Abstract In recent years, cryptocurrencies' price prediction has attracted the interest of many people including investors, researchers and practitioners. In this study, we proposed a hybrid model for predicting the daily close price of cryptocurrencies based on different neural networks such as long short‐term memory, convolutional neural network and attention mechanism. Using an ensemble of three pre‐trained language models, we extracted sentiment of cryptocurrency‐related tweets posted between 1 January 2021 and 31 December 2021. We constructed 20 different versions of our model and evaluated their performance on data of 27 most traded cryptocurrencies using a history of previous days' sentiment data along with close prices as input data. The flexible input layer of our model enables different ways of feeding data into the model to adjust it for different cryptocurrencies to obtain better predictions. Our analysis revealed several important findings. We showed that longer sequences of input data achieve most accurate predictions on average. More specifically, using a history of 14‐ and 21‐days' data results in lowest RMSE values on average compared to using a history of 7 days. However, there is no significant difference between the results related to the input sequences with lengths of 14 and 21. In addition, our findings suggest that sentiment data can be useful in predicting prices for more than 70% of the studied cryptocurrencies. Thus, peoples' emotions, opinions, and sentiment that are expressed through their posts on Twitter platform play a significant role in prediction of cryptocurrencies' prices.
The study aims to investigate the causality relationship between investor happiness and cryptocurrency returns. The study is focused on the five largest cryptocurrencies, specifically Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Ripple (XRP), and Cardano (ADA). Twitter-based Happiness Index is used to measure investor happiness. The sample period covers the period between January 1, 2019, and October 2, 2021. The Zivot-Andrews test is employed to detect stationary of covariates. After ensuring that all variables are stationary at levels, the Granger causality test is adopted to understand the relationship between the happiness index and cryptocurrency returns. The impulse-response functions are illustrated. The results indicate that there is a uni-directional relationship from BTC to Happiness Index, and Happiness Index to ETH. Considering that the causal relationship between cryptocurrency returns and investor happiness differs between cryptocurrencies, it is thought that investors should closely monitor the happiness index and make adjustments in their portfolios in response to changes in investor happiness.
The emergence of non-fungible tokens (NFTs) has created a new market with significant implications for stakeholders, particularly in industries such as art, fashion, gaming, and real-world assets, leading to challenges in finance, financial pricing, financial management, risk management, and cryptocurrency issues. This research paper adopts a quantitative approach to provide a comprehensive analysis of the challenges associated with NFTs, including their impact on the art market, risks related to ownership rights, and proper financial statement treatment. Additionally, the paper examines the challenges of accounting for NFTs under the International Financial Reporting Standards (IFRS), including valuation, tax treatment, and accounting considerations. The use of artificial intelligence (AI) in creating, verifying, and authenticating NFTs, as well as detecting potential fraud and valuing them in the market, is also discussed. Finally, the paper provides recommendations for companies and accounting professionals on addressing the challenges associated with NFTs under IFRS. The research contributes to the ongoing debate on the best practices for NFT accounting, the evolving nature of digital assets, and the role of AI in this emerging market.
OBJECTIVES: With the expansion of social networks such as Twitter, many experts share their opinions on various topics. The opinions of experts, who are also known as influencers, can be very influential. Combining these tweets and the historical prices of cryptocurrencies makes it possible to predict their price trends accurately. A Hybrid of RoBERTa deep neural network and BiGRU has been used for Sentiment Analysis (SA). Sentiments of tweets can be of great help to investors to understand the future behavior of the market and manage the stock portfolio. Unlike the tweets that are only extracted using the cryptocurrency name hashtag, the tweets of this dataset have specialized opinions and can determine the market trend. DATA DESCRIPTION: The dataset created in this research concerns the opinions of more than 52 influencers (persons or companies) regarding eight cryptocurrencies. This dataset was collected through the Apify Twitter API for eight months, from February 2021 to June 2023. This dataset contains five Excel files and tweets, compound score, importance coefficient of each tweet, sentiment polarity, and historical prices of four cryptocurrencies: Bitcoin, Ethereum, Binance, and other information. These tweets cover the opinions of 52 influencers on more than 300 cryptocurrencies, although most comments are related to Bitcoin, Ethereum, and Binance. For this reason, three Excel files containing the historical prices of polarity and compound sentiment related to Bitcoin, Ethereum, and Binance cryptocurrencies have been placed separately in the dataset. The polarity of sentiment in these Excel shows the maximum number of polarities by applying the importance coefficient, which determines the dominant polarity of sentiment related to a particular day for the cryptocurrency.
