Quantitative Finance

What the sources say

These sources examine financial markets through quantitative models, algorithmic trading systems, and fundamental economic critiques to better measure risk and identify market manipulation.

Several researchers focus on refining how investors measure volatility and risk. Juan Andres Serur, Jose P. Dapena, and Julian R. Siri argue the VIX index measures both upside greed and downside fear, proposing a new index based on mathematical semi variance^[2]. Similarly, Stratos Chatzimanolakis uses a volatility filter in a Nasdaq Futures system to achieve returns independent of market risk^[3]. Meanwhile, Mohammed Mehdi Kaebi demonstrates that standard principal component analysis fails to separate systematic risk from market noise, suggesting a new method to fix this bias^[4].

Other analyses explore the disconnect between market narratives and actual asset behaviors. Murad Farzulla finds that the technical claims in cryptocurrency whitepapers completely fail to predict actual price movements^[5]. In the memecoin space, Yichen Luo, Jiahua Xu, Yebo Feng, and Yang Liu detail how automated bots manipulate markets, offering an artificial intelligence framework to detect these scams^[6]. Taking a macroeconomic view, Jeremy Grantham warns that artificial intelligence is driving a historic financial bubble that will ultimately collapse^[1].

Written by an AI model from Google, from the analyses on this page.