Abstract
The popularity of cryptocurrencies as alternative investments has grown in recent years. However, it remains unclear whether cryptocurrency investors behave irrationally in a similar way to emerging market investors. Using a systematic literature review, this study aims to compare the factors related to the presence of behavioural biases in the cryptocurrency and emerging stock markets. This study highlights similarities and differences between cryptocurrency and emerging stock market investor behaviour. Thus, the study's novelty arises from comparing the role of behavioural inclinations in cryptocurrency and emerging stock markets. The findings indicate that the small amount or lack of available information about small-cap emerging stocks or cryptocurrencies may reinforce investor sentiment and herding behaviour. The herding behaviour among investors in both markets may stem from following the most popular investment trends. Investors in cryptocurrency and emerging stock markets also tend to overreact to market sentiment and changes in market conditions. Extreme market conditions may affect the strength of herding behaviour, disposition effect, price clustering, anomalous behaviour, investor sentiment and uncertainty. Thus, cryptocurrency and emerging stock markets are informationally inefficient most of the time, whilst investors’ irrationality may be more pronounced during certain periods. Furthermore, investors’ behaviour in the cryptocurrency and emerging stock markets is more consistent with the adaptive market hypothesis than the efficient market hypothesis. This research suggests that cryptocurrency and emerging stock market investors should actively manage investment portfolios. Policymakers should be more concerned about information accessibility and quality, especially in the case of small-cap investment assets.
Keywords
Introduction
Behavioural finance relies on analysing the behaviour of market participants from a psychological perspective. This is a crucial issue in modern science as adaptation to rapidly changing market conditions becomes increasingly challenging. Under these circumstances, investors can make impulsive investment decisions. Furthermore, difficulties in processing all available information may lead them to use mental shortcuts (heuristics). Recent studies focus on the role of behavioural finance in various emerging markets (e.g., Duarte et al., 2023; Shrotryia & Kalra, 2023; Szczygielski et al., 2023). However, there remains a lack of knowledge about whether investors in cryptocurrency and other emerging markets show similar behavioural biases.
Some authors suggest cryptocurrency may represent an emerging market (e.g., Alvarez-Ramirez et al., 2018; Khuntia & Pattanayak, 2018; Kumar & Zargar, 2019). Omane-Adjepong et al. (2021) confirm that herding behaviour in emerging and cryptocurrency markets can be similar in some periods. Other similarities between cryptocurrency and emerging markets may include greater regulatory instability than in the more mature capital markets, the importance of international capital flow, weak economic integration with the global economy or the expectation of higher returns on investment than in developed markets. However, there is little evidence of whether cryptocurrencies are similar to emerging markets in the context of investor behaviour.
This study aims to compare the factors related to behavioural biases in the cryptocurrency and emerging stock markets. Using a systematic literature review, this study presents the existing evidence on a group of investor biases in both markets. The set of considered investor biases includes herding behaviour, investor sentiment, investor attention, the loss aversion and disposition effect, investor uncertainty, the anchoring heuristic, anomalous behaviour and overconfidence. Additionally, this article discusses the similarities and differences between drivers of investors’ behaviour in the cryptocurrency and emerging stock markets. Finally, the gaps in the empirical studies on investor behaviour in cryptocurrency and emerging stock markets are presented.
The answer to the question of why investors are irrational in the cryptocurrency and emerging stock markets is crucial. This may help investors make more efficient investment decisions. Moreover, this paper outlines which of the studied markets are characterised by the more irrational behaviour of investors. This can be interpreted as a comparison of informational inefficiency between markets (Fama, 1970). Notably, investors may benefit from this knowledge. When they consider investing in two alternative markets, such knowledge may help them select a market with slower/faster investment reactions to the available information. Furthermore, traders may construct better investment strategies based on investors’ overreactions to market sentiment. This study also indicates whether investors should manage an investment portfolio actively or passively. If investors’ irrational behaviour increases in both markets during financial crises, cryptocurrency and emerging markets investors should redirect capital to less risky markets (e.g,. gold or bond markets).
Most studies on investor behaviour in emerging markets focus on stocks. In traditional currency markets, investors' responses to market sentiment are different from that of the largest cryptocurrency - Bitcoin (Rognone et al., 2020). Moreover, the behaviour of daily logarithmic returns for Bitcoin is more similar to stocks than currencies in emerging markets (Kurtosis and standard deviations in the period from 13/09/2011 to 9/05/2024 have been approximately 7 and 0.003 for the MSCI emerging currency, 14 and 0.01 for the MSCI emerging stock index, and 25 and 0.05 for Bitcoin). This may be related to certain technical similarities between stock and cryptocurrency markets (e.g. capital raising mechanisms such as ICO (Initial Coin Offering) and IPO (Initial Public Offering), the occurrence of market trends, speculative bubbles, a limited supply of some cryptocurrencies, and the role of individual investors, which may be subject to heuristics among investors). Therefore, it may be expected that investors’ behaviour in the cryptocurrency market is similar to that of emerging stock markets.
Recently, it has been noted that cryptocurrency traders tend to use different investment strategies than in the case of stock or gold markets (Kogan et al., 2024). This may reflect some differences between cryptocurrency and emerging stock markets. For example, investment data and analytics services for professional investors provide more information about equity than cryptocurrency markets. This may indicate that the level of information asymmetry between investors in the cryptocurrency market differs from that of the emerging stock markets. Thus, it may be expected that the more analyses and information from professional companies available, the more efficient the allocation of resources will be.
In equity markets, index providers regularly evaluate the degree of market development. However, companies may classify a country as an emerging market based on different criteria. Differences in the classification of countries as emerging markets may lead to noise trading in some cases. Therefore, this study mainly refers to the countries included in the two most popular emerging market indexes: FTSE (Financial Times Stock Exchange) Russell and MSCI (Morgan Stanley Capital International). This narrows down the possible sample and allows for a comprehensive answer to the question of why investors’ behaviour in the cryptocurrency and emerging stock markets exhibit some (dis)similarities.
This study differs from previous investigations in several ways. Omane-Adjepong et al. (2021) only analysed the presence of herding behaviour in the cryptocurrency and emerging markets. Ballis and Verousis (2022) focused on the broader perspective because they conducted a literature review on behavioural finance in the cryptocurrency market. However, they failed to consider some important investor behaviours in the cryptocurrency market, such as the anchoring bias and the effect of social media influencers on other market participants. Influencers’ posts on social media seem to be crucial for the dynamics of cryptocurrency and Chinese stock prices (e.g. Gjerstad et al., 2021; Shahzad et al., 2022).
Another novelty of the paper arises from the possibility of verifying the adaptive market hypothesis (AMH) (Lo, 2004) in both markets simultaneously. The AMH states that the level of irrationality among market participants changes over time due to dynamics in market conditions. Thus, it can be expected that the presence of investor biases in cryptocurrency and emerging stock markets is subject to changes.
