Essays on machine learning and price impact in institutional finance
Abstract/Contents
- Abstract
- Institutional investors play crucial roles in financial markets. First, they delegate investment for individual investors. We show, using machine learning, that fund characteristics can consistently differentiate high from low-performing mutual funds. Fund momentum and fund flow are the most important predictors of future risk-adjusted fund performance, and the returns of predictive long-short portfolios are higher following a period of high sentiment. Second, institutional investors provide liquidity to investor demand. We hypothesize and provide evidence that prices are more inelastic when demand is less diversifiable. We decompose order-flow imbalances into components with varying degrees of diversifiability and estimate their price impacts. Our findings are consistent with weaker liquidity provision at less diversifiable levels.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Lin, Zihan, (Researcher in machine learning) |
|---|---|
| Degree supervisor | Pelger, Markus |
| Thesis advisor | Pelger, Markus |
| Thesis advisor | Giesecke, Kay |
| Thesis advisor | Krishnamurthy, Arvind |
| Degree committee member | Giesecke, Kay |
| Degree committee member | Krishnamurthy, Arvind |
| Associated with | Stanford University, Institute for Computational and Mathematical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Zihan Lin. |
|---|---|
| Note | Submitted to the Institute for Computational and Mathematical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/cc407xj6986 |
Access conditions
- Copyright
- © 2022 by Zihan Lin
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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