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The Indian stock market can be tricky when there's trouble in the world, like wars or big conflicts. It's like trying to read a secret message. We want to figure out what makes investors nervous or happy, because their feelings often affect how they buy and sell stocks. We're building a tool to make prediction that uses both numbers and people's opinions.

Hybrid approach leverages Twitter sentiment, market data, volatility index (VIX) and momentum indicators like moving average convergence divergence (MACD) and relative strength index (RSI) to deliver accurate market insights for informed investment decisions during uncertainty.

Our study reveals that geopolitical tensions' impact on stock markets is fleeting and confined to the short term. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.47% accuracy in forecasting stock market values during such events.

To the best of the authors' knowledge, this model's originality lies in its focus on short-term impact, novel data fusion and high accuracy. Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of geopolitical tensions on market behavior, a previously under-researched area. Novel data fusion: Combining sentiment analysis with established market indicators like VIX and momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods. Advanced predictive accuracy: Achieving the prediction accuracy (98.47%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

The study aims to explore how integrating recent fundamental values (RFVs) from conventional accounting studies enhances the accuracy of a machine learning (ML) model for predicting stock return movement in Indonesia.

The study uses multilayer perceptron (MLP) analysis, a deep learning model subset of the ML method. The model utilizes findings from conventional accounting studies from 2019 to 2021 and samples from 10 firms in the Indonesian stock market from September 2018 to August 2019.

Incorporating RFVs improves predictive accuracy in the MLP model, especially in long reporting data ranges. The accuracy of the RFVs is also higher than that of raw data and common accounting ratio inputs.

The study uses Indonesian firms as its sample. We believe our findings apply to other emerging Asian markets and add to the existing ML literature on stock prediction. Nevertheless, expanding to different samples could strengthen the results of this study.

Governments can regulate RFV-based artificial intelligence (AI) applications for stock prediction to enhance decision-making about stock investment. Also, practitioners, analysts and investors can be inspired to develop RFV-based AI tools.

Studies in the literature on ML-based stock prediction find limited use for fundamental values and mainly apply technical indicators. However, this study demonstrates that including RFV in the ML model improves investors’ decision-making and minimizes unethical data use and artificial intelligence-based fraud.

The primary objective of this research is to explore the potential of utilizing Global Consciousness Project (GCP) data as a tool for understanding and predicting market sentiment. Specifically, the study aims to assess whether incorporating GCP data into econometric models can enhance the comprehension of daily market movements, providing valuable insights for traders.

This study employs econometric models to investigate the correlation between the Standard & Poor's 500 Volatility Index (VIX), a common measure of market sentiment and data from the GCP. The focus is particularly on the largest daily composite GCP data value (Max[Z]) and its significant covariation with changes in VIX. The research employs interaction terms with VIX and daily returns from global markets, including Europe and Asia, to explore the relationship further.

The results reveal a significant relationship with the GCP data, particularly Max[Z] and VIX. Interaction terms with both VIX and daily returns from global markets are highly significant, explaining about one percent of the variance in the econometric model. This finding suggests that variations in GCP data can contribute to a better understanding of market dynamics and improve forecasting accuracy.

One limitation of this study is the potential for overfitting and P-hacking. To address this concern, the models undergo rigorous testing in an out-of-sample simulation study lasting for a predefined one-year period. This limitation underscores the need for cautious interpretation and application of the findings, recognizing the complexities and uncertainties inherent in market dynamics.

The study explores the practical implications of incorporating GCP data into trading strategies. Econometric models, both with and without GCP data, are subjected to an out-of-sample simulation where an artificial trader employs S&P 500 tracking instruments based on the model's one-day-ahead forecasts. The results suggest that GCP data can enhance daily forecasts, offering practical value for traders seeking improved decision-making tools.

Utilizing data from the GCP is found to be advantageous for traders as noteworthy correlations with market sentiment are found. This unanticipated finding challenges established paradigms in both economics and consciousness research, seamlessly integrating these domains of research. Traders can leverage this innovative tool, as it can be used to refine forecasting precision.

Fueled by the soaring popularity of the digital medium, consumers are increasingly relying on dynamic images to inform their decisions. However, little is known about how changes in the presentation of movement impacts these decisions. The purpose of this paper is to document whether and how movement speed–a fundamental characteristic of dynamic images in the digital medium–influences consumers' risk judgments and subsequent decisions.

