J.C. Chan, Michael Nayat Young, Yogi Tri Prasetyo, Reny Nadlifatin
This paper presents a comparative analysis of Mean-Variance Theory (MVT) and Safety - First model with SP/A criterion utilizing Non-Fungible Tokens and Collectibles in the crypto market. The criterion used to determine the investment pool is the mean volume price of the Top 100 NFTs and collectibles. Historical data are gathered and computed for return estimation with equal probability. Also, different portfolio weight threshold parameters were used for the mean-variance (risk-return threshold) and safety first (relative value for fear and hope). Using backtesting, safety-first portfolios show higher cumulative returns from the US dollar benchmark. Overall, this study offers portfolio optimization and an alternative portfolio selection model as references for generic investment procedures for digital asset investors, and for educational purposes.
Mohak Goyal, Geoffrey Ramseyer, Ashish Goel, David Mazières
Constant Function Market Makers (CFMMs) are a tool for creating exchange markets, have been deployed effectively in prediction markets, and are now especially prominent in the Decentralized Finance ecosystem. We show that for any set of beliefs about future asset prices, an optimal CFMM trading function exists that maximizes the fraction of trades that a CFMM can settle. We formulate a convex program to compute this optimal trading function. This program, therefore, gives a tractable framework for market-makers to compile their belief function on the future prices of the underlying assets into the trading function of a maximally capital-efficient CFMM. Our convex optimization framework further extends to capture the tradeoffs between fee revenue, arbitrage loss, and opportunity costs of liquidity providers. Analyzing the program shows how the consideration of profit and loss leads to a qualitatively different optimal trading function. Our model additionally explains the diversity of CFMM designs that appear in practice. We show that careful analysis of our convex program enables inference of a market-maker's beliefs about future asset prices, and show that these beliefs mirror the folklore intuition for several widely used CFMMs. Developing the program requires a new notion of the liquidity of a CFMM, and the core technical challenge is in the analysis of the KKT conditions of an optimization over an infinite-dimensional Banach space.
Everyone is eager for high yield and low risk. In this research, we use Markowitz's investment theory and Monte Carlo simulation to find the optimal investment portfolio and then study the impact of adding Bitcoin to the traditional investment portfolio on the cumulative rate of return. Our results show that the return performance of the investment portfolio with Bitcoin is better than that of the traditional investment portfolio. Moreover, despite the impact of COVID-19 on the global economy and the Federal Reserve's quantitative easing policy, it is beneficial for investors to include Bitcoin in their portfolio allocation.
Blanka Łęt, Konrad Sobański, Wojciech Świder, Katarzyna Włosik
Abstract This article sheds new light on the informational efficiency of the cryptocurrency market by analyzing investment strategies based on structural factors related to on-chain data. The study aims to verify whether investors in the cryptocurrency market can outperform passive investment strategies by applying active strategies based on selected fundamental factors. The research uses daily data from 2015 to 2022 for the two major cryptocurrencies: Bitcoin (BTC) and Ethereum (ETH). The study applies statistical tests for differences. The findings indicate informational inefficiency of the BTC and ETH markets. They seem consistent over time and are confirmed during the COVID-19 pandemic. The research shows that the net unrealized profit/loss and percent of addresses in profit indicators are useful in designing active investment strategies in the cryptocurrency market. The factor-based strategies perform consistently better in terms of mean/median returns and Sharpe ratio than the passive “buy-and-hold” strategy. Moreover, the rate of success is close to 100%.
This study analyses and compares the behavior of the gold-backed, conventional cryptocurrency, and gold markets capable of detecting the existence of herding and deducing the efficiency degree. In addition, this empirical work tried to examine the COVID-19 pandemic's influence on both cryptocurrency performances. This work developed a new method that discloses herding biases using persistence and efficiency metrics. Besides, this paper investigated the nonlinear dynamic properties of the gold-backed, conventional cryptocurrencies and Gold by estimating the Multifractal Detrended Fluctuation Analysis (MFDFA). It also assessed the inefficiency of these markets through an efficiency index (IEI) and tested the effect of COVID-19 on their dynamics. The findings of this investigation indicate that the gold-backed cryptocurrency (X8X) is the most efficient market in the long-term trading market. However, the conventional cryptocurrency market (Bitcoin) is the most efficient on the short trade horizon. Besides, gold-backed cryptocurrency markets present a smaller level of herding behavior than conventional cryptocurrencies on tall scales. Nevertheless, we noted the positive and negative effects of the pandemic on each cryptocurrency market dynamics. To the best of the authors' knowledge, this study is the first investigation that uses multifractal analysis to quantify the impact of the COVID-19 spread on gold-backed cryptocurrencies and detects the presence of herding behavior.
