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Forecasting Structural Breaks with Application to Algorithmic Trading

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Forecasting Structural Breaks in
Application to Algorithmic Trading
Results and Improvements
King Fung Wong

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Hidden Markov Model
• Initial Transition Matrix
• Emission Matrix
• Transition Matrix, e.g.
𝑃00 𝑃01
𝑃10 𝑃11

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Transition matrices as alpha signals
• Assuming features n.i.i.d., emitted by the hidden states
• Features have predictive...

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Forecasting Structural Breaks with Application to Algorithmic Trading

  1. 1. Forecasting Structural Breaks in Application to Algorithmic Trading Results and Improvements King Fung Wong
  2. 2. Hidden Markov Model • Initial Transition Matrix • Emission Matrix • Transition Matrix, e.g. 𝑃00 𝑃01 𝑃10 𝑃11
  3. 3. Transition matrices as alpha signals • Assuming features n.i.i.d., emitted by the hidden states • Features have predictive power on market dynamics • Change in transition probabilities imply structural breaks in shift of market regime • Long if the transition probability goes up, short vice versa
  4. 4. Further Improvements • Hyperparameters tuning (length of rolling window, baseline setting, trading rules etc.) • Features do not behave n. i. i. d. • No bootstrapping, stress period to backtest (doubling trade size, avoid overfitting etc.) • Does it create/ contribute to “a disorderly market”? MiFID ii
  5. 5. Thank you • king.wong@cass.city.ac.uk

Editor's Notes

  • Features: bid-ask spread, implied cross rate arbitrage
    Assuming features n.i.i.d., emitted by hidden states
    Features have predictive on market dynamics
    Transition matrix based on fitted time series
    Change in transition probabilities imply structural breaks in market/ shift of market regime
    Long if the transition probability goes up, short vice versa

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