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Our analysis is targeted at researchers in the fields of economics and finance, and we place emphasis on the incremental contributions of each paper, key research questions, study methodology, main conclusions and data and identification tactics. By focusing on these critical areas, our review seeks to provide valuable insights and guidance for future research in this rapidly evolving and complex field.

This paper conducts a structured literature review (SLR) of Bitcoin-related articles published in the leading finance, economics and accounting journals between 2018 and 2023. Following Massaro et al. (2016), SLR is a method for examining a corpus of scholarly work to generate new ideas, critical reflections and future research agendas. The goals of SLR are congruent with the three outcomes of critical management research identified by Alvesson and Deetz (2000): insight, critique and transformative redefinition.

The present state of research on Bitcoin lacks coherence and interconnectedness, leading to a limited understanding of the underlying mechanisms. However, certain areas of research have emerged as significant topics for further exploration. These include the decentralized payment system, equilibrium price, market microstructure, trading patterns and regulation of Bitcoin. In this context, this review serves as a valuable starting point for researchers who are unacquainted with the interdisciplinary field of bitcoin and blockchain research. It is essential to recognize the potential value of research in Bitcoin-related fields in advancing knowledge of the interaction between finance, economics, law and technology. Therefore, future research in this area should focus on adopting innovative and interdisciplinary methods to enhance our comprehension of these intricate and evolving technologies.

Our review encompasses the latest research on Bitcoin, including its market microstructure, trading behavior, price patterns and portfolio analysis. It explores Bitcoin's market microstructure, liquidity, derivative markets, price discovery and market efficiency. Studies have also focused on trading behavior, investors' characteristics, market sentiment and price volatility. Furthermore, empirical studies demonstrate the advantages of including Bitcoin in a portfolio. These findings enhance our understanding of Bitcoin's potential impact on the financial industry.

This paper investigates share price clustering and its determinants across Nasdaq Stockholm, Copenhagen, Helsinki, and Iceland.

This paper investigates share price clustering and its determinants across Nasdaq Stockholm, Copenhagen, Helsinki, and Iceland. Univariate analysis confirms widespread clustering, notably favouring closing prices ending in zero. Multivariate analysis explores the impact of firm size, price level, volatility, and turnover on clustering.

Univariate analysis confirms widespread clustering, notably favouring closing prices ending in zero. Multivariate analysis explores the impact of firm size, price level, volatility, and turnover on clustering. Results reveal pervasive clustering, strengthening with higher prices and turnover but weakening with larger trade volumes, firm size, and smaller tick sizes. These empirical findings support the theoretical expectations of price negotiation and resolution hypotheses.

The observed clustering presents an opportunity for investors to potentially capitalize on this market anomaly and achieve supra-normal returns.

Price clustering, the phenomenon where certain price levels are traded more frequently, challenges the efficient market hypothesis and has been extensively studied in financial markets. However, the Scandinavian stock markets, particularly those in the Nasdaq Nordic Exchange, remain unexplored in this context.

Although the effects of both news sentiment and expectations on price in financial markets have now been extensively demonstrated, the jointness that these predictors can have in their effects on price has not been well-defined. Investigating causal ordering in their effects on price can further our understanding of both direct and indirect effects in their relationship to market price.

We use autoregressive distributed lag (ARDL) methodology to examine the relationship between agent expectations and news sentiment in predicting price in a financial market. The ARDL estimation is supplemented by Grainger causality testing.

In the ARDL models we implement, measures of expectations and news sentiment and their lags were confirmed to be significantly related to market price in separate estimates. Our results further indicate that in models of relationships between these predictors, news sentiment is a significant predictor of agent expectations, but agent expectations are not significant predictors of news sentiment. Granger-causality estimates confirmed the causal inferences from ARDL results.

Taken together, the results extend our understanding of the dynamics of expectations and sentiment as exogenous information sources that relate to price in financial markets. They suggest that the extensively cited predictor of news sentiment can have both a direct effect on market price and an indirect effect on price through agent expectations.

Even traditional financial management firms now commonly track behavioral measures of expectations and market sentiment. More complete understanding of the relationship between these predictors of market price can further their representation in predictive models.

This article extends the frequently reported bivariate relationship of expectations and sentiment to market price to examine jointness in the relationship between these variables in predicting price. Inference from ARDL estimates is supported by Grainger-causality estimates.

This study aims to explore the relationship between house prices and time-on-market (TOM) in Silicon Valley. Previous findings have been inconclusive due to variations in property characteristics. This paper highlights the discrepancy between listing and selling prices and identifies differences among housing types such as condominiums, detached houses and townhouses based on housing orientations and customer groups. Additionally, this study considers the impact of the COVID-19 pandemic and the Fed’s interest rate policies on the housing market.

