This paper revisits the "too much finance" hypothesis by reassessing the relationship between financial depth and economic growth using an expanded dataset (1960-2019) and a systematic estimation strategy that avoids reliance on any single, potentially arbitrary sample window. We estimate both...

This research explores the application of extreme value theory in modelling and quantifying tail risks across different economic equity markets, with focus on the Nairobi Securities Exchange (NSE20), the South African Equity Market (FTSE/JSE Top40) and the US Equity Index (S&P500). The study...

Purpose - This study aims to forecast the stock market behavior of BRICS nations (Brazil, Russia, India, China and South Africa) using advanced machine learning models. The focus is on identifying market trends, predicting future index prices and analyzing returns. Design/methodology/approach -...

Despite rising stock markets in the United States and Europe from 2017 to 2024, we document that average daily stock market performance becomes negative when weighted by the amount of media coverage. We propose an explanation for this media negativity bias that does not rely on a bad-news bias...

This paper investigates persistence in the MINT (Mexico, Indonesia, Nigeria, Turkey) stock markets applying fractional integration methods to daily data from 1 January 2022 to 31 October 2025. Different model specifications are estimated for prices, log prices and log returns under the...

This paper examines how shifts in investor demand influence firm financing and investment decisions. For identification, the paper exploits a large-scale MSCI methodo logical reform that mechanically redefined the stock weights in major international equity benchmark indexes, changing the...

This study applies the Log-Periodic Power-Law (LPPL) framework to three major equity markets-Mexico (IPC), Brazil (IBOVESPA), and the United States (NYSE Composite)-using daily closes from 8 November 1991-30 January 2025 for IPC and NYSE, and 3 May 1993-30 January 2025 for IBOVESPA. Multi-window...

This study investigates the volatility spillover dynamics between the VIX fear index and the Magnificent Seven technology stocks - namely Microsoft, Apple, Nvidia, Amazon, Alphabet, Meta Platforms, and Tesla - over the period of June 2012 to March 2024. To achieve this objective, the variance...

In emerging markets, stock markets play a crucial role in supporting long-term growth. This study explores the causal relationship between stock market development and economic growth in the Fragile Five countries-Brazil, India, Indonesia, South Africa, and Turkey-covering the period from 2001...

This paper investigates the nonlinear and state-dependent relationship between inflation surprises and real equity returns across G7 economies. Using monthly data from January 1998 to May 2025, we employ nonlinear local projection models to estimate the dynamic responses of the equity market to...

This paper extends the gravity model to financial markets by examining how distance and bilateral linkages influence stock market correlations between the United States and selected emerging economies. To this end, the Poisson Pseudo Maximum Likelihood (PPML) estimator is used to account for...

This study explores how the volatility spillover mechanism and dynamic dependence among the founding BRICS equity markets, namely IBOVESPA, MICEX, Nifty 50, SSE, and JSE, have evolved over time using a multivariate DCC-GARCH model. The analysis is conducted across three distinct regimes: the...

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...

Stock price forecasting remains challenging due to the non-linear, noisy, and non-stationary nature of financial time series. Although LSTMs and Transformer-based models have improved sequential modeling, their ability to scale efficiently to long financial sequences remains limited. Recently,...

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 asymmetric responses of stock market indices in Türkiye and Kazakhstan to oil price shocks during the 2010-2025 period. Using the Nonlinear Autoregressive Distributed Lag (NARDL) model, the study decomposes the nonlinear effects of oil price fluctuations on financial...

This study quantifies Lithuania's Equity Risk Premium (ERP) by integrating Damodaran's country-risk premium (CRP) framework with a multiple regression on key market drivers. By using quarterly data from Q1 2015 to Q4 2024, the CRP model yields an implied cost of equity of 9.84%, corresponding to...

This study examines volatility spillovers among climate-policy-relevant sectors in the Chinese stock market across both time and frequency domains. It further explores the impact of climate policy uncertainty on the risk spillover effect in the Chinese stock market and its underlying mechanisms....

This paper uses a statistical test based on entropy theory to propose a new way to distinguish between interdependence, contagion, and the decoupling hypotheses in the context of shock transmission and spillover. Applying the proposed approach, the three hypotheses are examined when measuring...

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...

Stability in the global equity market relies on the existence of a favorable macroeconomic environment. This study examines the dynamic responses of these markets to geopolitical risks and oil price shocks from 1979Q2 to 2023Q3 using the global vector autoregressive (GVAR) approach. We introduce...

This paper examines the spillover effects of policy uncertainty in Lebanon on stock market volatility in seven MENA countries using a network connectedness approach. For this purpose, it constructs an economic policy uncertainty index for Lebanon using data extracted from Twitter (now X) for the...

This study examines the performance of trading strategies based on beta, idiosyncratic volatility (IVOL), MAX (lottery behavior), skewness, and tail risk in five major Asian markets, using data from 1999 to 2021. The most important determinant of cross-sectional differences in strategy premiums...

In this study, we employ the synthetic control method to evaluate the transmission of external shocks, taking COVID-19 as an example, on the stock market in the Chinese hospitality and tourism industry, specifically in the catering, hotel, cultural landscape, natural landscape, and travel agency...