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# Effective Testing for Investment Strategies and Portfolios in Quantitative Wealth and Investment Management
- URL: https://fsc.stevens.edu/effective-testing-for-investment-strategies-and-portfolios-in-quantitative-wealth-and-investment-management/
- Published: 2023-05-17T13:41:51.000Z
- Updated: 2026-05-16T22:38:15.000Z
- Author: Pooja V Mule
- Tags: 2020 Spring, FA 800 Projects, Research, #Import 2024-05-30 10:26, #Import 2025-11-08 22:14

## Researchers

Yinchi Chen  
Yufei Zha  
Shijie Cai

## Faculty Advisor

Zhenyu Cui  
Dr. Christian Homescu

### Abstract

The research aims to create an effective testing framework for investment strategies and portfolios in QWIM. The performance is evaluated by various testing methods, including back testing, bootstrapping, scenario testing, and in-sample out of sample testing, to assess their ability to predict the market. Ultimately, all methods are integrated to develop a comprehensive testing framework.

### Result

Different testing methods such as Portfolio Testing, Scenario Testing, BackTesting, and Bootstrapping. The formula used in Portfolio and Scenario testing is shown below with different weights for different variables.

![](https://fsc.stevens.edu/content/images/2023/04/Screenshot-2023-04-10-at-11.16.14-AM.png)

The test framework helps determine which method generates data that is closest to real historical data. 10 portfolios are tested to obtain 4 indicators: Sharpe Ratio, Annual Return, Standard Deviation, and Max Breakdown

The results of all 4 indicators against different testing methods are as follows

![](https://fsc.stevens.edu/content/images/2023/04/Screenshot-2023-04-10-at-11.32.23-AM.png)

![](https://fsc.stevens.edu/content/images/2023/04/Screenshot-2023-04-10-at-11.33.36-AM.png)

![](https://fsc.stevens.edu/content/images/2023/04/Screenshot-2023-04-10-at-11.33.55-AM.png)

![](https://fsc.stevens.edu/content/images/2023/04/Screenshot-2023-04-10-at-11.34.12-AM.png)

By merging together all frameworks, following is concluded:

![](https://fsc.stevens.edu/content/images/2023/04/Screenshot-2023-04-10-at-11.35.01-AM.png)

### Conclusion

The research finds out that based on the existing portfolios available, backtesting is an efficient method in predicting Sharpe ratio, annual return and standard deviation. Bootstrapping works well when it comes to the prediction of Max.drawdown, and scenario-based testing should be preferred when it comes to predicting Sharpe ratio.