Philippe Bergault, Louis Bertucci, David Bouba, Olivier Guéant
With the emergence of decentralized finance, new trading mechanisms called Automated Market Makers have appeared. The most popular Automated Market Makers are Constant Function Market Makers. They have been studied both theoretically and empirically. In particular, the concept of impermanent loss has emerged and explains part of the profit and loss of liquidity providers in Constant Function Market Makers. In this paper, we propose another mechanism in which price discovery does not solely rely on liquidity takers but also on an external exchange rate or price oracle. We also propose to compare the different mechanisms from the point of view of liquidity providers by using a mean / variance analysis of their profit and loss compared to that of agents holding assets outside of Automated Market Makers. In particular, inspired by Markowitz' modern portfolio theory, we manage to obtain an efficient frontier for the performance of liquidity providers in the idealized case of a perfect oracle. Beyond that idealized case, we show that even when the oracle is lagged and in the presence of adverse selection by liquidity takers and systematic arbitrageurs, optimized oracle-based mechanisms perform better than popular Constant Function Market Makers.
Essais sur la dynamique du prix des crypto-monnaies Cet essai, qui comprend trois recherches empiriques originales, met en lumière les caractéristiques du comportement du marché des crypto-monnaies en fonction des effets macroéconomiques exogènes, du réseau interne et de leurs “matières premières" - les marchés de l'énergie. Le premier chapitre étudie les réponses des rendements et de la volatilité des crypto-monnaies aux annonces de nouvelles macroéconomiques américaines. En utilisant les données \textit{intraday} de 5 minutes sur les prix des crypto-monnaies, nous trouvons des preuves de la réaction des rendements et de la volatilité des crypto-monnaies aux nouvelles macroéconomiques et les possibilités d'utiliser les crypto-monnaies comme un outil de refuge en raison de la différence de réponse aux nouvelles macroéconomiques américaines entre les crypto-monnaies et les autres actifs financiers conventionnels. Dans le deuxième chapitre, nous étudions la causalité dynamique sur le marché des crypto-monnaies du point de vue du rendement et de la liquidité en utilisant une méthode de réseau de causalité stable. Les résultats mettent en évidence la spéculation du marché en montrant que les principales crypto-monnaies ne sont pas les acteurs les plus influents du réseau. Le chapitre 3 traite du sujet controversé de la relation entre les crypto-monnaies et les marchés de l'énergie. En outre, la consommation d'énergie des crypto-monnaies augmente considérablement suite à la difficulté croissante du minage et à une gamme plus complète d'application de la technologie blockchain telle que NFT et DeFi. En utilisant le modèle VAR à paramètres variables dans le temps, ce chapitre complète la littérature existante sur la liaison entre les crypto-monnaies et les marchés de l'énergie. Contrairement à la plupart des chercheurs existants, notre étude aborde non seulement les principales crypto-monnaies consommatrices d'énergie, le Bitcoin et l'Ethereum, mais aussi d'autres actifs basés sur la blockchain.
In this research, the U.S. investor sentiment effect on cryptocurrency returns and volatility is examined by separating it into irrational and rational parts. According to the data, an unforeseen rise in the rational part of U.S. individual investor attitude influences cryptocurrency returns statistically and positively. In other words, rational sentiment can result in rising cryptocurrency returns. Additionally, a positive significant association exists between cryptocurrency volatility and the rational part of the individual U.S. investor sentiment. The findings confirm the hypothesis that the behavior of rational investors who utilize and study the impact of economic factors on asset prices reduces cryptocurrency volatility.
This paper examines the trading performances of several technical oscillators created using crypto-asset pricing methods for short-term bitcoin trading. Seven pricing models proposed in the professional and academic literature were transformed into oscillators, and two thresholds were introduced to create buy and sell signals. The empirical back-testing analysis showed that some of these methods proved to be profitable with good Sharpe ratios and limited max drawdowns. However, the trading performances of almost all methods significantly worsened after 2017, thus indirectly confirming an increasing financial literature that showed that the introduction of bitcoin futures in 2017 improved the efficiency of bitcoin markets.