Previous studies primarily focused on comparing the occurrence of the given behavioural bias between different markets. For example, studies analysed the effects of investor attention on stocks, bonds and commodity markets (Han et al., 2018; Tantaopas et al., 2016; Vozlyublennai, 2014). To the best of the author's knowledge, no research to date has conducted a comprehensive analysis of different types of investors’ behaviour in the cryptocurrency and emerging stock markets.
The contributions of this paper are fourfold. First, investor irrationality may be interpreted as a signal of market inefficiency. Therefore, this research adds to the literature on market efficiency (e.g. Fama, 1970; Shiller, 2003). Second, the conducted literature review provides additional insights into the validity of the AMH. Changes in investors’ irrationality during different market conditions give support to the AMH. This implies that an investment portfolio consisting of cryptocurrencies and stocks in emerging markets should be managed actively rather than passively. Thus, this paper adds to the literature on portfolio management (Lo, 2004). Finally, this research deepens knowledge about the drivers of the strength of behavioural biases in both markets.
The structure of this paper is as follows. The second chapter describes the data and methodology applied. The next parts include a review of studies on herding behaviour, investor sentiment, the loss aversion and disposition effect, investor uncertainty, investor attention, the anchoring heuristic, anomalous behaviour and overconfidence among investors. The final section provides the discussion and conclusions.
Data and Methodology
Inspired by Ballis and Verousis (2022), a systematic literature review was applied in this study. Initially, the time frame of the search for articles was limited to the period from 2000 to 2023. During this period, a few crises occurred: the 2007–2008 financial crisis, the European debt crisis that intensified between 2010 and 2012, and the COVID-19 pandemic. Due to this, there is a chance that many articles in the sample may reflect investors’ behaviour during different market dynamics. Thus, this paper joins the discussion on the validity of the AMH.
The most popular keywords referring to suboptimal investor behaviours in cryptocurrency and traditional markets were identified based on the behavioural finance literature (e.g. Ballis & Verousis, 2022; López-Martín, 2022; Ritter, 2003; Zielonka, 2003). These include ‘herding behaviour’, ‘investor sentiment/market sentiment’, ‘social media impact’, ‘influencer impact’, ‘availability/anchoring/representativeness/affect heuristic’, ‘endowment effect’, ‘loss aversion’, ‘investor attention/search volume predictability’, ‘overconfidence’, ‘regret aversion’, ‘disposition effect’, ‘self-attribution’, ‘framing effect’, ‘break-even effect’, ‘house-money effect’, ‘mental accounting’, ‘hindsight bias’, ‘sunk cost effect’, ‘illusion of control’, ‘investor uncertainty’, ‘market efficiency’, ‘calendar/market anomaly’, ‘calendar effect’, and ‘price clustering’. Abstracts related to these keywords were retrieved from Scopus, the largest international bibliographic database providing better journal coverage than the second most popular service, Web of Science (Wouters et al., 2015).
The process of searching for articles consisted of the following parts. In the first step, the list of investigated emerging markets was created (Brazil, Chile, China, Columbia, the Czech Republic, Egypt, Greece, Hungary, India, Indonesia, Kuwait, Malaysia, Mexico, the Philippines, Qatar, Saudi Arabia, South Africa, Taiwan, Thailand, Turkey, and the United Arab Emirates). Next, the combinations of words referring to the name of the selected behavioural bias and markets were searched in the Scopus database. Synonyms in the context of the given investor bias also were included. For example, in the case of investor sentiment, the following phrases may be searched: ‘investor sentiment Bitcoin’, ‘investor sentiment cryptocurrency’, ‘sentiment emerging market’, ‘sentiment emerging stock’ and ‘investor sentiment China’ (similarly for other markets). Moreover, the names of the most popular proxies for investor behavioural biases were used during searching, such as ‘Twitter (actually known as ‘X’) sentiment …’, ‘Google sentiment…’ and ‘GSV sentiment…’ for investor sentiment.
Selected abstracts were sorted by the highest number of citations in the context of the given investor behaviour. Next, they were filtered by their content. In particular, the articles were reviewed to determine whether they met the following criteria: the scope of the paper related to behavioural finance, the studied markets included and the article being written in English. Due to limited space, the study period was set to the years 2008-2023, and only articles mentioned in the text were left in the final sample. 2008 is the year of publication of the white paper describing the concept of the first cryptocurrency (Nakamoto, 2008). The procedure of the final sample selection is presented in Figure 1.
The procedure of the final sample selection.
The final sample includes 168 articles: 94 of them are about emerging markets and 74 concern cryptocurrencies. In this research, most articles are from the years 2018–2023. Therefore, this study does not favour articles that were published at the beginning of the study period. The number of articles published over time that are included in the sample is shown in Figure 2.
Number of articles published in subsequent years.
Importance of behavioural factors for cryptocurrency and emerging stock markets
Research on behavioural factors in the cryptocurrency market is not as advanced as in the case of stocks. This may be because cryptocurrency investors are geographically dispersed and may wish to remain anonymous. In effect, it is difficult to conduct representative surveys and cognise investor behaviour more precisely. Another reason may be that stocks have been traded over a longer period than cryptocurrencies. Therefore, cryptocurrency research mainly covers investor’s inclinations, which can be measured indirectly. These include herding behaviour, investor sentiment, investor attention, the loss aversion and disposition effect, investor uncertainty, the anchoring heuristic, anomalous behaviour and overconfidence. This paper mainly focuses on these investor behaviours.
Herding Behaviour
One type of investor inclination in cryptocurrency and emerging stock markets is herding behaviour, which can simply be understood as imitating the actions of other market participants. Most studies confirm herding behaviour among investors in cryptocurrency and emerging stock markets (e.g., Cakan et al., 2019; Omane-Adjepong et al., 2021). However, the results of surveys contradict the existence of herding behaviour among investors in both these markets (e.g. Kumar & Goyal, 2016; Nurbarani & Soepriyanto, 2022). Surveys represent the market perception or investor’s abilities at a specific time. Thus, this is a time-dependent approach, which may determine the obtained results.
Researchers find that herding behaviour in cryptocurrency and emerging stock markets may be dependent on economic turmoil or uncertainty (e.g. Bouri et al., 2019; Choi, Kang & Yoon, 2022; Najmudin et al., 2017). As examples of high economic uncertainty, they include the global financial crisis in 2007–2008 (Cakan et al., 2019) and the COVID-19 pandemic (Dhall & Singh, 2020; Luu & Luong, 2020). During this time, irrational investment decisions may occur because increasing fear may contribute to the rapid reactions of investors, which may not be based on the analysis of available information (Choi, Kang & Yoon, 2022). This indicates that the integration of cryptocurrency and emerging markets may strengthen with economic uncertainty and contribute to the ‘spillover’ of herding behaviour across markets.
Further analyses show that herding is most present in the case of the largest and smallest investment assets by market cap in cryptocurrency and emerging stock markets (Vidal-Tomás et al., 2019; Yao et al., 2014). These studies explain that the behaviour of market participants may be justified by minimal or a lack of information about small-cap stocks or cryptocurrencies. In effect, investors in small-cap assets tend to use information concerning the largest cryptocurrencies or stocks. This may also signal the low quality of information in the case of small-cap assets.
