Search Results (32)

Cryptocurrency markets are characterized by high volatility, nonlinear dependencies, and limited transparency, making short-term forecasting particularly challenging for both researchers and practitioners. To address these complexities, this study introduces a Temporal Fusion Transformer (TFT)-based forecasting framework that integrates on-chain and technical indicators to improve predictive performance and inform tactical trading decisions. By combining multi-source features—such as Spent Output Profit Ratio (SOPR), Total Value Locked (TVL), active addresses (AA), exchange net flow (ENF), Realized Cap HODL Waves, and the Crypto Fear and Greed Index—with classical signals like Relative Strength Index (RSI) and moving average convergence divergence (MACD), the model captures behavioral patterns, investor sentiment, and price dynamics in a unified structure. Five major cryptocurrencies—BTC, ETH, USDT, XRP, and BNB—serve as the empirical basis for evaluation. The proposed TFT model is benchmarked against LSTM, GRU, SVR, and XGBoost using standard regression metrics to assess forecasting accuracy. Beyond prediction, a signal-based trading strategy is developed by translating model outputs into daily buy, hold, or sell signals, with performance assessed through a comprehensive set of financial metrics. The results suggest that integrating attention-based deep learning with domain-informed indicators provides an effective and interpretable approach for multi-asset cryptocurrency forecasting and real-time portfolio strategy optimization. Full article

In recent years, corporate ESG performance has been widely incorporated into investment decisions and capital allocation considerations, becoming a focal point and hot topic for research by governments and organizations worldwide. However, due to various reasons, significant discrepancies have emerged in ESG ratings for the same company across different institutions, and this growing divergence in ESG ratings has increasingly drawn the attention of scholars. Studying the differences in ESG (environmental, social, and corporate governance) ratings is of great significance. This not only helps to understand the root causes of differences, improve the objectivity, consistency, and comparability of ratings, but also helps users better understand the meaning and limitations of rating results. It is beneficial for investors to understand the focus of different ratings and develop more effective investment strategies. It can promote rated companies to improve the quality and transparency of ESG-related information disclosure. It can also provide a reference for regulatory agencies and policymakers, identify market failures and potential risks, and promote the development of more unified standards and frameworks. At the same time, this study can also promote the in-depth development of relevant academic research and theories. Based on this, this study systematically reviews the relevant literature on ESG rating divergence, focusing on its existence, causes, influencing factors, and impacts. The study finds that, in addition to the widespread existence of rating divergence in corporate ESG performance, scholars also disagree on the measurement and methods of this divergence. The reasons for rating divergence are mainly that ESG is a qualitative indicator; top-level design, intermediate calculations, and bottom-level data collection across multiple stages exacerbate divergence; and controversies in practice further deepen divergence, among others. The influencing factors and impact effects of ESG rating divergence are diverse. Given the existence of ESG rating divergence, all parties should treat ESG ratings with caution. This paper offers corresponding recommendations and looks forward to the future, providing a foundation for subsequent research. Full article

Amidst global economic fluctuations and escalating geopolitical risks, gold futures, as a pivotal safe-haven asset, demonstrate price dynamics that directly impact investor decision-making and risk mitigation effectiveness. Traditional forecasting models face significant limitations in capturing long-term trends, addressing abrupt volatility, and mitigating multi-source noise within complex market environments characterized by nonlinear interactions and extreme events. Current research predominantly focuses on single-model approaches (e.g., ARIMA or standalone neural networks), inadequately addressing the synergistic effects of multimodal market signals (e.g., cross-market index linkages, exchange rate fluctuations, and policy shifts) and lacking the systematic validation of model robustness under extreme events. Furthermore, feature selection often relies on empirical assumptions, failing to uncover non-explicit correlations between market factors and gold futures prices. A review of the global literature reveals three critical gaps: (1) the insufficient integration of temporal dependency and global attention mechanisms, leading to imbalanced predictions of long-term trends and short-term volatility; (2) the neglect of dynamic coupling effects among cross-market risk factors, such as energy ETF-metal market spillovers; and (3) the absence of hybrid architectures tailored for high-frequency noise environments, limiting predictive utility for decision support. This study proposes a three-stage LSTM–Transformer–XGBoost fusion framework. Firstly, XGBoost-based feature importance ranking identifies six key drivers from thirty-six candidate indicators: the NASDAQ Index, S&P 500 closing price, silver futures, USD/CNY exchange rate, China’s 1-year Treasury yield, and Guotai Zhongzheng Coal ETF. Second, a dual-channel deep learning architecture integrates LSTM for long-term temporal memory and Transformer with multi-head self-attention to decode implicit relationships in unstructured signals (e.g., market sentiment and climate policies). Third, rolling-window forecasting is conducted using daily gold futures prices from the Shanghai Futures Exchange (2015–2025). Key innovations include the following: (1) a bidirectional LSTM–Transformer interaction architecture employing cross-attention mechanisms to dynamically couple global market context with local temporal features, surpassing traditional linear combinations; (2) a Dynamic Hierarchical Partition Framework (DHPF) that stratifies data into four dimensions (price trends, volatility, external correlations, and event shocks) to address multi-driver complexity; (3) a dual-loop adaptive mechanism enabling endogenous parameter updates and exogenous environmental perception to minimize prediction error volatility. This research proposes innovative cross-modal fusion frameworks for gold futures forecasting, providing financial institutions with robust quantitative tools to enhance asset allocation optimization and strengthen risk hedging strategies. It also provides an interpretable hybrid framework for derivative pricing intelligence. Future applications could leverage high-frequency data sharing and cross-market risk contagion models to enhance China’s influence in global gold pricing governance. Full article

