Blockchain Papers

Surinder Singh Khurana, Parvinder Singh, Naresh Kumar Garg

No abstract is available for this record.

Zhunzhun Liu, Ruidong Zhang

In recent years cryptocurrency trading has been very active and received attention from worldwide investors. Given its short history and lack of effective regulatory frameworks, cryptocurrency trading is still like the wild west. This study selected the top 21 dominant cryptocurrencies to analyze the trading behavior associated with them over the period from 2016 to May 2023. We adopted the cross-sectional absolute deviations (CSAD) model to investigate investors’ trading behavior. Two calculation methods are used: the capitalization-weighted method and the equally weighted method. Our analysis results have indicated that no matter which calculation method is used, the cryptocurrency marketplace has shown strong and significant herd behavior from 2016 to March 12, 2020. However, significant herd behavior couldn’t be detected after March 12, 2020. This is a very interesting and yet important finding from our study. The implication is that March 12, 2020 appeared to be a turning point where important market conditions might have changed, such as the dominant cryptocurrency trading force has transformed from individuals to institutional investors.

Indranil Ghosh, Rabin K. Jana

No abstract is available for this record.

Xavier Brouty, Matthieu Garcin

Considering that both the entropy-based market information and the Hurst exponent are useful tools for determining whether the efficient market hypothesis holds for a given asset, we study the link between the two approaches. We thus provide a theoretical expression for the market information when log-prices follow either a fractional Brownian motion or its stationary extension using the Lamperti transform. In the latter model, we show that a Hurst exponent close to 1/2 can lead to a very high informativeness of the time series, because of the stationarity mechanism. In addition, we introduce a multiscale method to get a deeper interpretation of the entropy and of the market information, depending on the size of the information set. Applications to Bitcoin, CAC 40 index, Nikkei 225 index, and EUR/USD FX rate, using daily or intraday data, illustrate the methodological content.

Jiliang Li, Ziyi Zhao, Zhou Su, Weizhi Meng

No abstract is available for this record.

Ahmad El Majzoub, Fethi Rabhi, Walayat Hussain

Summary This study explores various machine learning and deep learning applications on financial data modelling, analysis and prediction processes. The main focus is to test the prediction accuracy of cryptocurrency hourly returns and to explore, analyse and showcase the various interpretability features of the ML models. The study considers the six most dominant cryptocurrencies in the market: Bitcoin, Ethereum, Binance Coin, Cardano, Ripple and Litecoin. The experimental settings explore the formation of the corresponding datasets from technical, fundamental and statistical analysis. The paper compares various existing and enhanced algorithms and explains their results, features and limitations. The algorithms include decision trees, random forests and ensemble methods, SVM, neural networks, single and multiple features N‐BEATS, ARIMA and Google AutoML. From experimental results, we see that predicting cryptocurrency returns is possible. However, prediction algorithms may not generalise for different assets and markets over long periods. There is no clear winner that satisfies all requirements, and the main choice of algorithm will be tied to the user needs and provided resources.

AMAL SAAD ALSHEHRI

People are starting to see the cryptocurrency market as a viable source of income and investment, similar to the stock market, as the concept of cryptocurrencies continues to gain popularity. Predicting Bitcoin returns is related to financial machine learning, which uses time series to forecast price variance. This study starts with the daily close price of Bitcoin for its initial dataset. The price is transformed into percentages and binary classes, which categorize into “Up” and “Down”, after which a time series is applied to produce two datasets: a categorical dataset for classification and a numerical dataset for regression. For classification that represents a Binary classification in asset-price forecasting, k-fold cross-validation is applied to ensure that the best classifiers are selected for testing and analysis. Most of the regression analysis was based on visualization, which displayed the predicted prices by each regressor in front of the original values and helped analyze the models’ results more accurately. The outcomes of this study were achieved by anticipating bitcoin returns using classification and regression machine learning models, despite the approaches’ low accuracy and significant precision rate to the “Up” class. At this stage, with a significant limitation regarding the dataset and a lack of other indicators, a model capable of predicting future variations is considered a beneficial addition for many trading tools or even for crypto market analysts.

