RFinance 20 May 2016 Rishi K Narang Founding Principal T2AM What the hell are we talking about What the hell is machine learning How the hell does it relate to investing Why the hell am I mad at it ID: 554342
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Slide1
Rage Against the Machine (Learning)
R/Finance20 May 2016Rishi K Narang, Founding Principal, T2AMSlide2
What the hell are we talking about?
What the hell is machine learning?How the hell does it relate to investing?Why the hell am I mad at it?
2Slide3
What the hell is machine learning?
Method for automating design of models by algorithmically studying data
Traditionally, model design is a human activity (e.g., first and second steps of the Scientific Method)
Related (read: conflated) terms:
Data mining – attempts to discover previously unknown properties in data
Artificial intelligence – sort of the parent field of ML. seeks to replicate (general) intelligence within a computer. learning is one (very crucial) kind of intelligence
Data science – umbrella covering all of these terms
Consider “data driven investing” instead of ML
3Slide4
No, seriously, what the hell is it?
Supervised learning: non-parametric (model-free) input-output functionsclassification (e.g., Trees, SVM)regression (e.g., Gaussian processes)
Unsupervised learning: non-parametric data representation
clustering (e.g., k-means)
dimensionality reduction (e.g., ISOMAP)
density estimation (e.g., kernel density)Reinforcement learning:learning + dynamic control: learn to behave in an environment to maximize cumulative reward
credit:
Balasz
Kegl4Slide5
Ok, let’s try a different tack: What the hell are we talking about when we talk about investing?
5Slide6
So what the hell do people usually do for Alpha Models?
6
What
Return Category
Input Type
Phenomenon
Alpha
Price
How
Implementation
Specification
Time Horizon
High Frequency
Long Term
Bet Structure
Directional
Relative
Instruments
Liquid
Illiquid
Forecast Target
Model Specification
Conditioning Variables
Run Frequency
Trend
Reversion
Technical Sentiment
Quality
Yield
Growth
FundamentalSlide7
How the hell do you use machine learning to forecast returns?
What defines the current market condition?By what technique do you identify conditions and expected outcomes?What data should you (let the machine) study?
7Slide8
What the hell is the problem, exactly?
1. It’s really hard...very difficult to separate signal from noise, even with strong priorsvery difficult to prove your algorithm is doing what you meant it to do
...so most people attempting to utilize these approaches are simply not qualified
2. It’s a buzzword...
my guess is that there are now ~100-200 quant funds claiming to utilize ML techniques, versus maybe 10 three years ago
investors are also very excited...so much of what is being paraded about as “ML” is in practice just linear regression
poseurs are annoying
3. Almost no one utilizing ML is successful
especially in the alpha model itself (as opposed to the meta-alpha / signal combination phase) is successful...so all the fuss is for no particularly good reasonHOWEVER, done well, ML has great promise as a way to discover subtler, less intuitive alphas8