Developing efective strategies to earn excess returns in the stock market is a cuttingedge topic in the feld of economics. At the same time, stock price forecasting that supports trading strategies is considered one of the most challenging tasks. Therefore, this study analyzes and extracts news...

This study investigates tail dependence among five major cryptocurrencies, namely Bitcoin, Ethereum, Litecoin, Ripple, and Bitcoin Cash, and uncertainties in the gold, oil, and equity markets. Using the cross-quantilogram method and quantile connectedness approach, we identify cross-quantile...

This study examines the role of market sentiment in predicting the price bubbles of four strategic metal commodities (gold, silver, palladium, and platinum) from January 1985 to August 2020. It is the first to investigate this topic using sentiment indices, including news-based economic and...

A number of studies have investigated the predictability of Chinese stock returns with economic variables. Given the newly emerged dataset from the Internet, this paper investigates whether the Baidu Index can be employed to predict Chinese stock returns. The empirical results show that 1) the...

This study examines herding behavior in the Pakistani Stock Market under different market conditions, focusing on the Ramadan effect and Crisis period by using data from 2004 to 2014. Two regression models of Christie and Huang (Financ Analysts J 51:31–37, 1995) and Chang et al., (J Bank...

Eye tracking can facilitate understanding irrational decision-making in contexts such as financial risk-taking. For this purpose, we develop an experimental framework in which participants trade a risky asset in a simulated bubble market to maximize individual returns while their eye movements...

The subprime crisis was quite damaging for hedge funds. Using the local projection method (Jordà 2004, 2005, 2009), we forecast the dynamic responses of the betas of hedge fund strategies to macroeconomic and financial shocks-especially volatility and illiquidity shocks-over the subprime crisis...

Tail risk is a classic topic in stressed portfolio optimization to treat unprecedented risks, while the traditional mean-variance approach may fail to perform well. This study proposes an innovative semiparametric method consisting of two modeling components: the nonparametric estimation and...

This study investigates the factors of Bitcoin's tail risk, quantified by Value at Risk (VaR). Extending the conditional autoregressive VaR model proposed by Engle and Manganelli (2004), I examine 30 potential drivers of Bitcoin's 5% and 1% VaR. For the 5% VaR, quantity variables, such as...

A well-documented finding is that explicitly using jumps cannot efficiently enhance the predictability of crude oil price volatility. To address this issue, we find a phenomenon, "momentum of jumps" (MoJ), that the predictive ability of the jump component is persistent when forecasting the oil...