# Equities Tactical

Cumulative Performance |
55.24% |

Annualized Performance |
27.25% |

Annualized Volatility |
5.62% |

Max Drawdown (Day) |
-1.08% |

Average Daily Exposure |
38% |

Sharpe Ratio |
4.49 |

__1) Strategy Specific information__

Equities Tactical seeks an absolute return by taking positions on two underlyings, the S&P 500 and the Euro Stoxx 50.

The strategy implements temporary exposures to the underlyings, therefore representing a purely tactical and timing-based process, without any factor (value, growth), market-cap (small cap) or risk-weighted (equal-weight, minimum variance, etc.) approach.

The strategy’s default mode is 100% cash. When probable bullish or bearish movements are detected the strategy enters tactical tilts of limited duration, long or short the underlyings 50%, respectively. A behavioural analysis determines the excesses of the underlyings, and contrarian tilts are therefore implemented to trade against those excesses (underweight when bullish excess, overweight when bearish excess).

__2) The “Complexity” family of strategies__

All the strategies in the “Complexity” range are grounded in the research in complex systems, as those systems offer a better analogy with financial markets than Modern Financial Theory (MFT). The frequent bouts of exuberance, intense risk-aversion or herd behaviour observed on financial markets invalidate the main assumptions and conclusions of MFT and question the validity of long-term forecasting. In fact, prediction in financial markets more closely resembles weather forecasting than odds calculation in a lottery: in meteorology as in financial markets, the accuracy of predictions decreases rapidly as the length of the prediction increases. “Better predict 7 times the weather for tomorrow than once for the whole week!” Long-term statistics in weather forecasting actually back this saying, with around 85% success rate for overnight prediction versus slightly more than 50% over a 10-day horizon.

The endogenous instability and non-linearity observed on financial markets render inefficient the traditional equilibrium approaches and normal distribution assumptions: prediction has to accommodate the existence of unpredictable chaotic phases, and the appropriate tools should therefore be used. This toolbox is found in statistical physics, at its root the study of large numbers of particles and their interactions. This strategy makes large use of such tools. Moreover, the strategy uses concepts first introduced by B. Mandelbrot, the 20^{th}-century mathematician, who coined concepts such as self-similarity, Noah & Joseph effects, etc.

This strategy makes full use of those concepts to achieve a robust, dynamic and auto-adaptive pattern-recognition process. This performance engine is built on sound statistical physics methods, such as numerical analysis, denoising, filtering, etc.

High-frequency and high-quality data is needed for this strategy as only price data is used as raw material in the process. The quality of data is monitored continuously and 2 separate sources are used to ensure continuity and reliability. The numerical analysis relies on the use of 1-, 5- and 15-minute data points.