Just about any field one can think of—finance, industry, national security, science, medicine, sports, and many others—can utilize simulation analysis to evaluate risks and explore new possibilities.
The quality of such analysis depends on how realistic the simulated environment is.
This blog post uses a Wasserstein GAN + gradient penalty to create lifelike financial data. Two features lead to greatly improved results:
Using recurrent neural networks. RNNs can produce a correlation structure in the generated data that fully connected neural networks weren’t able to accomplish.
Providing the generator and discriminator with conditioning information that tells them which environment they are operating in when evaluating a sample.
The additional information, in this case it’s a market volatility index, also offers a major benefit when we want to use the generator for a practical purpose. We can create different types of data depending on what kind of setting we tell the generator it’s working in.
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