Uwais Suliman, Terence L. van Zyl, Andrew Paskaramoorthy
Cryptocurrencies are peer-to-peer digital assets monitored and organised by a blockchain network. Price prediction has been a significant focus point with various machine learning algorithms, especially concerning cryptocurrency. This work addresses the challenge faced by traders of short-term profit maximisation. The study presents a deep reinforcement learning algorithm to trade in cryptocurrency markets, Duelling DQN. The environment has been designed to simulate actual trading behaviour, observing historical price movements and taking action on real-time prices. The proposed algorithm was tested with Bitcoin, Ethereum, and Litecoin. The respective portfolio returns are used as a metric to measure the algorithm's performance against the buy-and-hold benchmark, with the buy-and-hold outperforming the results produced by the Duelling DQN agent.
This paper investigates how changes in investor base is related to idiosyncratic volatility in cryptocurrency markets. For each cryptocurrency, we set change in its subreddit followers as a proxy for the change in its investor base, and find out that the latter can significantly increase cryptocurrencies idiosyncratic volatility. This finding is not subsumed by effects of size, momentum, liquidity and volume and is robust to various measures of idiosyncratic volatility.
Being archetypal complex systems, financial markets exhibit rich set of dynamics in their interactions. In this paper, we focus on the recently evolved cryptocurrency market as an example of a complex system and analyse the evolution of cross correlation structure of cryptocurrencies in the 5 year period from 2017 to 2022. We observe characteristic correlation structures in the observation time window duration and use these specific structures to cluster the cryptocurrency market in 4 market states.
The total capital in cryptocurrency markets is around two trillion dollars in 2022, which is almost the same as Apple’s market capitalisation at the same time. Increasingly, cryptocurrencies have become established in financial markets with an enormous number of transactions and trades happening every day. Similar to other financial systems, price prediction is one of the main challenges in cryptocurrency trading. Therefore, the application of artificial intelligence, as one of the tools of prediction, has emerged as a recently popular subject of investigation in the cryptocurrency domain. Since machine learning models, as opposed to traditional financial models, demonstrate satisfactory performance in quantitative finance, they seem ideal for coping with the price prediction problem in the complex and volatile cryptocurrency market. There have been several studies that have focused on applying machine learning for price and movement prediction and portfolio management in cryptocurrency markets, though these methods and models are in their early stages. This survey paper aims to review the current research trends in applications of supervised and reinforcement learning models in cryptocurrency price prediction. This study also highlights potential research gaps and possible areas for improvement. In addition, it emphasises potential challenges and research directions that will be of interest in the artificial intelligence and machine learning communities focusing on cryptocurrencies.
Most of the literature on life cycle investment portfolio analysis focuses on the allocation between risky stocks and safe bonds. We introduce a new risky asset class, cryptocurrency, to a standard consumption-investment life cycle model. Our model suggests that the optimal investment profile in cryptocurrencies declines with age. Young investors mainly invest in cryptocurrency. As age and wealth increase, investors transition to mostly stocks mid-career and mostly bonds in retirement. A welfare analysis shows significant utility losses from not participating in the cryptocurrency market or not adjusting cryptocurrency portfolio shares throughout the life cycle.
The cryptocurrency market is understood as being more volatile than traditional asset classes. Therefore, modeling the volatility of cryptocurrencies is important for making investment decisions. However, large swings in the market might be normal for cryptocurrencies due to their inherent volatility. Deviations, along with correlations of asset returns, must be considered for measuring the degree of market anomaly. This paper demonstrates the use of robust Mahalanobis distances based on shrinkage estimators and minimum covariance determinant for observing anomaly scores of cryptocurrencies. Our analysis shows that anomaly scores are a critical complement to volatility measures for understanding the cryptocurrency market. The use of anomaly scores is further demonstrated through portfolio optimization and scenario analysis.
Investors looking to integrate digital assets into a traditional, diversified multi-asset portfolio need to formulate appropriate risk and return assumptions for them. Using the case of bitcoin, we argue that due to the short duration of available returns and the extreme volatility of the asset, historical returns are an unreliable basis for directly formulating forward return expectations. We also show that bitcoin’s return characteristics require an emphasis on such portfolio construction considerations as rebalancing frequency that are often peripheral in traditional asset allocation studies. We then demonstrate an allocation approach that addresses these concerns. The main idea is to extract required return thresholds for a small bitcoin investment (1% or 5%) that need to be underwritten by the investor, rather than relying on explicit return expectations as the input. We show that these return thresholds are surprisingly low, illustrating that the broader multi-asset portfolio perspective is critical when making investment decisions regarding high-volatility assets like bitcoin.