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This study aims to investigate the occurrence of the decoy effect in stock investment decisions based on fundamental analysis.

In this study, the decoy effect was investigated by applying two questionnaires, one of them with the presence of a decoy alternative, to a set of 224 respondents with knowledge of business fundamentals, simulating investment decisions in stocks of companies listed on the Brazilian Stock Exchange. The data analysis was performed using the Fisher's exact test, Student's t-test and ANOVA. The research also aimed to detect a potential relationship between the variables gender, age, degree and professional experience with the type of decision made.

The results pointed to the occurrence of the decoy effect when analysing the general response data. However, such evidence was not confirmed when the sample was analysed by classes (gender, course, age and professional experience). There is no statistical evidence that the decoy effect influences classes.

The recent decoy effect literature is little explored in investment decision-making. This study is unique in examining the decoy effect in investment decisions in the Brazilian context.

By extending Edmans et al.’s (2021) music sentiment measures to the Vietnam market, the authors aim to investigate the impacts of music sentiment on stock market returns and volatility.

The authors adopted Edmans et al.’s (2021) music-based sentiment to proxy for investor mood. The current study uses linear regression analysis.

The authors find that music sentiment is significantly and positively related to both stock returns and stock market volatility. The authors also show that music sentiment has a contagious effect: Global music sentiment and those in the United States, France and Hong Kong are significant drivers of the Vietnamese stock market. The authors also examine the effect on different industry returns and find that returns on stocks of firms in the communication services, consumer discretionary, consumer staples, energy, financials, healthcare, real-estate, information technology and utility sectors are significantly related to music sentiment. In addition to valence, the authors find that other Spotify audio features can be used to quantify music sentiment.

This study contributes to the behavioral finance literature that focuses on investor sentiment. The authors address this topic in Vietnam using high-frequency data.

This paper aims to provide empirical evidence for using the prospect theory (PT) basic assumptions in the Argentine context. Mainly, this study analysed the financial decision-making process in students of the economic-administrative academic area of two universities, one public and one private, in Córdoba.

The analysis methodology included (1) the descriptive statistical analysis to identify the presence of the certainty, reflection and isolation effects; (2) the construction of a set of indicators on the application of the PT; (3) the chi-squared independence test, to determine if the decisions made are independent of the degree course taken; (4) the non-parametric Kruskal–Wallis test, to determine if the decisions made by individuals vary according to the semesters taken or students' levels of progress; and (5) the non-parametric Mann–Whitney test, to determine if there are differences between the decisions made by men and women.

The empirical results provided evidence on the effects of certainty, reflection and isolation in both universities, concluding that the study participants make financial decisions in situations of uncertainty based more on PT than on expected utility theory.

This study contributes to the empirical evidence in a different Latin-American context, confirming that individuals make financial decisions based on the PT independently of their degree course, semester, level of advance, gender or the kind of university where they belong (public or private).

Investors' behavior in financial markets is often under the influence of various psychological and cognitive factors, as well as personality characteristics. This research explores which behavioral factors and personality traits affect investment decisions and, consequently, investment performance.

A survey analysis was conducted on a sample of 310 investors in Croatia. Partial least squares structural equation modeling was used to obtain the results.

Overconfidence heuristic, prospect theory elements, emotions and stability and plasticity (as big two personality dimensions) positively affect investment decisions, while herding has a negative effect. Investment decisions, observed through the preference for long-term investments, consequently have a positive effect on the investment performance satisfaction.

This research proposes a unique comprehensive model of the effect of numerous different cognitive and psychological behavioral factors on investment decisions. Furthermore, the influence of investment decisions on investment performance is observed simultaneously. Understanding human behavior based on their personal characteristics can help investors to make better investment decisions. Advisors can learn from human behavior and guide their clients in the right direction when it comes to stock investment. Scientists will be able to replicate the model with other data and make comparative analyses.

This paper aims to investigate feedback trading and autocorrelation behavior in the cryptocurrency market.

