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Multi-level deep Q-networks for Bitcoin trading strategies
16 references·0 citing·42 second hop
Multi-level deep Q-networks for Bitcoin trading strategies (2024)Multi-level deep Q-networks for Bitcoin t…Bitcoin price change and trend prediction through twitter sentiment and data volume (2022)Bitcoin price change and tren…Forecasting and trading cryptocurrencies with machine learning under changing market conditions (2021)Forecasting and trading crypt…Bitcoin, gold, and commodities as safe havens for stocks: New insight through wavelet analysis (2020)Bitcoin, gold, and commoditie…An Application of Deep Reinforcement Learning to Algorithmic Trading (2020)An Application of Deep Reinfo…Cryptocurrency reaction to FOMC Announcements: Evidence of heterogeneity based on blockchain stack position (2019)Cryptocurrency reaction to FO…Cryptocurrency market contagion: Market uncertainty, market complexity, and dynamic portfolios (2019)Cryptocurrency market contagi…Bitcoin price prediction using machine learning: An approach to sample dimension engineering (2019)Bitcoin price prediction usin…Predicting the Price of Bitcoin Using Machine Learning (2018)Predicting the Price of Bitco…Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques (2018)Predicting the direction, max…Predicting bitcoin returns using high-dimensional technical indicators (2018)Predicting bitcoin returns us…Is Bitcoin a bubble? (2018)Is Bitcoin a bubble?Volatility estimation for Bitcoin: A comparison of GARCH models (2017)Volatility estimation for Bit…On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier? (2016)On the hedge and safe haven p…What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis (2015)What Are the Main Drivers of …Social signals and algorithmic trading of Bitcoin (2015)Social signals and algorithmi…Using genetic algorithms to find technical trading rules (1999)Using genetic algorithms to f…Efficient Capital Markets: A Review of Theory and Empirical Work (1970)EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* (1970)Expected stock returns and volatility (1987)An Anatomy of Trading Strategies (1998)Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation (2000)Stock market's price movement prediction with LSTM neural networks (2017)Using wavelets to decompose the time–frequency effects of monetary policy (2008)Can volume predict Bitcoin returns and volatility? A quantiles-based approach (2017)Datestamping the Bitcoin and Ethereum Bubbles (2017)Dynamic Conditional Correlation (2002)Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold (2009)Bitcoin, gold and the dollar – A GARCH volatility analysis (2015)Exploring the Dynamic Relationships between Cryptocurrencies and Other Financial Assets (2017)Bitcoin is not the New Gold – A comparison of volatility, correlation, and portfolio performance (2018)The contagion effects of the COVID-19 pandemic: Evidence from gold and cryptocurrencies (2020)Designing a neural network for forecasting financial and economic time series (1996)A hybrid ARIMA and support vector machines model in stock price forecasting (2004)Practical Bayesian Optimization of Machine Learning Algorithms (2012)An innovative neural network approach for stock market prediction (2018)Deep learning for financial applications : A survey (2020)Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN) (2021)Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin (2015)The Inefficiency of Bitcoin (2016)Does economic policy uncertainty predict the Bitcoin returns? An empirical investigation (2018)Forecasting stock market movement direction with support vector machine (2004)Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques (2019)Financial applications of machine learning: A literature review (2023)Efficient Tests for an Autoregressive Unit Root (1996)Generalized impulse response analysis in linear multivariate models (1998)Efficiency in the markets of crypto-currencies (2019)Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions (2020)Quantile connectedness in the cryptocurrency market (2021)BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era (2013)A Gated Recurrent Unit Approach to Bitcoin Price Prediction (2020)A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms (2021)The Extreme Value Method for Estimating the Variance of the Rate of Return (1980)Deep Learning-Based Cryptocurrency Price Prediction Scheme With Inter-Dependent Relations (2021)Cue the volatility spillover in the cryptocurrency markets during the COVID-19 pandemic: evidence from DCC-GARCH and wavelet analysis (2022)Analysis of Bitcoin Price Prediction Using Machine Learning (2023)Are Bitcoin and Ethereum safe-havens for stocks during the COVID-19 pandemic? (2020)Is Gold a Hedge or a Safe-Haven Asset in the COVID–19 Crisis? (2020)Static and Dynamic Connectedness Between NFTs, Defi and Other Assets: Portfolio Implication (2021)
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References

  • 2016On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?1,569 cited
  • 2017Volatility estimation for Bitcoin: A comparison of GARCH models1,177 cited
  • 2015What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis989 cited
  • 1999Using genetic algorithms to find technical trading rules868 cited
  • 2018Predicting the Price of Bitcoin Using Machine Learning702 cited
  • 2020Bitcoin, gold, and commodities as safe havens for stocks: New insight through wavelet analysis524 cited
  • 2019Bitcoin price prediction using machine learning: An approach to sample dimension engineering460 cited
  • 2019Cryptocurrency market contagion: Market uncertainty, market complexity, and dynamic portfolios307 cited
  • 2018Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques253 cited
  • 2021Forecasting and trading cryptocurrencies with machine learning under changing market conditions240 cited
  • 2020An Application of Deep Reinforcement Learning to Algorithmic Trading225 cited
  • 2019Cryptocurrency reaction to FOMC Announcements: Evidence of heterogeneity based on blockchain stack position217 cited
  • 2015Social signals and algorithmic trading of Bitcoin207 cited
  • 2018Predicting bitcoin returns using high-dimensional technical indicators

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177 cited
  • 2018Is Bitcoin a bubble?137 cited
  • 2022Bitcoin price change and trend prediction through twitter sentiment and data volume97 cited
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