Most firms and portfolio managers rely on backtests (or historical simulations of performance) to select investment strategies and allocate them capital. Standard statistical techniques designed to prevent regression over-fitting, such as hold-out, tend to be unreliable and inaccurate in the...

A large number of quantitative hedge funds have historically sustained losses. In this study we argue that the backtesting methodology at the core of their strategy selection process may have played a role.* Most firms and portfolio managers rely on backtests (or historical simulations of...

We prove that high simulated performance is easily achievable after backtesting a relatively small number of alternative strategy configurations, a practice we denote “backtest overfitting”. The higher the number of configurations tried, the greater is the probability that the backtest is...

* It has been estimated that the current size of the asset management industry is approximately US$58 trillion.* Portfolio optimization is one of the problems most frequently encountered by financial practitioners. It appears in various forms in the context of Trading, Risk Management and...

Multiple empirical studies have shown that Order Flow Imbalance has predictive power over the trading range.The PIN Theory (Easley et al. [1996]) reveals the Microstructure mechanism by which: Market Makers adjust their trading range to avoid being adversely selected by Informed Traders;...

SEC and CFTC reports estimate that High Frequency strategies are responsible for about 60% of all transactions on U.S. shares. In Europe, this percentage is around 40% and growing. High Frequency strategies are those characterized by a brief holding period, which can range from a split second to...

Execution traders know that market impact greatly depends on whether their orders lean with or against the market. We introduce the OEH model, which incorporates this fact when determining the optimal trading horizon for an order, an input required by many sophisticated execution strategies....

Investing can be characterized as a data science problem. While investment firms have attracted scientific talent, they have done a poor job at developing it. Firms hire specialists, but entice them to become generalists (e.g., portfolio managers). Under the ubiquitous silo/platform structure,...

A large portion of Macroeconomic and Financial research is built upon classical applications of Linear Algebra (such as regression analysis) and Stochastic Calculus (such as valuation models). As a result, most Macroeconomic and Financial research has inherited a focus on geometric locations...

Inspired by visualization techniques à la Feynman, we introduce Stochastic Flow Diagrams (SFDs), a new mathematical approach to represent complex dynamic systems into a single weighted digraph. This topological representation provides a way to visualize what otherwise would be a morass of...