The authors analyze 63,853 transactions from the Bay East Board of Realtors’ Multiple Listing Service during 2018 to 2022. The study uses a multiple-stage methodology, including a nonlinear hedonic pricing model, search theory and two-stage least squares method to address concerns relating to endogeneity.

The Silicon Valley housing market shows resilience, with low-end properties giving buyers more bargaining power without significant price drops. High-end properties, on the other hand, attract more attention over time, leading to aggressive bidding and higher final sale prices. The pandemic, despite reducing housing supply, did not dampen demand, leading to price surges. Post-COVID, price correlations with TOM changed, indicating a more cautious buyer approach toward high premiums. The Fed’s stringent monetary policies post-2022 intensified these effects, with longer listing times leading to greater price disparities due to financial pressures on buyers and shifting dynamics in buyer interest.

Results reveal a nonlinear positive correlation between TOM and the price formation process, indicating that the longer a listed property is on the market, the greater the price changes. For low-end properties, TOM becomes significantly negative, while for high-end properties, the coefficient becomes significantly positive, with effects and magnitudes varying by type of dwelling. Moreover, external environmental factors, especially those leading to financial strain, can significantly impact the housing market.

The experience of Silicon Valley is valuable for cities using it as a development model. The demand for talent in the tech industry will stimulate the housing market, especially as the housing supply will not improve in the short term. It is important for government entities to plan for this proactively.

This study examines the impact of outcome uncertainty on the National Football League (NFL) secondary ticket market prices. As a demand-driven market, it is essential to comprehend how resellers respond to outcome uncertainty, one of the consumer demand factors in sports.

Using real-time ticket prices and money lines as a proxy of the probabilities of winning, this study employs a regression analysis and examines 33,554 price observations from the NFL’s secondary ticket market partner, StubHub.

The result shows a positive relationship between outcome uncertainty and secondary market ticket prices, indicating that resellers adjust the prices in response to the level of outcome uncertainty and put more value on games with greater uncertainty. This finding confirms the demand-driven nature of the secondary ticket market, as outcome uncertainty is one of the demand factors in sports.

This study links the uncertainty of outcome hypothesis with secondary ticket market pricing and fills a gap in the literature by providing an important perspective on games with uncertainty in the secondary ticket market. Outcome uncertainty has limited understanding in relation to secondary ticket market pricing despite its relationship with consumer demand. The positive relationship between outcome uncertainty and the ticket prices, grounded in real-time price data and win probability from sport betting markets, enhances our understanding of price determinations in the secondary ticket market.

The recent Russia–Ukraine conflict highlights the geopolitical importance of natural gas, especially in Europe. In this light, this study examines the impact of the Russia–Ukraine conflict on the spread of price bubbles from European natural gas to international energy prices.

The Generalized Supremum Augmented Dickey-Fuller (GSADF) test is employed to detect the occurrence of price bubble episodes while the Dynamic Logit Model is used to examine price bubble contagion between the two markets. Further, a tri-variate VAR model is used to examine the determinants of the price bubble.

The findings reveal multiple bubble episodes in both European natural gas and international energy prices. Further, evidence of bilateral contagion between European natural gas and the international energy market is found. In addition, the Russia–Ukraine conflict triggers price bubble episodes in both markets. Finally, a counterfactual analysis suggests that the conflict increases the bubble contagion from the European natural gas market to the international energy market by about 40%. These findings imply that the Russia–Ukraine conflict is a significant driver of high upside risks to bubble occurrence and subsequent contagion to both European natural gas and international energy prices.

To the best of our knowledge, this study contributes new empirical evidence that the Russian–Ukrainian conflict significantly impacts the spread of price bubbles from the European natural gas market to international energy markets.

In this paper, we provide new evidence to strengthen the stock market overreaction hypothesis by examining a new context that has not been explored before. Our research is inspired by the widely held belief that investor sentiment experiences abrupt changes from optimism to pessimism as the market switches between bull and bear states.

If the stock market overreaction hypothesis is correct, it implies that investors are inclined to become excessively optimistic during bull markets and overly pessimistic during bear markets, resulting in overreaction and subsequent market correction. Consequently, the study first develops two testable hypotheses that can be used to uncover the presence of stock market overreaction with subsequent correction. These hypotheses are then tested using long-term data from the US market.

The study's findings support the hypothesis while also revealing a significant asymmetry in investor overreaction between bull and bear markets. Specifically, our results indicate that investors tend to overreact towards the end of a bear market, and the subsequent bull market starts with a prompt and robust correction. Conversely, investors appear to overreact only towards the end of a prolonged bull market. The correction during a bear market is not confined to its initial phase but extends across its entire duration.

