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All Journal Jurnal Ekonomi & Bisnis PNJ (JEKBIS PNJ) Infotech Journal Journal of Economic, Bussines and Accounting (COSTING) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Revenue : Jurnal Ilmiah Akuntansi Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Lamahu: Jurnal Pengabdian Masyarakat Terintegrasi Jurnal Sistem Informasi Jurnal Ekbis (Ekonomi & Bisnis)
Ni Putu Noviyanti Kusuma
Universitas Primakara
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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The Capital Asset Pricing Model Forecast Using Artificial Intelligence Ni Putu Noviyanti Kusuma; I Ketut Budiartha
Budapest International Research and Critics Institute (BIRCI-Journal): Humanities and Social Sciences Vol 5, No 1 (2022): Budapest International Research and Critics Institute February
Publisher : Budapest International Research and Critics University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33258/birci.v5i1.3677

Abstract

The application of Artificial Intelligence (AI) with the Recurrent Neural Network (RNN) Long Short-Term Memory (LSTM) algorithm which has excellent accuracy in predicting stock prices, but still needs to be developed optimally in the accounting and finance fields. In many cases it was found that the value of the results of the traditional Capital Asset Pricing Model (CAPM) calculations was consistently below the estimated return. Therefore, it is necessary to calculate the CAPM that can optimize the estimated returns more accurately, by combining it using AI technology with the LSTM method RNN algorithm. The purpose of this study is to prove the accuracy of the CAPM calculation results generated by AI compared to the traditional CAPM calculation method in providing an estimate of the return on the best blue-chip group in the LQ45 index during the 2015-2019 observation period. The sample of this study uses data on closing prices adjusted for companies with the lowest Debt to Equity Ratio (DER). The results of the adjusted closing price prediction using AI with the LSTM method have high accuracy. From the analysis of the different paired sample t-test, it was found that there was a significant difference in the mean Absolute Percentage Error (MAPE), where the AI-optimized CAPM model had a lower MAPE value than the traditional CAPM model. This study concludes that the AI-optimized CAPM calculation method has proven to be able to provide more accurate return estimates than traditional CAPM calculation methods.
Co-Authors Abdullah, Mikail Anak Agung Gede Adi Mega Putra Budiartini, Ni Kadek Ayu Dewi, Desak Putu Nitya Eddy Muntina Dharma I Gede Putu Krisna Juliharta I Gst. Agung Pramesti Dwi Putri I Gusti Agung Pramesti Dwi Putri I Gusti Agung Pramesti Dwi Putri, I Gusti Agung Pramesti Dwi I Gusti Ayu Agung Esa Mayra I Nyoman Yudi Anggara Wijaya I Nyoman Yudi Anggara Wijaya Jauzaa Maylia Suhendro K. Budiartha Kadek Intan Janeta Pratiwi Lestari, Ni Putu Yowi Nengah Widya Utami Ni Made Esa Purnama Sari Ni Made Indah Posawati Ni Made Satvika Iswari Ni Putu Ayu Paramita Ni Putu Puspa Astiti Asih Ni Putu Tia Ananda Ni Wayan Nanik Suaryani Taro Putri Nyoman Purnama, Nyoman Putra, Anak Agung Gede Adi Mega Putri, Ni Wayan Nanik Suaryani Taro Sekartini, Made Suhendro, Jauzaa Maylia