Saachin Bhatt, Mustansar Ali Ghazanfar, Mohammad Hossein Amirhosseini

The purpose of this research is to investigate the impact of social media sentiments on predicting the Bitcoin price using machine learning models, with a focus on integrating onchain data and employing a Multi Modal Fusion Model. For conducting the experiments, the crypto market data, on-chain data, and corresponding social media data (Twitter) has been collected from 2014 to 2022 containing over 2000 samples. We trained various models over historical data including K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Extreme Gradient Boosting and a Multi Modal Fusion. Next, we added Twitter sentiment data to the models, using the Twitter-roBERTa and VADAR models to analyse the sentiments expressed in social media about Bitcoin. We then compared the performance of these models with and without the Twitter sentiment data and found that the inclusion of sentiment feature resulted in consistently better performance, with TwitterRoBERTa-based sentiment giving an average F1 scores of 0.79. The best performing model was an optimised Multi Modal Fusion classifier using Twitter-RoBERTa based sentiment, producing an F1 score of 0.85. This study represents a significant contribution to the field of financial forecasting by demonstrating the potential of social media sentiment analysis, onchain data integration, and the application of a Multi Modal Fusion model to improve the accuracy and robustness of machine learning models for predicting market trends, providing a valuable tool for investors, brokers, and traders seeking to make informed decisions

Tushar Maheshwari, Shivani Bharadwaj, Neeraj Kumar Sharma, Gajanan Mudegol · 5 authors

Bitcoin is a kind of Cryptocurrency and now is one of type of investment on the stock market.Stock markets are influenced by many risks of factor.And bitcoin is one kind of cryptocurrency that keep rising in recent few years, and sometimes sudden fall without knowing influence behind it on the stock market.Because it's fluctuations, there's a need and automation tool to predict bitcoin on the stock market.This research study learns how to create model prediction bitcoin stock market prediction using LSTM, LSTM (Long Short Term Memory) is another type of module provided for RNN later developed and popularized by many researchers, like RNN, the LSTM also consists of modules with recurrent consistency.The contribution of this study is providing a new forecasting framework for bitcoin price prediction can overcome and improve the problem of input variables selection in LSTM without strict assumptions of data assumption.The results revealed its possible applicability in various cryptocurrencies prediction, industry instances such as medical data or financial timeseries data.The Method that we apply on this research, also technique and tools to predict Bitcoin on stock market yahoo finance can predict the result above $ 12600 USD for next days after prediction, in the last section we make conclusions and discuss future works.The proposed methodology is then applied to train a simple Long Short Term Memory (LSTM) model to predict the bitcoin price for the upcoming 5 days.When the LSTM model is trained with a suitable data chunk, thus identified, sustainable results are found for the prediction.In the end of this paper, the work culminates with future improvements.

Omkar Ingale

This research paper presents a stacked ensemble model for next day Bitcoin price prediction, incorporating diverse look-back windows and evaluating the performance of various models within the ensemble framework using metrics like Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The base layer, layer-0, comprises LSTM and GRU models with different look-back windows. The layer-1 models, including CNN, SVR, Linear Regression, Random Forest Regressor, LSTM, and KNN, are tested individually in conjunction with the base layer models. Extensive experiments demonstrate the effectiveness of the stacked ensemble approach, improving prediction accuracy. The comparative analysis provides insights into the strengths and weaknesses of each model, aiding in the identification of optimized combinations for Bitcoin price prediction. This research contributes to the field by showcasing the value of diverse look-back windows and evaluating models in a stacked ensemble framework, enhancing the accuracy of Bitcoin price forecasting.

Prisha Negi, Riddhi Dhawad, Nekita Chavhan Morris, Rahul Agrawal · 5 authors

Crypto currency is an immerging field for investments and trading which attracts many businessmen, investors, and most importantly a generation of aspiring youth which understands the future of money transactions and its security. Cryptocurrency price prediction problem solutions can provide extremely useful information which will prevent investors from losing money invested on cryptocurrency. The ability to forecast the price of an asset such as crypto offers the opportunity for profit by trading it. The goal of this study is to use LSTM (Long-Short Term Memory) and RNN (Recurrent Neural Network) with Sentiment Analysis to create an algorithm model that can accurately predict the price of cryptocurrency the following day.

