Generating Financial Series with Generative Adversarial Networks
The scarcity of historical financial data is a big hindrance in algorithmic trading model development. In the ever-changing economic reality we live in, countless models are tried and evaluated. Most of these models seek to extract information from the market by measuring a set of reasonable variables. Through backtesting, an overwhelming amount of these models are seen not to perform and are thus routinely discarded; some, however, do appear to work well. Out of these models, how many of their performances are just a product of overfitting? Given the lack of available historical data, this is a well-founded concern.