Rage Against the Machine Learning RFinance 20 May

Rage Against the Machine (Learning) R/Finance 20 May 2016 Rishi K Narang, Founding Principal, T 2 AM

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? 2

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 3

No, seriously, what the hell is it? • Supervised learning: non-parametric (model-free) input-output functions • classification (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 Kegl 4

Ok, let’s try a different tack: What the hell are we talking about when we talk about investing? 5

So what the hell do people usually do for Alpha Models? Return Category What Alpha Price Input Type Phenomenon Specification How Trend Reversion Forecast Target Fundamental Technical Sentiment Model Specification Yield Growth Conditioning Variables Quality Run Frequency Time Horizon High Frequency Bet Structure Directional Instruments Liquid Long Term Relative Illiquid Implementation 6

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? 7

What the hell is the problem, exactly? 1. It’s really hard. . . • very difficult to separate signal from noise, even with strong priors • very 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 reason HOWEVER, done well, ML has great promise as a way to discover subtler, less intuitive alphas 8