This thesis examines the roots of price instability in cryptoasset markets. Using daily data from 2016 to 2022, it estimates volatility for Ethereum, XRP, Doge- coin, Stellar Lumen and Litecoin with the Garman-Klass range-based estima- tor. This exploits intraday price information that is usually not taken into con- sideration when using return-based metrics. A log-log two-stage least-squares framework relates volatility to exchange volume, on-chain transfers, active ad- dresses, stable-coin supply, own price, lagged volatility and Bitcoin's domi- nance, instrumenting endogenous variables with hash rate, circulating supply and traditional-market indicators. Results show that volatility of alternative cryptoassets reacts only weakly to conventional fundamentals: for Ethereum, user activity and stablecoin liquidity temper volatility while speculative trad- ing, price surges and persistence amplify it; for XRP and Dogecoin, none of the tested drivers matter besides persistence; for Stellar Lumen and Litecoin, Bit- coin dominance slightly stabilizes prices. Overall explanatory power is modest, suggesting that other undefined e!ects drive the volatility in alternative cryp- toasset markets. The study pioneers the combination of range-based volatility and instrumental-variables analysis in this field and...
Market sentiment in the cryptocurrency market is a crucial determinant of price movements and investment strategies. This study conducts a bibliometric analysis to explore the relationship between investor sentiment, behavioural biases, and cryptocurrency market dynamics from 2009 to 2023. Utilising a dataset of 5,184 records from the Web of Science and advanced bibliometric tools (Citespace, VOSviewer, and Nvivo), we identify key research trends, influential contributions, and emerging areas of study. Our findings highlight that investor sentiment, herding behaviour, and social media influence play significant roles in shaping cryptocurrency prices. Additionally, momentum and contagion effects emerge as dominant factors, underscoring the presence of psychological biases in cryptocurrency trading. This study provides critical insights into the evolving landscape of digital finance by mapping research clusters and citation networks. The results reveal key gaps in the literature, such as the need for further research on sentiment-driven contagion, the role of alternative sentiment proxies (Google Trends, Twitter), and cross-market influences. These insights offer valuable implications for academics, policymakers, and market participants by advancing the understanding of behavioural dynamics in cryptocurrency markets and guiding future research directions.
This study shows that returns of cryptocurrencies with similar prices move together. This price-based comovement is independent of comovements caused by other cryptocurrencies’ well-known common risk factors including size, momentum, past returns, past trading volume, or market returns. The results are robust to alternative estimation methods and data frequencies. Additional analysis shows that the relationship becomes stronger during periods of high investor sentiment, exhibits a long-run reversal, and holds within a sample of memecoins. These findings support a sentiment-based explanation of return comovement.
Ovaj rad istražuje ulogu kriptovaluta u optimizaciji portfelja koristeći Markowitzev model moderne teorije portfelja. Cilj je bio analizirati u kojoj mjeri uključivanje kriptovaluta može poboljšati omjer rizika i prinosa u odnosu na portfelje koji se sastoje isključivo od tradicionalnih instrumenata. Analiza je provedena na odabranim kriptovalutama (Bitcoin, Ethereum, Solana, Binance Coin i XRP) te na dva tradicionalna tržišna indeksa (S&P 500 (SPY) i CROBEX), u razdoblju od 2023. do 2025. godine. Metodološki pristup uključivao je izračun logaritamskih tjednih prinosa, varijanci i standardnih devijacija, te konstrukciju matrice kovarijanci i korelacija između instrumenata. Na temelju tih podataka definirano je pet različitih portfelja s različitim razinama izloženosti kriptovalutama: portfelj sastavljen isključivo od kriptovaluta, kombinirani portfelj kriptovaluta i indeksa, defenzivni portfelj s većinskim udjelom indeksa, portfelj temeljen na indeksima i Bitcoinu, te diverzificirani portfelj u kojem svaki instrument ima minimalni udio. Za svaki portfelj konstruirana je efikasna granica kako bi se prikazao odnos rizika i prinosa. Rezultati pokazuju da kriptovalute, zbog svoje visoke volatilnosti, značajno povećavaju rizik portfelja, ali istodobno omogućuju postizanje viših očekivanih prinosa. Najveći potencijal za smanjenje rizika i postizanje uravnoteženog odnosa rizika i prinosa pokazali su portfelji koji uključuju kombinaciju tradicionalnih indeksa i Bitcoina, dok su diverzificirani portfelji ponudili najbolju stabilnost. Portfelj sastavljen isključivo od kriptovaluta, iako je donosio najviše prinose, bio je izložen i najvećem riziku, što potvrđuje da kriptovalute same po sebi nisu dovoljne za optimalnu strategiju ulaganja. Zaključno, rad pokazuje da kriptovalute mogu igrati važnu ulogu u optimizaciji portfelja, ali prvenstveno u kombinaciji s tradicionalnim instrumentima. Na taj način mogu pridonijeti diversifikaciji i poboljšanju efikasnosti portfelja, dok istodobno smanjuju ekstremne rizike povezane s isključivim oslanjanjem na kriptovalute.
