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Fundamental analysis and relative valuation multiples: a determination of value drivers and development of a value model for the US and UK markets

Student thesis: Doctoral Thesis

  • Kim Ehab Shelbaya Ali
The main objective of this study was to develop an algorithmic financial model to determine and examine the characteristics of key value drivers, earnings, net income, EBITDA, sales, and book value, that formulate the value aspects of a company to compute raw value multiples using multi-linear regression analyses of scaled value driver, Price-to-Earnings (PE), Price-to-Net_Income (PX_Earn_Com), Price-to-EBITDA (PEBITDA), Price-to-Sales (PS), and Price-to-Book (PB), against a comprehensive list of independent proxy variables. The resulting spectrum of raw value multiples is utilised in further computation that encompass the triangulation of the spectrum raw value multiples in a weighted process based on the adjusted coefficient of determination measurement, which would synthesise a raw market share price of the company (Adj. Vs_PX) comparable to Bloomberg-based share prices (PX). Effectively, the multi-linear regressive algorithmic financial model would be used for assessing market value signalling a buy or sell based on the position of synthesised market share price relative to current market share prices. The amalgamated data sample for this study comprises of the market indices representing the Anglo-Saxon and European markets, namely the FTSE-All-Share (ASX) of UK, S&P 500 (SPX) of the USA and STOXX Europe 600 (SXXP) of Europe with a data availability ranging from 2001 to 2011 obtained from Bloomberg. The main objective was successfully completed by the analysis of 170 regression models based on 5 scaled dependent variables regressed against 56 independent proxy variables for 8,851 company-years out of 14,340 company-years representing the 3 market indices, ASX, SPX, and SXXP. The descriptive statistics measures of the computed raw value multiples and share prices relative to the Bloomberg-based values have overall generated robust and significant results. Generally reflecting a low standard error, consistent standard deviation and yielding sample means that are very similar. Relating the computed raw value multiples of PE, PX_Earn_Com, PEBITDA, PS, and PB, against the respective Bloomberg-based multiples has mostly shown similar company values for ASX and SPX, signifying that the listed companies are efficiently valued. Whereas for the companies listed on the SXXP index, the results highlighted that there were differences in values observed between the synthesised raw multiples and the Bloomberg-based multiples, implying that companies are either over-valued or under-valued. Overall the corresponding PS and PB multiples displayed the most consistent and explanatorily significant results compared to the three earnings multiples. However, the observed discrepancies in the synthesised values relative to the Bloomberg-based values would mostly be offset collectively between PE, PX_Earn_Com, and PEBITDA, thus presenting consistent and significant results. This study concludes that the cross-sectional relative valuation analysis of any fully-listed company in the Anglo-Saxon and European markets in an identical process to be achievable. Hence, the process of valuation analysis using independent proxy variables can be standardised for the Anglo-Saxon and European markets and the triangulation of value multiples to synthesise comparable market share prices. The various aspects of the methodologies applied are founded on multi-linear regression analysis and relative valuation using a standardised database for all the data obtained from the three market indices: ASX, SPX, and SXXP. Thus, the multi-linear regressive algorithmic financial model is capable of computing cross-sectional valuation, as well as cross-market valuation for any fully-listed company, to compute value multiples that can be triangulated to synthesise respective share prices premised on standardised proxy variables.
Original languageEnglish
Awarding Institution
Supervisors/Advisors
Award dateAug 2014

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