Trendy topics in investing may support herding in cryptocurrencies and emerging stock markets. Recently, the popularity of eco-friendly attitudes has increased in many activities. In this context, Ren and Lucey (2022) report that herding behaviour mainly occurs in the case of more energy-intensive cryptocurrencies. In emerging stock markets, it is documented that investors may herd with high-tech companies to other industries (Lee et al., 2013). The high-tech sector can be also considered trendy. Moreover, it may be difficult to understand the impact of uncertain information related to trendy investments due to noise in the news. Therefore, it can be assumed that investors are subject to accessibility heuristics and are concentrated on the most popular trends.
Investment decisions in cryptocurrency and emerging stock markets may be affected by market cycles. In the cryptocurrency market, studies confirm that herding behaviour strengthens mainly with a bull market (Ballis & Drakos, 2020; Jalal et al., 2020; Kallinterakis & Wang, 2019). In emerging markets, there is no consensus on whether investors tend to follow others more during a bear or bull market (Chaffai & Medhioub, 2018; Chen, 2013; Demirer et al., 2010; Lee et al., 2013; Poshakwale & Mandal, 2014; Yao et al., 2014). This difference may result from a greater share of institutional investors in emerging stock markets than in the cryptocurrency market in the early period of its existence (Huang, Lin & Wang, 2022; Xiong & Wang, 2023). Institutional investors may assume a momentum strategy (Li & Wang, 2010) and affect other market participants with a short-term time horizon (e.g. by providing analyst recommendations) (Chong et al., 2017). Thus, cryptocurrency investors may be more inclined to follow others during a bull market. This may reflect more over-optimism among cryptocurrency investors when compared to emerging stock markets (Rognone et al., 2020).
In summary, investors tend to herd in both markets. This may be explained by uncertainty in information, a fear reinforced by the tone of information, low quality, minimal or no available information, and the rising popularity of certain topics in investing. One difference in herding behaviour between cryptocurrency and emerging markets participants is manifested by the strength of investor reaction to market cycles. This may result from differences in the share of professional investors between cryptocurrency and emerging markets. It can be expected that this discrepancy will be mitigated in the future as the cryptocurrency market develops.
Investor Sentiment
Investors in cryptocurrency and most emerging stock markets tend to make investment decisions based on market sentiment (e.g. Chen, Chen & Lee, 2013; Zhang et al., 2018; Guégan & Renault, 2021). One explanation of this phenomenon may be investors’ tendency to anchor on existing market conditions (e.g. Gurdgiev & O'Loughlin, 2020). Furthermore, it may be related to the reversal effect of returns which is commonly observed in cryptocurrency and emerging stock markets (Dong & Gil-Bazo, 2020; Karalevicius et al., 2018; Ni et al., 2015). This could be interpreted in the following manner. In the short term, investors may overreact to positive market sentiment. However, in the long run, the market value of an investment asset is reverted closer to its fundamental value. This may result from over-optimistic expectations and speculation based on uncertain information.
Another similarity between cryptocurrencies and emerging stock markets is the effect of investor sentiment conditioned by the size of firms and cryptocurrencies. The influence of investor sentiment is most evident in small-size cryptocurrencies and stocks in emerging markets (e.g. Anamika & Subramaniam, 2022; Xu & Zhou, 2018). The reason behind this may be less information between market participants in the case of smaller stocks or cryptocurrencies (Vidal-Tomás et al., 2019; Yao et al., 2014). In effect, it may be difficult to value such investment assets. Therefore, investors may be driven by market sentiment.
Previous evidence has shown that the effect of investor sentiment on some emerging markets and cryptocurrencies is more mixed (e.g. Anamika & Subramaniam, 2022; Chu et al., 2019; Corredor et al., 2015; Daszyńska-Żygadło et al., 2015; Oprea, 2014; Rognone et al., 2020; Wang, Su & Duxbury, 2021). These studies used consumer confidence indexes or news-based measures of investor sentiment. The dissemination of information does not guarantee its consideration by market participants. Moreover, these proxies for investor sentiment may reflect more market-specific (local) than global sentiment. Therefore, it may be assumed that in recent decades, the importance of local market sentiment has been less persistent and more volatile than in the case of global sentiment towards cryptocurrency and emerging stock markets.
Studies conducted in cryptocurrencies and emerging markets use different proxies for investor sentiment, e.g. mutual funds flows, turnover ratio, surveys, trading volume or X-based measures (Anusakumar et al., 2017; Canbaş & Kandır, 2009; Cheema et al., 2020; Chen, Chen & Lee, 2013; Kling & Gao, 2008; Kumari & Mahakud, 2015). However, the effect of social media sentiment towards cryptocurrency and emerging stock markets on investor behaviour is more frequently confirmed (e.g. Dong & Gil-Bazo, 2020; Kraaijeveld & Smedt, 2020; Tan & Tas, 2021; Zhang et al., 2018). This may reflect a very rapid process of information sharing on a given topic between social media users (Guo et al., 2017), especially in the case of popular investment assets. In effect, these communications may be reflected faster in investor reaction (e.g. following others or searching for interesting topics) (Da et al., 2011).
Cultural factors may be more related to the effect of market sentiment on investor behaviour in emerging versus cryptocurrency markets. This may be explained by the fact that cryptocurrencies are not assigned to any specific geographic territory (Statista, 2022). Thus, it is difficult to use heuristics based on cultural tendencies in one country. For example, investors in Middle Eastern markets (e.g. Saudi Arabia, Turkey, Egypt and Kuwait) may treat Ramadan month as a period of positive mood. During Ramadan, the Muslim community may pay more attention to mental health and spend more time in prayer, meeting with friends and improving their well-being. When a large group of investors hold this belief, it may result in higher returns on mutual funds (Białkowski et al., 2013) or profitable investment strategies (Al-Hajieh et al., 2011).
Recent studies mainly focus on the sentiment expressed in influencers’ comments regarding investments or future market policies. This phenomenon may cause excessive emotions among investors and lead to the affect heuristic. In the cryptocurrency and emerging stock markets, researchers confirm that sentiment based on the opinions of so-called influencers drives market dynamics such as volatility, prices and returns (e.g. Bouteska et al., 2023; Cerda & Reutter, 2019; Chahooki et al., 2023; Gjerstad et al., 2021; Guo et al., 2021). However, investors’ reactions to influencers’ tweets may be short-lived in both markets (e.g. Tandon et al., 2021; Machus et al., 2022). This may reflect the speed of incorporating information into prices because information on social media may be shared and included in market prices very quickly. In cryptocurrency and emerging markets, investors likely attempt to preprocess more information noise than in more mature markets. Difficulties in this task may lead them to speculate following tweets with unclear content. In this manner, investors may want to obtain a greater information advantage over other market participants (Huynh, 2022).
