Valuation of common shares using fundamental analysis
|Title||Valuation of common shares using fundamental analysis|
|Institution||University of Prince Edward Island|
|Degree earned||Master of Business Administration|
|Place published||Charlottetown, P.E.I.|
|Abstract||As a young investor, it is essential that I take advantage of investment principles such as compounding interest rates and the time value of money. History has proven that although riskier than other forms of investments, Canadian and U.S. common shares have yielded double digit percentage returns. Therefore, as a young investor with ample time for investment earnings growth, even with the volatility of the stock market, investing in Canadian and U.S. common shares can be a very rewarding investment strategy. Therefore, to become more knowledgeable about investing, I completed a valuation exercise on five Canadian and five American publicly companies. The ten companies were selected from five different industries which were as follows: • financial services – banks • oil and gas – integrated oils • merchandise and lodging • communications and media - telecommunications • industrial products – transportation equipment and components. To begin this exercise, I conducted a thorough review of existing literature relating to both fundamental analysis and technical analysis. The sources of literature included, but were not limited to, peer reviewed (scholarly) journals, company reports, and case studies. For the fundamental analysis portion, the literature reviewed included broad areas such as its history, the role of financial variables, financial ratios, and criticisms. Specific research focused on the application of Piotroski`s F_SCORE approach by Piotroski (2000) himself, Nguyen (2004), Lopes and Galdi (2007), Pettersson and Maican (2007), along with several others. Introduced by Piotroski, the F_SCORE approach captures the financial health of companies. This approach 4 consists of the application of financial variables such as return on assets, inventory turnover, change in total asset turnover, as well as several other variables to companies. The company then receives scores based on how well each of these variables is scored in the company’s financial performance. The research has identified that firms with high F_SCOREs tend to have higher earnings growth compared to firms that possess lower F_SCOREs. Due to timeframes associated with completing this paper, I had reviewed literature on technical analysis but did not incorporate its findings into my own analysis. Specific aspects of technical analysis such as its history, price patterns, point and figure charts, moving averages, candlestick charts, and criticisms were analyzed from the existing literature. From reviewing the existing literature, it was evident that the F_SCORE is a successful predictor of future earnings growth. Therefore, I had conducted a thorough analysis which included conducting a detailed ratio analysis for the past 20 fiscal quarters and a vertical analysis. The ratios which were used were based on the findings of the literature reviewed. Industry specific ratios, which were captured from investment reports and company annual reports, were also utilized to offset any anomalies, including the small selection of the companies. The completion of these ratios was critical for calculating F_SCOREs for each company, which would then be compared to future earnings as part of a regression analysis. However, before I completed the regression analysis, return data for each company’s stock was captured. Based on these returns, I then proceeded to calculate the monthly return, monthly excess return, quarterly return, quarterly excess return, average quarterly return, average quarterly excess return, six month return, six month excess return, average six month return, and average six month excess return. By having all of these returns and F_SCOREs calculated, I was 5 then able to complete a regression analysis for each company which identified the relationship between F_SCORE and subsequent returns, as represented by linear equations. The findings of the regression analysis revealed conflicting results compared to that of the literature reviewed. With 11 types of returns displaying a significantly positive relationship, 18 types of returns displaying a significantly negative relationship, and 51 types of returns displaying a no significant relationship, it was found that the findings of my study do not support the F_SCORE literature reviewed. In terms of raw returns and excess returns, it was found that there was a more significant relationship between F_SCOREs and future excess returns as opposed to future raw returns. Limitations that may have impacted my results included the small sample size of companies included in the study, time-frame of when the study was administered, exclusion of dividends in return calculations, and company selection.|
|Use/Reproduction||In presenting this signature project report in partial fulfilment of the requirements for a Master of Business Administration degree from the University of Prince Edward Island, the author has agreed that the Robertson Library, University of Prince Edward Island, may make this signature project freely available for inspection and gives permission to add an electronic version of the signature project to the Digital Repository at the University of Prince Edward Island. Moreover the author further agrees that permission for extensive copying of this signature project report for scholarly purposes may be granted by the professor or professors who supervised the author’s project work, or, in their absence, by the Dean of the School of Business. It is understood that any copying or publication or use of this signature project report or parts thereof for financial gain shall not be allowed without the author’s written permission. It is also understood that due recognition shall be given to the author and to the University of Prince Edward Island in any scholarly use which may be made of any material in the author’s report.
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