Our study has some limitations related to its focus on investigating stock market overreaction in the US market and analyzing the pattern of mean returns during bull and bear market states. Expanding our study to different global markets would be necessary to understand whether the same stock market overreaction effect exists universally. Furthermore, exploring the relationship between volatility and overreaction during different market phases would be an exciting direction for future research, as it could provide a more complete picture of market dynamics.

Our study confirms the presence of the stock market overreaction effect, which contradicts the efficient market hypothesis. We have observed specific price patterns during bull and bear markets that investors can potentially exploit. However, successfully capitalizing on these patterns depends on accurately predicting the turning points between bull and bear market states.

The results of our study have significant implications for market regulators. Stock market overreactions resulting in market corrections can severely disrupt the market, leading to significant financial losses for investors and undermining investor confidence in the overall market. Further, the existence of overreactions suggests that the stock market may not always be efficient, raising regulatory concerns. Policymakers and regulators may need to implement policies and regulations to mitigate the effects of overreactions and subsequent market corrections.

This paper aims to provide additional support for the stock market overreaction hypothesis using a new setting in which this hypothesis has not been previously investigated.

This paper aims to examine whether China's grain price support policy has distorted the grain market price.

The time-varying differences-in-differences (DID) model is used to study the impact of support policies on grain prices, and it is combined with the event study method to explore the dynamic effects of price support policy. Panel data model is used to study the effect of the price support policy on price formation for national grain market prices. In addition, we apply the smooth transformation (STR) model to verify whether there is a distortion in the transmission of grain prices among different markets in China and from the international market to China’s market.

China’s grain price support policy plays a significant role in rising grain market prices, weakens the decisive role of the market mechanism in the formation of grain prices, hinders the spatial transmission of market price signals and decreases the effect of price transmission from the world market to China’s market.

In order to ensure both the stability of grain production as well as the market stability, and also to ensure that intervention policies do not distort the food market, the minimum purchase price of grain and market regulation policies should be adjusted as follows: (1) price support policy should be shifted to an income support policy and (2) reasonably determine the scale of reserves and implement a grain minimum purchase price policy in limited areas.

Our findings are relevant for understanding the effect of China's grain price support policies on the implementation regions and the price transmission effect, which provide reference experience for developing countries to implement food price policies.

The present study reviews the literature on the history and evolution of investment strategies in the stock market for the period from 1900 to 2022. Conflicts and relationships arising from such diverse seminal studies have been identified to address the research gaps.

The studies for this review were identified and screened from electronic databases to compile a comprehensive list of 200 relevant studies for inclusion in this review and summarized for the cognizance of researchers.

The study finds a coherence to complex theoretical documentation of more than a century of evolution on investment strategy in stock markets, capturing the characteristics of time with a chronological study of events.

There were complications in locating unpublished studies leading to biases like publication bias, the reluctance of editors to publish studies, which do not reveal statistically significant differences, and English language bias.

Practitioners can refine investment strategies by incorporating behavioral finance insights and recognizing the influence of psychological biases. Strategies span value, growth, contrarian, or momentum indicators. Mitigating overconfidence bias supports effective risk management. Social media sentiment analysis facilitates real-time decision-making. Adapting to evolving market liquidity curbs volatility risks. Identifying biases guides investor education initiatives.

This paper is an original attempt to pictorially depict the seminal works in stock market investment strategies of more than a hundred years.

This study aims to analyse the effect of competition on retail fuel prices in a small European Union (EU) country with high market concentration.

The researchers use a panel data set to estimate a fuel price equation that includes supply and demand factors as well as time-fixed effects.

The study finds that more competitors in the local market decrease prices, whereas the high market share of oligopoly brands does not condition this effect. Additionally, independent brands set lower prices than wholesalers, and gas stations located near the borders of almost all neighbouring countries are associated with higher prices.

The study suggests that Slovenia’s retail fuel market maintains competitive pricing despite high oligopolistic shares because of historical regulatory influences that shaped firm behaviour and pricing strategies, along with geographical and economic factors such as Slovenia’s role as a transit country. External competitive pressures from neighbouring countries and high levels of traffic, combined with the remnants of regulatory structures, help prevent market abuses and keep fuel prices lower than in other EU countries.

It also indicates that policy should encourage fiercer competition in the local market by increasing the density of gas stations, especially from independent brands.

These findings may be associated with specific country characteristics. This paper introduces unique findings that shed light on the impact of a small market on competition, with a particular focus on highlighting the effect of oligopolistic brands.