The portfolio beta βp is quite an important coefficient in modern portfolio theory since it efficiently measures portfolio volatility relative to the benchmark index or the capital market. βp is usually employed for portfolio evaluation or prediction but scarcely for portfolio construction process. The main objective of this paper is to propose a portfolio algorithm that engages βp in its portfolio construction process and studies its strengths. Our portfolio algorithm termed as β-G portfolio algorithm selects stocks based on their market capitalization and optimizes them in terms of the standard deviation of βp. The optimizing process or finding optimal weights is done by the genetic algorithm. Our major findings on β-G portfolio algorithm are: (i) its performance depends on market volatility, i.e. it is expected to work well for a stable market whether it is bullish or bearish (ii) it tends to register outstanding performance for short-term applications.
Bibliographical noteFunding Information:
We are very grateful to two anonymous referees for their valuable comments which led to significant improvement of our manuscript. This work is supported by Korea Science and Engineering Fund (KOSEF R01-2003-000- 105890-0).
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence