Blockchain Papers

J. M. Toledo, Damires Yluska Souza

No abstract is available for this record.

Vasileios Kochliaridis, Eleftherios Kouloumpris, Ioannis Vlahavas

No abstract is available for this record.

Chenyang Xu

Bitcoin's performance during the COVID-19 pandemic has drawn a lot of attention, with many researchers wondering whether bitcoin can act as a hedge against the stock market, and how exactly the COVID-19 pandemic has changed bitcoin's connection to the world. This paper aims to investigate the dynami

Şule Öztürk Birim, Filiz Erataş Sönmez, Yağmur Sağlam

No abstract is available for this record.

Jingying Wei, Jiajun Chen, Zhanchao Zhou

As investment fever rises, investment strategy is a critical choice for investors. In this paper, based on the price data of gold and bitcoin from 9/11/2016 to 9/10/2021, the corresponding mathematical models are established by the LSTM, evaluation model, and single-objective optimization model in a

Azeez A. Oyedele, Anuoluwapo Ajayi, Lukumon O. Oyedele, Sururah A. Bello · 5 authors

No abstract is available for this record.

Eduardo Lopes

Cryptocurrency has become a popular asset in global financial markets, meaning that not only individual investors but also asset management companies around the world are considering this new investment class. The main contribution of this research is to address an intra-day forecasting problem with hourly granularity by comparing deep network architectures, including ones with and attention mechanisms for the Ethereum intrinsic cryptocurrency (ETH). The results showed that the TCN outperformed other architectures considered for a short-term forecast period in terms of processing time and it is amongst the most accurate models using an ARIMA model as a baseline.

Nashirah Abu Bakar, Sofian Rosbi

The cryptocurrency namely Bitcoin is a decentralized cryptocurrency considered a type of digital asset that uses public-key cryptography to record, sign and send transactions over the Bitcoin blockchain. All transaction processes are performed without the oversight of a central authority. The time series data for Bitcoin price movement exhibit time-varying volatility and volatility clustering. This study aims to evaluate the time-varying volatility of Bitcoin price using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. This study uses daily share prices starting from July 2017 until July 2022. The mean equation was developed using the ARMA (1,1) for Bitcoin return. Next, this study evaluated OLS, GARCH, GARCH-M, and E-GARCH models. The result shows the EGARCH (1,1) model exhibits its lowest error of AIC with a value of 5.5984. The autocorrelation test was performed using Q-statistics indicating EGARCH (1,1) model is free from the autocorrelation problem. In addition, ARCH-LM test indicates EGARCH (1,1) is free from heteroscedasticity problems. The EGARCH (1,1) shows there is a leverage effect for volatility clustering. This explained the behavior of bad news effect more than positive news. The finding of the study can act as a guideline to help investors to analyze their investment behavior. At the same time, the finding of this study helps investors to understand the cryptocurrency dynamics behavior.

R Q Li

In this paper, we aimed to build a quantitative investment trading model based on a combination of a multivariate cycle ARMA model and Apriori. We first note that in order to have a sound investment strategy, a forecast for the next trading day needs to be made. To do this, a basic time series forecasting model was first built to predict the value of gold and bitcoin for the next day based on the market volatility of the previous 40 days. The next step is developing a trading strategy model with a stable rate of return and some risk tolerance. At the same time, we developed a fixed stop-loss strategy to protect the strategy's stability and improve the risk resistance performance. Ultimately, using this model, we calculated that on 10 September 2021, we will have a return of $4816941 in Bitcoin and $1129.0503 in gold.