Studies report that the sentiment expressed in Donald Trump’s comments on social media is related to volatility in cryptocurrency and emerging stock markets (Huynh, 2021; Nishimura & Sun, 2021). Additionally, it is noted that cryptocurrency prices or returns react to the tone of Elon Musk’s messages on social media (Huynh, 2022; Lennart, 2023; Tandon et al., 2021). The reason behind this may be the popularity of the X (formerly Twitter) accounts belonging to Donald Trump (the 45th president of the United States during the years 2017–2021) and Elon Musk. They are among the top 10 X users by number of followers (Statista, 2024b). In effect, their posts may be spread very quickly among cryptocurrency and emerging stock market communities. However, the content of Donald Trump's comments may differ from the information included in Elon Musk’s posts. It has been noted that Donald Trump's posts may reflect political risk (Huynh, 2021) and mainly past events (Machus et al., 2022). Thus, in emerging markets, investors may use heuristics based on information concerning political risk. However, cryptocurrency price dynamics may be more complex, depending on rumours from influencers' online posts. Thus, cryptocurrency investors seem to be more irrational in the context of market sentiment.
Overall, the effect of investor sentiment on cryptocurrency and emerging stock markets may be similar due to the rapid diffusion of information on social media, investors’ tendency to refer to existing market conditions in the process of making investment decisions, their over-optimistic expectations, and less available information concerning small-cap investment assets. Investors tend to react to the sentiment expressed in influencer comments due to difficulties in preprocessing information noise or to gain an information advantage over others. Differences between the importance of investor sentiment for cryptocurrency and emerging stock markets may result from using proxies for investor sentiment that reflect more local than global perspectives. Moreover, culture may be more influential in shaping the mood of investors in emerging markets when compared to cryptocurrencies.
Loss Aversion and Disposition Effect
Kahneman and Tversky (1979) confirm that investors take more risk in the context of losses than equivalent gains. This may be due to losses ‘hurting’ them more than the pleasure they obtain from equivalent gains. Therefore, investors tend to avoid losses at all costs (Zielonka, 2003). Most studies indicate that cryptocurrency and emerging stock markets investors have different risk aversion in the context of losses and gains (e.g. Al-Mansour, 2020; Haryanto et al., 2020; Ma et al., 2018; Sood et al., 2023; Tekçe et al., 2016). This may be explained by more investors being sensitive to bad versus good news (or bear versus bull market). Investors may perceive that the chance to experience losses is higher during an economic downturn than in the case of a bull market (representativeness heuristic).
A greater aversion to losses than gains may lead to the disposition effect (e.g. an investor’s tendency to hold stock too long when its price has decreased). This phenomenon may be more pronounced in cryptocurrency and emerging stock markets because high market volatility may make investment analysis more difficult. For example, Zhang et al. (2022) noticed a stronger disposition effect when investors enter the stock market during periods of higher market volatility or economic policy uncertainty. Higher market volatility or economic uncertainty may cause difficulties in investment valuation due to the unpredictability of earnings or market policy. In effect, the ambiguity of information may result in investors being inclined to use heuristics referring to market trends.
Some studies contradict the conclusion that the disposition effect in cryptocurrency and emerging stock markets is an all-or-nothing phenomenon. One reason may be the time-varying nature of investors’ risk aversion to losses. For example, Schatzmann and Haslhofer (2023) find that the disposition effect is more present during dynamic changes in market conditions. Thus, studies conducted using surveys (e.g. Alsedrah & Ahmed, 2018) in specific time frames may provide contradicting results to others.
Another factor that may drive the disposition effect in both markets is the share of institutional investors. At the beginning of the existence of the cryptocurrency market, the share of institutional investors in this market was at a lower level (Huang, Lin & Wang, 2022) than in emerging stock markets (e.g. Xiong & Wang, 2023). Studies confirm that with the greater role of professionals in emerging markets, loss aversion is less evident (Bashall et al., 2018; Hincapié-Salazar & Agudelo, 2020). The reason behind this may be that institutional investors have sufficient experience and resources to be less inclined to heuristics than individuals.
In summary, the strength of the disposition effect in cryptocurrency and emerging stock markets may be conditioned by market trends and volatility. Investors in both markets tend to have a greater aversion to negative news than the pleasure they seek from positive information. The higher share of institutional investors in emerging markets may be related to less aversion to losses than in the case of cryptocurrencies due to the knowledge and experience of professionals.
Investor Uncertainty
Kahneman and Tversky (1979) state that economic uncertainty may affect loss aversion. The degree of uncertainty in investing may be a proxy for availability heuristics (i.e. when more information about economic uncertainty is available, the easier it may be to remember it). Previous studies confirm the importance of economic uncertainty for investors in cryptocurrency and emerging stock markets (e.g. Balcilar et al., 2018; Colon et al., 2021; Li et al., 2016; Sum, 2012; Wu et al., 2021). This similarity in investor behaviour may be caused by the perception of these markets as very risky. When economic uncertainty increases, investors may over- or underreact to news, and large capital outflows from more to less risky markets may be observed (Caldara & Iacoviello, 2018).
To date, studies conducted in cryptocurrency and emerging stock markets have mainly used two proxies for investor uncertainty: EPU (Economic Policy Uncertainty) (e.g. Demir et al., 2018; Kirikkaleli, 2020; Tsai, 2017) and geopolitical risk indexes (e.g. Bossman & Gubareva, 2023; Bouri, Gupta & Vo, 2020; Caldara & Iacoviello, 2018). In emerging stock markets, the effect of the EPU index on stock returns is higher when compared to other measures of economic uncertainty, such as geopolitical risk and financial stress indicators (Das et al., 2019; Kannadhasan & Das, 2020). This may reflect the greater information capacity of the EPU index than the geopolitical risk measure. A similar result would be expected in the cryptocurrency market. However, studies in the context of the cryptocurrency market are limited (e.g. Choi & Shin, 2022).
Investors in cryptocurrency and emerging stock markets tend to react differently to changes in economic uncertainty. In emerging markets, most studies report that the changes in EPU are negatively related to stock returns (e.g. Chen et al., 2017; Chen & Chiang, 2020; Chiang, 2021; Donadelli & Persha, 2014; Hoque & Zaidi, 2019; Kang & Ratti, 2015). In the cryptocurrency market, results on the direction of the impact of EPU in some countries are more mixed (e.g. Cheng & Yen, 2020; Shaikh, 2020; Wang et al., 2020; Yen & Cheng, 2021). This discrepancy may be due to market specificity. Only in some cases, investors may treat cryptocurrencies as an alternative to risky assets and independent from government policy (Choi & Shin, 2022). In other words, not all local shocks included in the EPU measure may be important for cryptocurrency market movement due to differences in the importance of the country's policy for this market.
