Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)

Best Portfolio Choice by Using Markowitz Model

A Case Study of Ten Stocks from S&P500

Authors
Keyu Chen1, *
1Chongqing No.1 International Studies School, Chongqing, 401122, China
*Corresponding author. Email: guanghua.ren@gecacademy.cn
Corresponding Author
Keyu Chen
Available Online 26 March 2022.
DOI
10.2991/aebmr.k.220307.034How to use a DOI?
Keywords
portfolio optimization; Markowitz model; S&P 500; case study
Abstract

Our paper practically implements the idea that people can have the best portfolio choice by Markowitz Model. Using a recent 20 years of historical daily total return data for ten stocks from S&P 500, one equity index (S&P 500) and a proxy for risk-free rate (1-month Fed Funds rate), we use Excel to arrange those data and categorize them with Solver Table. Then, to reduce the non-Gaussian effects, we aggregate the daily data into the monthly observations based on those monthly observations. Finally, we calculate all proper optimization inputs for the full Markowitz Model (“MM”).

Copyright
© 2022 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

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Volume Title
Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)
Series
Advances in Economics, Business and Management Research
Publication Date
26 March 2022
ISBN
978-94-6239-554-1
ISSN
2352-5428
DOI
10.2991/aebmr.k.220307.034How to use a DOI?
Copyright
© 2022 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Keyu Chen
PY  - 2022
DA  - 2022/03/26
TI  - Best Portfolio Choice by Using Markowitz Model
BT  - Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)
PB  - Atlantis Press
SP  - 215
EP  - 220
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.220307.034
DO  - 10.2991/aebmr.k.220307.034
ID  - Chen2022
ER  -