# Exploring Classic Quantitative Strategies

arXiv: https://arxiv.org/abs/2202.11309 · HTML: https://mlsanity.com/paper/2202.11309/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 23 Feb 2022.

## Builds on (most cited first, 3 of 3)

- [Matrix Decomposition and Applications](https://mlsanity.com/paper/2201.00145/) (2022)
- [A rigorous introduction to linear models](https://mlsanity.com/paper/2105.04240/) (2021)
- [Machine learning modeling for time series problem: Predicting flight ticket prices](https://mlsanity.com/paper/1705.07205/) (2017)

## Built on by (most cited first, 4 of 4)

- [Reducing overestimating and underestimating volatility via the augmented blending-ARCH model](https://mlsanity.com/paper/2203.12456/) (2022)
- [Gradient Descent, Stochastic Optimization, and Other Tales](https://mlsanity.com/paper/2205.00832/) (2022)
- [AdaSmooth: An Adaptive Learning Rate Method based on Effective Ratio](https://mlsanity.com/paper/2204.00825/) (2022)
- [A note on VIX for postprocessing quantitative strategies](https://mlsanity.com/paper/2207.04887/) (2022)

## Abstract (arXiv)

The goal of this paper is to debunk and dispel the magic behind the black-box quantitative strategies. It aims to build a solid foundation on how and why the techniques work. This manuscript crystallizes this knowledge by deriving from simple intuitions, the mathematics behind the strategies. This tutorial doesn't shy away from addressing both the formal and informal aspects of quantitative strategies. By doing so, it hopes to provide readers with a deeper understanding of these techniques as well as the when, the how and the why of applying these techniques. The strategies are presented in terms of both S&P500 and SH510300 data sets. However, the results from the tests are just examples of how the methods work; no claim is made on the suggestion of real market positions.