The rapid advancement of digital technology has transformed how investors gather financial information, with platforms like Google Trends providing valuable insights into investor behavior through the Google Search Volume Index (GSVI). While the relationship between the GSVI and market behavior has been explored in developed markets, its application in emerging markets like Pakistan remains underexplored. This study investigates how investor attention, measured by the GSVI, influences market volatility, liquidity, and stock price movements in the Pakistan Stock Exchange (PSX), using weekly data from the KSE-100 Index between 2019 and 2024. The findings reveal that the GSVI significantly impacts market volatility and liquidity, particularly in retail-driven markets with high information asymmetry. Additionally, this research shows that the GSVI is a reliable predictor for stock price fluctuations, with heightened investor attention correlating with increased market activity. Despite the limitations of the GSVI in fully capturing investor sentiment, this study contributes to behavioral finance literature by demonstrating the role of digital information flows in shaping market behavior in emerging markets. It offers actionable insights for investors, financial institutions, and policymakers in Pakistan while suggesting areas for future research in applying the GSVI to global contexts and exploring alternative proxies for investor sentiment in emerging economies. Full article

Despite calls to counter unsustainable urbanization, dense, concrete, and overheated urban spaces remain a reality. However, ideological assumptions and priorities related to creating livable urban spaces are only a part of a broader chain of actions that is the construction investment process (CIP), and its participants (Theoreticians, Investors, Designers, Contractors, Controllers, and Users) can play a significant role in addressing negative urban changes through their attitudes. The starting point for analyzing attitudes was Jung’s theory, which led to identifying key attitudes: Thinking, Feeling, Perception, Intuition, Creativity, and Equilibrium. This study aimed to determine the values reflected in CIP participants’ attitudes, identified through individual in-depth interviews with selected experts. Statements were divided into phrases referring to specific features or values and were subjected to qualitative (and limited quantitative) content analyses, supplemented by thematic and sentiment analyses. Feeling emerged as the most frequently mentioned value, followed by Thinking. Designers and Investors attracted the most attention, while Designers and Users were seen as the most controversial groups due to the values and stances they represented. Contractors (especially general and specialized) received the most favorable evaluations. This study concludes with a detailed characterization of the attitudes and values of the CIP participants, highlighting their contributions to the CIP. Full article

Saudi Vision 2030, a strategic framework aimed at diversifying the economy and enhancing societal inclusivity, aligns with the UN’s Sustainable Development Goals (SDGs) by promoting gender equality and sustainable economic growth. Sustainability is central to fostering women’s entrepreneurship, as it drives social equity, economic diversification, and innovation, elements which are crucial to sustainable development. While the existing literature has primarily focused on women’s entrepreneurship in the Western world, limited attention has been given to its development in the Global South, particularly in Saudi Arabia. As a nation undergoing transformative social, cultural, and economic shifts, women entrepreneurs play a critical role in aligning entrepreneurial efforts with global sustainability goals. This research investigates the factors influencing Saudi women to become entrepreneurs, specifically examining the factors that inspire or hinder them from creating their own ventures. Drawing upon cognitive and social capital theories, which have proven their soundness in the existing literature, this research utilizes a dataset of 1715 women entrepreneurs analyzed through binomial logistic regression. The findings indicate that social desirability, relational capital, experience as angel investors, age, income, and education significantly increase the likelihood of women’s entrepreneurship. By contextualizing women’s entrepreneurship within Saudi Arabia’s evolving societal and economic landscape, this research highlights their potential as drivers of inclusive growth and sustainable economic empowerment. Furthermore, the research outlines strategies to enhance women’s entrepreneurial participation, contributing both to the entrepreneurship literature and the realization of Saudi Vision 2030. Full article

