# Generative Adversarial Networks in Business and Social Science
## 4 Statistics on GAN Publications in Economics
### Financial Economics  
The publications on GANs in the field of Financial Economics are displayed in Table 4. The first paper [189] proposes an innovative optimisation framework for the portfolio optimisation problem by leveraging GANs to develop a heuristic approach to the classic Markowitz model. This involves three steps: (i) using a GAN to select the initial set of assets for investment, (ii) solving the portfolio optimisation problem to determine the weights of the assets from the previous step, and (iii) enhancing the obtained solutions.  
In the year 2020, [190], proposed the use of CGAN for the calibration and aggregation of trading strategies. They designed an experiment involving multiple trading strategies across 579 assets and yielded successful results and clear advantages over other tools.  
In [191], a data-driven approach is proposed to calibrate local stochastic volatility models using GANs, and it demonstrates its feasibility and showcases several significant advantages over other methods.  
An approach using a GAN-based model, referred to as Quant GAN, is introduced in [192] for modelling discrete-time financial time series. Although the models of time series have historically been notably challenging to train, advances in GANs showcase their potential to provide competitive outcomes. Thus, as training procedures continue to evolve, GANs hold the promise of delivering even better performance in the future.  
In [193], a GAN model named WCGAN-GP is developed to address the challenges existing in actuarial domains, since actuaries often require a great amount of data. Furthermore, the datasets employed by actuaries frequently encounter issues with imbalanced classes, wherein events of interest related to risk are underrepresented. The authors provide several contributions to actuaries, with applications including the generation of new samples, data augmentation, enhancement of predictive models, anomaly detection, and imputation of missing data.  
Another paper from 2022 in the financial domain is [194], which addresses the problem of maximising utility under uncertain parameters through a robust optimisation process that can be interpreted as a stochastic differential game for two players with a zero-sum. This algorithm is tested in real markets and shows that robust portfolios tend to have higher expected utility and are more stable during market downturns. To solve the value function, the authors derive the analytical solution in the case of logarithmic utility and obtain numerical approximations using various methods, including GANs, and demonstrate a high level of accuracy in the numerical results.  
It is well-known that the major complexity in valuing convertible bonds lies in modelling the underlying stock-return process. A GAN called DeepPricing that generates financial time series capable of replicating a risk-neutral stock return process while preserving statistical properties is presented in [195]. The authors declare that it is more flexible and accurate at capturing the dynamics of the underlying stock-return process.  
The GANs used in [196] as generators of economic scenarios can be expanded into an entire internal market risk model by encompassing a suitable number of risk factors. The results obtained are comparable to regulatory-approved internal models in Europe. Hence, GANs can be considered as an alternative method for market risk modelling. Furthermore, the authors show how a GAN can serve as an economic scenario generator for market risk calculation within insurance companies.  
A Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is given in [197], where it is applied to the stock market, futures market, and cryptocurrency market. The results demonstrate that datasets generated from original asset prices by WGAN-GP simulate asset prices well, thereby showcasing the potential of a market simulator for trading analysis.