# Can robust optimization offer improved portfolio performance?: An empirical study of Indian market

## TL;DR

This paper empirically evaluates robust optimization models for portfolio selection in the Indian market, comparing their performance to the traditional Markowitz model using real market data.

## Contribution

It provides the first empirical analysis of three robust optimization models in the Indian market, demonstrating their viability as alternatives to the Markowitz model.

## Key findings

- Robust models perform comparably or better than Markowitz in real market data.
- Robust approaches are effective with different sample sizes and data types.
- Empirical evidence supports robust optimization as a practical portfolio tool.

## Abstract

The emergence of robust optimization has been driven primarily by the necessity to address the demerits of the Markowitz model. There has been a noteworthy debate regarding consideration of robust approaches as superior or at par with the Markowitz model, in terms of portfolio performance. In order to address this skepticism, we perform empirical analysis of three robust optimization models, namely the ones based on box, ellipsoidal and separable uncertainty sets. We conclude that robust approaches can be considered as a viable alternative to the Markowitz model, not only in simulated data but also in a real market setup, involving the Indian indices of S&P BSE 30 and S&P BSE 100. Finally, we offer qualitative and quantitative justification regarding the practical usefulness of robust optimization approaches from the point of view of number of stocks, sample size and types of data.