It uses the GJR-GARCH model to investigate feedback trading in the cryptocurrency market.

The findings show a negative relationship between trading volume and autocorrelation in the cryptocurrency market. The GJR-GARCH model shows that only the USD Coin and Binance USD show an asymmetric effect or leverage effect. Interestingly, other cryptocurrencies such as Ethereum, Binance Coin, Ripple, Solana, Cardano and Bitcoin Cash show the opposite behavior of the leverage effect. The findings of the GJR-GARCH model also show positive feedback trading for USD Coin, Binance USD, Ripple, Solana and Bitcoin Cash and negative feedback trading for Ethereum and Cardano only.

This paper contributes to the literature by extending Sentana and Wadhwani (1992) to explore the presence of feedback trading in the cryptocurrency market using a sample of the most active cryptocurrencies other than Bitcoin, namely, Ethereum, USD coin, Binance Coin, Binance USD, Ripple, Cardano, Solana and Bitcoin Cash.

The authors explore how the sentiment expressed by emojis in comments on stocks is associated with the stocks' subsequent returns.

By applying our own analyzer, the authors find a sentiment effect of emojis on stocks returns separately to the plain text-expressed sentiment in Reddit posts about meme stocks such as Gamestop during the Covid-19 pandemic.

The authors document that a one-standard deviation change in emoji sentiment magnitude measured as the quantity of positive emoji sentiment posts over the previous hour is associated with an 0.06% (annualized: 109.2%) one-hour abnormal stock return compared to a mean of 0.03% (annualized: 54.6%). If the stock exhibits a higher intra-hour volatility, a proxy for uninformed noise trading, this relation is more pronounced and even stronger compared to stock return's relation to plain text sentiment.

The authors are not able to show causation that is open to future research. It also remains an open question how emojis impact market price efficiency.

Emojis are positively related to stock returns in addition to plain text-expressed content if they are discussed heavily by retail investors in Internet boards such as Reddit.

Shared emotions expressed by emojis might have an influence on how disconnected individuals make homogeneous decisions. This argument might explain our found relation of emojis and stock returns.

So, the study findings provide empirical evidence that emojis in Reddit posts convey information on future short-term stocks returns distinct from information expressed in plain text, in the case of volatile stocks, with a higher magnitude.

This study aims to investigate the interconnectedness across the risk appetite of distinct investor types in Borsa Istanbul. This study also examines the causal impact of global implied volatility indices on the risk appetite of these investor groups.

The authors use a novel time-varying frequency connectedness framework of Chatziantoniou et al. and a new time-varying Granger causality test with a recursive evolving procedure by Shi et al. over June 2008 and July 2022.

The results show a high level of interconnectedness across the risk appetite of different investor types. The sizable spillovers to domestic types of investors either occur from professional or foreign investors, indicating the long-term dominant effect of foreign and more qualified investors on the domestic investors in Borsa Istanbul. The authors provide significant evidence of causality from the global implied volatility to the Borsa Istanbul risk appetite indices, which are getting stronger after the COVID-19 outbreak.

Unlike the previous studies, the authors analyze the risk appetite sub-indices of various types of investors to reveal behavioral distinctions and interconnectedness across them. The authors use a novel econometric framework to assess investors’ risk appetite in different investment horizons in a time-varying system. Together with volatility index (VIX), the authors also use volatilities of oil (OVX), gold (GVZ) and currency (EVZ), considering the information transmission not only from stock markets but also energy, metals and currency markets. The present data set covers significant financial crises, socioeconomic events and the COVID-19 outbreak.

This paper aims to recognize the direct influence of audit quality (AQ) on earnings management practices (EMP) and company value (CV), as well as the mediating role of EMP in the link between AQ and CV. It presents new factual proof from the Jordanian market, which is still in its early stages.