Kittiwin Kumlungmak

Reinforcement learning has emerged as a promising approach for enhancing profitability in cryptocurrency trading. However, the inherent volatility of the market, especially during bearish periods, poses significant challenges in this domain. Existing literature addresses this issue through the adoption of single-agent techniques such as deep Q-network (DQN), advantage actor-critic (A2C), and proximal policy optimization (PPO), or their ensembles. Despite these efforts, the mechanisms employed to mitigate losses during bearish market conditions within the cryptocurrency context lack robustness. Consequently, the performance of reinforcement learning methods for cryptocurrency trading remains constrained within the current literature. To overcome this limitation, we present a novel cryptocurrency trading method, leveraging multi-agent proximal policy optimization (MAPPO). Our approach incorporates a collaborative multi-agent scheme and a local-global reward function to optimize both individual and collective agent performance. Employing a multi-objective optimization technique and a multi-scale continuous loss (MSCL) reward, we train the agents using a progressive penalty mechanism to prevent consecutive losses of portfolio value. In evaluating our method, we compare it against multiple baselines, revealing superior cumulative returns compared to baseline methods. Notably, the strength of our method is further exemplified through the results obtained from the bearish test set, where only our approach demonstrates the ability to yield a profit. Specifically, our method achieves an impressive cumulative return of 2.36%, while the baseline methods result in negative cumulative returns. In comparison to FinRL-Ensemble, a reinforcement learning-based method, our approach exhibits a remarkable 46.05% greater cumulative return in the bullish test set.

Anas Elmelki, Najeh Chaâbane, Riadh Benammar

The increasing interest in digital currencies, their exceptional price rise, and the continuous discussions about their benefits raise the question: to what extent can it be an alternative to the traditional currencies in the future? It has become a prominent topic of discussion among several investments and market stakeholders seeking to enhance the growth of their wealth. This paper aims to bring answers to the nature of relationships between four major cryptocurrencies (Bitcoin, Ethereum, Litecoin, and Ripple), and the stock market return (S&P500). This study applies the wavelet method to daily data from 1 June 2017 to 15 November 2021, in COVID-19 sanitary crisis time. According to the results, this study shows a positive co-movement in the medium and long run between the four studied cryptocurrencies and S&P500 during different periods, especially in times of uncertainty. These findings have practical implications as they can be used strategically to make optimal investment decisions and build portfolio diversification strategies with the conventional financial market asset.

Huali Zhao, Martin Crane, Marija Bezbradica

Cryptocurrencies have won a lot of attention as an investment tool in recent years. Specific research has been done on cryptocurrencies’ price prediction while the prices surge up. Classic models and recurrent neural networks are applied for the time series forecast. However, there remains limited research on how the Transformer works on forecasting cryptocurrencies price data. This paper investigated the forecasting capability of the Transformer model on Bitcoin (BTC) price data and Ethereum (ETH) price data which are time series with high fluctuation. Long short term memory model (LSTM) is employed for performance comparison. The result shows that LSTM performs better than Transformer both on BTC and ETH price prediction. Furthermore, in this paper, we also investigated if sentiment analysis can help improve the model’s performance in forecasting future prices. Twitter data and Valence Aware Dictionary and sEntiment Reasoner (VADER) is used for getting sentiment scores. The result shows that the sentiment analysis improves the Transformer model’s performance on BTC price but not ETH price. For the LSTM model, the sentiment analysis does not help with prediction results. Finally, this paper also shows that transfer learning can help on improving the Transformer’s prediction ability on ETH price data.

Gabriel Borrageiro, Nick Firoozye, Paolo Barucca

We demonstrate a novel application of online transfer learning for a digital assets trading agent. This agent uses a powerful feature space representation in the form of an echo state network, the output of which is made available to a direct, recurrent reinforcement learning agent. The agent learns to trade the XBTUSD (Bitcoin versus US Dollars) perpetual swap derivatives contract on BitMEX on an intraday basis. By learning from the multiple sources of impact on the quadratic risk-adjusted utility that it seeks to maximise, the agent avoids excessive over-trading, captures a funding profit, and can predict the market's direction. Overall, our crypto agent realises a total return of 350\%, net of transaction costs, over roughly five years, 71\% of which is down to funding profit. The annualised information ratio that it achieves is 1.46.

Milos Ciganovic, Federico D’Amario

Cryptocurrencies have become a trendy topic recently, primarily due to their disruptive potential and reports of unprecedented returns. In addition, academics increasingly acknowledge the predictive power of Social Media in many fields and, more specifically, for financial markets and economics. In this paper, we leverage the predictive power of Twitter and Reddit sentiment together with Google Trends indexes and volume to forecast the log returns of ten cryptocurrencies. Specifically, we consider $Bitcoin$, $Ethereum$, $Tether$, $Binance Coin$, $Litecoin$, $Enjin Coin$, $Horizen$, $Namecoin$, $Peercoin$, and $Feathercoin$. We evaluate the performance of LASSO-VAR using daily data from January 2018 to January 2022. In a 30 days recursive forecast, we can retrieve the correct direction of the actual series more than 50% of the time. We compare this result with the main benchmarks, and we see a 10% improvement in Mean Directional Accuracy (MDA). The use of sentiment and attention variables as predictors increase significantly the forecast accuracy in terms of MDA but not in terms of Root Mean Squared Errors. We perform a Granger causality test using a post-double LASSO selection for high-dimensional VARs. Results show no "causality" from Social Media sentiment to cryptocurrencies returns