This thesis investigates the effectiveness of technical analysis, focusing on can- dlestick patterns, in cryptocurrency markets characterized by high volatility and continuous trading. Using statistical methods, including skewness-adjusted t-test and binomial test, the study evaluates 41 bullish and bearish patterns across five datasets: four datasets covering cryptocurrencies in general (excluding stable- coins) and one specific to stablecoins. Gap-dependent patterns were rare due to the continuous trading nature of cryptocurrency markets. Eight patterns demon- strated predictive potential in the non-stablecoin datasets, though two produced returns contrary to their bearish classification. The most compelling patterns are Hammer Bullish, Rising Window Bullish, On Neek Bearish, and Shooting Star Bearish, which produced returns contrary to its bearish classification, as they ap- pear in three datasets. In contrast, the stablecoin dataset showed Doji Star Bullish and Doji Star Bearish as significant; however, these likely reflect price-stabilization mechanisms rather than intrinsic predictive properties. By leveraging large, di- verse datasets and employing modern trend-definition methodology, the study highlights the limited applicability of traditional candlestick patterns and ques- tions the...
This thesis consists of three essays that provide a comprehensive exploration of the cryptocurrency market, addressing significant gaps in the literature through a systematic review and empirical investigations into the asset pricing process.The first essay systematically reviews 2,098 cryptocurrency publications in finance, identifying three primary research streams: features of cryptocurrencies, market behaviour, and blockchain implications. It synthesises diverse findings, resolves contradictions, and maps future research directions. Additionally, it highlights two critical research topics that form the foundation for the subsequent essays.The second essay examines the impact of economic shocks on cryptocurrency asset pricing. The results show that incorporating sensitivities to unexpected changes in economic variables, such as global stock market returns, financial stress, and inflation expectations, significantly enhances the explanatory power of asset pricing models in explaining both time-series returns and cross-sectional expected returns. Furthermore, the sensitivities to economic shocks yield substantial abnormal returns over the long run, suggesting the presence of economic risk premia within the cryptocurrency market. For example, cryptocurrencies with higher sensitivities to global stock market shocks and inflation expectations outperform their counterparts by average weekly returns of 0.48% and 0.49%, respectively. Similarly, cryptocurrencies more vulnerable to spikes in fear sentiment and financial stress deliver long-run outperformance of 0.47% and 0.44% per week, compared with the more resilient coins.The third essay investigates the time variation of factor premia in the cryptocurrency market, focusing on six categories of long-short factors, including size, momentum, liquidity, volatility, psychological, and economic sensitivities. The essay explores the influence of behavioural finance variables, economic and financial indicators, and cryptocurrency market state variables on factor returns. The findings reveal substantial time variation in cryptocurrency factor returns, shaped by three pivotal mechanisms: behavioural finance-driven mispricing, investor behaviour amid different market states, and fundamental forces from economic and financial conditions. For example, heightened fear sentiment enhances the performance of larger and more liquid cryptocurrencies, while bullish market conditions amplify returns for lottery-like assets. This research extends traditional asset pricing models to the cryptocurrency market, offering novel insights into the predictability of factor premia and providing practical implications for investors navigating this dynamic and volatile market.
This paper investigates the speed of price discovery when information becomes publicly available but requires costly processing to become common knowledge. We exploit the unique institutional setting of hacks on decentralized finance (DeFi) protocols. Public blockchain data provides the precise time a hack’s transactions are recorded—becoming public information—while subsequent social media disclosures mark the transition to common knowledge. This empirical design allows us to isolate the price impact occurring during the interval characterized by information asymmetry driven purely by differential processing capabilities. Our central empirical finding is that substantial price discovery precedes common knowledge: approximately 36 percent of the total 24-hour price decline (∼27 percent) materializes before the public announcement. This evidence suggests sophisticated traders rapidly exploit their ability to process complex, publicly available on-chain data, capturing informational rents. We develop a theoretical model of informed trading under processing costs which predicts strategic, slow information revelation, consistent with our empirical findings. Our results quantify the limits imposed by information processing costs on market efficiency, demonstrating that transparency alone does not guarantee immediate information incorporation into prices.
