Blockchain Papers

Burcu Selçuk, Tacha Şerif

No abstract is available for this record.

Chenglin Zhao

Bitcoin, a decentralized digital currency, has gained widespread acceptance and recognition in recent years. The prediction of Bitcoin prices is a challenging task due to its relatively young age and high volatility. Therefore, this study explores the accuracy of price prediction for Bitcoin using machine learning models and makes comparsion on the outcome of different models, Linear Regression, Long Short-Term Memory, and Recurrent Neural Network. This study utilizes the closing price of Bitcoin in USD from a Kaggle dataset as the independent variable. The study also adopts Mean Absolute Error (MAE) as the measurement indicators, and comparative performance analysis is conducted under various circumstances. The experimental results demonstrate that LR performs poorly in Bitcoin price prediction, while LSTM and RNN outperform LR. Further analysis reveals that LSTM performs better during price apexes, while RNN performs better during price recessions. Graphical representations illustrate the strengths and weaknesses of each model under different market scenarios. Through comparison, the article provides an insight for other researchers to choose corresponding machine learning models under different circumstances to predict bitcoin price.

Radisha Fanni Sianti, Nur Iriawan, Irhamah

No abstract is available for this record.

Minglei Lyu

No abstract is available for this record.

Rosalie Speeckaert, Xing Wang

No abstract is available for this record.

Muhammad Muneeb, Noman Islam, Mana Saleh Al Reshan, Mohammed Hamdi · 6 authors

No abstract is available for this record.

Rishabh Kumar, Gagandeep Marken, Aman Singh

No abstract is available for this record.

Andrea Colombo, Teodoro Baldazzi, Luigi Bellomarini, Andrea Gentili · 5 authors

Recent extensions of Datalog that consider the temporal dimension as a first-class citizen have unlocked the possibility of using its temporal variants, such as DatalogMTL, to model and reason about complex financial domains. Very relevant ones are crypto-activity markets, which, according to the recent Markets in Crypto-Assets Regulation (MiCAR) of the EU, are described by white papers published by crypto-assets issuers. In particular, the issuers publish semi-structured information about the assets they are willing to offer. Then, the assets are implemented in decentralized finance contexts (i.e., in a blockchain) as executable scripts known as smart contracts. However, these scripts are often criticized for their complexity, which makes them challenging to understand and communicate. On the other hand, in our experience, the availability of a declarative and executable representation of a crypto-activity market fosters a better understanding of that market as well as improved transparency, reproducibility and, as a consequence, increased fairness. These characteristics are of major interest to the financial authorities for example for supervision purposes. In this paper, we study the problem of automatically translating textual descriptions of crypto-assets, written according to the MiCAR specifications, into DatalogMTL programs that represent and capture the respective crypto-activity market. To this end, we opt for a machine translation approach and leverage a Large Language Model. We discuss promising techniques and preliminary experimental results.

Dayan A. Perera, Jessica Lim, Shuta Gunraku, Wern Han Lim

This research introduces an innovative approach to forecasting cryptocurrency prices by combining user-generated content (UGC) and sentiment analysis with quantitative data. The primary goal is to overcome limitations in existing methods for market forecasting, where accurate forecasting is crucial for informed decision-making and risk mitigation. The paper suggests a robust prediction methodology by integrating sentiment analysis and quantitative data. The study reviews prior research on sentiment analysis and quantitative analysis of cryptocurrency and stock price prediction. It explores the integration of machine learning and deep learning techniques, an area not extensively explored before. The methodology employs Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Bidirectional LSTM and Gated Recurrent Unit (GRU) models to capture temporal dependencies. Prediction accuracy is assessed using metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and a confusion matrix. Results show that GRU models excel in prediction, while RNN models outperform in predicting price movements; with an emphasis on the significance of a suitable data preprocessing pipeline towards improving model performance. In summary, this study demonstrates the effectiveness of integrating sentiment analysis and quantitative data for cryptocurrency price forecasting using UGC data.