Jingreng Lei

As a new investment method, quantitative investment is expanding its market scale and share due to its stable investment performance. In this paper we propose a prediction model based on LSTM. It is helpful for the traders to predict the future price to formulate the best trading strategy. Using this model, we can precisely forecast each price separately to determine when the asset should be traded based on future price fluctuations. Simulation results show that our model can successfully predict the future price trend of the two assets within the acceptable range of error, which helps us to better optimize our portfolio. In addition, the RMSE (root mean square error) is selected as the loss function to describe the accuracy of our prediction model.

Zhenyuan Wu

The price of cryptocurrencies is predicted in this paper based on their intrinsic interrelationship with Bitcoin. The Kaggle dataset is gathered, standardized, collated, and extracted. Convolutional Neural Network (CNN) is compared to other machine learning methods such as Linear Regression and K-Nearest Neighbor (KNN), and then parameter optimization is performed. The empirical results show that Linear Regression is less accurate than the other two models, whereas the CNN model employing end-to-end solutions outperforms other models with the best accuracy (overall above 0.95) forecasting the price quantitatively and directly of the majority of cryptocurrencies, despite the fact that forecasting takes a long time and tweaking its parameters is extremely time-consuming. This paper proposes using research object interrelationships rather than extrinsic relationships.

Ramit Sawhney, Shivam Agarwal, Vivek Mittal, Paolo Rosso · 6 authors

The rapid spread of information over social media influences quantitative trading and investments. The growing popularity of speculative trading of highly volatile assets such as cryptocurrencies and meme stocks presents a fresh challenge in the financial realm. Investigating such "bubbles" - periods of sudden anomalous behavior of markets are critical in better understanding investor behavior and market dynamics. However, high volatility coupled with massive volumes of chaotic social media texts, especially for underexplored assets like cryptocoins pose a challenge to existing methods. Taking the first step towards NLP for cryptocoins, we present and publicly release CryptoBubbles, a novel multi-span identification task for bubble detection, and a dataset of more than 400 cryptocoins from 9 exchanges over five years spanning over two million tweets. Further, we develop a set of sequence-to-sequence hyperbolic models suited to this multi-span identification task based on the power-law dynamics of cryptocurrencies and user behavior on social media. We further test the effectiveness of our models under zero-shot settings on a test set of Reddit posts pertaining to 29 "meme stocks'', which see an increase in trade volume due to social media hype. Through quantitative, qualitative, and zero-shot analyses on Reddit and Twitter spanning cryptocoins and meme-stocks, we show the practical applicability of CryptoBubbles and hyperbolic models.

Wipawee Nayam

Unlike traditional currencies that rely on centralized such as banks or governments, cryptocurrencies today have become popular due to its decentralized transactions. Decentralization takes advantage of no requirement for intermediaries, thus reducing transaction fees and processing time. However, investing in cryptocurrencies incurs risks and uncertainties due to price volatility and rapid changes. The fact that prediction of asset prices is complex due to the influence of multiple factors on price movements. This paper studied the technical factor to analyze the short-term returns of Ethereum in the periods of 1-10 days. The historical data containing Ethereum closing price are collected from CoinGecko. The twenty-two indicators are chosen from Momentum, Volatility, and Sentiment factors as candidates to provide valuable insights in market trends. The values of these indicators are calculated based on past Ethereum closing prices and then used for XGBoost learning to discover patterns in previous trading. The model performance is evaluated using the multi-class AUC-ROC metric, which measures the accuracy of predicting three types of Ethereum returns: Downtrend, Sideway, and Uptrend. The experimental results reported that the models achieved the values of micro-average ROC curve ranging from 0.65 to 0.67. Moreover, the study emphasizes the importance of considering momentum indicators when making investment decisions in Ethereum.