The role of EPU in China may be crucial for the behaviour of cryptocurrency market participants because China was the leader in Bitcoin mining. In effect, most studies confirm the positive association between the behaviour of cryptocurrency returns and EPU in China (Cheng & Yen, 2020; Shaikh, 2020). The relationship between economic policy uncertainty in China and future volatility in the Bitcoin or Litecoin markets is negative (Yen & Cheng, 2021). In emerging stock markets, findings about the impact of EPU in China on other markets are more mixed (Arouri & Roubaud, 2016; Tsai, 2017). For China, this discrepancy in findings may be due to the delayed effect (Kang & Ratti, 2015) or the time-varying impact of economic uncertainty on stock market performance (Xiong et al., 2018). Moreover, these results may reflect the difference between the main components of EPU in emerging markets. Compared to cryptocurrency markets, measures of economic uncertainty in emerging markets may reflect more global than local uncertainty events (e.g. Bouras et al., 2019). Thus, it can be expected that these markets are more strongly driven by global uncertainty (e.g. Caldara & Iacoviello, 2018) than others. For example, institutional investors who may be exposed to international risk are the main stakeholders in Poland (e.g. Statista, 2024a). This may explain the lack of an EPU index for some post-communist European countries.
To summarise, investors in cryptocurrency and emerging stock markets may be driven by economic uncertainty. This may be due to investors perceiving these markets as being more risky during times of turmoil. Investors in emerging markets are more inclined to react to economic uncertainty caused by global events rather than local ones. In the case of cryptocurrencies, they may be more sensitive to economic uncertainty concerning the most important countries for market capital flow.
Investor Attention
Investor attention may lead to poor investment decisions. For example, increasing Google-specific searches or social media posts may strengthen investors’ tendency to succumb to the accessibility heuristic. Thus, investor attention to a given market indicates how much investors overestimate the impact of the most popular information on market prices, causing them to underweight the importance of other market data (informational inefficiency). Social media posts or search engine queries may be the most reliable in measuring investor attention to markets because they provide useful data on the demand for certain information. In other words, an investor’s interest does not have to be manifested by the supply of information on a given topic (Da et al., 2011).
Most empirical investigations confirm that investor attention is related to cryptocurrency and emerging stock market returns (e.g. Akarsu & Süer, 2022; Bakas et al., 2022; Kristoufek, 2013; Polasik et al., 2015; Smales, 2022; Swamy et al., 2019; Zhang et al., 2013). However, some studies have not found a significant relationship between investor attention and stock prices in emerging markets (Chen, 2017). An explanation for this lack of investor attention effect on stock prices in emerging markets could be differences in access to information across emerging markets.
Using the same proxy for investor attention, studies report the opposite reaction of returns to changes in investor attention to cryptocurrency or emerging stock markets (e.g. Bleher & Dimpfl, 2019; Panagiotidis et al., 2019; Shen et al., 2017; Urquhart, 2018; Yang et al., 2021). This discrepancy may be due to the time-varying relationship between investor attention and market characteristics. In cryptocurrency and emerging stock markets, the direction of association between returns and investor attention is reversed in the short term (Smuts, 2019; Ying et al., 2015; Zhang & Wang, 2015). The initial overreaction of investors to information may result from the high uncertainty presented in the media (Philippas et al., 2019). Thus, the impact of investor attention on cryptocurrency and emerging stock market returns may reflect more interest in uncertain information, which is similar to the argument of Szczygielski et al. (2023).
Separate studies find that the effect of investor attention on cryptocurrency and emerging stock market returns is short-lived (Li et al., 2021; Tantaopas et al., 2016). This similarity between markets may be due to the short-term nature of the drivers of investor attention in cryptocurrency and emerging stock markets. These factors include previous returns, volatility, and trading volume (Smales, 2022; Tantaopas et al., 2016; Urquhart, 2018), which are subject to dynamic changes.
It may be assumed that the effect of the popularity of investment on investor behaviour in cryptocurrency or emerging stock markets may not be as long-lived in the case of information from X versus the Google search engine. This may be due to the characteristics of X, which involve emotional reactions to posts published about trendy topics on social media (Li et al., 2021). Furthermore, in some emerging markets, investors may be less inclined to search for information on the Internet (Han et al., 2018). This may lead to a marginal presence of investor attention proxied by X. However, the specificity of cryptocurrencies may ‘require’ access to and knowledge about information technology. Thus, social media may drive investor attention more to cryptocurrency than emerging stock markets.
The above evidence indicates that investor attention is related to cryptocurrency and emerging stock market returns. On the other hand, investor attention is driven by previous returns, volatility and trading volume in cryptocurrency and emerging stock markets. Therefore, this relationship between investor attention and returns is bi-directional. The effect of investor attention on cryptocurrency and stock market returns may change over time due to the time-varying uncertainty presented in media. Investor attention proxied by social media may drive investor behaviour more in cryptocurrency than in emerging stock markets due to market specificity.
The Anchoring Heuristic
Studies on price clustering confirm that investors may be subject to the anchoring heuristic, finding that the prices of investment assets may cluster on round numbers in cryptocurrency and emerging stock markets (e.g. Hu et al., 2017; Lien et al., 2019; Mbanga, 2019; Urquhart, 2017). The round prices may be preferred because they are simpler to recall. However, Hu et al. (2019) do not confirm the validity of this psychological explanation. Another reason for the occurrence of this phenomenon may be reflected by the negotiation hypothesis. According to Harris (1991), price negotiations are costly (e.g. time-consuming). Traders may want to ‘follow’ round numbers because this may facilitate negotiations and move the transaction forward. In other words, it may diminish the set of information that must be exchanged between parties of the trade. Furthermore, investors may be more affected by the negotiation hypothesis during periods of high market sentiment or uncertainty (Baig et al., 2019). When uncertainty increases, then valuations of investments may become more uncertain and make negotiations more difficult.
In some cases, the culture may be more important for the presence of price clustering. For example, stock prices in the Chinese market often end with the digit ‘8’, which is a symbol of prosperity for the Chinese (e.g. Brown & Mitchell, 2008). However, in the case of the cryptocurrency market, it is difficult to assign price clustering to one specific culture due to geographically dispersed investors. Thus, in the cryptocurrency market, price clustering may primarily be driven by trade efficiency (negotiation hypothesis) or psychological factors.
In cryptocurrency and emerging stock markets, studies find that historical price peaks are perceived as important points of reference in making investment decisions (e.g. Sood et al., 2023; Wang & Lien, 2023; Wang, Wu & Wang, 2023). This may be because the prices of cryptocurrencies and stocks in emerging markets are more volatile than in other markets (e.g. Dwyer, 2015; Michelfelder & Pandya, 2005). Thus, it may be difficult to carefully analyse them. Therefore, investors may be subject to heuristics based on information about historical prices (e.g. the distance of prices from all-time highs).
In summary, investors in cryptocurrency and emerging stock markets are subject to the anchoring heuristic based on historical prices and trade efficiency. Culture may be important for the presence of price clustering only in emerging markets. The reason for this may be the cultural diversity of cryptocurrency investors.
