Stock Prediction with Random Forests and Long Short-term Memory

Machine learning as a popular computer science area has been promoted and developed for more than two decades. It has been applied in many fields in our life, like domestic products such as Alexa from Amazon, photographic products such as Mavic from Dji and so many other areas. This report represents an interesting way to apply machine learning and deep learning technologies on the stock market. We explore multiple approaches, including Long Short-Term Memory (LSTM), a type of Artificial Recurrent Neural Networks (RNN) architectures, and Random Forests (RF), a type of ensemble learning methods. The goal of this report is to use real historical data from the stock market to train our models, and to show reports about the prediction of future returns for picked stocks

Utilizing Machine Learning to Reassess the Predictability of Bank Stocks

Objectives: Accurate prediction of stock market returns is a very challenging task due to the volatile and non-linear nature of the financial stock markets. In this work, we consider conventional time series analysis techniques with additional information from the Google Trend website to predict stock price returns. We further utilize a machine learning algorithm, namely Random Forest, to predict the next day closing price of four Greek systemic banks. Methods/Analysis: The financial data considered in this work comprise Open, Close prices of stocks and Trading Volume. In the context of our analysis, these data are further used to create new variables that serve as additional inputs to the proposed machine learning based model. Specifically, we consider variables for each of the banks in the dataset, such as 7 DAYS MA,14 DAYS MA, 21 DAYS MA, 7 DAYS STD DEV and Volume. One step ahead out of sample prediction following the rolling window approach has been applied. Performance evaluation of the proposed model has been done using standard strategic indicators: RMSE and MAPE. Findings: Our results depict that the proposed models effectively predict the stock market prices, providing insight about the applicability of the proposed methodology scheme to various stock market price predictions. Novelty /Improvement: The originality of this study is that Machine Learning Methods highlighted by the Random Forest Technique were used to forecast the closing price of each stock in the Banking Sector for the following trading session. Doi: 10.28991/ESJ-2023-07-03-04 Full Text: PD

Prediksi Fluktuasi Harga Bitcoin Dengan Menggunakan Random Forest Classifier

Bitcoin merupakan salah satu cryptocurrency paling berharga di dunia dan diperdagangkan di lebih dari 40 bursa di seluruh dunia dan menerima lebih dari 30 mata uang berbeda dengan 250.000 transaksi per hari. Dalam perdagangannya, Bitcoin menunjukkan fluktuasi pada pasar yang diperdagangkan, dalam hal ini fluktuasinya dapat mencapai 10 kali lebih tinggi daripada fluktuasi nilai tukar mata uang asing. Karena fluktuasi harga bitcoin yang masif dan tinggi, prediksi fluktuasi harga sangat dibutuhkan, terutama karena harga bitcoin bergerak dengan sangat acak. Untuk melalukan prediksi flutuktuasi harga, Random Forest classifier merupakan salah satu algoritma machine learning yang sering digunakan untuk prediksi, kesehatan, artificial intelligence, dll. K-means clustering juga dipergunakan untuk membantu algoritma random forest classifier dalam hal mengkluster data. Hasil dari penelitian ini yaitu melakukan prediksi terhadap naik atau turunnya harga bitcoin dengan akurasi sebanyak 71% yang didapatkan dari perbandingan hasil prediksi dan data asli dengan bantuan algoritma confusion matrix