Hongze Guo, Ke Gao, Yue Yu, Yingchang Liu · 5 authors

This paper introduces a diluted prediction method for bitcoin and gold based on cycle prediction. This method does not need to quantify the external parameters like robot learning and neural network autoregressive model, but mainly uses ARIMA to feedback the parameter values into risk coefficients under the condition of obtaining the optimal solution circularly, and the price prediction of a single period in the future is carried out with a fixed number of samples, thus realizing the high-precision prediction of bitcoin and gold prices. In the application simulation, the real data of bitcoin and gold from 2016 to 2021 are selected. After 1000 times of Monte Carlo simulations, 919 times of the yield is more than 3 times, 157 times of the yield is more than 8 times, and the minimum yield is about 2 times. At the same time, this paper puts forward an investment strategy for this prediction method, which realizes a very safe profit with a final return rate of 6.2 times under the condition of making full use of the prediction risk coefficient. The prediction method and investment scheme bring a brand-new high-precision prediction method and targeted investment strategy with high safety coefficient to all the investors, which has great economic value.

Xiaoxu Du, Zhenpeng Tang, Junchuan Wu, Kaijie Chen · 5 authors

The return series of cryptocurrencies, which are emerging digital assets, exhibit nonstationarity, nonlinearity, and volatility clustering compared to other traditional financial markets, making them exceptionally difficult to forecast. Therefore, accurate cryptocurrency price forecasting is important for both market participants and regulators. It has been demonstrated that improved data forecasting accuracy can be achieved through decomposition, but few researchers have performed information extraction on the residual series generated by data decomposition. Based on the construction of a "decomposition-optimization-integration" hybrid model framework, in this paper, we propose a multi-scale hybrid forecasting model that combines the residual components after primary decomposition for secondary decomposition and integration. This model uses the variational modal decomposition (VMD) method to decompose the original return series into a finite number of components and residual terms; then, the residual terms are decomposed and the features are extracted using the completed ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method. The components are predicted by an extreme learning machine optimized by the sparrow search algorithm, and the final predictions are summed to obtain the final results. Forecasts for the returns of Bitcoin and Ethereum, which are major cryptocurrency assets, are compared with other benchmark models constructed based on different ideas, and we find that the proposed quadratic decomposition VMD-Res.-CEEMDAN-SSA-ELM hybrid model demonstrates the optimal and most stable forecasting performance in both one-step and multi-step ahead prediction of the cryptocurrency return series.

Sarah Sylvester, Kevin McCabe, Aleksander Psurek, Nalin Bhatt

No abstract is available for this record.

Zubair Ahmad, Zahra Almaspoor, Faridoon Khan, Sharifah E. Alhazmi · 7 authors

<abstract><p>Cryptocurrency is a digital currency and also exists in the form of coins. It has turned out as a leading method for peer-to-peer online cash systems. Due to the importance and increasing influence of Bitcoin on business and other related sectors, it is very crucial to model or predict its behavior. Therefore, in recent, numerous researchers have attempted to understand and model the behaviors of cryptocurrency exchange rates. In the practice of actuarial and financial studies, heavy-tailed distributions play a fruitful role in modeling and describing the log returns of financial phenomena. In this paper, we propose a new family of distributions that possess heavy-tailed characteristics. Based on the proposed approach, a modified version of the logistic distribution, namely, a new modified exponential-logistic distribution is introduced. To illustrate the new modified exponential-logistic model, two financial data sets are analyzed. The first data set represents the log-returns of the Bitcoin exchange rates. Whereas, the second data set represents the log-returns of the Ethereum exchange rates. Furthermore, to forecast the high volatile behavior of the same datasets, we apply dual machine learning algorithms, namely Artificial neural network and support vector regression. The effectiveness of these models is evaluated against self exciting threshold autoregressive model.</p></abstract>

Pavlo Seroyizhko, Zhanel Zhexenova, Muhammad Zohaib Shafiq, Fabio Merizzi · 6 authors

Pavlo Seroyizhko, Zhanel Zhexenova, Muhammad Zohaib Shafiq, Fabio Merizzi, Andrea Galassi, Federico Ruggeri. Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP). 2022.

David L. John, Bela Stantić

No abstract is available for this record.