BEN ABDALLAH Mohamed, TALBI Omar

The cryptocurrency market has experienced significant growth in international finance in recent years, attracting a large number of investors. This has led to a substantial increase in the overall trading volume[1], surpassing 1.2 trillion USD in July2023. Bitcoin in particular, has garnered significant interest from both advanced and emerging economies. Notably, Bitcoin is legal tender in El Salvador and the Central African Republic. The cryptocurrency market possesses distinct characteristics that set it apart from traditional markets such as exchanges, commodities, and equities. Its decentralization, facilitated by Blockchain technology, stands as a key differentiating factor. This technology enables anonymous trading of cryptocurrencies, with the identities of market participants, account holders, and electronic wallet managers remaining unknown. Consequently, there is an inherent ambiguity when it comes to characterizing the behavior of these investors or determining their preferred trading horizons. In this context, an important question arises: how do investors in the cryptocurrency market navigate price volatility? Furthermore, how do their trading strategies, which are inherently tied to their investment horizons, impact each other and consequently influence market prices? The heterogeneity hypothesis, which is a prevalent assumption in financial markets, is particularly relevant when exploring how investors in the cryptocurrency market navigate price volatility. The diverse trading strategies and approaches to handling price fluctuations among heterogeneous investors impact and influence the overall volatility and price dynamics of cryptocurrencies. The heterogeneity market hypothesis[2] refers to the existence of differences among investors or traders regarding their beliefs, information, risk preferences, investment strategies, and time horizons. In a heterogeneous market, investors, traders and financial institutions have varying views and expectations about the future performance of assets. These differences can manifest in various ways, such as market information access, market liquidity provision, trading strategies and market structure. While numerous empirical studies have explored the concept of market heterogeneity in relation to traditional assets such as those conducted by Müller et al. (1993;1997), Lux and Marchesi (2000), LeBaron (2000), Dacorogna et al. (2001) and Benhmad (2011), there is a paucity of research on this topic specifically focusing on cryptocurrencies. This paper aims to contribute to the existing literature that investigates the hypothesis of investor heterogeneity in traditional markets, including forex, stocks, and commodities. Specifically, we focus on the cryptocurrency market, which is predominantly dominated by Bitcoin. As of November 2021[3], Bitcoin holds the distinction of being the most actively traded cryptocurrency, with a market capitalization exceeding 1.2 trillion USD. Given its significance, Bitcoin garners substantial interest from investors across various categories. Therefore, this research seeks to uncover potential synergies among participants[4] in the cryptocurrency market and identify investor types and investment horizons prevalent in this market. By understanding these factors, we can gain insights into the dynamic volatility displayed by Bitcoin’s price. To tackle this issue, we investigate Bitcoin’s price volatility, analyzing causal relationships among short-term, medium and long-term traders by using wavelet transform to decompose volatility at different trading frequency scales[5] considered, then Granger causality test will be employed to determine if changes in one scale impact others. Furthermore, we extend this analysis by employing the nonlinear causality test proposed by Hmamouche. Y (2020). This nonlinear causality test goes beyond the limitations of the linear causality test and helps identifying potential nonlinear causal effects that may exist between the volatilities at different frequency scales, which could be overlooked by the linear causality test. The rest of this paper is organized as follows: Section 2 presents the theoretical framework relatively to the heterogeneity market hypothesis. Section 3 focuses on the empirical review. Section 4 and 5 turns to the data and methodology. Section 6 provides the empirical findings. Section 7 concludes.

Levent Kutlu

No abstract is available for this record.

Janek Ratnatunga

It has been an explosive start to 2024 in terms of the share market performance. Driven by a wave of enthusiasm for tech heavyweights like Meta and Nvidia, USA’s S&P 500 index of large American firms is up 5% and has crossed the 5,000 mark for the first time ever. On February 22, Japan's Nikkei 225 broke its own record – which it had established in 1989. In Australia, despite some volatility due to speculation on the direction of Reserve Bank interest rates, its share market also has boomed. Given such share market performance, this article asks if it is time to think about investing exclusively in shares. To answer this, the article will consider two fundamental questions that affect investors in capital markets: (1) what is meant by investment risk vs. return, and (2) can investors optimise their risk-return relationship by holding a single asset type (like stocks), or by holding a diversified portfolio of different asset classes? It will also consider a third question:(3) can using cryptocurrencies such as bitcoin help investors to better diversify their portfolio?

