what makes underwriting and non-underwriting clients of brokerage firms receive different recommendations? an application of uplift random forest model
Clicks: 216
ID: 186715
2016
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Popular Article
30.0
/100
216 views
54 readers
AI Quality Assessment
Not analyzed
Readership in this journal
PopularRanked #17 of 41 articles by views in optics and laser technology
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
I explore company characteristics which explain the difference in analysts’ recommendations for companies that were underwritten (affiliated) versus non-underwritten (unaffiliated) by analysts’ brokerage firms. Prior literature documents that analysts issue more optimistic recommendations to underwriting clients of analysts’ brokerage employers. Extant research uses regression models to find general associations between recommendations and financial qualities of companies, with or without underwriting relationship. However, regression models cannot identify the qualities that cause the most difference in recommendations between affiliated versus unaffiliated companies. I adopt uplift random forest model, a popular technique in recent marketing and healthcare research, to identify the type of companies that earn analysts’ favor. I find that companies of stable earnings in the past, higher book-to-market ratio, smaller sizes, worsened earnings, and lower forward PE ratio are likely to receive higher recommendations if they are affiliated with analysts than if they are unaffiliated with analysts. With uplift random forest model, I show that analysts pay more attention on price-related than earnings-related matrices when they value affiliated versus unaffiliated companies. This paper contributes to the literature by introducing an effective predictive model to capital market research and shedding additional light on the usefulness of analysts’ reports.
| Reference Key |
hua2016internationalwhat
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Shaowen Hua |
| Journal | optics and laser technology |
| Year | 2016 |
| DOI |
10.20525/ijfbs.v5i3.278
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.