Bitcoin Futures Research Articles

Cryptocurrency hacking and trader behavior in bitcoin futures

We investigate the effect of cryptocurrency hacking on trader positions in CME bitcoin futures. Our results indicate that the occurrence of hacking and fraudulent incidents lead to more net-short positions and fewer participation ratio for leveraged funds in bitcoin futures. Additionally, U.S. dollar index, futures basis, and price efficiency also play significant roles in the trading of leveraged funds. Lastly, we find that the number of hacking incidents negatively affects dealers’ net-long positions. This study provides new insights into trader behaviors in bitcoin futures and offers practical implications for bitcoin investors and regulators.

Bitcoin returns and YouTube news: a behavioural time series analysis

ABSTRACT This study investigates whether investor’s sentiment and attention information collected via YouTube can improve bitcoin return forecasts. Accordingly, we collected daily data over the period 2017–2023, covering calm and turbulent periods marked by different types and episodes of emotions. Unlike previous studies, we used YouTube videos to propose two sentiment proxies: investor attention to YouTube (daily number of YouTube video views) and investor sentiment on YouTube (number of positive and negative videos on YouTube). Interestingly, we break down both attention and sentiment per subject. Econometrically, we assess lead-lag effects between sentiment/attention and bitcoin return using causality tests and Vector Auto-regressive (VAR) model. We also evaluate the forecasting power of YouTube attention/sentiment data using a deep learning LSTM model. Our study shows two main results. First, we find lead-lag effects between bitcoin returns and per subject investor’s attention and sentiment proxies. Second, we show that our deep learning LSTM model relying on the information provided by attention and sentiment supplants benchmark Buy and Hold Strategy to forecast future bitcoin returns.

Investor Sentiment, Unexpected Inflation, and Bitcoin Basis Risk

ABSTRACTThe introduction of regulated CME futures contracts on Bitcoin in 2017 raised an expectation that cryptocurrencies would become part of mainstream financial markets. This also heightened links between traditional markets and Bitcoin, implying that the cryptocurrency would be subject to systematic spillovers. This paper uses high‐frequency data to examine whether Bitcoin basis risk is linked to investor sentiment from established financial markets. Our findings indicate that extreme investor sentiment, as reflected by the tail risk in various volatility indices, including the VIX, consistently correlates with a negative Bitcoin basis, where Bitcoin futures prices are lower than spot prices. Fluctuations significantly influence this relationship in the trading volume of Bitcoin futures and are more pronounced during periods of substantial unexpected inflation and deflation. These results underline the complex dynamics between market sentiment and cryptocurrency pricing, offering insights with substantial implications for investors and policymakers.

Time-varying expected returns, conditional skewness and Bitcoin return predictability

We employ a GARCH-type model to jointly estimate returns, conditional variance and skewness and show that conditional skewness outperforms sample skewness and conditional and sample variance in predicting future Bitcoin returns. Interestingly, the results show that the relationship between conditional skewness and future Bitcoin returns is different depending on the sample period. In the first subsample (2018–2020), a period of relative calm in the Bitcoin market, the relationship is negative, which is in line with that found in the literature. However, in the second subsample (2021–2022), a period of major turmoil in the Bitcoin market, the relationship is positive, which is consistent with that found in previous papers on the relationship between conditional market skewness and future index returns during crisis periods. Based on these results, a dynamic buy and sell strategy of buying or selling Bitcoin based on the estimated conditional skewness is proposed. This dynamic strategy outperforms a static buy-and-hold strategy. The profitability of this strategy can be viewed as the reward that investors demand for bearing the risk associated with the changing conditions in the cryptocurrency market that generate time-varying expected returns.

Bitcoin Price Prediction Using Sentiment Analysis and Empirical Mode Decomposition

