World Business StrategiesServing the Global Financial Community since 2000

Thursday 5th July, 17.30 BST

Overview, Math Background, Trend Following, Mean Reversion

Quant Trading – Definitions and Motivation

  • Passive vs Active, Where’s the value?
  • Alternatives to passive
  • Quant trading flavours – CTAs, Quant Funds, Quant Equities Funds, E-trading and HFT

Quant Trading as an Industry

  • Systematic Trading as an Industry:
    • Structure of Quantitative/CTA market 
    • Trends in AUM
    • Performance
    • Where to find out more
  • Shared infrastructure for algo traders
    • Platforms and APIs
    • Python libraries
    • SQL, NOSQL, etc
    • Process pipelines
  • Overview of Strategies
    • Momentum or Trend Following
    • Mean reversion and RV
    • Carry
    • Value
    • Vol Selling, Vol Risk Premium
    • Statistical Arbitrage
    • Equities Quant
    • Overview of pitfalls

The Basics: Quick Overview of Background Maths/Stats

  • Time-series stats/econometrics (Discrete time)
  • Stochastic Differential Equations (Continuous time)
  • Stationarity, Non-stationarity, Cointegration and Tests

Emphasis on modern fitting techniques, methods of solution, properties of solutions

Fads, Fancies and Trends

  • Momentum
    • Rationales
    • Persistence and History
    • Mathematical/Statistical Properties
      • Continuous time characterisations – power options and underlying Sharpes
      • Gaussian returns –  Correlation, Sharpe, Skewness, Kurtosis and Stderrs
      • Discrete time – skewness term-structure, autocorrelation and volatility
      • Non-linear filter – Impact and benefits
      • Returns distribution engineering, limitations and further direction
      • Forecasting, Causation and Correlation
  • Impact on design – option value vs reactivity, skewness and Sharpe
    • Momentum signals in practice.
      • Crossing moving averages
      • Z-scores
      • Filters
      • Technical indicators
      • Econometric forecasting, ARIMA models
    • Timeseries vs Cross-sectional Momentum
    • On the street–CTAs and Quant Trend following vs Quant Equities

Mean-Reversion Indecisive Markets? 

  • Mean Reversion
    • Rationales: Liquidity provision or Overreaction
    • Measures of Liquidity, Measures of MR profitability
    • Relationships of measures of liquidity
    • When should you expect to make money?

ASSIGNMENT

  • Indices, Futures, Coding trend-following rules and mean-reversion rules, summary statistics

Thursday 12th July, 17.30 BST

Mean-Reversion and RV trading

Mean-Reversion Indecisive markets- Cont’d

  • Mean Reversion
    • Formal tests – Stationary vs Non-Stationary processes (traditional timeseries analysis)
      • Univariate Tests – ADF, KPSS, Var-Ratio, Trend-efficiency
      • Multivariate tests – Johansen, Nyblom
      • Cointegration and PCA
      • Shortcomings – Time-variation
      • Change-points
    • Relation to Relative Value (RV) Trading

Catching falling knives

  • RV Trading and its flavours
    •  I(1) vs I(0): RV vs Trend
    •  RV Trades in Delta-One space
      • Pairs trading / Spread trading
      • Butterflies, baskets, condors and so on.
    •  Timing Entry points and mean reversion. Optimising
    •  Stationarity: Are RV trades stationary? Macroeconomic trends and RV.

When trades go bad…

  • Change-point Detection and Regime Switches
    • Breakpoint tests
    • Switching Kalman-Filters, Regime switching
    • In Practice – OOS vs IS fits
  • Stop Losses and scaling

Seeing things more simply

  • Robust prediction using mean reversion
    • Exponentially weighted moving averages (EWMA), Double exponential
    • Moving averages as Kalman filters, EWMA as best OOS performers
    • Signal vs Noise

Carry – When things stay the same

  • Carry and Roll
    •  P vs Q measures.
      •      Carry as P measure “expectation”
    •  Calculating Carry and Roll Carry strategies and performance
      •      Instruments: Futures, swaps, bonds, equities, fx, options
  • Elements of expected returns, Decompositions, and forecasting.
    • How much carry can you expect to take home?

 What should it really be worth? 

  • What is value?
    • Value Trading, Value Investing, Valuations vs Pricing
    • Measures of Value:
      • Equities/Credit value
      • Value in rates
      • Securitized transactions
      • Long-term proxies for value outside equities
  • Timeseries and Stationarity vs  Horizon
  • Recap: Timeseries – mean-reverting/trending/mean-reverting

ASSIGNMENT

  • Coding trend-following rules and mean-reversion rules, summary statistics

Thursday 19th July, 17.30 BST

Portfolio Strategies and Equities Quant

Combining lots of things

  • Measures of performance and risk:
    • Sharpe, Sortino, Calmar
    • Skewness, Kurtosis
    • VaR, CVaR
    • Downside Measures
  • Portfolio Strategies
    • MVO review
      • Optimal Shape Ratios, Risk Parity and Min Variance
      • MVO as regression – tests of optimality
    • Bootstrap methods in MVO
    • Black-Litterman and other Bayesian Approaches
    • Restrictions – Min-Variance, Risk-Parity and Hierarchical Risk Parity
    • Portfolio Strategy Design
      • Forecasting vs optimal weights-measurement of ‘goodness’
      • Objectives without theory – overfitting
      • Sharpe Ratios – distributions and significance (t stats and asymptotic normality)

Factoring Equities

  • Risk Premia and getting compensated
    • CAPM
    • Arbitrage Pricing Theory (APT)
    • Finding Risk Factors
  • Equities Factors
    • Fama-French
    • Momentum
    • Size
    • The Factor Zoo, Finding new premia
  • The Equities Quant Industry
  • Alternative data-sources and Machine Learning in Equities quant

ASSIGNMENT

  • Performance measurement, Quadratic Programming, and MVO.

Thursday 25th July, 17.30 BST

Overfitting, Data snooping, and Rehash

Data snooping, P-Hacking and Bad Science

  • Data snooping
    • Definition
    • Type I vs Type II errors
    • Snooping outcome –  Bad models. Poor OOS performance
  • P-hacking and non-reproducible results
    • Irreproducibility crisis in science
    • Overfitting in finance
  • Standard fixes – Train/Test/Holdout
    • Holdout overfitting, Kaggle competitions
  • Multiple testing methods
    • P-value adjustments
    • Bootstrap based tests
    • Tests of Data snooping
    • Cross-Validation, Covariance Penalities
    • Various other studies / tests
  • Data snooping and (Robust) Machine Learning
  • Preventing data-snooping in practice
  • Summary

 Rehash 

  • Key take-aways
  • Role of Machine Learning / Big Data
  • Designing your own strategies
  • Doing active research
  • Next steps

Thursday 30th August

To final project will be discussed and agreed with the instructor.

Thursday 6th September (Start Time: 17.30 BST) 

Final Project Review, Catch up & Feed Back Webinar Week.

  • Discount Structure
  • Special Offer
    When two colleagues attend the 3rd goes free!

  • 50% Academic Discount
    (FULL-TIME Students Only)

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