Projection of PT Aneka Tambang Tbk Share Risk Value Based on Backpropagation Artificial Neural Network Forecasting Result

M. A. Haris, L. Setyaningsih, Fatkhurokhman Fauzi, Saeful Amri
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Abstract

PT Aneka Tambang Tbk (ANTAM) received an award as the most sought-after stock issuer in Indonesia in 2016. That stock continued to attract investors in 2022 due to a 105% increase in net profit and a 19% increase in sales from the previous year. Despite the upward trend, investors still had doubts due to the fluctuating movement of ANTAM's stock prices. Therefore, forecasting was needed to determine the future movement of stock prices. The Backpropagation Neural Network method had good capabilities for fluctuating data types. However, this method has the disadvantage of a lengthy iteration process. To handle this limitation, The Nguyen-Widrow weighted setting was applied to address this constraint. The expected Shortfall (ES) method used the forecasting results to measure investment risk. This research uses ANTAM stock closing price data from May 2, 2018, to May 31, 2023. Based on the analysis results, the best architecture was obtained with a configuration of 5-11-1, using Nguyen-Widrow weight initialization and a combination of a learning rate of 0.5 and momentum of 0.9. This architecture yielded a prediction error based on the Mean Absolute Percentage Error (MAPE) of 1.9947%. Risk measurement with the ES method based on the prediction for the next 60 periods showed that at a 95% confidence level, the risk value was 0.002181; at a 90% confidence level, it was 0.002165; at an 85% confidence level, it was 0.002148, and at an 80% confidence level, it was 0.002132.
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基于反向传播人工神经网络预测结果的 PT Aneka Tambang Tbk 股票风险价值预测
PT Aneka Tambang Tbk(ANTAM)在2016年获得了印尼最受欢迎股票发行商奖。2022 年,由于净利润比上一年增长了 105%,销售额增长了 19%,该股票继续吸引着投资者。尽管呈上升趋势,但由于 ANTAM 股票价格的波动,投资者仍心存疑虑。因此,需要通过预测来确定股价的未来走势。反向传播神经网络方法对波动数据类型有很好的处理能力。但是,这种方法的缺点是迭代过程较长。为了解决这一限制,我们采用了 Nguyen-Widrow 加权设置法。预期缺口(ES)法使用预测结果来衡量投资风险。本研究使用了 2018 年 5 月 2 日至 2023 年 5 月 31 日的 ANTAM 股票收盘价数据。根据分析结果,采用 5-11-1 的配置,使用 Nguyen-Widrow 权重初始化以及 0.5 的学习率和 0.9 的动量组合,获得了最佳架构。根据平均绝对百分比误差 (MAPE) 计算,该架构的预测误差为 1.9947%。根据 ES 方法对未来 60 期的预测进行的风险测量显示,在 95% 的置信度下,风险值为 0.002181;在 90% 的置信度下,风险值为 0.002165;在 85% 的置信度下,风险值为 0.002148;在 80% 的置信度下,风险值为 0.002132。
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