PREDICTING BITCOIN PRICE WITH THE LSTMA MODEL

Abstract

Purpose- Forecasting techniques and models are extremely important for people and organizations that are in the right decision making and investment stage. Forecast accuracy enables successful decisions and allows investors to maximize their profits. The development of finance and related technologies in the world and innovative financial instruments have made it interesting for investors. The most popular of these developments is undoubtedly Bitcoin, a product of blockchain technology. The purpose of this study is to predict the future values of Bitcoin. Methodology- In this study, future predictions are made using an LSTM model based on Bitcoin's historical data and indicators of key market forecasters. In this study, 3 different data sets were created by selecting 1 indicator from 4 different indicator types. The 10 Bitcoin data coming after the last value is estimated. Findings- In this study, 3 different data sets were created by selecting an indicator from 4 different indicator groups. These datasets were first trained with the iterative neural network LSTM model and then tested with real values. At the same time, the next 10 bitcoin price values were also predicted in a 15-minute period. Error rates at the end of the model were compared with each other. The 1st dataset, with the most used indicators in the datasets, produced the lowest error rate. Conclusion- The dataset 1, which consists of the most used indicators of the datasets, gave the lowest error rate. According to this result, the rate of reaching realistic values increases as the use of indicators increases. Keywords: LSTM, bitcoin, cryptocurrency, neural network, prediction JEL Codes: C53, C45, G10