.00     CS -0.01 0.02 -0.01 -0.11 0.92 0.26 3.81 CA 0.01 0.00 0.13 1.99 0.05* 0.59 1.69 FH -0.14 0.01 -0.73 -15.05 0.00** 1.00 1.00 LR -0.03 0.06 -0.03 -0.46 0.65 0.69 1.45 ROP 0.10 0.27 0.03 0.38 0.71 0.55 1.83 IND -0.10 0.05 -0.18 -2.18 0.03* 0.34 2.96 NEOIY 0.01 0.01 0.06 0.80 0.42 0.39 2.59 * Significant at the 0.05 level , ** Significant at the 0.01 level.

17. Final Multiple linear regression analysis Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .758 .575 .568 .18 ANOVA Model Sum of Squares Df Mean Square F Sig. 1 Regression 8.085 3 2.695 83.873 .00** Residual 5.976 186 .032     Total 14.061 189      

18. Final Multiple linear regression analysis Coefficients Model Unstandardized Coefficients Standardized Coefficients T Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 1 (Constant) 0.88 0.04   33.34 0.00     CA 0.01 0.00 0.11 2.06 0.04* 1.00 1.00 FH -0.14 0.01 -0.73 -15.18 0.00** 1.00 1.00 IND -0.10 0.03 -0.20 -4.13 0.00** 1.00 1.00 * Significant at the 0.05 level , ** Significant at the 0.01 level.

19. Multiple linear regression analysis (Residual) Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 1.00 1.00 1.00 .00 ANOVA Model Sum of Squares Df Mean Square F Sig. 1 Regression 14.061 4 3.515 .00 .00** Residual .000 185 .000     Total 14.061 189      

20. Multiple linear regression analysis (Residual) Coefficients Model Unstandardized Coefficients Standardized Coefficients Sig. Collinearity Statistics   B Std. Error Beta Tolerance VIF   1 (Constant) 0.88 0.00   0.00       CA 0.01 0.00 0.10 0.00** 1.00 1.00   FH -0.14 0.00 -0.73 0.00** 1.00 1.00   IND -0.10 0.00 -0.20 0.00** 1.00 1.00   Unstandardized Residual 1.00 0.00 0.65 0.00** 1.00 1.00   * Significant at the 0.05 level , ** Significant at the 0.01 level.

21. Summary of findings H Hypotheses Finding Decision H1 There is a statistically significant positive relationship between company size and absolute profit forecast accuracy. Insignificant negative relationship Rejected H2 There is a statistically significant positive relationship between company age and absolute profit forecast accuracy Significant positive relationship Accepted H3 There is a statistically significant negative relationship between forecast horizon and absolute profit forecast accuracy Significant negative relationship Accepted H4 There is a statistically significant negative relationship between leverage ratio and absolute profit forecast accuracy Insignificant negative relationship Rejected H5 There is a statistically significant positive relationship between retained ownership percentage and absolute profit forecast accuracy Insignificant positive relationship Rejected H6 There is a statistically significant negative relationship between industry type and absolute profit forecast accuracy Significant negative relationship Accepted H7 There is a statistically significant positive relationship between national economic growth of IPO year and absolute profit forecast accuracy Insignificant positive relationship Rejected

22. Practical implications for managers of companies The companies’ managers can use the result of this study as a supportive guide to enhance their practices while they are preparing the future profit forecast for example: For manager of new established firms, it is preferred don’t hurry to go public and wait till have sufficient historical operation period that can support them have enough information to well understanding the market situation. Use trustable consultant agency that can built the forecast figures based on scientific methods or by using advanced technology tools that take in consideration different scenarios and expected risks related to the industry type. Use other companies that have long operating history as a model for comparison and these companies should have similar characteristics (for example size and industry type), this will help them to be more realistic in the predication of profit growth rate.

23. Practical implications for regulatory authorities It is recommended that the regulator can apply new rules that can protect the investors and the company itself that will go public as well as support the market at all to be more efficient, for examples: Don’t allow the companies that are newly established to go public till they have sufficient operating history that can be used to predict the profit in accurate way. Apply limitation on those companies that have short operating history by using short forecast period for example don’t place forecast for period more than three years with limited profit growth factor that will help to decrease the variation between the actual and forecasted figures. Apply a standard guide on all companies can be used in preparing the profit forecast figures, the guide should define the profit growth rate that will be used based on different factors such as historical performance data, company age and industry type.

24. Practical implications for investors The study revealed some facts that can support investors to make efficient investment decision such as following: There is no relationship between the company size and the quality of their profit forecast, and so it is not necessary to trust in big company or not trusting in small companies because the company size is not significant factors that can be used to measure the accuracy of profit forecast . Study carefully the data disclosed in the prospectus and consider the companies that have short operating history is high risk and low accurate data as well as the manufacturing companies as industry type.

25. Research limitations and recommendations This study conducted in Egyptian market that has special business conditions and the firms have different behavior than other similar firms in characteristics but work in different business environment, accordingly the results cant apply on other firms work out of Egyptian market. It is recommended to conduct comparative study between different countries that reveal the common factors and what are the regions that have similar behavior. It is recommended to use other independent variables not used in this study to reveal more factors that may have significant impact on the profit forecast accuracy. Apply the study on the companies already trade their stocks in secondary market by using their published reports for their future business plan and measure the accuracy of published data.