Background

Stock market process is full of uncertainty; hence stock prices forecasting very important in finance and business. For stockbrokers, understanding trends and supported by prediction software for forecasting is very important for decision making. This paper proposes a data science model for stock prices forecasting in Indonesian exchange based on the statistical computing based on R language and Long Short-Term Memory (LSTM).

Findings

The first Covid-19 (Coronavirus disease-19) confirmed case in Indonesia is on 2 March 2020. After that, the composite stock price index has plunged 28% since the start of the year and the share prices of cigarette producers and banks in the midst of the corona pandemic reached their lowest value on March 24, 2020. We use the big data from Bank of Central Asia (BCA) and Bank of Mandiri from Indonesia obtained from Yahoo finance. In our experiments, we visualize the data using data science and predict and simulate the important prices called Open, High, Low and Closing (OHLC) with various parameters.

Conclusions

Based on the experiment, data science is very useful for visualization data and our proposed method using Long Short-Term Memory (LSTM) can be used as predictor in short term data with accuracy 94.57% comes from the short term (1 year) with high epoch in training phase rather than using 3 years training data.

中文翻译：

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使用长短期记忆 (LSTM) 在 Covid-19 期间预测印度尼西亚股票价格的数据科学方法

背景

股市过程充满不确定性；因此股票价格预测在金融和商业中非常重要。对于股票经纪人来说，了解趋势并在预测软件的支持下进行预测对于决策非常重要。本文提出了一种基于 R 语言和长短期记忆（LSTM）的统计计算的印尼交易所股票价格预测的数据科学模型。

发现

印度尼西亚首例 Covid-19（冠状病毒病 19）确诊病例发生在 2020 年 3 月 2 日。此后，综合股价指数自年初以来暴跌 28%，印尼卷烟生产商和银行的股价在冠状病毒大流行期间，2020 年 3 月 24 日达到最低值。我们使用来自雅虎财经的中亚银行 (BCA) 和印度尼西亚曼迪里银行的大数据。在我们的实验中，我们使用数据科学可视化数据，并使用各种参数预测和模拟称为开盘价、最高价、最低价和收盘价 (OHLC) 的重要价格。

结论

根据实验，数据科学对可视化数据非常有用，我们提出的使用长短期记忆（LSTM）的方法可以用作短期数据的预测器，准确率 94.57% 来自短期（1 年）训练阶段的时期，而不是使用 3 年的训练数据。