The rapid expansion of blockchain technology has created both opportunities and challenges in financial markets, particularly in the investment of blockchain-based real estate tokens. Unlike traditional financial assets, these investments exhibit high volatility, decentralized trading mechanisms, and complex transaction fee structures, all of which significantly influence portfolio management strategies. This study tackled the core issue of portfolio optimization in blockchain asset markets by incorporating both proportional and fixed transaction costs, factors often overlooked in conventional models. To address this, we proposed a multi-period investment optimization framework that leveraged Lagrange multipliers and dynamic programming to determine optimal asset allocation. A key feature of our model was its ability to define an optimal no-trade region, balancing transaction costs with investment returns under varying fee structures. Through numerical experiments, we analyzed how different levels of transaction costs impacted trading frequency, risk exposure, and portfolio efficiency. Our findings indicated that higher transaction costs expanded the no-trade region, reducing trading frequency, while lower costs encouraged more frequent rebalancing. Additionally, we highlighted the practical benefits of blockchain real estate tokenization, including lower investment barriers, enhanced market liquidity, and seamless cross-border transactions. By providing a robust theoretical and empirical framework, this research offered valuable insights for investors navigating blockchain-based financial markets and contributed to the broader discourse on decentralized finance (DeFi) and tokenized real estate investments.
This thesis aims to understand the nature of Bitcoin and the characteristics of Bitcoin-related equities using established asset pricing frameworks. It involves the empirical testing of two hypotheses. The first hypothesis posits that Bitcoin returns should be priced in the cross-section of expected stock returns, with a negative risk premium. Using a sample of 5,091 U.S.-listed stocks from March 2011 to April 2024, the cross-sectional analysis indicates that the risk premium associated with Bitcoin returns is not statistically significant. This finding challenges the “digital gold” narrative, which implies that Bitcoin functions as a safe-haven asset. Instead, the evidence suggests that portfolios with extreme Bitcoin betas consistently yield abnormal negative future returns, revealing a non-linear, inverted U-shaped relationship between Bitcoin beta and expected stock returns. While abnormal negative returns align more closely with speculative behavior, the interpretation regarding Bitcoin’s role remains theoretically challenging, as portfolios with the lowest Bitcoin betas also exhibit abnormal negative returns. The second hypothesis examines the risk determinants of Bitcoin-related stocks. This analysis is based on a sample of 20 Bitcoin-holding firms listed in the U.S. market, covering the three-year period from January 2020 to December 2022. The results indicate that the stock returns of these firms are significantly exposed to daily Bitcoin price fluctuations, exhibiting a positive beta. Additionally, the stock returns of Bitcoin-mining firms in the sample are significantly influenced by changes in Bitcoin mining difficulty, with a negative sensitivity—an effect not observed in other types of Bitcoin-holding firms. This suggests that Bitcoin-specific risk factors beyond price fluctuations may play a role in the risk-return dynamics of Bitcoin-related equities. Furthermore, a reverse size effect is observed within this sector: Bitcoin-related firms with larger market capitalizations tend to generate higher returns compared to smaller firms. This finding holds important implications for industry practice since it challenges the conventional belief that smaller stocks typically yield higher returns.
We selected the daily trading data of BTC, SPY, DXY, GLD, and QQQ from Yahoo Finance, aiming to analyze the role of BTC in portfolios. This paper believes that BTC, as a high-risk asset, is speculative. Through correlation analysis, its returns were found to be independent of other traditional assets, proving that applying BTC to investment strategies could create arbitrage opportunities. Through various asset combinations in investment portfolio experiments, we found that the intervention of BTC could enhance the returns and optimal Sharpe ratio of the original investment portfolio, and the increase in the optimal Sharpe ratio decreased as the number of assets in the portfolio except for BTC increased. Therefore, for ordinary investors, we suggest adding 10% - 20% of BTC to a single asset. Through out-of-sample testing, we found that the investment strategy that includes BTC investment based on historical data, although it could not achieve the optimal Sharpe ratio, would have higher returns than the optimal Sharpe ratio investment portfolio without BTC intervention in the current period, considering that investors have certain risk tolerance, we believe that the effectiveness of historical investment strategies can be verified.