AbstractCryptocurrencies have garnered significant attention recently due to widespread investments. Additionally, researchers have increasingly turned to social media, particularly in the context of financial markets, to harness its predictive capabilities. Investors rely on platforms like Twitter to analyze investments and detect trends, which can directly impact the future price movements of Bitcoin. Understanding and analyzing Twitter sentiments can potentially provide insights into future Bitcoin price movements and can shed light on how investor sentiment affects cryptocurrency markets. In this study, we explore the correlation between Twitter activity and Bitcoin prices by examining tweets related to Bitcoin price sentiments. Our proposed model consists of two distinct networks. The first network exclusively utilizes historical price data, which is further decomposed into various components using the Empirical Mode Decomposition method. This decomposition helps mitigate the impact of irregular fluctuations on Bitcoin price predictions. Each of these components is then separately processed by Long Short-Term Memory (LSTM) networks. The second network focuses on modeling user sentiments and emotions in conjunction with Bitcoin market data. User opinions are categorized into positive and negative classes and are integrated with historical data to predict the next-day price using LSTM networks. Finally, the outputs of each network are combined to form the ultimate prediction values. Experimental results demonstrate that Twitter sentiment can effectively helps us predict Bitcoin price trends. Furthermore, to validate our proposed model, we compared it with several state-of-the-art methods. The results indicate that our approach outperforms these existing models in terms of accuracy.

Bitcoin Price Prediction Based on Multiple Machine Learning Algorithms

In this paper, we performed bitcoin price prediction based on bitcoin price dataset using Support Vector Machine model, Random Forest model, Neural Network model, XGBoost model and LightGBM model and evaluated the performance of these models. We divided the Bitcoin price dataset into training and test sets in a ratio of 7:3, where 70 were used as the training set and 30 as the test set. The models were trained with the training set and tested with the test set using the stock price change (yield) as the target variable and other variables as input variables. By comparing the MSE, RMSE, MAE, MAPE and R of the different models were evaluated and it was found that XGBoost has the best performance and the best prediction. The performance of the other four models ranged from good to poor, including LightGBM, Random Forest, Support Vector Machine and Neural Network. Among them, the neural network, whose MSE is tens of times higher than the other four models, performs the worst. The research results in this paper can provide reference value for future Bitcoin price prediction, and also provide some reference for choosing appropriate machine learning models.

Diversifying and hedging REIT portfolios with cryptocurrencies: Evidence from global and regional REIT indices

Hedging the financial risk of portfolios of securitized real estate assets is daunting because of the unique nature of the underlying assets and because no direct market exists to trade on property-related derivatives. In this paper, we conduct a global study of the diversification and risk mitigation benefits of cryptocurrencies for REIT stocks. Specifically, we examine the dynamic relationship between Bitcoin and REIT indices obtained from multiple countries and regions of the world. We also explore whether portfolios of REIT stocks that are hedged with Bitcoin futures obtain significant improvement in portfolio risk mitigation and return performance. Furthermore, given that Bitcoin and REIT stocks are prone to large volatility swings, we examine the nature of the tail dependences in the multivariate distributions of our REIT indices universe and Bitcoin using Archimedean copula functions. Lastly, we compare the market risk of Bitcoin-hedged REIT portfolios to the unhedged portfolios of REIT stocks using value-at-risk. We find region-specific benefits from using Bitcoin to hedge REIT exposure. Furthermore, we show that using Bitcoin to dynamically hedged REIT portfolios had minimal impact on the bivariate and multivariate joint distributions of REIT portfolios. We also find evidence of region-specific reductions in the market risk exposure of dynamically hedged REIT portfolios relative to unhedged and naively hedged REIT portfolios. We posit that Bitcoin could improve the mean-variance frontiers of REIT investors in countries with higher market friction and lower global integration.

Price-Volume Relationship in Bitcoin Futures ETF Market: An Information Perspective

Bitcoin futures exchange-traded funds (ETFs) are recent innovations in cryptocurrency investment. This article studies the price-volume relationship in this market from an information perspective. We first propose effective mutual information which has better estimation accuracy to analyze the contemporaneous relationship. Using half-hourly trading data of the world’s largest Bitcoin futures ETF, we find that trading volume changes and returns contain information about each other and are contemporaneously dependent. Then, we employ effective transfer entropy to examine the intertemporal relationship. The results show that there exists information transfer from volume changes to returns in most of our sample period, suggesting the presence of return predictability and market inefficiency. However, information transfer in the opposite direction occurs much less frequently, and the amount is typically smaller.