Rasoul Amirzadeh, Dhananjay Thiruvady, Asef Nazari, Mong Shan Ee

Abstract Cryptocurrencies have gained widespread attention, particularly in finance and investment sectors. Despite their growing popularity, cryptocurrencies can be a high-risk investment due to their price volatility. The inherent volatility in cryptocurrency prices, coupled with the effects of external global economic factors, makes predicting their price movements challenging. To address this challenge, we propose a dynamic Bayesian network (DBN)-based approach to uncover potential causal relationships among various features including social media data, traditional financial market factors, and technical indicators. This study focuses on six major cryptocurrencies, including Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether. The proposed model’s performance is compared to five baseline models of auto-regressive integrated moving average, support vector regression, long short-term memory, random forests, support vector machines, and a large language model. Results demonstrate that while DBN performance varies across cryptocurrencies, with some cryptocurrencies exhibiting higher predictive accuracy than others, the DBN significantly outperforms the baseline models.

Parth Daxesh Modi, Kamyar Arshi, Pertami J. Kunz, Abdelhak M. Zoubir

Bitcoin as a cryptocurrency has been one of the most important digital coins and the first decentralized digital currency. Deep neural networks, on the other hand, has shown promising results recently; however, we require huge amount of high-quality data to leverage their power. There are some techniques such as augmentation that can help us with increasing the dataset size, but we cannot exploit them on historical bitcoin data. As a result, we propose a shallow Bidirectional-LSTM (Bi-LSTM) model, fed with feature engineered data using our proposed method to forecast bitcoin closing prices in a daily time frame. We compare the performance with that of other forecasting methods, and show that with the help of the proposed feature engineering method, a shallow deep neural network outperforms other popular price forecasting models.

Nitin N. Sakhare, Imambi S. Shaik

No abstract is available for this record.

Amine Mtiraoui, Heni Boubaker, Lotfi Belkacem

No abstract is available for this record.

Neil Archein I. Gomez, Gernel S. Lumacad, Isabela Loren R. Saludes, Princess Aravela A. Castino · 5 authors

MIR4, is a play to earn game that uses Non-Fungible Tokens (NFT) and cryptocurrency- or in MIR4, Draco Tokens- as a reward. Draco is obtained through mining an in-game resource called Darksteel and is then traded to Wemix Wallet, where real-world money is obtained. Cryptocurrencies are volatile, which gives MIR4 players and traders a decision dilemma of when is the preferable time to buy, sell, or trade Draco Tokens. In this study we present deep learning models, specifically the Long-Short Term Memory (LSTM) neural network, and Neural Prophet (NP) time series machine learning models to forecast future Draco-token exchange value. Historical data of Draco-token value from Yahoo Finance is utilized as a univariate parameter for the analysis, model development, and the forecasting of the future Draco-token exchange values. Performance of formulated models are assessed and compared based on the following regression metrics: RMSE, MSE, MAE and MAPE. Experimental results indicated that the LSTM Neural Network yielded better forecast estimates with lower error than the Neural Prophet. Findings of the study showed that LSTM can be utilized as a tool for forecasting future Draco token exchange values. future research direction suggests improving prediction accuracy by incorporating other parameters such as MIR-4 players sentiments, newly added players, and google search interest over time.

Farman Ullah Khan, Faridoon Khan, Parvez Ahmed Shaikh

Abstract The study aims at forecasting the return volatility of the cryptocurrencies using several machine learning algorithms, like neural network autoregressive (NNETAR), cubic smoothing spline (CSS), and group method of data handling neural network (GMDH-NN) algorithm. The data used in this study is spanning from April 14, 2017, to October 30, 2020, covering 1296 observations. We predict the volatility of four cryptocurrencies, namely Bitcoin, Ethereum, XRP, and Tether, and compare their predictive power in terms of forecasting accuracy. The predictive capabilities of CSS, NNETAR, and GMDH-NN are compared and evaluated by mean absolute error (MAE) and root-mean-square error (RMSE). Regarding the return volatility of Bitcoin and XRP markets, the forecasted results remarkably suggest that in contrast to rival approaches, the CSS can be an effective model to boost the predicting accuracy in the sense that it has the lowest forecast errors. Considering the Ethereum markets’ volatility, the MAE and RMSE associated with NNETAR are smaller than the MAE and RMSE of CSS and GMDH-NN algorithm, which ensures the effectiveness of NNETAR as compared to competing approaches. Similarly, in case of Tether markets’ volatility, the corresponding MAE and RMSE reveal that the GMDH-NN algorithm is an efficient technique to enhance the forecasting performance. We notice that no single tool performed uniformly for all cryptocurrency markets. The policymakers can adopt the model for forecasting cryptocurrency volatility accordingly.

