---
title: Global FOMO in Financial Markets
url: https://www.ml-quant.com/papers/ssrn/5214893/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 5214893
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5214893
featured: 2025-04-16
citations: unknown
topic: Derivatives & Volatility
---


# Global FOMO in Financial Markets

The Global Fear of Missing Out (FOMO) Index, using Google Trends data, forecasts lower stock returns, decreased volatility, and weaker Sharpe ratios, especially in democratic countries, showing the role of psychology and politics in finance.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5214893
- Identifier: SSRN 5214893
- Released: 2025-04-12
- First featured: Quant Letter No. 93 (2025-04-16): https://www.ml-quant.com/issues/2025-04-16/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- [Risk Revisited](https://www.ml-quant.com/papers/ssrn/4825844/): The study identifies recency, cluster, and sign as three factors shaping investors' risk perceptions of a stock, influencing trading volume and future volatility.
- [Rough Volatility: Fact or Artefact?](https://www.ml-quant.com/papers/arxiv/2203.13820/): Fact or Artifact: The study proposes a new method to estimate the roughness of financial asset volatility, attributing observed roughness to microstructure noise.
- [On the Rate of Convergence of Estimating the Hurst Parameter of Rough Stochastic Volatility Models](https://www.ml-quant.com/papers/arxiv/2504.09276/): The research extends the convergence result of a scale-invariant estimator, proving its consistent estimation of the Hurst parameter in rough stochastic volatility models.
- [Unified GARCH-Recurrent Neural Network in Financial Volatility Forecasting](https://www.ml-quant.com/papers/arxiv/2504.09380/): The paper proposes a new GARCH-GRU model for financial volatility forecasting, showing better computational efficiency and forecasting accuracy than other models.
- [Deep Hedging with Options Using the Implied Volatility Surface](https://www.ml-quant.com/papers/arxiv/2504.06208/): A new deep hedging framework for index option portfolios, which includes surface-informed decisions and transaction costs, has been proposed and outperforms traditional methods in both simulated and historical data from 1996 to 2020.
- [Deep Reinforcement Learning Algorithms for Option Hedging](https://www.ml-quant.com/papers/arxiv/2504.05521/): A comparison of eight Deep Reinforcement Learning algorithms for dynamic hedging found that Monte Carlo Policy Gradient and Proximal Policy Optimization performed best, with the former outperforming the Black-Scholes delta hedge baseline.