Cryptocurrency Adoption in Traditional Financial Markets in the United States

Purpose: The aim of the study was to assess the cryptocurrency adoption in traditional financial markets in the United States. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Cryptocurrency adoption in traditional financial markets in the United States has been marked by a gradual but significant increase in interest and integration. Studies indicate that while the adoption rates vary across different sectors, there's a growing acceptance and utilization of cryptocurrencies among institutional investors, particularly in the form of investment products like Bitcoin futures and exchange-traded funds (ETFs). Regulatory clarity and institutional infrastructure developments have played crucial roles in fostering this adoption. Additionally, the involvement of prominent financial institutions and corporations, such as banks offering cryptocurrency custody services and major companies integrating Bitcoin into their balance sheets, highlights a broader acceptance of cryptocurrencies within traditional finance. Implications to Theory, Practice and Policy: Technology acceptance model (tam), diffusion of innovations theory and institutional theory may be use to anchor future studies on assessing the cryptocurrency adoption in traditional financial markets in the United States. Develop educational initiatives aimed at enhancing financial literacy and cryptocurrency awareness among investors, financial professionals, and policymakers. Advocate for clear and coherent regulatory frameworks that provide legal certainty and consumer protection while fostering innovation and market development.

Assessing the Risk of Bitcoin Futures Market: New Evidence

AbstractThe main objective of this paper is to forecast the realized volatility (RV) of Bitcoin futures (BTCF) market. To serve our purpose, we propose an augmented heterogenous autoregressive (HAR) model to consider the information on time-varying jumps observed in BTCF returns. Specifically, we estimate the jump-induced volatility using the GARCH-jump process and then consider this information in the HAR model. Both the in-sample and out-of-sample analyses show that jumps offer added information which is not provided by the existing HAR models. In addition, a novel finding is that the jump-induced volatility offers incremental information relative to the Bitcoin implied volatility index. In sum, our results indicate that the HAR-RV process comprising the leverage effects and jump volatility would predict the RV more precisely compared to the standard HAR-type models. These findings have important implications to cryptocurrency investors.

Unravelling the global landscape of Bitcoin research: insights from bibliometric analysis

ABSTRACT Bitcoin has been gaining increasing attention in academia and industry. This article investigates Bitcoin’s research status and evolution via bibliometrics using a dataset of 3,873 publications between 2012 and 2022 from the Web of Science Core Collection. The findings reveal a significant increase in research on Bitcoin since 2017, coinciding with the cryptocurrency bull market. The article identifies publication trends, influential contributors, collaboration networks, and topics evolution in Bitcoin research. The three Bitcoin research stages are conceptualisation and fundamentals of Bitcoin (2012–2016), cryptocurrency and market efficiency (2017–2018), and technical analysis, big data, data privacy, and the connection between Bitcoin and financial markets (2019–2022). The four prominent emerging areas for future Bitcoin research are decentralised finance (DeFi), non-fungible tokens (NFTs), clean energy and mining, and monetary policy. The article offers valuable insights for researchers, policymakers, and practitioners, facilitating a better understanding of the status quo of Bitcoin research.

Analysis of Legal Risks of Transactions of Bitcoin Futures in Chinese Mainland

Analysis of Legal Risks of Transactions of Bitcoin Futures in Chinese Mainland

The impact of index futures crash risk on bitcoin futures returns and volatility

This study examines the relationship between E-mini S&P 500 futures' crash risk and Bitcoin futures' returns and volatility using data from 2017 to 2021. While E-mini S&P 500's crash risk doesn't significantly influence Bitcoin returns, it correlates with its volatility, especially during events like the COVID-19 pandemic and U.S. elections. Furthermore, as global and emerging market indices rise, Bitcoin futures volatility decreases, suggesting its role as a hedging tool. These findings are pivotal for investors aiming to construct informed trading strategies, leverage Bitcoin futures as a hedging asset during economic instability, and keep tabs on traditional market indicators like E-mini S&P 500 crash risk for anticipating fluctuations in Bitcoin futures.

Applications and the future of bitcoin through analysing potential practical applications and prospects in finance in Venezuela

Applications and the future of bitcoin through analysing potential practical applications and prospects in finance in Venezuela

Cryptocurrency Market Dynamics Around Bitcoin Futures Expiration Events

Cryptocurrency Market Dynamics Around Bitcoin Futures Expiration Events

BiBitcoin Price Prediction using Recurrent Neural Networks and Long Short-Term Memory

