This WBG study examines what makes distressed asset markets work — and how governments can create the right conditions for private sector engagement. It provides policymakers, regulators, and financial institutions with a practical framework to assess market readiness, identify policy and...

This paper quantifies how demand and supply shocks transmit to yields in Colombia's sovereign bond market by estimating investor-level demand elasticities and translating them into equilibrium price effects. Using investor-security microdata and two complementary identification strategies, I...

The diverse rating scales used by brokerage firms pose significant challenges for aggregating analyst recommendations in financial research. We develop a momentum-based normalization framework that transforms heterogeneous rating changes into standardized sentiment signals using firm-relative,...

The Financial Lobster Bias describes how SMEs, driven by distorted liquidity perceptions, engage in aggressive expansion until financial breakdown occurs. Using data from 10,412 Spanish SMEs (2000-2024), this study shows that liquidity misperception-measured through two versions of the Liquidity...

We propose a macro-finance model in which inflation, growth, and the policy rate are driven by unobservable long-run trends and transitory cycles that investors must infer from aggregate data. Their subjective estimates of these trends, and the uncertainty surrounding them, are priced into the...

As China's population ages and the sustainability of the public pension system is at risk, personal savings become crucial. As such, the quality of financial planning for retirement (FPR) has been recognized as a key to safeguarding financial well-being during retirement. This study examines the...

Most news stories contain both granular quantitative information and coarse categorizations. For instance, company earnings are reported as a dollar figure alongside categorizations, such as whether earnings beat or missed market expectations. We formalize and study the hypothesis that when a...

In this paper we employ the natural experiment of German Division and Reunification in order to study the effect of institutional reform on the decision to hold risky assets. We present empirical evidence indicating that even 16 years after German Reunification risky portfolios of East and West...

We study how confidence bias affects investment in learning via experimentation, a mechanism critical for technology adoption under uncertainty. We hypothesize that bias direction and strength predict how willingness to experiment diverges from unbiased agents. We measure revealed and stated...

As global climate and sustainable challenges gain more attention, green finance has emerged as a significant focus of worldwide financial reform, with green bonds serving as a key indicator. Retail investors, as an important part of the financial market, have a significant impact on the...

This study builds on the concept of loss tolerance by introducing investment grip, a behavioral interpretation that captures investors' commitment to long-term objectives under adverse market conditions. While loss tolerance traditionally measures the maximum financial loss an investor can...

We study how sustainability disclosure regulation affects mutual fund flows and portfolio choices, accounting for investor heterogeneity. Guided by a model of ESG investing under uncertainty, we exploit the introduction of the European Sustainable Finance Disclosure Regulation (SFDR) as a...

Managers of actively managed funds do not maintain a constant investment style. Instead, their factor loadings change over time. These changes are especially large following quarters with extreme returns and fund flows and arise from both active portfolio reallocations and passive style drift....

We present improved out-of-sample predictability of future fund flows using state-of-the-art machine learning methods. Nonlinear machine learning models significantly outperform linear models in terms of out-of-sample R-squared. Using interpretable ML methods, we identify past flows and the...

We test whether forecast bias affects individual investors' stock trading by combining bias measures from laboratory experiments with administrative trade data. Forecast bias is positively associated with past excess returns of purchased stocks: Compared to contrarians, extrapolators purchase...

Understanding how investors maintain positions during adverse market conditions, investment grip, is increasingly important as retail participation rises and information environments diversify. While prior research identifies demographic, psychological, and economic determinants of investment...

This study examines how effective the Dogs of the Dow (DoD) investment strategy, popular in the USA, was for the Polish blue-chip stock market between 2002-2023. This strategy involves investing the same amount of funds each year in shares of ten companies called Dogs of Dow with the highest...

I develop a model-based definition of time-varying sovereign bond safety, and apply it empirically by constructing a news-based index, the FLY, that measures global safe-assets demand. The FLY captures flight-to-safety episodes, the savings glut, and natural interest rate declines. Estimated FLY...

This study investigates how the origin and language of public attention influence financial markets during geopolitical conflict, using Israel's experience during the 2023-2025 Gaza War as a case study. We use Google Trends data-in Hebrew, English, and Arabic, sourced both worldwide and...

Prediction of stock closing price plays a critical role in financial planning, risk management, and informed investment decision-making. In this study, we propose a novel model that synergistically amalgamates Bidirectional GRU (BiGRU) with three complementary attention techniques-Top-k Sparse,...

This study examines the determinants of equity crowdfunding (ECF) campaign success in Malaysia through the lens of quality signaling theory and behavioral herding dynamics. Using a manually collected dataset of 335 completed campaigns launched on pitchIN, MyStartr, and AtaPlus between 2016 and...

This study investigates the asymmetric responses of AI and ESG Exchange Traded Funds (ETFs) to geopolitical and financial uncertainty, with a focus on resilience across market regimes. The NASDAQ-100 and MSCI ESG Leaders indices are used as proxies for thematic ETFs, and their dynamic...

This work introduces a novel method for sentiment analysis in the stock market by combining Deep Neural Networks (DNN) and an improved Firefly Algorithm (FA). It is essential to comprehend investor sentiment in financial markets in order to forecast stock price movements and make wise investment...