Cite this DOI

Satya, M. P. S. (2023). CRYPTOCURRENCY PRICE ANALYSIS WITH ARTIFICIAL INTELLIGENCE. *Journal of Science & Technology*, *8*(4), 18–24. https://doi.org/10.46243/jst.2023.v8.i04.pp18-24

@article{satya2023cryptocurrency,
  author    = {Satya, Mrs. P.Lakshmi Satya},
  title     = {{CRYPTOCURRENCY PRICE ANALYSIS WITH ARTIFICIAL INTELLIGENCE}},
  journal   = {Journal of Science \& Technology},
  year      = {2023},
  month     = {oct},
  volume    = {8},
  number    = {4},
  pages     = {18--24},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2023.v8.i04.pp18-24},
  url       = {https://doi.org/10.46243/jst.2023.v8.i04.pp18-24},
  language  = {en},
  abstract  = {Cryptocurrency is playing an increasingly important role in reshaping the financial system due to its growing popular appeal and merchant acceptance. While many people are making investments in Cryptocurrency, the dynamical features, uncertainty, the predictability of Cryptocurrency are still mostly unknown, which dramatically risk the investments. It is a matter to try to understand the factors that influence the value formation. In this study, we use advanced artificial intelligence frameworks of fully connected Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) Recurrent Neural Network to analyze the price dynamics of Bitcoin, Ethereum, and Ripple. We find that ANN tends to rely more on long-term history while LSTM tends to rely more on short-term dynamics, which indicate the efficiency of LSTM to utilize useful information hidden in historical memory is stronger than ANN. However, given enough historical information ANN can achieve a similar accuracy, compared with LSTM. This project provides a unique demonstration that Cryptocurrency market price is predictable. However, the explanation of the predictability could vary depending on the nature of the involved machine-learning model}
}

TY  - JOUR
TI  - CRYPTOCURRENCY PRICE ANALYSIS WITH ARTIFICIAL INTELLIGENCE
AU  - Satya, Mrs. P.Lakshmi Satya
JO  - Journal of Science & Technology
PY  - 2023
DA  - 2023/10/04/
VL  - 8
IS  - 4
SP  - 18
EP  - 24
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Cryptocurrency is playing an increasingly important role in reshaping the financial system due to its growing popular appeal and merchant acceptance. While many people are making investments in Cryptocurrency, the dynamical features, uncertainty, the predictability of Cryptocurrency are still mostly unknown, which dramatically risk the investments. It is a matter to try to understand the factors that influence the value formation. In this study, we use advanced artificial intelligence frameworks of fully connected Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) Recurrent Neural Network to analyze the price dynamics of Bitcoin, Ethereum, and Ripple. We find that ANN tends to rely more on long-term history while LSTM tends to rely more on short-term dynamics, which indicate the efficiency of LSTM to utilize useful information hidden in historical memory is stronger than ANN. However, given enough historical information ANN can achieve a similar accuracy, compared with LSTM. This project provides a unique demonstration that Cryptocurrency market price is predictable. However, the explanation of the predictability could vary depending on the nature of the involved machine-learning model
DO  - 10.46243/jst.2023.v8.i04.pp18-24
UR  - https://doi.org/10.46243/jst.2023.v8.i04.pp18-24
ER  -

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    "URL": "https://doi.org/10.46243/jst.2023.v8.i04.pp18-24",
    "title": "CRYPTOCURRENCY PRICE ANALYSIS WITH ARTIFICIAL INTELLIGENCE",
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    "language": "en",
    "abstract": "Cryptocurrency is playing an increasingly important role in reshaping the financial system due to its growing popular appeal and merchant acceptance. While many people are making investments in Cryptocurrency, the dynamical features, uncertainty, the predictability of Cryptocurrency are still mostly unknown, which dramatically risk the investments. It is a matter to try to understand the factors that influence the value formation. In this study, we use advanced artificial intelligence frameworks of fully connected Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) Recurrent Neural Network to analyze the price dynamics of Bitcoin, Ethereum, and Ripple. We find that ANN tends to rely more on long-term history while LSTM tends to rely more on short-term dynamics, which indicate the efficiency of LSTM to utilize useful information hidden in historical memory is stronger than ANN. However, given enough historical information ANN can achieve a similar accuracy, compared with LSTM. This project provides a unique demonstration that Cryptocurrency market price is predictable. However, the explanation of the predictability could vary depending on the nature of the involved machine-learning model",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2023.v8.i04.pp18-24 gives all four in one JSON answer.