Bitcoin's decentralized nature has made it a popular mode of payment for buyers and sellers, but its highly volatile nature poses a challenge for investors. This study aims to predict future Bitcoin prices using a combination of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. The non-stationary nature of Bitcoin prices is addressed using RNN, which is particularly useful for analyzing sequential data.The dataset used in this research is sourced from the Kraken exchange and includes various factors that are believed to influence Bitcoin prices, such as transaction volume, hash rate, and Google search trends. The data is preprocessed and cleaned to ensure accuracy, and then fed into the RNN and LSTM models for training and testing.The study's use of RNN and LSTM algorithms demonstrates the effectiveness of these methods in predicting Bitcoin prices, particularly in the context of sequential data. The results of the study, provide insights into potential future trends in Bitcoin prices and identify key indicators that significantly influence Bitcoin prices. The findings of this research have important implications for investors and traders looking to make informed decisions in the cryptocurrency market, as well as for researchers seeking to improve our understanding of Bitcoin's price dynamics. By predicting future prices, the study provides insights that can mitigate the risks associated with Bitcoin's volatility, making it a more viable investment option.

Relations among Bitcoin Futures, Bitcoin Spot, Investor Attention, and Sentiment

This research investigates the function of price discovery between the Bitcoin futures and the spot markets while also analyzing the impact of investor sentiment and attention on these markets. This study utilizes various statistical models to examine the short-term and long-term relations between these variables, including the bivariate Granger causality model, the ARDL and NARDL models, and the Johansen cointegration procedure with a vector error correction mechanism. The results suggest that there is no statistical evidence of price discovery between the Bitcoin spot price and futures, and the term structure of the Bitcoin futures neither enriches nor impairs this lead lag relation. However, the study finds robust evidence of a long-run cointegrating relation between the two markets and the presence of asymmetry in them. Moreover, this research indicates that investor sentiment exhibits a lead lag relation with both the Bitcoin futures and the spot markets, while investor attention only leads to the Bitcoin spot market, without showing any lead lag relation with the Bitcoin futures. These findings highlight the crucial role of investor behavior in affecting both Bitcoin futures and spot prices.

The Bitcoin volume-volatility relationship: A high frequency analysis of futures and spot exchanges

We examine the volume-volatility relationship across Bitcoin futures and spot markets, using daily realised volatility measures estimated from high frequency intraday data. We estimate realised spot volatility across five major exchanges using both the standard volume weighted price and using a new approach, inspired by the CME Bitcoin Reference Rate methodology. We find that unexpected trading volume is the most important explanatory variable for BRR spot volatility, explaining 20% of variation in price volatility at exchange level. Conversely, we find that both expected and unexpected CME Bitcoin futures volumes play a very limited or even calming role in systemic volatility. Our findings suggest that CME Bitcoin futures are not independently contributing to systemic risk in Bitcoin over the period studied.

Visualizing profitability: A heatmap approach to evaluate Bitcoin futures trading using VMA trading rules

Given that technical trading charts are publicly available on popular financial websites such as Bloomberg and MarketWatch, it stands to reason that the same technical trading approaches may be applied to cryptocurrency markets. One of these trading strategies is the variable length moving average (VMA), whose flexibility benefit has not been fully explored in prior research. To fill this gap, we evaluate Bitcoin futures using VMA trading rules and provide the results in a heatmap diagram. This approach allows investors to choose the most effective VMA rules, potentially leading to profits. Furthermore, our approach may shed new light on previously unexplored investment thinking and practices that have the potential to improve investment outcomes.

Arbitrage opportunities and feedback trading in regulated bitcoin futures market: An intraday analysis

The study uses 5-min intraday Bitcoin futures and spot prices to investigate the additive and multiplicative impact of arbitrage opportunities on feedback trading in the Bitcoin futures market. The basis, equal to the difference between Bitcoin spot and futures prices, is used to proxy arbitrage opportunities. The study uses quantile regressions to capture different market conditions. The study documents episodic cases of positive and negative feedback trading. The resulting evidence reveals that the intensity and type of feedback trading largely depend on market conditions and the degree of arbitrage opportunities. From the multiplicative model, the impact of arbitrage opportunities on positive feedback trading is strongest in the extreme bear market, lowest in the regular market conditions, and absent in the bull market conditions. Channel “breakouts” analysis indicates positive feedback traders dominate during bear and normal market conditions. When the basis declines by varying magnitudes across quantiles, positive feedback trading shifts to negative feedback trading. The findings suggest widening basis intensifies negative feedback trading during bull market conditions. However, when the basis decreases by varying scales across upper quantiles, negative feedback trading switches to positive feedback trading. By observing the trading signals, investors can adjust their portfolio positions to maximize returns and minimize risk.