What is Quant?

Quantitative analysis (QA) in finance is an approach that emphasizes mathematical and statistical analysis to help determine the value of a financial asset, such as a stock or option. Quantitative trading analysts (also known as “quants”) use a variety of data—including historical investment and stock market data—to develop trading algorithms and computer models.

The information generated by these computer models helps investors analyze investment opportunities and develop what they believe will be a successful trading strategy. Typically, this trading strategy will include very specific information about entry and exit points, the expected risk of the trade, and the expected return. The ultimate goal of financial quantitative analysis is to use quantifiable statistics and metrics to assist investors in making profitable investment decisions. In this article, we review the history of quantitative investing, compare it to qualitative analysis, and provide an example of a quant-based strategy in action.

Did you know!

As of 2019, ETFs and index funds together manage more US equities than human-managed asset managers. Across the $31 trillion of US stock market value, quant funds now own 35.1% of market capitalization, compared to 24.3% of human-managed funds.

How it works?

Quantitative value investing, also known as Systematic value investing, is a form of value investing that analyzes fundamental data such as financial statement line items, economic data, and unstructured data in a rigorous and systematic manner. Quantitative investing made three things possible – studying larger numbers of stocks simultaneously, decisions based on empirical evidence rather than on subjective forecasts, and a systematic approach to portfolio management. Quantitative investing models are based on probabilities and an expected distribution of returns. This means the expected risk and return can be more accurately predicted, but this also requires a large enough sample size to be effective. Quant funds therefore typically hold a higher number of securities than actively managed funds.

Process

Robust multi-layer risk management systems and an automated execution platform.
Leading systematic trader, all our quant-based proprietary trading models go through a rigorous process of development in-house and are back tested over long periods of time before going live.

  • Our automated strategies are deployed across asset classes (equity, currency, commodities, debt) and instruments (futures and options).
  • Once the models are built and the information is gathered, quants use the data to set up automated trades of securities.
  • Quantitative trading analysts (quants) identify trading patterns, build models to assess those patterns, and use the information to make predictions about the price and direction of securities.
  • Quantitative analysis is different from qualitative analysis, which looks at factors such as how companies are structured, the makeup of their management teams, and what their strengths and weaknesses are.

1. Hypothesis

Market Data Study, Generation of trading ideas. Deployment of strategy using automated trading systems.

2. Back-testing

It’s a process of testing a trading strategy on historical market data to see how it would have performed under those trading conditions. Quantitative Developers and Analysts will use a market simulator to evaluate the trading strategy. Back-testing and validation using statistical analysis on historical data for the last 10-15 years.

3. Automated Engine

Deployment of strategy using automated trading systems. Opportunity Scanning and Position Building and gradual scale-up and increase in leverage.

4. Risk management

Real Time Risk Monitoring for positions and exposure • Automated alerts and hedging strategies in place. The idea is that investors should take no more risk than is necessary to achieve their targeted level of return.

5. Optimization

Enabling pre-sales tasks and post trade analytics for optimization. Running the model with live market data, Optimization, Scaling and Portfolio selection.