Abstract
This chapter aims to analyze the price efficiency of Bitcoin (BTC), DASH, EOS, Ethereum (ETH), LISK, Litecoin (LTC), Monero, NEO, QUANTUM, RIPPLE, STELLAR, and ZCASH in their weak form between March 1, 2018 and March 1, 2023 and determine whether they experience overreactions. The results show that cryptocurrencies exhibit positive and negative autocorrelations, which can reduce volatility and moderate price fluctuations. The results also show persistence in cryptocurrency returns, suggesting long-term trends or market patterns that individual and institutional investors can exploit. It is essential to recognize that cryptocurrencies are characterized by a high degree of complexity and instability. Investors need to monitor market trends and make the necessary adjustments to their investment strategies to anticipate market changes.
TopIntroduction
The efficiency of markets is a fundamental concept in financial theory, whereby financial asset prices serve as appropriate signals for the purchase of resources. The market efficiency hypothesis is part of the concept that an investor cannot earn an extraordinary risk-adjusted return. Nevertheless, certain empirical investigations have demonstrated a contrary outcome, whereby an investor could potentially achieve a return that surpasses the market average (Fama, 1965a, 1970).
According to the efficient market hypothesis, stock prices incorporate all the relevant information that is available, thereby posing a challenge for investors to obtain abnormal profits by predicting future returns. Notwithstanding, several academic studies have contested this notion since the mid-1980s. Bondt and Thaler (1985) conducted a study which demonstrated that investing in stocks that have previously performed poorly (early extreme losers) and shorting those that have performed exceptionally well can result in abnormal long-term returns. (Initial extreme winners). According to the authors, the observed returns that are in opposition to the expected trend are attributed to the tendency of investors to overreact to information, leading to an excess of both positive and negative feelings in the market.
The occurrence of exaggerated reaction is a financial market anomaly that has been extensively researched. A significant alteration in price that partially reverts is commonly characterized as such (Chen and Zhu, 2005). This phenomenon can be elucidated by the psychological tendencies exhibited by investors (Achleitner et al., 2012; Chopra et al., 1992; De Bondt and Thaler, 2012), where this concept is frequently denoted as the “overreaction hypothesis” within the literature (Bondt and Thaler, 1985; De Bondt and Thaler, 1985) which has been overly studied and documented for the stock markets (Campbell et al., 1997; Lo and MacKinlay, 1988). Chopra et al. (1992) demonstrated in their complementary research that abnormal returns can still be achieved through the implementation of an opposing strategy, even after accounting for variables such as size, beta, and past returns.
Excessive fluctuations in cryptocurrency prices can be seen as a remarkable example of exaggerated reaction that has persisted for six months and deserved considerable attention from investors, regulators, policy makers and the media (Urquhart, 2018). According to the authors Amini et al. (2013), exaggerated reactions can also occur in shorter periods lasting a few minutes on various asset classes, resulting in the observation of a recurrent price pattern in financial markets with a gain followed by a collapse (or vice versa). This emphasizes the significance of doing a thorough investigation into the occurrence of exaggerated reactions to cryptocurrency prices.
Although conventional literature focuses on stock markets, this study intends to investigate empirical evidence and assess the behavior of digital currencies, a new class of assets that has piqued the interest of investors and gained importance in financial circles in recent years. As such, our research evaluates the prevalence of excessive reactions in the digital currency markets Bitcoin (BTC), DASH, EOS, Ethereum (ETH), LISK, Litecoin (LTC), MONERO, NEO, QUANTUM, RIPPLE, STELLAR, and ZCASH, as well as whether these reactions create efficiency imbalances.
This chapter adds to the current literature by modeling excessive price reaction as a price change based on 16-day lags, without the need to select any specific threshold parameter (such as a price change that exceeds 10% within a day or is two standard deviations over average). Previous work, on the other hand, was based on statistical modeling of exaggerated reactions, which includes the selection of one or more arbitrary parameters. Because the selection of such factors is such an important component of the study, the results may be biased when using this approach. Thus, our study extends to the empirical literature by assessing the scope of over-reaction behavior across various digital currencies, as well as their efficiency, in its weak form.
Key Terms in this Chapter
Long Memories: In finance or statistics, “long memories” relate to the persistence of past knowledge or events in impacting current or future results. In contrast to the concept of perfect randomness, it implies that historical facts or patterns have a long-term impact on the present.
Efficiency in Its Weak Form: In its weak form, efficiency implies that all previous market prices and information are already reflected in the current market price, making it challenging to achieve consistently greater returns using historical data or public information.
Short-Term Reactions: Short-term reactions among cryptocurrency investors pertain to the prompt and frequently instantaneous responses exhibited by investors in response to new information, events, or market developments within the cryptocurrency market. These reactions might result in rapid price swings or trading determinations within a short-term context.
Price Reversal: In the realm of financial markets, mean reversal refers to the occurrence wherein the prices of assets, having strayed considerably from their historical average or mean, exhibit a tendency to revert to said mean over a time period.
Serial Autocorrelation: Serial autocorrelation, regardless of its directionality, pertains to the statistical association observed between successive data points within a given time series. Positive serial autocorrelation refers to a situation where there is a positive correlation between consecutive data points. This implies that a high value is more likely to be followed by another high value, while a low value is more likely to be followed by another low value. Negative serial autocorrelation refers to a situation where there is a negative correlation between consecutive data points. This implies that a high value is likely to be followed by a low value, and vice versa.
Underreaction: Underreaction among cryptocurrency investors refers to the phenomenon in which certain cryptocurrency market players fail to immediately modify their investment strategies or asset valuations in response to new information or events, resulting in delayed or inadequate responses.
Cryptocurrencies: Digital or virtual currencies that operate on a decentralized network known as blockchain and use cryptographic techniques to assure security are referred to as cryptocurrencies. Cryptocurrencies enable safe peer-to-peer exchanges and are widely used as a store of value, means of exchange, or digital asset.
Martingale: High-risk trading strategy in which investors double their position size following a loss in an attempt to recoup losses through subsequent profitable transactions. This strategy assumes that future gains will compensate for past losses but entails substantial risk.
Overreaction: Overreaction in cryptocurrency investors refers to the tendency of some cryptocurrency market players to overreact to new information or events, resulting in exaggerated and frequently irrational buying or selling decisions.