---
title: Google Search Volume Index and Investor Attention
url: https://www.ml-quant.com/papers/repec/spr-fininn-v-10-y-2024-i-1-d-10-1186-s40854-023-00606-y/
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: RePEc:spr:fininn:v:10:y:2024:i:1:d:10.1186_s40854-023-00606-y
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1186%2Fs40854-023-00606-y%3Bh%3Drepec%3Aspr%3Afininn%3Av%3A10%3Ay%3A2024%3Ai%3A1%3Ad%3A10.1186_s40854-023-00606-y
featured: 2024-08-07
citations: unknown
topic: Derivatives & Volatility
---


# Google Search Volume Index and Investor Attention

The study finds that Google Search Volume Index can be used to predict stock market movements and volatility, improving forecasting models.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1186%2Fs40854-023-00606-y%3Bh%3Drepec%3Aspr%3Afininn%3Av%3A10%3Ay%3A2024%3Ai%3A1%3Ad%3A10.1186_s40854-023-00606-y
- Identifier: RePEc:spr:fininn:v:10:y:2024:i:1:d:10.1186_s40854-023-00606-y
- Released: 2024-08-07
- First featured: Quant Letter No. 60 (2024-08-07): https://www.ml-quant.com/issues/2024-08-07/
- 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.
- [Hedge Fund Portfolio Construction Using PolyModel Theory and iTransformer](https://www.ml-quant.com/papers/arxiv/2408.03320/): The article explores the use of PolyModel theory and deep learning in creating hedge fund portfolios for high returns and low risks.
- [PDEs for pricing interest rate derivatives under the new generalized Forward Market Model (FMM)](https://www.ml-quant.com/papers/doi/10-1016-j-camwa-2024-06-010/): The paper discusses the use of partial differential equations in pricing interest rate derivatives under the generalized Forward Market Model.
- [Existence, uniqueness and positivity of solutions to the Guyon-Lekeufack path-dependent volatility model with general kernels](https://www.ml-quant.com/papers/arxiv/2408.02477/): The study confirms the existence and uniqueness of a solution to a path-dependent volatility model used to predict the price of an equity index and its spot volatility.
- [CLVR Ordering of Transactions on AMMs](https://www.ml-quant.com/papers/arxiv/2408.02634/): The study introduces the CLVR algorithm, which organizes transactions to reduce price volatility in Automated Market Maker trading, balancing price stability and inequality reduction.