The popularity of cryptocurrencies has skyrocketed in recent years, with blockchain technologies enabling the development of new digital assets. However, along with their advantages, such as lower transaction costs, increased security, and transactional transparency, cryptocurrencies have also become susceptible to various forms of market manipulation. The pump and dump (P&D) scheme is of significant concern among these manipulation tactics. Despite the growing awareness of P&D activities in cryptocurrency markets, a comprehensive survey is needed to explore the detection methods. This paper aims to fill this gap by reviewing the literature on P&D detection in the cryptocurrency world. This survey provides valuable insights into detecting and classifying P&D schemes in the cryptocurrency market by analyzing the selected studies, including their definitions and the taxonomies of P&D schemes, the methodologies employed, their strengths and weaknesses, and the proposed solutions. Presented here are insights that can guide future research in this field and offer practical approaches to combating P&D manipulations in cryptocurrency trading.
Abstract Security token offerings (STOs) are a new means for ventures to raise funding, where digital tokens are issued as regulated investment products on the blockchain. We study market outcomes in the primary and secondary markets for security tokens and examine the associated determinants in the context of signaling theory. We analyze success determinants of 138 STOs and find that a pre-sale and the announcement of token transferability are positively related to the funding success and serve as positive quality signals for investors to overcome information asymmetries. We examine 108 security tokens traded on centralized and decentralized exchanges related to the rapidly evolving area of decentralized finance. There is hardly any underpricing in the market, and it is positively associated with the crypto market sentiment as an external signal. When traded on the secondary market, security tokens generate both extremely positive and negative returns for various short-term time horizons. We disentangle the liquidity situation in the market between centralized and decentralized exchanges and find that decentralized marketplaces are less liquid and offer lower barriers to entry, indicating slow market completion.
Using the capital asset pricing model, this article critically assesses the relative importance of computing ‘realized’ betas from high-frequency returns for Bitcoin and Ethereum—the two major cryptocurrencies—against their classic counterparts using the 1-day and 5-day return-based betas. The sample includes intraday data from 15 May 2018 until 17 January 2023. The microstructure noise is present until 4 min in the BTC and ETH high-frequency data. Therefore, we opt for a conservative choice with a 60 min sampling frequency. Considering 250 trading days as a rolling-window size, we obtain rolling betas < 1 for Bitcoin and Ethereum with respect to the CRIX market index, which could enhance portfolio diversification (at the expense of maximizing returns). We flag the minimal tracking errors at the hourly and daily frequencies. The dispersion of rolling betas is higher for the weekly frequency and is concentrated towards values of β > 0.8 for BTC (β > 0.65 for ETH). The weekly frequency is thus revealed as being less precise for capturing the ‘pure’ systematic risk for Bitcoin and Ethereum. For Ethereum in particular, the availability of high-frequency data tends to produce, on average, a more reliable inference. In the age of financial data feed immediacy, our results strongly suggest to pension fund managers, hedge fund traders, and investment bankers to include ‘realized’ versions of CAPM betas in their dashboard of indicators for portfolio risk estimation. Sensitivity analyses cover jump detection in BTC/ETH high-frequency data (up to 25%). We also include several jump-robust estimators of realized volatility, where realized quadpower volatility prevails.
Cryptocurrencies are significant improvements in the digital age that have changed the way we think about money. The first cryptocurrency was Bitcoin, introduced in 2009 and was created by Nakamoto. Due to their potential ups and downs, many people now think that cryptocurrencies are appropriate for use as an investment instrument, especially millennials who are attracted to higher-risk investment alternatives. A number of different investing options such as cryptocurrencies, gold, and other conventional assets like equities and bonds have unique characteristics and advantages. It’s essential for investors to understand the similarities and differences between cryptocurrencies and other assets in order to create diversified portfolios. In this study, the optimum portfolio will be constructed using Bitcoin, Gold, LQ45 Index, and ABF IBI as the representative of Indonesia Bond Index. Mean-Variance Optimization will be used as an asset allocation method, and will be compared to the other methods such as Risk Parity, 60/40 Portfolio, and Equally Weighted to find a better risk-adjusted return. The Sharpe ratio analysis is used to evaluate the portfolio performance resulting from every method. The investment strategy will be simulated to know which strategy will result the best total return in the end of simulation period. According to risk, return, and the Sharpe ratio, Bitcoin could perform better than gold, LQ45, and ABF IBI. Furthermore, the Mean-Variance Optimization resulted the highest Sharpe ratio compared to the other methods. The optimal weight from the portfolio construction using Mean-Variance Optimization allocated 53% to ABFI index, 40% to Bitcoin, and 7% to gold, which resulted 48.2% portfolio return, 40.44% portfolio risk, and 1077.8% Sharpe ratio. From the investment strategy simulation, the quarterly rebalancing strategy was found to be the best strategy with the total return 223.36%.