Three experimental studies investigate the impact of movement speed displayed in the digital medium, focusing on different risk-laden domains including health (pilot study), gambling (Study 1) and stock market decisions (Study 2).

The authors find that faster movement speed displayed in the digital medium elevates consumers’ feelings of risk and elicits cautionary actions in response. The authors reveal a mechanism for this effect, showing that faster movement reduces feelings of control over outcomes, which predicts greater feelings of risk.

Future work could expand upon these findings by systematically examining whether certain individuals are more susceptible to movement speed effects in the digital medium. Research could also investigate whether different ways of experiencing movement speed (e.g. physical movement) similarly influence risk judgments and whether movement speed can have positive connotations outside of risky domains.

The authors offer important insights to marketing practitioners and public policymakers seeking to guide consumers’ judgments and decisions in risk-laden contexts through the digital medium.

By showing how movement speed alters judgments in risk-laden contexts, the authors contribute to literature on risk perception and the growing body of literature examining how moving images shape consumers’ behaviors.

This study aims to provide empirical evidence on the return and volatility spillover structures between Bitcoin, Fintech stocks and Asian-Pacific equity markets over time and during different market conditions, and their implications for portfolio management.

We use Time-varying parameter vector autoregressive and quantile frequency connectedness approach models for the connectedness framework, in conjunction with Diebold and Yilmaz’s connectivity approach. Additionally, we use the minimum connectedness portfolio model to highlight implications for portfolio management.

Regarding the uncertainty of the whole system, we show a small contribution from Bitcoin and Fintech, with a higher contribution from the four Asian Tigers (Taiwan, Singapore, Hong Kong and Thailand). The quantile and frequency analyses also demonstrate that the link among assets is symmetric, with short-term spillovers having the largest influence. Finally, Bitcoins and Fintech stocks are excellent diversification and hedging instruments for Asian equity investors.

There is an instantaneous, symmetric and dynamic return and volatility spillover between Asian stock markets, Fintech and Bitcoin. This conclusion should be considered by investors and portfolio managers when creating risk diversification strategies, as well as by policymakers when implementing their financial stability policies.

The study’s major contribution is to analyze the volatility spillover between Bitcoin, Fintech and Asian stock markets, which is dynamic, symmetric and immediate.

This study aims to identify the financial attributes of non-fungible tokens (NFTs) as safe havens, hedges or diversifiers against traditional (stock indices, foreign exchange, gold and government bonds) and digital (Bitcoin and Ethereum) assets.

The quantile via moments was utilized, and the data spanned from 20 September 2021 to 31 January 2022. The authors incorporated feasible generalized least squares (FGLS) and difference-generalized method of moments (diff-GMM) as the robustness check.

Overall, NFTs offer strongly safe havens, hedging and diversifier attributes against cryptocurrencies, while weak properties for traditional assets. The specific findings are: (1) Bored Ape Yacht Club (BAYC) serves as a strong hedge for Bitcoin during market rise; (2) Mutant Ape Yacht Club (MAYC) serves as a strong safe haven against Bitcoin during market bull; (3) Crypto punk (CP) provides strong safe havens properties for gold during market turmoil while serving as a strong hedge against gold and Bitcoin on average and (4) the three blue-chip NFTs are powered by Ethereum blockchain, thus serving as a diversifier against Ethereum.

Bitcoin investors are suggested to include NFTs in their investment portfolio to mitigate the losses when Bitcoin falls. Meanwhile, the inclusion of crypto punk is advised for risk-averse investors who invest in gold. NFTs are powered by the Ethereum blockchain, indicating co-movement among them and thus, serve as diversifiers. Policymakers and regulators are suggested to watch closely over NFTs' great development and restructure the existing policies and thus, stabilization of asset markets can be achieved.

The originality aspects are: (1) focusing on the three blue-chip NFTs (i.e. BAYC, MAYC and CP) that are categorized as the largest NFTs by floor market capitalization; (2) testing the NFT attributes (safe havens, hedges or diversifiers) against traditional and digital assets, a.k.a., cryptocurrencies and (3) panel setting on 14 countries with the highest NFT users.

This study aims to analyze the entrepreneurial intention (EI) manifested by potential entrepreneurs for LGBT (lesbian, gay, bisexual, and transgender) and traditional markets, thereby tracing a comparative EI for both markets. The intention is to understand the vision of potential future entrepreneurs related to markets focused on the LGBT public (i.e. if entrepreneurs perceive this market as an option for future business).