In recent years, we have witnessed a massive increase in the number of startups, which are also producing significant amounts of digital data. This poses a new challenge for expert analysts due to their limited attention spans and knowledge, also considering the low success rate of empirical startup evaluation. However, this new era also presents a great opportunity for the application of artificial intelligence (AI) towards intelligent startup investments. There are only a few works that have considered the potential of AI for startup recommendation, and they have not paid attention to the actual requirements of investors, also neglecting to investigate the desirability, feasibility, and value proposition of this venture. In this paper, we answer these questions by conducting a survey in collaboration with three major organizations of the Greek startup ecosystem. Furthermore, this paper also presents the design specifications for an AI-based decision support system for forecasting startup sustainability that is aligned with the requirements of expert analysts. Preliminary experiments with 44 Greek startups demonstrate Random Forest’s strong ability to predict sustainability scores. Full article

With their potential for high returns and expanding role in the financial landscape, cryptocurrency investments have garnered the attention of the financial press and investors. Applying an integrated research model based on the Unified Theory of Acceptance and Use of Technology (UTAUT), this study investigates the factors influencing individual investors’ attitudes toward cryptocurrency investments and their intention to continue investing. The model incorporates constructs such as performance expectancy, effort expectancy, social influence, perceived risk, perceived privacy, technology competency, perceived enjoyment, and prior experience. Data from 506 cryptocurrency investors located in the United States were collected through a 50-item questionnaire. The findings indicate that performance expectancy and perceived enjoyment positively impact attitudes toward cryptocurrency investments, which, in turn, influence the intention to continue investing. Perceived privacy positively affects performance expectancy, while technology competency enhances effort expectancy. These results offer valuable insights for policymakers and cryptocurrency exchanges to foster sustainable growth in the cryptocurrency market. Despite its contributions, the study acknowledges limitations, including a focus on current investors in the US and the exclusion of factors such as optimism and innovativeness. Future research should explore these aspects across different populations and regions to gain a more comprehensive understanding of cryptocurrency investment behavior. Full article

Predicting stock trends in financial markets is of significant importance to investors and portfolio managers. In addition to a stock’s historical price information, the correlation between that stock and others can also provide valuable information for forecasting future returns. Existing methods often fall short of straightforward and effective capture of the intricate interdependencies between stocks. In this research, we introduce the concept of a Laplacian correlation graph (LOG), designed to explicitly model the correlations in stock price changes as the edges of a graph. After constructing the LOG, we will build a machine learning model, such as a graph attention network (GAT), and incorporate the LOG into the loss term. This innovative loss term is designed to empower the neural network to learn and leverage price correlations among different stocks in a straightforward but effective manner. The advantage of a Laplacian matrix is that matrix operation form is more suitable for current machine learning frameworks, thus achieving high computational efficiency and simpler model representation. Experimental results demonstrate improvements across multiple evaluation metrics using our LOG. Incorporating our LOG into five base machine learning models consistently enhances their predictive performance. Furthermore, backtesting results reveal superior returns and information ratios, underscoring the practical implications of our approach for real-world investment decisions. Our study addresses the limitations of existing methods that miss the correlation between stocks or fail to model correlation in a simple and effective way, and the proposed LOG emerges as a promising tool for stock returns prediction, offering enhanced predictive accuracy and improved investment outcomes. Full article

The efficient capital markets hypothesis (EMH) posits that security prices incorporate all available information in capital markets. Nevertheless, real stock markets often exhibit speculative behavior due to information asymmetry and the limited rationality of investors. This paper employs statistical analysis, a multiple regression approach, and robustness tests to investigate the impact of investor attention and accounting information comparability on stock returns. We collected monthly data from all Chinese A-share stocks listed on the main board of the Shanghai Stock Exchange for the period 2017–2021. Our findings reveal a significant positive correlation between current investor attention and current monthly stock returns and a significant negative correlation between lagged investor attention and current monthly stock returns. Moreover, accounting information comparability serves as a substantial moderator, amplifying the positive effect of current investor attention on current stock returns and mitigating the negative impact of lagged investor attention. We investigate the indicator of accounting information comparability from the perspective of investor attention. Significantly, we use accounting information comparability as a moderating variable for the first time to assess its influence on stock returns. Our results demonstrate that accounting information comparability significantly contributes to mitigating excessive share price declines and stimulating share price increases. This discovery also acts as an internal driver for listed companies to proactively improve accounting information comparability. Full article