Illia Uzun, M. Lobachev

Abstract. This article presents an analysis of the historical dynamics of the cryptocurrency market based on time series data using the OHLCV dataset. The study presents the results of calculations of the main cryptocurrency market momentum technical indicators. Using intelligent computational methods, the paper assesses patterns and trends in the data of major cryptocurrencies. The study emphasizes the importance of technical analysis in understanding the volatile landscape of digital currencies. Key words: time-series, data analysis, cryptocurrency market, momentum indicators, technical analysis indicators, OHLCV.

Daniel Cahill, Zhangxin Liu, Lee A. Smales

We investigate whether investors rely more on technical trading language to rationalise price movements in the absence of substantive information. Compared to equity markets, cryptocurrency markets are characterised by high volatility, often occurring without clear explanation from new information. We apply a machine-learning-based vocabulary of technical trading terms to comments from cryptocurrency- and equity-related subreddits on Reddit.com and analyse how investors use technical talk in different market conditions. We find a U-shaped relationship between technical talk and Bitcoin returns, with higher usage during extreme price movements, while technical talk on equity subreddits is concentrated around median market returns. Technical talk increases in cryptocurrency markets when news is scarce but rises in equity markets alongside greater news availability. Our results suggest that technical talk provides an important communication channel for social media users to describe price variation when information is scarce.

Meijun Gao

No abstract is available for this record.

Ali Asare Nezhad, Ahmad Kalhor, Reshad Hosseini, Babak Nadjar Araabi · 5 authors

No abstract is available for this record.

Aditya Nagdiya, Vivek Kapoor, Vrinda Tokekar

A growing number of people and organizations are choosing to invest in cryptocurrencies. The development of precise forecasting models for cryptocurrencies is crucial due to their very volatile market. Financial forecasting has long made use of time series analysis and prediction, but conventional time series analysis techniques have trouble capturing intricate patterns and nonlinear relationships. On the other hand, although deep learning models show promise in time series analysis, their effectiveness depends on large amounts of data, which might result in overfitting. In order to predict bitcoin prices, this research presents a hybrid model that combines long short-term memory and convolutional neural networks. The CNN is used to extract features from the time series data, while the LSTM is used to capture persistent.

F Farnuod Ahmadi, Abbas Toloie Eshlaghy, Reza Radfar

Cryptocurrencies have been widely identified and established as a new form of electronic currency exchange, carrying significant implications for emerging economies and the global economy. This research focused on the "examination and comparison of the efficiency of MLP and SimpleRNN algorithms in predicting cryptocurrency prices" using the Python programming language. Price predictions for Bitcoin, Ethereum, Binance Coin, Cardano, and Ripple were made using two deep learning algorithms (including the MLP algorithm and the SimpleRNN algorithm) over the period from 2017 to 2023. The results of cryptocurrency price prediction using deep learning algorithms were satisfactory; and the comparison of predictions across all cryptocurrencies indicated minimal differences between the algorithms studied, suggesting that they were efficient and had low error rates. Based on the obtained results regarding Bitcoin price prediction, the best algorithm was SimpleRNN; for Ethereum price prediction, the best algorithm was MLP; for Binance Coin price prediction, the best algorithm was SimpleRNN; for Cardano price prediction, the best algorithm was MLP; and for Ripple price prediction, the best algorithm was MLP.

