Fundamental Information Analysis: An Extension and Uk Evidence

Al-Debi’e, Mamoun M.; Walker, Martin

doi:10.1006/bare.1999.0101

Cited by 14 publications

(4 citation statements)

References 10 publications

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“…This provides the justification for the dichotomization of sub-components of income in the single income statement (G4C1, 1998: 4Ð24-30). In support, evidence exists that earnings response co-efficients are positively associated with earnings persistence and that persistence varies for different components of income, in particular gross profit (Al-Debie & Walker, 1999). Further support for the perceived value relevance of operating income is the application of the EVA TM residual income-based metric devised by Stern Stewart (1991) that is based on enterprise (debt plus equity) returns rather than on equity performance.…”

Section: Income Measurement and The Influence Of The G4c1mentioning

confidence: 89%

Corporate Governance and Accounting Models of the Reporting Entity

Forker¹,

Green

2000

The British Accounting Review

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Section: Income Measurement and The Influence Of The G4c1mentioning

confidence: 89%

Corporate Governance and Accounting Models of the Reporting Entity

Forker¹,

Green

2000

The British Accounting Review

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“…An alternative approach would be to include several variables that have the ability to predict earnings and/or returns, rather than a single variable. Authors such as Lev and Thiagarajan (1993), Bushee (1997, 1998), Al-Debie and Walker (1999), Dowen (2001) and Skogsvik (2008) have chosen this alternate approach and have included so-called fundamental signals that can be used to predict earnings and/or returns. Lev and Thiagarajan (1993) (hereafter LT) identify various financial variables that are believed to be useful for valuation purposes.…”

Section: Fundamental Analysismentioning

confidence: 99%

Fundamental Analysis and the Prediction of Earnings

Seng¹,

Hancock²

2012

IJBM

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This paper takes fundamental analysis research beyond the spatial and temporal bounds of previous studies. We
investigate how detailed financial statement data enter the decisions of market makers by examining how current
changes in the fundamental signals chosen can provide information on subsequent earnings changes. Using
global data from 1990 to 2000, we extend the body of research using fundamental signals for prediction of future
earnings changes. Contextual factors that may influence this predictive ability are also investigated. Results
indicate that the fundamental signals are significant predictors of both short- and long-term future earnings
changes. Contextual factors that include prior earnings news, industry membership, macroeconomic conditions
and country of incorporation are all demonstrated to influence this relationship. Research results provide
evidence to support the use of fundamental analysis.

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“…This, however, becomes almost impossible if incorrect information on such stocks is relied on due to the use of ineffective model(s). Authors such as Lev and Thiagarajan (1993), Wu et al (2006) and Al-Debie & Walker (1999) mentioned some of the studies which used the fundamental analysis methods, a well-known method for stock analysis. These authors explained that such studies developed trading rules on the basis of the information linked to macroeconomics, industry, and companies.…”

Section: Introductionmentioning

confidence: 99%

The Performance of Maximum Likelihood Factor Analysis on South African Stock Price Performance

Khula

Moroke

2017

JEBS

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The purpose of this paper is to explore the effectiveness and applicability of Maximum Likelihood Factor Analysis (MLFA) method on stock price performance. This method identifies the variables according to their co-movement and variability and builds a model that can be useful for prediction and ranking or classification. The results of factor analysis in this study provide a guide as far as investment decision is concerned. Stock price performance of the seven well-known and biggest companies listed in the Johannesburg stock exchange (JSE) was used as an experimental unit. Monthly data was available for the period 2010 to 2014.Details of a trivariate factor model is: Factor 1 comprises of Absa and Standard Bank (Financial sectors), Factor 2 has Shoprite and Pick â€˜n Pay (Retail sectors) while Factor 3 collected Vodacom MTN and Sasol (Industrial sectors). The companies contribute 46.9%, 12.7% and 10.8% respectively to the three sectors and these findings are confirmed by a Chi-square and the Akaike information criterion to be valid. The three factors are also diverse and reliable according to Tucker and Lewis and Cronbachâ€™s coefficients. The findings of this study give economic significance and the study is relevant as it gives investors and portfolio managerâ€™s sensible investment reference.Keywords: Maximum Likelihood Factor Analysis, stock prices

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Fundamental Information Analysis: An Extension and Uk Evidence

Cited by 14 publications

References 10 publications

Corporate Governance and Accounting Models of the Reporting Entity

Corporate Governance and Accounting Models of the Reporting Entity

Fundamental Analysis and the Prediction of Earnings

The Performance of Maximum Likelihood Factor Analysis on South African Stock Price Performance

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