You Liang, A. Thavaneswaran, Alex Paseka, Sulalitha Bowala · 5 authors

A profitable data-driven algorithmic trading algorithm will benefit from a dynamic system that can produce accurate hedge ratio estimates and short-term innovation volatility forecasts. Commonly used pairs and multiple trading strategies are constructed using the Kalman Filter (KF) and exploiting mean reversion in co-integrated nonstationary stock prices. However, KFs are sensitive to model errors. Misspecified modelling produces unstable solutions for dynamic systems. Fading-Memory Filter (FMF) uses a discounting weight to past observations. Compared to a standard KF, FMF addresses more recent observations and is more resilient (less sensitive) to modelling errors. However, the FMF algorithm does not provide slope parameter covariance matrix updates and innovation volatility forecasts. This paper proposes a novel resilient FMF algorithm for pairs trading and multiple trading by defining an appropriate data-driven innovation volatility forecasting model. The FMF-based strategies are implemented through some experiments on the hourly prices (high-frequency data) of Bitcoin, Ethereum and Litecoin. It is shown that the proposed FMF trading strategies outperform the existing KF trading strategies and they are more profitable in the bear market over time, especially for continuous falling of prices and the short-lived and sharp rally recovery where prices are not stationary.

NURAY ERGÜL

Kripto para piyasası ulaştığı işlem hacmiyle geleneksel para piyasasına rakip duruma gelmiştir. Kripto para piyasasında coinlere alternatif olarak altcoinler piyasaya sunulmuştur. Kripto para piyasasına binlerce coin ve altcoin sunulmasına karşın bitcoinin büyüklüğüne ulaşamamışlardır. Kripto piyasası tezgahüstü bir piyasadır. Bu piyasanın volatilitesi ve riski oldukça yüksektir. Bu piyasanın yüksek getiri imkanı vermesi nedeniyle yatırımcıların ilgi odağı olmaktadır. Çalışmanın amacı kripto para birimlerinin fiyat hareketliliği temel alınarak, bu kripto paralar arasındaki eş-bütünleşme ve nedensellik ilişkileri incelenmektedir. Çalışma kapsamındaki kripto paralar Johansen Eş-bütünleşme Analizi ve Granger Nedensellik Testi kullanılarak incelenmiştir. Johansen eş-bütünleşme test sonucunda iz istatistiği ve max öz değer istatistikleri %5 anlamlılık düzeyindeki kritik değerden yüksek olduğu, H0 hipotezinin reddedildiği ve kripto para serileri arasında eş-bütünleşme ilişkisinin bulunduğunu ortaya koymuştur. Granger Nedensellik Test sonuçları, ADA, BNB, DOGE ve ETH’nin BTC’nin ‘nedeni’ ve BTC’nin ADA, BNB, DOGE ve ETH’nin ‘nedeni’ olduğu ve aralarında çift taraflı bir ilişkisinin bulunduğu belirlenmiştir. ETH ve SOL’un BTC’nin ‘nedeni’ olduğu ve aralarında tek taraflı bir ilişkisinin olduğu görülmüştür. Anahtar Kelimeler: Cryptocurrency, Johansen Cointegration Analysis, Granger Causality Test

Japjeet Singh, Ruppa K. Thulasiram, A. Thavaneswaran, Alex Paseka

There have been several studies in the literature discussing the profitability with various trading strategies. Two common strategies are pairs trading and momentum strategies. The momentum strategy aims to exploit the phenomenon of momentum, where securities that have performed well in the past are likely to continue performing well in the future. The concept behind a pairs trading of stocks is similar to the statistical idea of cointegration. The goal of pairs trading is to profit from the relative price movements of the two assets, rather than from the absolute price movements of either asset. This strategy is generally implemented using algorithmic trading techniques, and it is often used by traders and investors to take advantage of mispricing in the market. In this study we first compare these two strategies and implement them to study for their profitability. We considered two major cryptocurrencies (Bitcoin and Ethereum) for these two trading strategies and show that with daily price data, dual momentum strategy generates significantly better results than the pairs trading strategy.

