Aplikasi Capital Asset Pricing Model dalam Evaluasi Kelayakan Investasi Daerah

Harijono, Harijono; Kristanto, Ari Budi; Atahau, Apriani Dorkas Rambu

doi:10.24246/persi.v4i1.p1-12

Cited by 1 publication

(1 citation statement)

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“…e larger the index is, the more the enterprise's income increases, and the stronger the enterprise's business performance and market competitiveness are [20]. e calculation equation is the ratio of current net profit to base period net profit [21][22][23]. If the net profit growth rate of the enterprise is higher than the sales growth rate, it indicates that the profitability of the enterprise's products is constantly improving, and the enterprise is in the stage of rapid growth and has good growth ability.…”

Section: Bpnn's Sample Selection Of E-commerce Enterprisementioning

confidence: 99%

Application of Capital Asset Pricing Model Based on BP Neural Network in E-commerce Financing

Geng

Guan

2022

Computational Intelligence and Neuroscience

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The study explores the risks and benefits of investors in e-commerce financing under the background of “double carbon” to maximize investors’ interests and reduce investment losses. The Back Propagation Neural Network (BPNN) algorithm model of e-commerce enterprise financing based on the Capital Asset Pricing Model (CAPM) is mainly studied. First, according to the worldwide literature, the theoretical concept and principle of the CAPM are deeply studied and analyzed. Then, from the perspective of “double carbon,” with the financing risk characteristics of listed companies responding to the “double carbon” policy as samples, the CAPM model of e-commerce financing under the BPNN algorithm is established. Next, the BPNN is used to input the financing samples of e-commerce enterprises and train the model. The verification experiment of the capital asset financing model of e-commerce enterprises is further conducted. The experimental results show that the model error is the smallest when the number of neurons in the hidden layer reaches about 20. Therefore, the number of neurons in the hidden layer of the model is set to 20. When the number of iterations in training reaches 3000, the financing risk model begins to show a convergence trend. Finally, it can be determined that the number of adaptive iterations of the model is 3000. When the learning rate is 0.03, the oscillation of the model is smaller and stabler, so the model learning rate is 0.03, and the final model error is only 9.96 × 10 − 8 . Based on this, e-commerce enterprises can achieve the purpose using this model to adjust the coefficient in financing in the future. The results have certain reference significance for e-commerce financing risk assessment under a “double carbon” background.

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Section: Bpnn's Sample Selection Of E-commerce Enterprisementioning

confidence: 99%