Nitish Maini, General Manager, WorldQuant LLC
| May 22, 2018
Session held at the London Big Data and Machine Learning Revolution event in April 2018.You can also access the full slides (video pending compliance approval).
Nitish focuss on the similarities and differences between quantitative and discretionary investing with an emphasis on the importance of data. Watch as he hosts a demonstration of quantitative alpha using a web based simulator.
Three main questions are addressed:
: is a rule-based strategy that utilizes computer models to identify investments and to execute most of the trades.
: By testing against historical data, Quants can set leverage based on the worst drawdown a given strategy has endured.
: Quants identify, trade and monitor multiple strategies across many markets for a large number of instruments.
: Quants can take advantage of the big data revolution
: Because of the minimal human involvement required, burnout and execution mistakes can be reduced for Quants.
: Quants have nearly doubled their share of stock trades from 14% in 2013 to 27% in Q2 2017.
: In the past five years (as in Q2 2017), quant strategies gained about 5.1% a year, while the average hedge fund rose 4.3%.
: Quant strategies account for more than 30% ($932 billion) of all hedge fund assets in Q2 2017, up from 25% ($408 billion) in 2009.
An alpha is a mathematical, predictive model of the performance of financial instruments.
On the ideas axis, they can be very simple ideas, or there can be a lot of individual complex ideas to generate.
There are a lot of datasets to generate alphas and there are alot of datasets available in the marketplace that can be used.
One must identify where you want to focus on in order to build the alpha. This decision is based on the capitalization of the stock market in that particular country or region or it depends on the rules or regulations govern by that country.
In terms of universes, a Quant may want to trade the stocks based on liquidity, a particular sector, industry or they might want to build their own groups depending on their objective statement.
In terms of the last axis and the most interesting, is the performance parameters of this strategy. We can think that the sharp returns can be used to analyse the results and are used of analysing the results, but for me it's a big source of alpha ideas and a big source of identification.
First we put in a very basic expression using mathematical techniques which have already been coded into the platform, then we will identify the data and then we will execute our strategy.
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