A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions

Patel, Mohil Maheshkumar; Tanwar, Sudeep; Gupta, Rajesh; Kumar, Neeraj

doi:10.1016/j.jisa.2020.102583

Cited by 177 publications

(104 citation statements)

References 13 publications

Supporting

Mentioning

100

Contrasting

Unclassified

Order By: Relevance

“…This corpus uses the conventional time series models like the GARCH family models, which were extended recently in light of outliers that characterize cryptocurrency markets (Aslan & Sensoy, 2020 ; Charles & Darné, 2019 ; Catani et al, 2019 ; Trucíos, 2019 , among others). A second subset of the literature involves approaches inspired by operations research, such as neural networks (Adcock & Gradojevic, 2019 ; Jay et al, 2020 ; among others), machine learning, and deep learning (Lahmiri & Bekiros, 2019 ; Patel et al, 2020 ; Akyildirim et al, 2020 , 2021 ; Sensoy, 2019 ; among others).…”

Section: Introductionmentioning

confidence: 99%

Cryptocurrency volatility forecasting: What can we learn from the first wave of the COVID-19 outbreak?

2021

View full text Add to dashboard Cite

This study aims to examine the issue of cryptocurrency volatility modelling and forecasting based on high-frequency data. More specifically, this study assesses whether crisis periods, particularly the coronavirus disease pandemic, influence the dynamic of cryptocurrency volatility. We investigate the four main cryptocurrency markets (Bitcoin, Ethereum Classic, Ethereum, and Ripple) from April 2018 to June 2020. The realized volatility measure is computed and decomposed to various components (continuous versus discontinuous, positive and negative semi-variances, and signed jumps). A variety of heterogeneous autoregressive (HAR) models are developed including these components, thereby enabling assessment of different assumptions (including persistence and asymmetric dynamic) of modelling and volatility forecasting based on in-sample and out-of-sample forecasting strategies, respectively. Our results reveal three main findings. First, the extended HAR model that includes the positive and negative jumps appears to be the best model for predicting future volatility for both crisis and non-crisis periods. Second, during the crisis period, only the negative jump component is statistically significant. Third, in terms of volatility forecasting, the results show that the extended HAR model that includes positive and negative semi-variances outperform the other models.

show abstract

Section: Introductionmentioning

confidence: 99%

Cryptocurrency volatility forecasting: What can we learn from the first wave of the COVID-19 outbreak?

2021

View full text Add to dashboard Cite

show abstract

“…The decision-making process needs to make the appropriate decision at the right time, reducing the risks associated with the investment process. In [28], a hybrid cryptocurrency prediction system based on LSTM and GRU is presented, focusing on two cryptocurrencies, Litecoin and Monero. The authors of [29] use minute-sampled Bitcoin returns over 3 h periods to aggregate RV data.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms

Hamayel

Owda

2021

157

View full text Add to dashboard Cite

Cryptocurrency is a new sort of asset that has emerged as a result of the advancement of financial technology and it has created a big opportunity for researches. Cryptocurrency price forecasting is difficult due to price volatility and dynamism. Around the world, there are hundreds of cryptocurrencies that are used. This paper proposes three types of recurrent neural network (RNN) algorithms used to predict the prices of three types of cryptocurrencies, namely Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The models show excellent predictions depending on the mean absolute percentage error (MAPE). Results obtained from these models show that the gated recurrent unit (GRU) performed better in prediction for all types of cryptocurrency than the long short-term memory (LSTM) and bidirectional LSTM (bi-LSTM) models. Therefore, it can be considered the best algorithm. GRU presents the most accurate prediction for LTC with MAPE percentages of 0.2454%, 0.8267%, and 0.2116% for BTC, ETH, and LTC, respectively. The bi-LSTM algorithm presents the lowest prediction result compared with the other two algorithms as the MAPE percentages are: 5.990%, 6.85%, and 2.332% for BTC, ETH, and LTC, respectively. Overall, the prediction models in this paper represent accurate results close to the actual prices of cryptocurrencies. The importance of having these models is that they can have significant economic ramifications by helping investors and traders to pinpoint cryptocurrency sales and purchasing. As a plan for future work, a recommendation is made to investigate other factors that might affect the prices of cryptocurrency market such as social media, tweets, and trading volume.

show abstract

“…The results of the GRU and LSTM models are very close and outperform the multilayer perceptron (MLP), which was also used in this study. In [ 12 ] Patel et al propose a model incorporating LSTM and GRU for one, three, and seven days ahead prediction of Litecoin and Monero cryptocurrency. In order to build a threshold based portfolio Lee and Yoo [ 13 ] develop three types of RNN models : classical RNN, LSTM and GRU to forecast one month ahead, the top ten stocks in Standard and Poor’s 500 index using monthly data (OHLCV: open,high,low,close and volume).…”

Section: Related Workmentioning

confidence: 99%

An LSTM and GRU based trading strategy adapted to the Moroccan market

Touzani

Douzi

2021

J Big Data

View full text Add to dashboard Cite

Forecasting stock prices is an extremely challenging job considering the high volatility and the number of variables that influence it (political, economical, social, etc.). Predicting the closing price provides useful information and helps the investor make the right decision. The use of deep learning and more precisely of recurrent neural networks (RNNs) in stock market forecasting is an increasingly common practice in the literature. Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures are among the most widely used types of RNNs, given their suitability for sequential data. In this paper, we propose a trading strategy designed for the Moroccan stock market, based on two deep learning models: LSTM and GRU to predict the closing price in the short and medium term respectively. Decision rules for buying and selling stocks are implemented based on the forecasting given by the two models, then over four 3-year periods, we simulate transactions using these decision rules with different settings for each stock. The returns obtained will be used to estimate an expected return. We only hold stocks that outperform a benchmark index (expected return > threshold). The random search is then used to choose one of the available parameters and the performance of the portfolio built from the selected stocks will be tested over a further period. The repetition of this process with a variation of portfolio size makes it possible to select the best possible combination of stock each with the optimized parameter for the decision rules. The proposed strategy produces very promising results and outperforms the performance of indices used as benchmarks in the local market. Indeed, the annualized return of our strategy proposed during the test period is 27.13%, while it is 0.43% for Moroccan all share Indice (MASI), 15.24% for the distributor sector indices, and 19.94% for the pharmaceutical industry indices. Noted that brokerage fees are estimated and subtracted for each transaction. which makes the performance found even more realistic.

show abstract

A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions

Cited by 177 publications

References 13 publications

Cryptocurrency volatility forecasting: What can we learn from the first wave of the COVID-19 outbreak?

Cryptocurrency volatility forecasting: What can we learn from the first wave of the COVID-19 outbreak?

A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms

An LSTM and GRU based trading strategy adapted to the Moroccan market

Contact Info

Product

Resources

About