A pattern of 43 service firms listed on the Amman Stock Exchange (ASE) was collected for the timeframe (2012–2019), giving an amount of 344 firm-year observances. The data was collected from the annual reports extracted from the ASE’s database and tested with panel data analysis.

The results show that audit firm industry specialization positively affects EMP while its size and tenure do not, which implies that its industry specialization does not restrict earnings management but rather leads to an increase in opportunistic behaviors. Audit firm size and audit firm industry specialization positively affect CV, whilst audit firm tenure does not. Additionally, the findings indicate that EMP negatively affect CV, and EMP act as a mediator for the AQ–CV nexus.

Stakeholders can use the findings to enhance the capacity and effectiveness of Jordan’s fiscal market. For example, our results will boost policymakers’ eagerness to institute suitable statutes improving Jordan’s fiscal market performance. Besides, the results can assist existing and potential investors make sound adjudication by using AQ proxies and earnings management as signals to predict future company’s value.

The paper differentiates itself from previous papers through initiating a new proposed model by exploring the role of earnings management as a mediator in the nexus between AQ and CV by presenting new factual proof from the Jordanian market.

This work aims at designing an indicator for detecting and forecasting price volatility and speculative bubbles in three markets dealing with agricultural and soft commodities, i.e. Intercontinental Exchange Futures market Europe, (IFEU), Intercontinental Exchange Futures market United States (IFUS) and Chicago Board of Trade (CBOT). This indicator, designed as a demand/supply odds ratio, intends to overcome the subjectivity limits embedded in sentiment indexes as the Bull and Bears ratio by the Bank of America Merrill Lynch.

Data evidence allows for the parameter estimation of a Jacobi diffusion process that models the demand share and leads the forecast of speculative bubbles and realised volatility. Validation of outcomes is obtained through the dynamic regression with autoregressive integrated moving average (ARIMA) error. Results are discussed in comparison with those from the traditional generalized autoregressive conditional heteroskedasticity (GARCH) models. The database is retrieved from Thomson Reuters DataStream (nearby futures daily frequency).

The empirical analysis shows that the indicator succeeds in capturing the trend of the observed volatility in the future at medium and long-time horizons. A comparison of simulations results with those obtained with the traditional GARCH models, usually adopted in forecasting the volatility trend, confirms that the indicator is able to replicate the trend also providing turning points, i.e. additional information completely neglected by the GARCH analysis.

The authors' commodity demand as discrete-time process is capable of replicating the observed trend in a continuous-time framework, as well as turning points. This process is suited for estimating behavioural parameters of the agents, i.e. long-term mean, speed of mean reversion and herding behaviour. These parameters are used in the forecast of speculative bubbles and realised volatility.

Peer-to-peer (P2P) lending facilitates direct online lending and aims to provide financial inclusion and investment returns. Lender goals range from for-profit to pro-social and objective information is limited, which highlights the need to examine heuristics.

This study examines 1,347 lending decisions by finance students on a mock P2P site. Testimonials were used to randomly condition the financially literate lenders towards for-profit or pro-social decision-making. Each investor evaluated three loans. The three loan applications were identical except for a female or male headshot (vs an icon) and random reports of 50% funding for the female or male loan in 3 days (vs 11 days for opposite gender and 7 for icon). Previous research surveys students on a mock platform (Gonzalez, 2020) and reports similar heuristics and lifelike decisions in student and general population samples (Gonzalez and Komarova, 2014).

Lenders randomly conditioned towards pro-social lending state lower trust in borrowers. However, pro-social investors state lower risk in P2P lending and higher financial literacy. Second, pro-social investors are more confident when lending to borrowers highly trusted by other lenders, especially if the popular loan applicant is female. Third, pro-social conditioning increases lending to male applicants when the popular loan applicant is female. Fourth, pro-social investors who have experienced financial trauma have greater confidence in bad loan recovery.

This is the first study of heuristics in pro-social vs for-profit P2P lending. In addition, it shows that testimonials can effectively condition lending goals and affect trust and risk perceptions.