Abstract Bitcoin (BTC) perpetual futures contracts are highly leveraged speculative trading instruments with daily market trading of $45 Billion. BTC perpetual futures are derivative contracts, which depend upon the underlying BTC SPOT (current) price. Pricing perpetual futures fairly is hard, using traditional arbitrage arguments, because of the volatile nature of the so called funding rate, which is used as the replacement of risk free rate in the Cryptocurrency market. This work presents a novel technique for pricing BTC futures contracts using conditional volatility and mean models. Intra‐day high‐frequency futures' return volatility and mean are modelled using different ML and econometric techniques. A comparison is made using statistical measures to find the model that best captures the intra‐day conditional mean and volatility. Exponential generalized autoregressive conditional heteroskedasticity is shown to be an almost unbiased predictor of intra‐day volatility, while a constant autoregressive moving average (0, 0) model best captures the conditional mean of the returns. A market directional high frequency trading algorithm is developed using the volatility and mean models. The algorithm first prices the futures contract at some future point of time using the volatility and mean regression models. Next, the slope between the current futures price and the expected price are used to predict the market direction. A long or short position is taken depending upon the expected market direction movement. Extensive back‐testing results show absolute returns of 1500%–8000% depending upon the transaction fees and leverage used. On average, the market direction is predicted correctly 85% of the time by the best model. Finally, the trading technique is market neutral, in that it gives large positive returns, with low SD, in both bull and bear markets.
Hilda Hadan, Leah Zhang-Kennedy, Lennart E. Nacke, Ville Mäkelä
With the increasing popularity of cryptocurrency, many people are interested in cryptocurrency investments, but have so far hesitated. Many others have made investments without adequate preparation. To help interested investors improve their understanding of cryptocurrency and make rational investment decisions, it is important to study their concerns and motivations and to draw upon experienced investors’ experiences and practices. Therefore, we surveyed crypto investors and inexperienced potential investors interested in trading cryptocurrency (n = 395). Our results showed that extreme price volatility is the primary incentive and a substantial obstacle to market participation. Fraud risks, lack of personal funds, insufficient knowledge, and difficulty identifying credible information sources are also common barriers. Our findings highlight the need to build trustworthy exchange platforms and integrate educational features. Based on the reported concerns and experiences, we (1) identify learning components for new investors, and (2) formulate design recommendations for beginner-friendly exchange platforms.
Despite the growing literature on Bitcoin and other cryptocurrencies, we know relatively little about who are involved in trading, transacting and using these assets and how they behave. Examining millions of Bitcoin transaction records, we show that less than 1% of Bitcoin users contribute to more than 95% of the market volumes. These ‘whales’ are often associated with strategic trading/transaction volumes, market reactions and timing patterns. Using K-means clustering on a comprehensive transaction dataset, we establish a typology of traders by learning their trading exchange patterns, strategies and impact risk and market microstructure. Our approach ‘learns’ and identifies five distinct groups or types of Bitcoin users, which are somewhat, though not entirely, comparable to popular categorisations used in conventional market such as fundamental, technical, retail and institutional traders as well as market makers. Four of these groups present distinguishable trading patterns with a strong impact on liquidity provision and trading signals.
Stablecoin is a medium of exchange with stable value in the world of decentralized finance (DeFi). In which, algorithmic stablecoins are one special type of stablecoins that are not backed by any asset. They stand to revolutionize the way a sovereign fiat operates. As implemented, algorithmic stablecoins are poorly stabilized in most cases; their prices easily deviate from the target or even fall into a catastrophic collapse, and are as a result often dismissed as a Ponzi scheme. However, what is the essence of Ponzi? In this paper, we try to clarify such a deceptive concept and reveal how algorithmic stablecoins work from a higher level. We find that Ponzi is basically a financial protocol that pays existing investors with funds collected from new ones. Running a Ponzi, however, does not necessarily imply that any participant is in any sense losing out, as long as the game can be perpetually rolled over. Economists call such realization as a rational Ponzi game . We thereby propose a rational model in the context of algorithmic stablecoins and draw its holding conditions. We apply the model to examine: whether or not the algorithmic stablecoin is a rational Ponzi game. Accordingly, we discuss two types of algorithmic stablecoins (Rebase & Seigniorage Shares) and dig into the historical market performance of a number of impactful projects to demonstrate the effectiveness of our model.
