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Financing Cash Flow Management and Financial Performance.

Über Financing Cash Flow Management and Financial Performance.

This study focused on effect of financing cash flow management on financial performance of mutual funds in Kenya. The study employed causal research design. In this study, theories of measuring financing cash flow management on financial performance of mutual funds in Kenya are addressed. In particular Trade Off theory, Agency Theory, Free Cash flow Theory and Financial Life cycle Theory were reviewed. Descriptive statistics namely; mean, median ,minimum, maximum and standard deviation were generated using Eviews software. The inferential statistics such as R square, t-test and F-test was used to test the significant of the relationship between the variables under the study and establish the degree to which the predictor variable explain the variation in dependent variable. The data was evaluated using the regression technique, random effect model and fixed effect model based on Hausman specification test. Jargue-Beta test, coefficients of skewness and kurtosis were used to assess normality of the data for modeling and parametric inference. Durbin-Watson test was used to test for autocorrelation.

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  • Sprache:
  • Englisch
  • ISBN:
  • 9786138835769
  • Einband:
  • Taschenbuch
  • Seitenzahl:
  • 56
  • Veröffentlicht:
  • 11. Juni 2019
  • Abmessungen:
  • 229x152x3 mm.
  • Gewicht:
  • 95 g.
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Beschreibung von Financing Cash Flow Management and Financial Performance.

This study focused on effect of financing cash flow management on financial performance of mutual funds in Kenya. The study employed causal research design. In this study, theories of measuring financing cash flow management on financial performance of mutual funds in Kenya are addressed. In particular Trade Off theory, Agency Theory, Free Cash flow Theory and Financial Life cycle Theory were reviewed. Descriptive statistics namely; mean, median ,minimum, maximum and standard deviation were generated using Eviews software. The inferential statistics such as R square, t-test and F-test was used to test the significant of the relationship between the variables under the study and establish the degree to which the predictor variable explain the variation in dependent variable. The data was evaluated using the regression technique, random effect model and fixed effect model based on Hausman specification test. Jargue-Beta test, coefficients of skewness and kurtosis were used to assess normality of the data for modeling and parametric inference. Durbin-Watson test was used to test for autocorrelation.

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