Anomalous Behaviour
A specific case of investors’ irrationality is so-called ‘anomalous behaviour’. This investor’s tendency is manifested by the presence of market anomalies (‘ordered’ patterns in returns on investment assets). The extant literature in this area mainly concerns calendar anomalies (López-Martín, 2022), which postulate that returns exhibit divergent behaviour during specific periods, attributable to factors such as investor emotions and feelings (Lopez-Martin, 2022).
The existence of market anomalies in cryptocurrencies and emerging stock markets is well documented. Most studies confirm the presence of the day-of-the-week, month-of-the-year, Monday and holiday effects in both market types (e.g. Aharon & Qadan, 2019; Bley & Saad, 2010; Caporale & Plastun, 2019; Guidi et al., 2011; Harshita et al., 2018; López-Martín, 2022). Furthermore, this phenomenon in cryptocurrencies and emerging stock markets tends to change over time (e.g. Harshita et al., 2018; Khuntia & Pattanayak, 2022; López-Martín, 2022). This may be explained by the occurrence of financial crises (e.g. Al-Khazali, 2014). During these periods, the asset returns may exhibit different patterns than previously, resulting in their lower predictability. Furthermore, as cryptocurrency markets develop, uncovered anomalies may disappear or diminish because they are arbitraged over long periods (Baur et al., 2019). Because the stage of cryptocurrency market development is unclear (e.g. Naz et al., 2023; Qadan et al., 2022), the presence of calendar anomalies may change depending on the market's temporal state.
The religious-related anomalies seem to be the most robust to changes in market conditions (Al-Khazali et al., 2017). Despite this fact, they are found in both markets, e.g. Ramadan effect (Al-Khazali et al., 2017; Białkowski et al., 2012; Lopez-Martin, 2022). One potential explanation for this similarity between cryptocurrency and emerging markets may lie in the common cultural or religious beliefs that characterised most market participants (Bley & Saad, 2010). Thus, Muslim culture and beliefs may affect investment decisions in both markets.
Most studies on cryptocurrencies report a reverse January effect (Kaiser, 2019; Kinateder & Papavassiliou, 2021; Naz et al., 2023) that is opposite to those observed in the case of emerging markets (Bley & Saad, 2010). This discrepancy may be due to the greater role of institutional investors in emerging markets than in cryptocurrencies. In particular, institutional investors in emerging markets may realize tax-selling of stocks at the end of the year and buy them back in January (Al-Khazali et al., 2008).
To conclude, market anomalies are found in cryptocurrency and emerging stock markets. Secondly, they are time-dependent in both market types. This phenomenon may be driven by changing market conditions in both markets. Additionally, the dynamics of anomalies seem to be connected to unclear market development in the case of cryptocurrencies. Religious-related anomalies are pronounced in cryptocurrency and emerging stock markets, highlighting the important role of Muslim beliefs among most of their investors.
Overconfidence Among Investors
Based on surveys, most research papers confirm the overconfidence among investors in cryptocurrency and emerging stock markets (e.g. Bakar & Yi, 2016; Foley et al., 2022; Raut et al., 2020; Sudzina et al., 2023). Other studies adopting proxies of overconfidence (e.g. volume) also document its effect among investors in cryptocurrency and emerging stock markets (e.g. Wen et al., 2022; Zhang et al., 2019). These results may suggest an overestimation of investment abilities by market participants (Huang, Wang, et al., 2022; Philander, 2023), which can be explained by the prospect theory (Kahneman & Tversky, 1979). According to prospect theory, investors feel the pain of losses more than they get pleasure from equivalent gains. When investors experience losses, they may perceive it as a result of coincidence. In the case of gains, investors may assign them to their skills. Thus, investors may overreact to private information about profits and underreact to losses.
Another factor that may be related to overconfidence among investors in cryptocurrency and emerging markets is investor experience (e.g. Aljifri, 2023; Sood et al., 2023). Since a high proportion of cryptocurrency users have little investment experience (Fonseca et al., 2020), they may be more inclined to make behavioural mistakes. This is also the case in emerging markets, such as Saudi Arabia's stock market (Aljifri, 2023). Furthermore, in the context of emerging markets, it has not been confirmed that the managers of funds listed on public markets are overconfident (e.g. Lowies et al., 2014). It may be assumed that managers have sufficient experience and knowledge about investing because they are professionals. Thus, in both markets, investors’ experience may drive overconfidence. However, in the cryptocurrency market, investors’ overconfidence may be more dependent on the future perspective of blockchain technology than the current state of knowledge (Rebman et al., 2022). This may signal the complexity of cryptocurrencies and difficulties in understanding this financial innovation.
During extreme market conditions, overconfidence among investors in cryptocurrency and emerging stock markets may be strengthened. In this line, it has been found that speculative bubbles in cryptocurrency and emerging stock markets are associated with the level of overconfident behaviour among investors because they may underestimate risk and make investment decisions based on the ‘fear of missing out’ (e.g. Abbes, 2013; Guzmán et al., 2021). However, there is no consensus on whether overconfidence was presented among Chinese investors during the COVID-19 pandemic (e.g. Hii et al., 2023; Shrotryia & Kalra, 2023). Hii et al. (2023) argue that the COVID-19 pandemic may have increased Chinese investors’ awareness of risk related to investing in the security market. Thus, investors in emerging stock markets may have learnt from the past. On the other hand, it may reflect the originality of the Chinese stock market, which is dominated by domestic retail investors. Thus, China’s stock market sometimes may behave differently from others.
In conclusion, the level of overconfidence among investors in cryptocurrency and emerging stock markets may be dependent on the strength of aversion to losses when compared to pleasure from equivalent gains, as well as the investor’s experience. However, cryptocurrency investors may be more strongly driven by the future perspective of the market than current knowledge. Overconfidence among investors in cryptocurrency and emerging markets may be affected by extreme market conditions and ‘fear of missing out’. The level of overconfidence among investors is not always presented in some emerging markets due to differences in investors’ awareness of risk. Examples of studies on investor behaviour in cryptocurrency and emerging stock markets are presented in Table 1.
Examples of Studies on Investor Behaviour in Cryptocurrency and Emerging Stock Markets.
Source: Own work.
Discussion and Conclusions
Cryptocurrencies have gained increasing attention from academics, the media and investors. Despite large capital flows between emerging and cryptocurrency markets (e.g. Statista, 2022), as well as some similarities between them, there remains a lack of knowledge about why investors behave irrationally in both markets. The purpose of this study is to compare the factors related to the presence of behavioural biases in the cryptocurrency and emerging stock markets. To this end, studies concerning behavioural biases in the cryptocurrency and emerging stock markets are reviewed, with articles sourced from the Scopus database. The sample includes papers concerning the following investor behaviours: herding behaviour, investor sentiment, the loss aversion and disposition effect, investor uncertainty, anchoring heuristics, investor attention, anomalous behaviour and overconfidence.
