PROTO:Smart Beta

PE and Devidend Yeild

Select stocks based on P/E and Devidend Yeild

Abstract

In this project, we use the P/E ratio and dividend yield to select and hold stocks in the Vietnamese stock market. The portfolio is rebalanced on the first day of each month; if that day is a holiday, rebalancing is carried out on the next trading day.

Introduction

In value investing, one common approach to stock selection involves identifying undervalued companies with strong income potential. This strategy leverages two key financial metrics: the Price-to-Earnings (P/E) ratio and the dividend yield. The P/E ratio helps assess whether a stock is trading at a reasonable price relative to its earnings, while the dividend yield highlights its income-generating potential.

The core idea is to select stocks with a low P/E ratio and a high dividend yield, as these stocks may represent undervalued opportunities that also offer steady cash returns. This method is particularly effective in mature or stable markets, such as the Vietnamese stock market, where dividend-paying companies are often more established and financially sound.

Hypothesis

We filter and maintain a portfolio of stocks with a price-to-earnings (P/E) ratio within the range of (0, 15) and a dividend yield (DY) greater than 0.01. The dividend yield is calculated as:

• DY = DPS / Price

where DPS denotes dividends per share. Existing holdings are sold before purchasing new stocks at each rebalancing period. The strategy does not yet account for trading volume; it is assumed that all stocks can be bought immediately without liquidity constraints.

Data

• Data source: Algotrade database
• Data period: from 2022-01-01 to 2025-01-01
• Financial and closing price data are extracted and stored in a .csv file to reduce the time required for subsequent actions.
• Each sell or buy side will be charge 0.035% fee.

Data collection

Daily closing price data

• The daily close price is collected from Algotrade database using SQL queries.
• The data is collected using the script data_loader.py
• The data is stored in the data/pe_dps.csv and data/vnindex.csv files.

Financial data

• P/E and DPS are calculated based on:
  • Net Profit After Tax Atributed To Shareholder
  • Outstanding share
  • Dividends paid
• The data is also collected using the data_loader.py file.

Implementation

Environment Setup

1. Set up python virtual environment

python -m venv venv
source venv/bin/activate # for Linux/MacOS
.\venv\Scripts\activate.bat # for Windows command line
.\venv\Scripts\Activate.ps1 # for Windows PowerShell

2. Install the required packages

pip install -r requirements.txt

3. (OPTIONAL) Create .env file in the root directory of the project and fill in the required information. The .env file is used to store environment variables that are used in the project. The following is an example of a .env file:

DB_NAME=<database name>
DB_USER=<database user name>
DB_PASSWORD=<database password>
DB_HOST=<host name or IP address>
DB_PORT=<database port>

Data Collection

Option 1. Download from Google Drive

Data can be download directly from Google Drive. The data files are stored in the data folder with the following folder structure:

data
├── is
│   ├── pe_dps.csv
│   └── vnindex.csv
└── os
    ├── pe_dps.csv
    └── vnindex.csv

You should place this folder to the current PYTHONPATH for the following steps.

Option 2. Run codes to collect data

To collect data from database, run this command below in the root directory:

python data_loader.py

The result will be stored in the data/pe_dps.csv and data/vnindex.csv

In-sample Backtesting

Specify period and parameters in parameter/backtesting_parameter.json file.

python backtesting.py

The results are stored in the result/backtest/ folder.

Optimization

To run the optimization, execute the command in the root folder:

python optimization.py

The optimization parameter are store in parameter/optimization_parameter.json. After optimizing, the optimized parameters are stored in parameter/optimized_parameter.json.

Out-of-sample Backtesting

To run the out-of-sample backtesting results, execute this command

python evaluation.py

The script will get value from parameter/optimized_parameter.json to execute. The results are stored in the result/optimization folder.

In-sample Backtesting

Running the in-sample backtesting by execute the command:

python backtesting.py

Evaluation Metrics

• Backtesting results are stored in the result/backtest/ folder.
• Used metrics to compare with VNINDEX are:
  • Sharpe ratio (SR)
  • Sortino ratio (SoR)
  • Information ratio (Inf)
  • Maximum drawdown (MDD)
• We use a risk-free rate of 6% per annum, equivalent to approximately 0.023% per day, as a benchmark for evaluating the Sharpe Ratio (SR) and Sortino Ratio (SoR).

Parameters

In-sample Backtesting Result

• The backtesting results with VNINDEX benchmark is constructuted from 2019-01-01 to 2022-01-01.

| Metric                    | Value                              |
|---------------------------|------------------------------------|
| Sharpe Ratio              | 1.2971                             |
| Information Ratio         | 0.4734                             |
| Sortino Ratio             | 1.7297                             |
| Maximum Drawdown (MDD)    | -0.2828                            |
| HPR (%)                   | 104.9                              |
| Excess HPR (%)            | 35.97                              |
| Monthly return (%)        | 2.2617                             |
| Excess monthly return (%) | 0.5012                             |
| Annual return (%)         | 27.09                              |

• The HPR chart. The chart is located at: result/backtest/hpr.svg
• Drawdown chart. The chart is located at result/backtest/drawdown.svg

Optimization

The configuration of optimization is stored in parameter/optimization_parameter.json you can adjust the range of parameters. Random seed is used for reconstructing the optimization process. The optimized parameter is stored in parameter/optimized_parameter.json The optimization process can be reproduced by executing the command:

python optimization.py

The currently found optimized parameters with the seed 2024 are:

{
    "pe": [0, 10],
    "dy": [0.056272982721535775, 1e6]
}

Out-of-sample Backtesting

• Specify the out-sample period and parameters in parameter/backtesting_parameter.json file.
• The out-sample data is loaded on the previous step. Refer to section Data for more information.
• To evaluate the out-sample data run the command below

python evaluation.py

Out-of-sample Backtesting Result

• The out-sample backtesting results with VNINDEX benchmark is constructuted from 2022-01-01 to 2024-01-01.

| Metric                    | Value                              |
|---------------------------|------------------------------------|
| Sharpe Ratio              | -0.3497                            |
| Information Ratio         | 0.7441                             |
| Sortino Ratio             | -0.4363                            |
| Maximum Drawdown (MDD)    | -0.4604                            |
| HPR (%)                   | -3.170                             |
| Excess HPR (%)            | 18.26                              |
| Monthly return (%)        | -0.1904                            |
| Excess monthly return (%) | 0.8778                             |
| Annual return (%)         | -4.243                             |

• The HPR chart. The chart is located at result/optimization/hpr.svg.
• Drawdown chart. The chart is located at result/optimization/drawdown.svg.

Reference

[1] ALGOTRADE, Algorithmic Trading Theory and Practice - A Practical Guide with Applications on the Vietnamese Stock Market, 1st ed. DIMI BOOK, 2023, pp. 64–67. Accessed: Apr. 30, 2025. [Online]. Available: Link

[2] ALGOTRADE, “Weighting Methods Used in Smart-Beta Strategy,” Website, Jul. 10, 2024. Available: Link (accessed Apr. 30, 2025).