This research examines the impact of the coronavirus index on the returns and volatility of ten major cryptocurrencies during the COVID-19 pandemic. For this purpose, we applied a multivariate volatility GARCH model with an integrated dynamic conditional correlation (DCC) approach to daily cryptocurrency values observed data during the January-December, 2020 period. Moreover, we used the Granger causality test to study return-volume correlations. The findings indicate that cryptocurrency volatility declined after the World Health Organization declared on March 11, 2020, that the coronavirus was a pandemic. Unlike most of the relevant previous studies, we found that the COVID-19 crisis did not have a long-term effect on cryptocurrency returns and volatility but only presented a short-term effect. Our results have implications for investors who need to determine an optimal portfolio for a scenario other than the base.
Dr.Alamelu Mangai Jothidurai, Pratheek D Kanchan, Rahul Raj
Abstract: Cryptocurrency price prediction is a challenging task due to the high volatility and uncertainty of the market. Machine learning techniques can provide useful insights and forecasts for investors and traders. In this paper, we propose a novel approach for cryptocurrency price prediction using machine learning models and sentiment analysis. We collect historical price data of Bitcoin from yahoo business. We then apply various machine learning models, such as LSTM for the price prediction of the cryptocurrency using the past data. LSTM is a type of recurrent neural network that can manage long-term dependencies and sequential data. LSTM has three gates: forget gate, input gate, and output gate, which control the flow of information in and out of the memory cell. LSTM can be implemented in Python using the Keras and TensorFlow library. In this paper, we use LSTM as one of the machine learning models for cryptocurrency price prediction. We then use the average of the next 5 days of the predicted data to implement a buy-sell call strategy that aims to maximize the profit and minimize the risk. We evaluate our framework on a popular cryptocurrency Bitcoin.
This research addresses the impact of individual investors on the cryptocurrency market, focusing specifically on the development of herd behavior. Although the phenomenon of herd behavior has been studied extensively in the stock market, it has received limited research in the context of cryptocurrencies. This study aims to fill this research gap by examining the impact of liquidity and sentiment on herd behavior using the CSAD model, considering small, medium, and large cryptocurrencies. The results show different outcomes for cryptocurrencies of different sizes, consistently demonstrating that the herding effect is more pronounced under conditions of lower liquidity, as determined by the turnover volume and liquidity ratio of cryptocurrencies. Proxy measures such as the Twitter Hedonometer and CBOE VIX were used to measure investor sentiment and show the prevalence of herding behavior in optimistic times for all cryptocurrencies, regardless of their market capitalization. Consequently, this study provides valuable insights into the manifestation of herd behavior in the cryptocurrency market and highlights the importance of liquidity and sentiment as influencing factors. These findings improve our understanding of investor behavior and provide guidance to market participants and policymakers on how to effectively manage the risks associated with herd effects.
We propose an ensemble method to improve the generalization performance of trading strategies trained by deep reinforcement learning algorithms in a highly stochastic environment of intraday cryptocurrency portfolio trading. We adopt a model selection method that evaluates on multiple validation periods, and propose a novel mixture distribution policy to effectively ensemble the selected models. We provide a distributional view of the out-of-sample performance on granular test periods to demonstrate the robustness of the strategies in evolving market conditions, and retrain the models periodically to address non-stationarity of financial data. Our proposed ensemble method improves the out-of-sample performance compared with the benchmarks of a deep reinforcement learning strategy and a passive investment strategy.
I Made Gede Abandi Semeru, Yunieta Anny Nainggolan
Abstract : In forming their portfolios, investors should analyze the risk and return of each investment instrument. This is aimed at preventing investors from speculating and gambling with their investments. Conducting an investment portfolio optimization study on LQ-45 stock index, government bond, USD, gold, and Bitcoin can provide valuable insights due to unique market characteristics in Indonesia. This research analyzes the formation of investment instruments over the last 60 months, specifically from January 2018 to December 2022. The research method used in this study is quantitative research aimed at selecting several investment instruments for a portfolio in Indonesia. The portfolio aims to minimize risk and maximize return using the Markowitz method, also known as the optimal portfolio. To fulfill the objectives of this research, data on the prices of each instrument are required. An optimal portfolio can be obtained by combining two instruments: 18% bitcoin and 82% gold. This optimal portfolio can achieve an expected return of 1.29% with a risk level of 5.15%. Considering a risk-free rate of 0.375%, this portfolio forms a slope of 0.1775, which is the largest slope formed between the combination of risk-free instruments and risky portfolios. Investors should allocate their funds more wisely, considering not only the highest return but also the associated risk. High returns often come with high risks, so investors need to assess the risk-return trade-off before making investment decisions.
