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All Journal TEKNIK INFORMATIKA Jurnal Keuangan dan Perbankan FIRM: Journal of Management Studies Proceeding of the International Conference on Family Business and Entrepreneurship (ICFBF) Prosiding Sesiomadika Journal of Actuarial, Finance, and Risk Managment
Edwin Setiawan Nugraha
President University
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences Other

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Forecasting the Weekly Stock Price of PT. OCBC NISP Tbk. using Auto Regressive Integrated Moving Average Elisabeth Gloria Manurung; Edwin Setiawan Nugraha
Journal of Actuarial, Finance, and Risk Management Vol 2, No 2 (2023)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v2i2.4812

Abstract

Stocks are widely used in financial markets and can be an option for companies seeking to raise funds. Additionally, investors often opt for stocks as an investment due to their potential for providing high returns. To aid investors in making informed decisions when buying and selling stocks and mitigating risks, professionals have developed different theories and analyses to forecast stock prices. Auto Regressive Integrated Moving Average (ARIMA) (p,d,q) technical analysis will be used in this study to predict the weekly stock price of PT Bank OCBC NISP Tbk (NISP.JK) for 7 weeks from Jan 7, 2022 to February 18, 2022. In this study, historical weekly stock price data for PT. Bank OCBC NISP Tbk (NISP.JK) from 1 January 2021, to 31 December 2021 was collected from Yahoo Finance website to create a forecast. The researches got 12 different ARIMA models, then the researcher determined that the second model (ARIMA (2,2,1) was the most effective. This model was chosen because it has second lowest AIC value and lowest MSE, RMSE, and MAE.
Forecasting of YG Entertainment Stock Prices February 2022-August 2022 Using Arima Model Novia Galuh Ramadhanty; Edwin Setiawan Nugraha
Journal of Actuarial, Finance, and Risk Management Vol 1, No 2 (2022)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v1i2.3972

Abstract

The stock price in investing is the main factor in determining whether an investor will invest there. With stock price prediction research, investors have an idea of whether to invest in the company. YG Entertainment is a public company in the entertainment sector with many artists and entertainment projects that have fluctuating prices. With the ARIMA (Autoregressive Integrated Moving Average) forecasting method, we can predict YG Entertainment's stock price. In this article, YG Entertainment's prediction using the ARIMA model results in a MAPE error rate of 11% with the best model being ARIMA (0,1,0). The error of the model are 33160 x103 MSE, 5758.543 RMSE, and 4366.446 MAE. This forecast will produce good output as consideration for investor who interesting buy YG Entertainment stock price
Forecasting of Retirement Insurance Filled via Internet by ARIMA Models Lovena Louisa; Rifky Fauzi; Edwin Setiawan Nugraha
Journal of Actuarial, Finance, and Risk Management Vol 1, No 1 (2022)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v1i1.3672

Abstract

Pension fund insurance is critical for everyone because it can guarantee a good life during retirement because retirement is a period when someone no longer gets a steady income. Technological advances make it easier for retirement insurance applications. By using ARIMA Models, we can predict the number of internet users who apply for retirement insurance via the internet, using the monthly data of the Social Security Administration from January 2008 to October 2020. The data used has a steady increasing trend with some seasonal components, so it needs to be removed first. ARIMA models use the assumption that the data is stationary, so the data must be tested using the ADF test command in R. After seeing the plotting of ACF and PACF, 9 ARIMA models are formed. ARIMA model is selected based on the smallest AIC. By using 95% confidence it can be concluded that ARIMA (9,1,9) is the best model for forecasting.
Prediction of Loan Status Using Logistics Regression Model and Naïve Bayes Classifier Christabell Christabell; Edwin Setiawan Nugraha; Karunia Eka Lestari
Journal of Actuarial, Finance, and Risk Management Vol 1, No 2 (2022)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v1i2.3968

