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Examining the Predictive Ability of Fundamental Analysis in Predicting Stock Prices

المصدر: المجلة العلمية للاقتصاد والتجارة
الناشر: جامعة عين شمس - كلية التجارة
المؤلف الرئيسي: Abu Qamar, Dina S. (Author)
مؤلفين آخرين: Ebeid, Said T. (Advisor) , Abdelkader, Hossameldin (Advisor)
المجلد/العدد: ع2
محكمة: نعم
الدولة: مصر
التاريخ الميلادي: 2022
الشهر: يوليو
الصفحات: 527 - 543
ISSN: 2636-2562
رقم MD: 1373097
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: EcoLink
مواضيع:
الأسواق المالية | أسعار الأسهم | التحليل المالي
كلمات المؤلف المفتاحية:
Fundamental Analysis | Panel Data Analysis | Earnings Per Share | Book Value Per Share | Return on Equity | Return on Assets | Operation Cash Flow Per Share | Free Cash Flow Per Share | Forecasting Stock Prices | Egyptian Exchange Market
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المستخلص: This paper evaluates the ability of fundamental analysis in predicting stock prices by applying panel data analysis to fundamental analysis dataset. It has been done by investigating the predictive ability of six fundamental analysis ratios: EPS, BVPS, ROE, ROA, FCFPS, and OCFPS. The research data consist of the yearly financial reports (income statements and balance sheets) for 29 companies listed on EGX30 in the Egyptian stock market over the period 2009-2018 to forecast the stock prices in 2019. For statistical inference, panel data analysis has been applied, in addition to, some statistical tests, for instance, Likelihood Ratio Test, Hausman test, Arellano-Bond Test, and Breusch–Pagan Test. The results of the study demonstrate that the OCFPS is the most important variable to explain the stock price. The remaining variables which involve FCFPS, EPS, BVPS, ROA did not seem to have any significant value on the stock price. The fixed effects model is the best econometric model to explain the data compared to the random effects model and the pooled model. The fundamental analysis dataset cannot be used to predict stock prices accurately in the Egyptian exchange market, because the forecasted prices are not close to the actual prices for many companies.
ISSN: 2636-2562

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