Forecasting high‐frequency excess stock returns via data analytics and machine learning-Reference-Cited by-同舟云学术

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Forecasting high‐frequency excess stock returns via data analytics and machine learning

Published:2021-12-07 Issue: Volume: Page:
ISSN:1354-7798
Container-title:European Financial Management
language:en
Short-container-title:Eur Financ Manag

Author:

Akyildirim Erdinc¹²,Nguyen Duc Khuong³⁴,Sensoy Ahmet⁵,Šikić Mario²

Affiliation:

1. Department of Banking and Finance Mehmet Akif Ersoy University Burdur Turkey

2. Department of Banking and Finance University of Zurich Zurich Switzerland

3. IPAG Lab IPAG Business School Paris France

4. International School Vietnam National University Hanoi Vietnam

5. Faculty of Business Administration Bilkent University Ankara Turkey

Publisher

Wiley

Subject

General Economics, Econometrics and Finance,Accounting

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/eufm.12345

Reference61 articles.

1. The role of big data analytics capabilities (BDAC) in understanding the challenges of service information and operations management in the sharing economy: Evidence of peer effects in libraries

2. Fundamentals and exchange rate forecastability with simple machine learning methods

3. Distilling liquidity costs from limit order books

4. Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards

5. Bitcoin price forecasting with neuro-fuzzy techniques

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2. Forecasting stock prices of fintech companies of India using random forest with high-frequency data;Journal of Open Innovation: Technology, Market, and Complexity;2024-03

3. Machine learning methods in finance: Recent applications and prospects;European Financial Management;2023-02-02

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