SPESIFIKASI MODEL REGRESI PENGARUH RASIO HUTANG, PEMBAYARAN DIVIDEN, DAN PROFITABILITAS TERHADAP HARGA SAHAM-SAHAM IDX 30 DI INDONESIA

Zulkarnain, Zulkarnain SPESIFIKASI MODEL REGRESI PENGARUH RASIO HUTANG, PEMBAYARAN DIVIDEN, DAN PROFITABILITAS TERHADAP HARGA SAHAM-SAHAM IDX 30 DI INDONESIA. Forum Bisnis dan Kewirausahaan Jurnal Ilmiah STIE Multi Data Palembang, 2 (2). pp. 73-192. ISSN 2089-0001

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Abstract

The Objective of this research is to determine the most efficient regression model by using five methods, i.e., 1) Enter, 2) Forward, 3) Backward, 4) Stepwise 5) Pooled Least Square. These methods are facilitated in SPSS and Eviews program. These method are based on the Henry (1993) statements about the general to specific and the specific to general model in the regression analyses. As stated that the regression model must be able to be used both as the explanation and as the prediction of the dependent variable future values. The object of this research is the stock prices of AALI, ASII, BBCA, BMRI, GGRM, SMGR, PGAS, UNVR, UNTR, AND CPIN. Those stocks are the best choice for the trader in order to maximize the profit while minimize the risk in daily trading activities. The period of observations in this research is from the year 2000 up to the year 2011. The predictors in this research are some indicators in financial theories, i.e., Price to Book Value (PBV), Earning Per Share (EPS),Dividend Per Share (DPS), Return On Asset (ROA), Return On Equity (ROE), Debt to Asset Ratio (DAR), and Debt to Equity Ratio (DER). The result shows that the most efficient model in this research is SP = 1950.24 + 16.375DPS + 4.451EPS + 558.523PBV ─ 210.983ROE + 260.009ROA ─82.520DAR. However this model is still contained the weakness, that is the sign of the negative in the ROE parameter that was contradictory towards the theory of the corporate financial management. In conclusion this contradictive is caused by the pooling data. As a result of the use pooling data in regression analysis with the program of SPSS become more ineffective because of weakening each other between the time series dan the data cross sectional data.

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