• Corpus ID: 208309898

Cryptocurrency Price Prediction and Trading Strategies Using Support Vector Machines

@article{Zhao2019CryptocurrencyPP,
  title={Cryptocurrency Price Prediction and Trading Strategies Using Support Vector Machines},
  author={David Zhao and Alessandro Rinaldo and C. Brookins},
  journal={arXiv: Trading and Market Microstructure},
  year={2019}
}
Few assets in financial history have been as notoriously volatile as cryptocurrencies. While the long term outlook for this asset class remains unclear, we are successful in making short term price predictions for several major crypto assets. Using historical data from July 2015 to November 2019, we develop a large number of technical indicators to capture patterns in the cryptocurrency market. We then test various classification methods to forecast short-term future price movements based on… 

Predictive analysis of Bitcoin price considering social sentiments

This report reports on the use of sentiment analysis on news and social media to analyze and predict the price of Bitcoin and finds that social sentiment gives a good estimate of how future Bitcoin values may move.

Real-time Bitcoin price tendency awareness via social media content tracking

Apache Spark logistic regression model is implemented to process the large-scale data after having a sentimental analysis of Twitter tweets regarding Bitcoin, to predict the upcoming price tendency and classify an awareness level to alert potential traders and investors in real time about such potential market changes.

Econophysics of cryptocurrency crashes: an overview

It has been shown that it is possible to build indicators-precursors of crisis phenomena in the cryptocurrency market and most of these measures behave characteristically in the periods preceding the critical event.

Comparison of Distance Measurement in Time Series Clustering for Predicting Bitcoin Prices

This work cluster time series through K-Medoids algorithm and train and evaluate each cluster with predictive models and examines the predictive performance in Bitcoin price according to the various distance measurement of clustering.

CBITS: Crypto BERT Incorporated Trading System

This study answers the question of “Is it possible that the LMs can profit by effectively applying the sentiment score of the natural language processing task with chart score in the BTC trading system?” by focusing on the effectiveness of both scores, which significantly affect the profit of the trading system.

References

SHOWING 1-10 OF 21 REFERENCES

Cryptocurrency portfolio management with deep reinforcement learning

This paper presents a model-less convolutional neural network with historic prices of a set of financial assets as its input, outputting portfolio weights of the set, trained with 0.7 years' price data from a cryptocurrency exchange.

Stacking with Neural Network for Cryptocurrency investment

This work has used generative and discriminative classifiers and optimized over one-layer Neural Network to model the direction of price cryptocurrencies and developed methodology for features importance for stacked model.

Bitcoin is not the New Gold – A comparison of volatility, correlation, and portfolio performance

Financial Stock Market Forecast using Data Mining Techniques

Various techniques which are able to predict with future closing stock price will increase or decrease better than level of significance are discussed.

Cryptoasset Factor Models

The empirical analysis identifies the leading factor that appears to strongly contribute into daily cryptoasset returns and suggests that cross-sectional statistical arbitrage trading may be possible for cryptoassets subject to efficient executions and shorting.

Price Manipulation in the Bitcoin Ecosystem

Become Your Own Technical Analyst

The author first observes that investment scandals and the dot.com fallout have wreaked havoc on the U.S. equity market since 1999. Dow components have fallen to depths many would never have imagined

The economics of BitCoin price formation

ABSTRACT This is the first article that studies BitCoin price formation by considering both the traditional determinants of currency price, e.g., market forces of supply and demand, and digital

Cautious Deep Learning

This work proposes constructing conformal prediction sets which contain a set of labels rather than a single label, and demonstrates the performance on the ImageNet ILSVRC dataset and the CelebA and IMDB-Wiki facial datasets using high dimensional features obtained from state of the art convolutional neural networks.