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Financial simulators play an important role in enhancing forecastingaccuracy, managing risks, and fostering strategic financial decision-making.Despite the development of financial market simulation methodologies, existingframeworks often struggle with adapting to specialized simulation context. Wepinpoint the challenges as i) current financial datasets do not contain contextlabels; ii) current techniques are not designed to generate financial data withcontext as control, which demands greater precision compared to othermodalities; iii) the inherent difficulties in generating context-aligned,high-fidelity data given the non-stationary, noisy nature of financial data. Toaddress these challenges, our contributions are: i) we proposed the ContextualMarket Dataset with market dynamics, stock ticker, and history state ascontext, leveraging a market dynamics modeling method that combines linearregression and Dynamic Time Warping clustering to extract market dynamics; ii)we present Market-GAN, a novel architecture incorporating a GenerativeAdversarial Networks (GAN) for the controllable generation with context, anautoencoder for learning low-dimension features, and supervisors for knowledgetransfer; iii) we introduce a two-stage training scheme to ensure thatMarket-GAN captures the intrinsic market distribution with multiple objectives.In the pertaining stage, with the use of the autoencoder and supervisors, weprepare the generator with a better initialization for the adversarial trainingstage. We propose a set of holistic evaluation metrics that consider alignment,fidelity, data usability on downstream tasks, and market facts. We evaluateMarket-GAN with the Dow Jones Industrial Average data from 2000 to 2023 andshowcase superior performance in comparison to 4 state-of-the-art time-seriesgenerative models.

Market-GAN: Adding Control to Financial Market Data Generation with Semantic Context