Joy Dip Das, Sulalitha Bowala, Ruppa K. Thulasiram, A. Thavaneswaran

Constructing resilient portfolios is of crucial and utmost importance to investment management. This study compares traditional and data-driven models for building resilient portfolios and analyzes their performance for stocks (S&P 500) and highly volatile cryptocurrency markets. The study investigates the performance of traditional models, such as mean-variance and constrained optimization, and a recently proposed data-driven resilient portfolio optimization model for stocks. Moreover, the study analyzes these methods with evolving S&P CME bitcoin futures index and the Crypto20 index. These analyses highlight the need for further investigation into traditional and data-driven approaches for resilient portfolio optimization, including higher-order moments, particularly under varying market conditions. This study provides valuable insights for investors and portfolio managers aiming to build resilient portfolios that could be used in different market environments.

Yu-Jin An, Hayoung Oh, Hyun-Jong Kim

Over the past decade, Bitcoin’s unprecedented performance has underscored its po-sition as the premier asset class. Starting from an insignificant value and reaching an astounding high of around 65,000 U.S dollars in 2021 – all without a central con-trolling authority – Bitcoin’s trajectory is undoubtedly a historical feat. Its intangible nature, initially a subject of skepticism, has turned into an attractive quality, leading many investors to allocate a significant portion of their portfolios to Bitcoin. The traditional banking and investment sectors have also turned their attention to Bitcoin’s exponential growth. Concurrently, research on macro-economic variables and investor sentiment explaining Bitcoin’s price fluctuations has seen considerable development. However, there is a notable absence of studies leveraging On-Chain Data, information derived from transaction data in Bitcoin’s blockchain network. This paper fills this gap by using LSTM (Long Short-Term Memory), a technique widely utilized for time-series data prediction, in conjunction with On-Chain Data, to predict Bitcoin prices.

Vibha Srivastava, Vijay Kumar Dwivedi, Ashutosh Kumar Singh

Abstract Due to the highly volatile tendency of Bitcoin, there is a necessity for a better price prediction model. Only a few researchers have focused on the feasibility to apply various modelling approaches. These approaches may prone to have low convergence issues in outcomes and acquire high computation time. Hence a model is put forward based on machine learning techniques using regression algorithm and Particle Swarm Optimization with XGBoost algorithm, for more precise prediction outcomes of three cryptocurrencies; Bitcoin, Dogecoin, and Ethereum. The approach uses time series that consists of daily price information of cryptocurrencies. In this paper, the XGBoost algorithm is incorporated with an enhanced PSO method to tune the optimal hyper-parameters to yield out better prediction output rate. The comparative assessment delineated that the proposed method shows less root mean squared error, mean absolute error and mean squared error values. In this aspect, the proposed model stands predominant in showing high efficiency of prediction rate.

Tiago E. Pratas, Filipe Ramos, Lihki Rubio

Abstract This study aims to evaluate forecasting properties of classic methodologies (ARCH and GARCH models) in comparison with deep learning methodologies (MLP, RNN, and LSTM architectures) for predicting Bitcoin's volatility. As a new asset class with unique characteristics, Bitcoin's high volatility and structural breaks make forecasting challenging. Based on 2753 observations from 08-09-2014 to 01-05-2022, this study focuses on Bitcoin logarithmic returns. Results show that deep learning methodologies have advantages in terms of forecast quality, although significant computational costs are required. Although both MLP and RNN models produce smoother forecasts with less fluctuation, they fail to capture large spikes. The LSTM architecture, on the other hand, reacts strongly to such movements and tries to adjust its forecast accordingly. To compare forecasting accuracy at different horizons MAPE, MAE metrics are used. Diebold–Mariano tests were conducted to compare the forecast, confirming the superiority of deep learning methodologies. Overall, this study suggests that deep learning methodologies could provide a promising tool for forecasting Bitcoin returns (and therefore volatility), especially for short-term horizons.