Network analysis of search dynamics: The case of stock habitats

Alvin Chung Man Leung, Ashish Agarwal, Prabhudev Konana, Alok Kumar

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

There is an increasing attention in information systems to be able to predict outcomes using search frequency on popular portals. This growing literature focuses on revealing demand patterns of individual assets (e.g., products, stocks, services). However, users typically search many different assets together (e.g., correlated searches) and leave a digital footprint, which can help provide insights on the behaviors of a group of assets. Furthermore, such group behavior can be used to predict outcomes (e.g., demand, stock returns) in the future.We analyze the underlying behavior of distinct subnetworks formed by correlated user searches for multiple items in the stock market and use such information for return prediction. Using cosearch data for stocks from Yahoo! Finance, we find 50-79 search clusters representing 230-349 stocks among Russell 3000 stocks at different time periods. These clusters reveal interesting habitats where the returns of stocks within a cluster tend to comove after controlling for known determinants of comovement. When a stock enters (departs) a cluster, the focal stock return comoves (detaches) with the cluster returns. Thus, search cluster-based habitats reveal aggregate investment preferences and are more granular than fundamental-based habitats. We find that search-based habitats can also improve return predictability of related stocks.

Original languageEnglish (US)
Pages (from-to)2667-2687
Number of pages21
JournalManagement Science
Volume63
Issue number8
DOIs
StatePublished - Aug 1 2017

Fingerprint

Network analysis
Habitat
Assets
Stock returns
Information use
Return predictability
Prediction
Comovement
Stock market
Aggregate investment
Group behavior
Finance
Information systems

Keywords

  • Comovement
  • Correlated search
  • Investment habitats
  • Network analysis
  • Online search
  • Stock returns
  • User attention

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research

Cite this

Network analysis of search dynamics : The case of stock habitats. / Leung, Alvin Chung Man; Agarwal, Ashish; Konana, Prabhudev; Kumar, Alok.

In: Management Science, Vol. 63, No. 8, 01.08.2017, p. 2667-2687.

Research output: Contribution to journalArticle

Leung, Alvin Chung Man ; Agarwal, Ashish ; Konana, Prabhudev ; Kumar, Alok. / Network analysis of search dynamics : The case of stock habitats. In: Management Science. 2017 ; Vol. 63, No. 8. pp. 2667-2687.
@article{20ceadbbf5b745a28866e7e9d6751ff6,
title = "Network analysis of search dynamics: The case of stock habitats",
abstract = "There is an increasing attention in information systems to be able to predict outcomes using search frequency on popular portals. This growing literature focuses on revealing demand patterns of individual assets (e.g., products, stocks, services). However, users typically search many different assets together (e.g., correlated searches) and leave a digital footprint, which can help provide insights on the behaviors of a group of assets. Furthermore, such group behavior can be used to predict outcomes (e.g., demand, stock returns) in the future.We analyze the underlying behavior of distinct subnetworks formed by correlated user searches for multiple items in the stock market and use such information for return prediction. Using cosearch data for stocks from Yahoo! Finance, we find 50-79 search clusters representing 230-349 stocks among Russell 3000 stocks at different time periods. These clusters reveal interesting habitats where the returns of stocks within a cluster tend to comove after controlling for known determinants of comovement. When a stock enters (departs) a cluster, the focal stock return comoves (detaches) with the cluster returns. Thus, search cluster-based habitats reveal aggregate investment preferences and are more granular than fundamental-based habitats. We find that search-based habitats can also improve return predictability of related stocks.",
keywords = "Comovement, Correlated search, Investment habitats, Network analysis, Online search, Stock returns, User attention",
author = "Leung, {Alvin Chung Man} and Ashish Agarwal and Prabhudev Konana and Alok Kumar",
year = "2017",
month = "8",
day = "1",
doi = "10.1287/mnsc.2016.2470",
language = "English (US)",
volume = "63",
pages = "2667--2687",
journal = "Management Science",
issn = "0025-1909",
publisher = "INFORMS Inst.for Operations Res.and the Management Sciences",
number = "8",

}

TY - JOUR

T1 - Network analysis of search dynamics

T2 - The case of stock habitats

AU - Leung, Alvin Chung Man

AU - Agarwal, Ashish

AU - Konana, Prabhudev

AU - Kumar, Alok

PY - 2017/8/1

Y1 - 2017/8/1

N2 - There is an increasing attention in information systems to be able to predict outcomes using search frequency on popular portals. This growing literature focuses on revealing demand patterns of individual assets (e.g., products, stocks, services). However, users typically search many different assets together (e.g., correlated searches) and leave a digital footprint, which can help provide insights on the behaviors of a group of assets. Furthermore, such group behavior can be used to predict outcomes (e.g., demand, stock returns) in the future.We analyze the underlying behavior of distinct subnetworks formed by correlated user searches for multiple items in the stock market and use such information for return prediction. Using cosearch data for stocks from Yahoo! Finance, we find 50-79 search clusters representing 230-349 stocks among Russell 3000 stocks at different time periods. These clusters reveal interesting habitats where the returns of stocks within a cluster tend to comove after controlling for known determinants of comovement. When a stock enters (departs) a cluster, the focal stock return comoves (detaches) with the cluster returns. Thus, search cluster-based habitats reveal aggregate investment preferences and are more granular than fundamental-based habitats. We find that search-based habitats can also improve return predictability of related stocks.

AB - There is an increasing attention in information systems to be able to predict outcomes using search frequency on popular portals. This growing literature focuses on revealing demand patterns of individual assets (e.g., products, stocks, services). However, users typically search many different assets together (e.g., correlated searches) and leave a digital footprint, which can help provide insights on the behaviors of a group of assets. Furthermore, such group behavior can be used to predict outcomes (e.g., demand, stock returns) in the future.We analyze the underlying behavior of distinct subnetworks formed by correlated user searches for multiple items in the stock market and use such information for return prediction. Using cosearch data for stocks from Yahoo! Finance, we find 50-79 search clusters representing 230-349 stocks among Russell 3000 stocks at different time periods. These clusters reveal interesting habitats where the returns of stocks within a cluster tend to comove after controlling for known determinants of comovement. When a stock enters (departs) a cluster, the focal stock return comoves (detaches) with the cluster returns. Thus, search cluster-based habitats reveal aggregate investment preferences and are more granular than fundamental-based habitats. We find that search-based habitats can also improve return predictability of related stocks.

KW - Comovement

KW - Correlated search

KW - Investment habitats

KW - Network analysis

KW - Online search

KW - Stock returns

KW - User attention

UR - http://www.scopus.com/inward/record.url?scp=85027382405&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85027382405&partnerID=8YFLogxK

U2 - 10.1287/mnsc.2016.2470

DO - 10.1287/mnsc.2016.2470

M3 - Article

AN - SCOPUS:85027382405

VL - 63

SP - 2667

EP - 2687

JO - Management Science

JF - Management Science

SN - 0025-1909

IS - 8

ER -