This study shows that changes in market conditions may strengthen herding behaviour, disposition effect, price clustering, investor sentiment and uncertainty, anomalous behaviour, and overconfidence among investors in cryptocurrency and emerging stock markets. The existence of time-dependent herding in both markets is consistent with findings by Omane-Adjepong et al. (2021). Additionally, herding behaviour in cryptocurrencies and emerging markets may be most pronounced for the most and the least valuable assets (cryptocurrencies or stocks) due to minimal—or a lack of—information being available about small-cap investments. This research indicates that the effects of investor sentiment change over time and may have short-term implications in cryptocurrency and emerging stock markets. In particular, the sentiment expressed in Donald Trump’s comments on social media may be an important driver of investors’ behaviour in both markets. Another finding is that the effect of EPU indexes on cryptocurrency and emerging stock markets returns may be higher when compared to other measures of investor uncertainty. In the context of investor attention, it has been stated that the impact of X-based measures may not last as long as Google Search Volume indexes. The anchoring heuristic is confirmed in both markets by the presence of a price clustering phenomenon or by referring to historical prices by investors. Finally, it can be concluded that in cryptocurrency and emerging stock markets, investor experience may be an important driver of the level of overconfidence among investors.
This study attempts to answer the question of why investors are irrational in both markets. To this end, several possible explanations are indicated. The common feature of cryptocurrency and emerging stock markets may be high information asymmetry between investors of different assets. It has been shown that minimal or a lack of available information about small-cap emerging stocks or cryptocurrencies (or their poor quality) may reinforce investor sentiment and herding behaviour. Investors in small-cap assets may use information concerning the largest investments due to more information being available about them. Thus, investors may be subject to accessibility heuristics and market sentiment concerning the largest assets, and, in effect, herding behaviour may be uncovered.
The prices of cryptocurrencies and stocks in emerging markets may be more volatile than in other markets (e.g. Dwyer, 2015; Michelfelder & Pandya, 2005). This may cause more difficulties in making efficient investment decisions and strengthen investors’ tendency to rely on heuristics. This high volatility may be connected to economic uncertainty. Economic turmoil may cause more difficulties in the valuation of investments. It is possible that in times of high uncertainty, investors massively exit from risky markets, thereby resulting in high asset price volatility (‘flight to quality’). During periods of high economic uncertainty, investors may feel increasingly uncomfortable and rely on heuristics. They may also overestimate their investment abilities, leading to overconfidence and holding losers in investment portfolios. This could be characteristic of markets with a high share of inexperienced investors, such as cryptocurrencies and the Chinese stock market (high share of retail investors). This is also likely true for other emerging markets since the present study finds that the reaction of investors may strengthen with increased uncertainty (e.g. the COVID-19 pandemic), which, for example, can lead to herding behaviour and the disposition effect.
It can be assumed that institutional investors have enough experience to make more efficient investment decisions. In this line, this paper reports that the disposition effect is less pronounced in markets with a high share of institutional investors. The cryptocurrency market is ‘developing’, with more institutional investors recently entering this market (Huang, Lin & Wang, 2022). Therefore, aversion to losses among cryptocurrency investors may decrease over time. On the other hand, institutional investors in the cryptocurrency market may primarily suffer from herding behaviour during a bull market (this is less likely to be the case in emerging stock markets). This may reflect the momentum investment strategy and the over-optimism of these market participants. Thus, the behaviour of institutional investors may differ from that of inexperienced market participants to some extent, but it is not always rational.
Another potential explanation for investor irrationality in both markets can be found in Behavioural Portfolio Theory (BPT). BPT posits that investors' decisions to allocate capital into investments are driven by various psychological motivations, such as a point of reference (enrichment), avoiding the realisation of losses, and benefiting from a perceived informational advantage (satisfaction). This theory suggests that investors may fail to consider the risk of the entire portfolio (the relationship between assets) when making investment decisions, which may lead them to allocate capital inefficiently (Shefrin & Statman, 2000). For instance, investors may over-allocate into high-risk or speculative assets, such as cryptocurrencies, due to the strong desire to become wealthy in a short period (high reference point). Conversely, they may also have long positions in emerging stocks due to the belief that they have access to private information. The prevalence of such strategies could potentially result in asset price bubbles and market inefficiencies.
Compared to cryptocurrency markets, economic uncertainty measures in emerging markets reflect more global than local uncertainty events (Bouras et al., 2019). It has been noted that EPU in China may have the greatest impact on investors’ behaviour in the cryptocurrency market. The reason for this may be that China was a leader in the context of Bitcoin mining over several years, and investors may follow information about economic policies from the largest cryptocurrency mining territory. However, in emerging markets, investors may be more concerned about global events due to the large share of foreign investors in these economies. Furthermore, in emerging markets, investors may be subject to heuristics based on social media posts created by the most popular politicians in the largest developed markets. Thus, investors in emerging markets may be more sensitive to global political risk than local ones. The dynamics of cryptocurrency prices may be more complex than in emerging stock markets, also depending on rumours from influencers' social media posts. Cryptocurrency investors may follow rumours because they may want to gain more information advantage over other market participants (Huynh, 2022). Following the rule ‘buy the rumour, sell the news’ may only allow investors to gain a short-term advantage. Nevertheless, cryptocurrency investors seem to be more irrational in the context of social media communication.
The role of global sentiment in cryptocurrencies is more complex than in traditional markets, such as stocks. In principle, global threats such as the ongoing pandemic of the Coronavirus (COVID-19) and geopolitical conflicts should cause increasing fear among investors. This, in turn, may result in capital outflow from both these markets to less risky assets or safe-havens (and vice versa). However, emerging stock markets can be more homogeneously affected by global threats than cryptocurrencies because the economic activities of firms are heavily related to consumer well-being. For instance, an economic downturn may result in decreased savings for consumers. As a result, the demand for some products offered by firms may be lower, which may incline investors to reduce their expectations regarding future firm revenues. Thus, a decline in stock prices may be observed. In contrast, cryptocurrencies may have exhibited price appreciation following both negative and positive news events (Rognone et al., 2020). This phenomenon can be attributed to the less important role of loss aversion among cryptocurrency investors in the investment decision-making process (Sood et al., 2023). Thus, cryptocurrency investors may be uniquely influenced by negative sentiment. Furthermore, during periods of heightened global uncertainty or geopolitical events, investors may seek to acquire cryptocurrencies due to their perceived independence from government policy (Choi & Shin, 2022) or to mitigate the impact of regulatory sanctions if they originate from conflicted regions, e.g. the Russia-Ukraine war (Sharma et al., 2024).