The valuation of cryptocurrencies is important given the increasing significance of this potential asset class. However, most state-of-the-art cryptocurrency valuation methods only focus on one of the fundamental factors or sentiments and use out-of-date data sources. In this study, a robust cryptocurrency valuation method is developed using the up-to-date datasets. Using various panel regression models and moving-window regression tests, the impacts of fundamental factors and sentiments in the valuation of cryptocurrencies are explored with data covering from January 1, 2009 to April 30, 2023. The research shows the importance of sentiments and suggests that fear and greed index can indicate when to make cryptocurrency investment, while Google search interest of cryptocurrency are crucial when choosing the appropriate type of cryptocurrency. Moreover, consensus mechanism and initial coin offering have significant effects on cryptocurrencies without stablecoins, while their impacts on cryptocurrencies with stablecoins are insignificant. Other fundamental factors, such as the type of supply and the presence of smart contracts, do not have a significant influence on cryptocurrency. Findings from this study can enhance cryptocurrency marketisation and provide insightful guidance for investors, portfolio managers and policymakers in assessing the utility level of each cryptocurrency.
Abstract In July 2020, the Chinese government warned that the Plus Token was a Ponzi scheme based on blockchain. More than 200 million investors were involved in this scam. We investigate how investors' search behaviour is associated with their decision making. We find that the bitcoin bag of words Baidu index is positively and significantly related to bitcoins transferred to Plus Token addresses, suggesting that public prominence of searches about bitcoin and blockchain tends to be related to investor decisions regarding the Plus Token project.
In view of the need for portfolio diversification, we investigate the interlinkages between a private equity ETF and a set of high-demand asset classes including bonds, equities, crude oil, gold, commodities, currency, Bitcoin, and shipping within a spillover framework. For this objective, we apply the enhanced modification of the Diebold and Yilmaz approach for the period 1 January 2010 to 31 January 2023. The empirical findings indicate a modest degree of connectedness among the investigated markets, whereas volatility spillovers showed acceleration during tumultuous periods. In addition, we assess the capacity of private equities for hedging, for the whole sample period and during COVID-19 infectious disease, in order to suggest investors for potential portfolio restructures. Results demonstrate that the short position in the volatility of private equity ETF can result in strong hedging effectiveness for investors holding long positions in Bitcoin, shipping, bonds, and crude oil. JEL Classification: C32, C58, G11, G15
Pairs trading is a popular quantitative trading strategy with the advantage of a similarity in price movement to financial assets. Assuming that the price spreads of trading pairs are mean-reverting, this strategy exploits the disequilibrium in financial markets to find arbitrage investment opportunities. Pairs trading has been widely applied to stock, ETF, and commodity markets. However, the effectiveness of this method for cryptocurrency markets has yet to be properly explored. Therefore, we examine the profitability of pairs trading for 26 cryptocurrencies traded on the Binance exchange at high frequencies of 1, 5, and 60 min. In addition to the traditional statistical methods of distance, correlation, cointegration, and stochastic differential residual (SDR), we focus on two evolutionary algorithms: genetic algorithm (GA) and non-dominated sorting genetic algorithm II (NSGA-II). During the 79-trading-day period from 11 January to 31 March 2018, NSGA-II showed the best results at all frequencies, with an average return of 2.84%. Among the statistical models, SDR ranks first, whereas Correlation ranks last, with average returns of 1.63% and −0.48%, respectively. The z-test results show that the models are statistically significantly different. We propose NSGA-II as the best candidate for use in pairs trading strategies in cryptocurrency markets.
The rise in artificial intelligence (AI) and machine learning (ML) in cryptocurrency trading has precipitated complex ethical considerations, demanding a thorough exploration of responsible regulatory approaches. This research expands upon this need by employing a consequentialist theoretical framework, emphasizing the outcomes of AI and ML’s deployment within the sector and its effects on stakeholders. Drawing on critical case studies, such as SBF and FTX, and conducting an extensive review of relevant literature, this study explores the ethical implications of AI and ML in the context of cryptocurrency trading. It investigates the necessity for novel regulatory methods that address the unique characteristics of digital assets alongside existing legalities, such as those about fraud and insider trading. The author proposes a typology framework for AI and ML trading by comparing consequentialism to other ethical theories applicable to AI and ML use in cryptocurrency trading. By applying a consequentialist lens, this study underscores the significance of balancing AI and ML’s transformative potential with ethical considerations to ensure market integrity, investor protection, and overall well-being in cryptocurrency trading.