Using a quantitative research design, data were collected from a sample of 157 students in Brazil and analyzed by applying structural equation modeling.

This study primarily identified a difference between EI when comparing the focus on LGBT and traditional markets. Results show that the impact of personal attitude is significantly higher on EI for general markets (all markets) than for markets focused on LGBT audiences. Furthermore, the impact on entrepreneurship for traditional markets is generally significantly lower than for the LGBT market.

The study explored the EI for LGBT markets, which has not been studied extensively. It aims to gain a better understanding of various aspects that may influence the decision-making and perceptions of potential future entrepreneurs. Furthermore, the study compares traditional and LGBT audiences, providing valuable insights for potential future entrepreneurs in both scenarios. This comparison is a unique contribution to the literature and contributes to important analyses and debates.

The study aims to identify and understand the underlying behavioral tendencies and motivations influencing investor sentiments and examines the relationship between these underlying factors and investment decisions during the COVID-19-induced financial risks.

The study uses the primary data and information collected from 300 Indian retail equity investors using a nonprobability sampling technique, specifically purposive and snowball sampling. This research uses the insights from Phuoc Luong and Thi Thu Ha (2011) and Shefrin (2002) to delineate behavioral factors influencing investment decisions. Structural equation modeling estimates the causal relationship between underlying behavioral factors and investment decisions during the COVID-19-induced financial risks.

The study establishes that the “Regret Aversion,” “Gambler’s Fallacy” and “Greed” significantly influence investment decisions, and provide a comprehensive understanding of how psychological motivations shape investor behavior. Notably, “Mental Accounting” and “Conservatism” exhibit insignificance, possibly influenced by the unique socioeconomic context of the pandemic. The research contributes to 35% of variance understanding and prompts the researchers and policymakers to tailor investment strategies aligned to these behavioral tendencies.

The findings hold policy implications for investors and policymakers and provide tailored recommendations including investor education programs and regulatory measures to ensure a resilient and informed investment community in the context of India's evolving financial landscapes.

Theoretically, behavior tendencies and motivations have been strongly linked to investment decisions in the stock market. Yet, empirical evidence on this relationship is limited in developing countries where investors focus on risk management. To the best of the authors’ knowledge, this study is among the first to document the influence of underlying behavioral tendencies and motivation factors on investment decisions regarding retail equity in a developing country.

The authors investigate the impact of the US stock market on the economies of the BRICS and major industrialized economies (G7).

The authors construct the world economy and the vulnerability between economies using three economic integration variables: bilateral trade, bilateral direct investment and bilateral equity positions. Global vector autoregressive (GVAR) empirical studies usually adopt trade integration to estimate models. The authors complement these studies by using bilateral financial flows.

The authors summarize the results in four points: (1) financial integration variables increase the effect of the US stock market on the BRICS and G7, (2) the US shock produces similar responses in these groups regarding industrial production, stock markets and confidence but different responses regarding domestic currencies: in the BRICS, the authors detect appreciation of the currencies, while in the G7, the authors find depreciation, (3) G7 stock markets and policy rates are more sensitive to the US shock than the BRICS and (4) the estimates point out to heterogeneities such as the importance of industrial production to the transmission shock in Japan and China, the exchange rate to India, Japan and the UK, the interest rates to the Eurozone and the UK and confidence to Brazil, South Africa and Canada.

The results reinforce the importance of taking into account different levels of economic development.

The authors construct the world economy and the vulnerability between economies using three economic integration variables: bilateral trade, bilateral direct investment and bilateral equity positions. GVAR empirical studies usually adopt trade integration to estimate models. The authors complement these studies by using bilateral financial flows.

The history of online trading began in the 1960s with the emergence of electronic communication networks, which allowed the electronic execution of trades outside traditional exchanges. The internet revolution led to the development of online brokerage platforms such as E*Trade and Schwab, enabling non-institutional investors to participate in the digital trading revolution. These platforms have evolved to serve the retail investor market, eventually adapting to mobile-first and commission-free models, significantly lowering the barriers to entry for financial markets. Platforms like Robinhood and other fintech firms have rapidly gained market share by offering services and products previously unavailable, such as commission-free trades, mobile trading, and novel products such as fractional shares and cryptocurrency investing. This chapter provides an overview of the history of online trading. It also introduces several new developments in fintech and the online trading industry and discusses various controversies and future implications of new technologies.