Abstract

Conducting an evaluation process of prospective debtors is important for creditors to reduce the risk of default. For this reason, the research aims to construct a model that can determine whether a prospective applicant's credit application is recommended to be accepted or rejected by using the method of logistic regression and naïve Bayes classifier. We used a dataset of gender, married, dependent, education, self-employed, applicant income, co-applicant income, loan amount, loan amount term, credit history, and property area as predictor variables and loan status as a response variable. The results show that the performance measures, including accuracy, precision, recall, and F1 score of the logistics regression method, are 85.9%, 83.82%, 100%, and 91.2%, while the naïve Bayes classifier is 84.62%, 83.58%, 98.2%, and 90.32%. Since the performance measures of logistic regression are bigger than naïve Bayes classifier, it suggests that logistic regression is better than naïve Bayes classifier
Forecasting the Monthly Stock Price per Share of Taiwan Semiconductor Manufacturing Company Limited (TSM) using ARIMA Box-Jenkins Method Gabriella Maria Singgih; Edwin Setiawan Nugraha
Journal of Actuarial, Finance, and Risk Management Vol 2, No 1 (2023)
Publisher : Journal of Actuarial, Finance, and Risk Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v2i1.4560

Abstract

Taiwan Semiconductor Manufacturing Company Limited is a Taiwanese multinational semiconductor contract manufacturing and design company. People can buy Stocks from Taiwan Semiconductor Manufacturing Company Limited. Stocks are one of the attractive investment instruments for companies and individuals. There are some theories and analyses to predict stock prices to help investors make wiser decisions when buying and selling stock portfolios. In this study, researchers will use ARIMA(p,d,q) technical analysis to predict the stock price of Taiwan Semiconductor Manufacturing Company Limited for the next 5 months from January 1, 2005 to May 1, 2005. For this forecasting, researchers used Taiwan Semiconductor Manufacturing Company Limited historical stock price data from January 1, 1998 to December 31, 2004 that was obtained from the Yahoo Finance website. Based on the test results of 8 ARIMA models, the best model that researchers got is model 2 ARIMA (2,1,1) with the equation Yt = 0.0759Yt-1 + 0.2706Yt-2 + et - 0.198et-1. This model is considered to be the best because it has the smallest MSE Value, which is 0.1076018; the smallest RMSE value, which is 0.0301156; the smallest MAE value, which is 0,2495926; and the smallest MAPE value, which is 3.0116%. This study shows that the stock price is predicted to rise for the next 5 months from January 1, 2005 to May 1, 2005.
A Backpropagation Artificial Neural Network Approach for Loan Status Prediction Edwin Setiawan Nugraha; Gabrielle Jovanie Sitepu
JURNAL TEKNIK INFORMATIKA Vol 15, No 2 (2022): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v15i2.27006

Abstract

Providing credit has become a main source of profit for financial and non-financial institutions. However, this transaction might lead into credit risk. This risk occurred if debtors unable to complete their obligations that will led loss for creditors.  It is necessity for company to create assessment in distinguishing eligible or non-eligible prospective customer. Artificial Neural Network (ANN) is introduced in solving this typical classification case. Furthermore, one of learning algorithm in ANN namely Backpropagation is able to minimizing error of output in order to receive accurate result. This research aims to form models that capable in classifying the loan status of applicants by utilizing historical data. The method developed in this research is Backpropagation with activation function is a sigmoid function. In addition, this research formed two data model for analyzed; with first data model is every variable given in dataset and for the second data model is the variables that influenced the loan acceptance. Backpropagation shows high performance with more or less data variables. The results of this research show that the both data model has highest accuracy of prediction is 94.37% while the lowest accuracy prediction is 80.28%.
Co-Authors Adi Saptari Agna Olivia Christabell Christabell Elisabeth Gloria Manurung Evelyn Priscilla Gabriella Maria Singgih Gabrielle Jovanie Sitepu Jeslyn Prinssesa Juliano Victor Christian Medellu Karunia Eka Lestari Lovena Louisa Mei Siang Jemima Aurelia Mei Siang Jemima Aurelia Moerpradighta Prayreyka Mokhammad Ridwan Yudhanegara Novia Galuh Ramadhanty Ranny Febrianti Rifky Fauzi Rosyid Nur Salam Shafa Luthfia Sari Haerani