利用深度学习预测比特币投资相关的金融风险

IF 1.7 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-06-06 DOI:10.14201/adcaij.27269
N. Aljojo
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引用次数: 1

摘要

投资比特币的金融风险正在增加,参与交易的每个人都意识到了这一点。比特币价值的涨跌很难预测,而且这个系统充满了不确定性。因此,本研究建议使用“深度学习”技术来预测与比特币投资相关的金融风险,这与比特币市场波动的“加权价格”有关。使用的数据集包括比特币历史数据,这些数据是在2012年1月至2020年12月期间从选定的交易所“每隔一分钟”获取的。采用基于深度学习线性耳svm的技术,在处理基于比特币的大数据量交易相关的高维挑战方面具有优势。四个变量(“高”、“低”、“收盘”和“交易量(比特币)”)被概念化以预测加权价格,以表明是否存在金融风险倾向于它们相互作用的影响。实验调查结果表明,与比特币投资相关的金融风险是准确预测的。这有助于发现与比特币投资交易相关的疑虑,从而增加对系统的信心和信任,并消除金融风险。我们的模型具有更高的预测准确性,证明了深度学习系统在检测与数字货币相关的金融问题方面的实用性。
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Predicting Financial Risk Associated to Bitcoin Investment by Deep Learning
The financial risk of investing in Bitcoin is increasing, and everyone partic-ipating in the transaction is aware of it. The rise and fall of bitcoin’s value is difficult to predict, and the system is fraught with uncertainty. As a result, this study proposed to use the «Deep learning» technique for predicting fi-nancial risk associated with bitcoin investment, that is linked to its «weighted price» on the bitcoin market’s volatility. The dataset used included Bitcoin historical data, which was acquired «at one-minute intervals» from selected exchanges of January 2012 through December 2020. The deep learning lin-ear-SVM-based technique was used to obtain an advantage in handling the high-dimensional challenges related with bitcoin-based transaction transac-tions large data volume. Four variables («High», «Low», «Close», and «Volume (BTC)».) are conceptualized to predict weighted price, in order to indi-cate if there is a propensity of financial risk over the effect of their interaction. The results of the experimental investigation show that the fi-nancial risk associated with bitcoin investing is accurately predicted. This has helped to discover engagements and disengagements with doubts linked with bitcoin investment transactions, resulting in increased confidence and trust in the system as well as the elimination of financial risk. Our model had a significantly greater prediction accuracy, demonstrating the utility of deep learning systems in detecting financial problems related to digital currency.
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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
22
审稿时长
4 weeks
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