Individual investors often struggle to predict stock prices due to the limitations imposed by the computational capacities of personal laptop Graphics Processing Units (GPUs) when running intensive deep learning models. This study proposes solving these GPU constraints by integrating deep learning models with technical analysis methods. This integration significantly reduces analysis time and equips individual investors with the ability to identify stocks that may yield potential gains or losses in an efficient manner. Thus, a comprehensive buy and sell algorithm, compatible with average laptop GPU performance, is introduced in this study. This algorithm offers a lightweight analysis method that emphasizes factors identified by technical analysis methods, thereby providing a more accessible and efficient approach for individual investors. To evaluate the efficacy of this approach, we assessed the performance of eight deep learning models: long short-term memory (LSTM), a convolutional neural network (CNN), bidirectional LSTM (BiLSTM), CNN Attention, a bidirectional gated recurrent unit (BiGRU) CNN BiLSTM Attention, BiLSTM Attention CNN, CNN BiLSTM Attention, and CNN Attention BiLSTM. These models were used to predict stock prices for Samsung Electronics and Celltrion Healthcare. The CNN Attention BiLSTM model displayed superior performance among these models, with the lowest validation mean absolute error value. In addition, an experiment was conducted using WandB Sweep to determine the optimal hyperparameters for four individual hybrid models. These optimal parameters were then implemented in each model to validate their back-testing rate of return. The CNN Attention BiLSTM hybrid model emerged as the highest-performing model, achieving an approximate rate of return of 5 percent. Overall, this study offers valuable insights into the performance of various deep learning and hybrid models in predicting stock prices. These findings can assist individual investors in selecting appropriate models that align with their investment strategies, thereby increasing their likelihood of success in the stock market. Full article

Predicting stock prices has long been the holy grail for providing guidance to investors. Extracting effective information from Limit Order Books (LOBs) is a key point in high-frequency trading based on stock-movement forecasting. LOBs offer many details, but at the same time, they are very noisy. This paper proposes a differential transformer neural network model, dubbed DTNN, to predict stock movement according to LOB data. The model utilizes a temporal attention-augmented bilinear layer (TABL) and a temporal convolutional network (TCN) to denoise the data. In addition, a prediction transformer module captures the dependency between time series. A differential layer is proposed and incorporated into the model to extract information from the messy and chaotic high-frequency LOB time series. This layer can identify the fine distinction between adjacent slices in the series. We evaluate the proposed model on several datasets. On the open LOB benchmark FI-2010, our model outperforms other comparative state-of-the-art methods in accuracy and F1 score. In the experiments using actual stock data, our model also shows great stock-movement forecasting capability and generalization performance. Full article

Multinational enterprises (MNEs) are increasingly expected to integrate sustainability into their core business activities, moving beyond philanthropy or public advocacy. In particular, the financial sector is expected to support the Sustainable Development Goals (SDGs) as it plays a critical role in promoting sustainable development through its key roles as risk managers, insurers, investors and lenders. It has been acknowledged that the sector has the power to direct investments towards sustainable activities, encourage sustainable business practices, and promote sustainable development more broadly. However, for MNEs, including insurance companies, examining sustainability practices across subsidiaries operating in expanded geographic contexts becomes complex. Implementing corporate sustainability strategies is challenging, particularly when their globally-developed strategy intersects with local operations. However, limited attention has been given to the sustainability practices adopted by the financial sector at the subsidiary or local levels. This study aims to fill this gap by examining how multinational insurance companies operating in emerging markets manage their sustainability practices, particularly in aligning their global sustainability strategy with local operations. Utilising a corporate sustainability assessment process framework and focusing on the case study context of Allianz in Malaysia, this study provides a comprehensive picture not only of the sustainability practices that have been implemented but also of the important role that global and local operations play in translating global strategies to achieve sustainability into meaningful and contextualised local agendas for sustainability. Full article

The current supply of finance to enable universal access to clean fuels and technology for cooking does not match the scale of Sustainable Development Goal 7’s access challenge. To date, little attention has been given to the modalities of funding the clean cooking transition at the macro level. Grounded in a review of academic and recent grey literature, this study’s research objective was to provide a granular understanding of gaps in finance flows and financial instruments, mapped against the innovation cycle of companies that provide clean cooking solutions. In the context of wide-ranging barriers to the clean cooking sector’s development, we found a chronic shortfall of finance for companies at the early stages of their business growth and poorly targeted public finance to support innovation and mitigate risk for later-stage investors. This is exacerbated by limited data sharing and knowledge exchange among a small number of funders. We recommend reforms to public funding for clean cooking enterprises, especially for research, development and demonstration (RD&D) and innovation, to mitigate risk for later-stage investors, as well as more effective data sharing, to help catalyse sufficient, appropriate finance through the value chain for universal access. Full article

Traditional asset pricing theory suggests that to compensate for the uncertainty that investors bear, risky assets should generate considerably higher rates of return than the risk-free rate. However, the overnight return anomaly in the Chinese stock market, which refers to the anomaly that overnight return is significantly negative, contradicts the risk–return trade-off. We find that this anomaly is asymmetrical, as the overnight return is significantly negative after a negative daytime return, whereas the anomaly does not occur following a positive daytime return. We explain this anomaly from the perspective of investor attention. We show that the attention of individual investors behaves asymmetrically such that they draw more attention on negative daytime returns, and play an essential role in explaining the overnight return puzzle. Full article