In the context of cryptocurrency trading, the regulatory framework is notably weaker than in the case of equities (Anselmi & Petrella, 2021; Reuters, 2022). Consequently, investors in the cryptocurrency market are exposed to frequent hacking episodes and new regulations (Khuntia & Pattanayak, 2020). This may cause panic among investors, resulting in large declines in cryptocurrency prices, particularly when these events are related to major bitcoin mining regions, e.g. China's ban on cryptocurrency mining in 2021 (Griffith & Clancey-Shang, 2023). Furthermore, in contrast to the regulatory framework in the case of equities, cryptocurrency market participants are less protected by existing regulations (Kokorin, 2023). Thus, cryptocurrency investors may be less cognisant of their investment decisions and potentially more influenced by volatile sentiment than in the context of emerging stock markets. Another issue related to cryptocurrency trading is the difference in fiscal regulation across countries (Coelho et al., 2024). This disparity can motivate investors to reallocate capital from one jurisdiction to another, where more accommodating legislation exists. For instance, in Germany, holding cryptocurrencies for one year is not subject to taxation (Reuters, 2022). This suggests that investors in such a jurisdiction may exhibit less sensitivity to losses (i.e. less aversion to risk) than in other countries. Besides, cryptocurrencies pose a major problem in the context of money laundering, as they allow for anonymised transactions (Naz et al., 2023). Thus, cryptocurrencies can be used to transfer money from illegal activities. This investor behaviour is likely to be less prevalent in stock markets due to their more stringent regulatory oversight.
Social media may be very important for cryptocurrencies because it ensures a global perspective consistent with the diversity of investors. Secondly, communication in English using many emoticons, replies and other interactive actions may be a habit of cryptocurrency market participants. Social media provides the rapid diffusion of information, thereby facilitating real-time investor reactions. This is more important when the market is open 24 hours and highly volatile, which is the case in the cryptocurrency market. Social media may be less popular in emerging stock markets due to regulatory and customary differences in social media usage between countries. Another reason may be that the stock market is less volatile than the cryptocurrency market. Furthermore, equity markets are usually closed at the weekend and open at regular hours, causing less emotion among traders than in the case of cryptocurrencies. Thus, in contrast to cryptocurrencies, emerging stock markets can reflect more economic cycles than speculation. The present study indicates that cultural factors may drive investor sentiment and anchoring bias in some emerging stock markets. Only some cryptocurrency investors may rely on heuristics based on a given cultural factor because they are more geographically dispersed than in the case of emerging stock markets. As a result, the presence of some behavioural biases may not be related to the culture from the perspective of the entire market. A summary of drivers of similarity and differences between the existence of behavioural biases in both markets is presented in Table 2.
Drivers of similarity and differences between the existence of behavioural biases in cryptocurrency and emerging stock markets.
Source: Own work.
The following implications emerge from the conducted literature review. It is evident that in cryptocurrency and emerging stock markets, investors are irrational and subject to heuristics relying on some pieces of available information. According to Fama (1970), markets are informationally efficient in a semi-strong form when prices reflect all publicly available information. Thus, it can be stated that cryptocurrency and emerging stock markets are not informationally efficient. Furthermore, the time-varying investors’ behavioural biases in cryptocurrency and emerging stock markets suggest that the AMH (Lo, 2004) is a proper description of investor behaviour in these markets. This paper also implies that in times of economic turmoil (e.g. COVID-19), investors in emerging and cryptocurrency markets can create profitable investment strategies. Thus, investors should pay attention to the behavioural tendencies of market participants (e.g. herding towards market trends) because they stand to lose or gain from them. Since investors can make significant mistakes when high uncertainty occurs (especially in times of financial crisis), clearer and faster communication about planned actions from policymakers is recommended. Moreover, policymakers could be more careful about the information provided and its quality in the case of small-cap investment assets.
This paper demonstrates that investors in both markets are heavily influenced by behavioural factors, including sentiment, emotions, etc. Therefore, the crucial question is how to manage behavioural biases. In order to mitigate the effects of such biases on the decision-making process, investors may use some tools like algorithmic trading (AT) or machine learning (ML) models. Algorithmic trading involves the automated execution of market transactions through the use of a programmed robot, thereby facilitating faster and less emotional investment decisions. The application of machine earning models in the formulation of investment strategies offers a distinct advantage in that these strategies possess the capacity to adapt to prevailing market conditions (learning algorithms). Furthermore, to improve price forecasts, ML models are commonly employed in conjunction with other tools, such as sentiment analysis programs, which combine data from various sources, e.g. social media, news, and market volume (Fang et al., 2022). However, it should be noted that the utilisation of these tools is not without its limitations. These are the lack of user control inherent to AI-driven investment strategies (‘black boxes’) and the exploitation of similar predictive models. Consequently, in a market where AI-driven investment strategies employing similar models are extensively adopted, unpredictable events may lead to larger negative returns than before (Coinmarketcap, 2025). As Bahoo et al. (2024) have highlighted, financial institutions may be the leaders of AI adoption. Because institutional investors have a greater share in emerging markets compared to cryptocurrencies (Huang, Lin & Wang, 2022; Xiong & Wang, 2023), this is more probable in the former than in the latter case. Conversely, the importance of social media for cryptocurrency prices is greater than for emerging equity markets. Thus, the role of AI-driven investment strategies combined with social sentiment analysis may be more prominent in cryptocurrencies, leading to more efficient investment decisions in this market.
In the literature review, several research gaps have been uncovered. Firstly, the relationship between the adoption of machine learning tools and behavioural biases pronounced among cryptocurrency and emerging stock market investors during different market conditions is not yet fully understood. Moreover, it remains unclear whether the strength of herding behaviour among institutional investors is weaker than that of inexperienced cryptocurrency market participants. Thirdly, the role of investor sentiment and uncertainty, investor attention, the loss aversion and disposition effect, sentiments expressed in influencers’ social media posts, and anchoring heuristics in cryptocurrency and emerging stock markets have not been compared. The necessity for further research in this area is evident, as it may facilitate more effective capital allocation over time. For instance, the finding that during high economic uncertainty or religious events, the effect of investor attention on cryptocurrency returns is stronger than in emerging stock markets could assist investors in formulating more effective investment strategies. Finally, there is a paucity of knowledge regarding the relationship between economic policy uncertainty in developing post-communist countries and cryptocurrency returns. Nevertheless, investors in emerging markets may utilise cryptocurrencies as a means of hedging against economic policy instability (e.g. inflation) in these countries.
Further research could compare drivers of the strength of presented behavioural biases in both markets. Additionally, there is a need to explore the role of machine learning tools in mitigating the irrational behaviour of investors in the cryptocurrency and emerging stock markets. Another promising area of research is the effect of behavioural factors on the changes in market efficiency. The extant literature in this field, concerning cryptocurrencies and emerging stock markets, is regrettably scarce.
Footnotes
Acknowledgements
The author would like to extend gratitude to Professor Jarosław Kubiak, Dr. Szymon Stereńczak and the anonymous referee for their useful suggestions, which contributed to enhancing the manuscript's quality.
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Funding
The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Supported by funds granted by the Minister of Science of the Republic of Poland under the “Regional Initiative for Excellence” Programme for the implementation of the project “The Poznań University of Economics and Business for Economy 5.0: Regional Initiative - Global Effects (RIGE)”.
Declaration of Conflicting Interests
The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Ethical approval and informed consent statements
Ethics approval was not required for this systematic review, as it does not contain any studies involving human or animal participants.
Informed consent was not required for this systematic review, as there are no human participants in this article.
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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.