Lead Data Scientist
S&P Global Ratings
Conference Day Two: 17 September 2019
Tuesday, September 17th, 2019
Financial traders typically assess their investment strategies against historical market prices. When using only data from the past, market conditions outside historical bounds are ignored. The augmentation of historical prices with synthetic prices generated using simulations provide an effective supplement. This talk describes an Agent Based Model to simulate market data for various what-if scenarios such as sudden price crash, bearish or bullish market sentiment and shock contagion. Unlike traditional agents that make trading decisions based on rules, heuristics or simple learners, a new class of deep intelligence agents that exploit the latest advances in artificial intelligence are used for decision making. The simulation model is validated by examining its ability to replicate the main statistical properties of financial markets.