Yu. V. Bezsmolnyi, Maksym Seniv

The work carried out a comparative analysis of scientific publications regarding the possibility of predicting the direction of the cryptocurrency exchange rate using the data of open numerical indicators, based on the results of which it can be concluded that due to the volatility of the cryptocurrency market and the need for accurate forecasting, there is a need to create an aggregated indicator that will take into account the current price exchange rate asset, parameters of simple indicators, trading volume, etc. In addition, this indicator will be a parameter for the application of a multi-criteria analysis model in the process of supporting decision-making for cryptocurrency trading. A software decision support system for cryptocurrency traders on the Trading View platform has also been developed, which allows the cryptocurrency trader to get the value of the current situation of the cryptocurrency market in the form of a value using the method of weighting coefficients and selected indicators. Among the selected indicators: RSI, MA, CCI, Stochastic Oscillator, OBV, ADX, CMF to determine the moment of opening a position, and Fibonacci Retracement, Ichimoku Cloud to determine the closing of positions. Taking into account all the indicators and the coefficients determined for them, the obtained range of values is from 0 % to 100 %. If the value of the indicator exceeds the threshold of 20 %, it means that it is necessary to inform the trader about a possible entry point. That is, a value of 20 % to 40 % is weak performance, 40 % to 60 % is medium performance, 60 % to 80 % is strong performance, and a value greater than 80 % will not be overlapped by new pyramiding values for a better overall indicator success rate. The value of the indicator determines the potential effectiveness of opening positions, and thanks to the RSI indicator, the direction of opening positions is determined. The direction of the position is divided into long and short. An indicator has been developed for the TradingView platform, which, unlike existing simple indicators, collects data from open access and calculates a potential point for opening a position. Obtaining the numerical value of a single indicator saves the trader time to review and analyze a collection of indicators and time to decide on opening a position, as the cryptocurrency market is known for its sudden volatility, where a decision must be made quickly.

Rizky Parlika, Mustafid Mustafid, Basuki Rahmat

Researchers and experts have developed various techniques, models, and methods to predict the price movements of cryptocurrencies, especially Bitcoin.However, among the many techniques studied in the literature, there is still a lack of focus on mining, creating, and developing datasets with specific patterns for predicting the next cryptocurrency price movement.This is an exciting reason to conduct further research.A web-based Patterned Dataset Application and a Telegram bot were constructed to address this issue.These tools read the price position of each cryptocurrency and predict the next price direction based on the last position indicated by the Patterned Dataset Application.The experiment's results show that when the Patterned Dataset Application shows a diamond crash position, it is time to make a purchase; conversely, when it shows a diamond moon position, it is time to make a sale.It is hoped that by utilizing the Patterned Dataset Application, potential losses can be minimized, and there is more potential for profit in cryptocurrency trading.Even though the initial data source comes from Indonesia's most prominent digital cryptocurrency trading market, according to coinmarketcap, namely Indodax, the results of this patterned dataset application can often describe the same cryptocurrency conditions globally.The novelty of this research is to produce a new way of predicting the next cryptocurrency price movement using patterned datasets.At the end of this paper, it will be proven that hypothesis 1 and hypothesis 2 on the results of the patterned dataset are true.

Jiakun Lian

This paper delves into the intriguing realm of cryptocurrency price prediction, with a specific focus on Zcash (ZEC), employing a cutting-edge deep learning approach.The study introduces two crucial features, "close_off_high" and "volatility", then systematically analyzes the correlations between these variables and the price of ZEC.By investigating the predictive accuracy of three prominent neural network architectures-Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and the Transformer model-the study discerns that LSTM and GRU models outperform the others in forecasting ZEC's price movements.Furthermore, the paper scrutinizes the influence of different activation functions on model performance, shedding light on the effectiveness of the linear activation function in this context.The research also addresses common challenges in predictive modeling, such as overfitting and multicollinearity.Moreover, it candidly acknowledges the limitations associated with solely focusing on a single cryptocurrency, recognizing that broader research efforts and interdisciplinary collaboration are required for a more comprehensive understanding of the ever-evolving cryptocurrency landscape.As the cryptocurrency market continues to evolve rapidly, this study provides invaluable insights for investors, offering a rational perspective on cryptocurrency investment.It underscores the importance of utilizing appropriate models and embracing interdisciplinary cooperation to navigate the complex and dynamic world of cryptocurrency.By bridging the gap between the cutting-edge world of deep learning and the financial market, this research paves the way for enhanced future investigations and more informed investment decisions.

Leonardo Enco, Alexander Mederos, Alejandro Paipay, Daniel Pizarro · 6 authors

No abstract is available for this record.

Tanya Kapoor, Laxmi Ahuja

No abstract is available for this record.

G P Sanjana, J. Jaswith, K. Aashritha, Nirmal Keshari Swain · 5 authors

No abstract is available for this record.