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Applications of GANs in Finance

This presentation highlights potential use cases of deep generative models, and Generative Adversarial Networks (GANs) in particular, in Finance. Essentially, these models are useful to generate realistic synthetic datasets. Quantitative Strategists, Traders, Asset and Risk Managers can find these novel techniques useful. Auditors and Regulators should also become aware of their existence as they may be source of new accounting frauds and misleading financial statements (deepfakes).

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Applications of GANs in Finance

  1. 1. Generating Realistic Synthetic Data in Finance Applications of GANs in Finance Gautier Marti HKML Research 15 October 2020 Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 1 / 61
  2. 2. Table of contents 1 Introduction 2 GANs explained GANs Milestones & Major Achievements How do GANs work? 3 Applications of GANs in Finance Applications Generating Synthetic Datasets to Avoid Strategy Overfitting Generating Alternative Realistic Historical Paths for Risk Estimation Training Machine Learning Models in the Cloud on Synthetic Data A Larger Data Market: Synthetic Datasets, A New Product Deepfakes of Financial Statements and Tools to Find Them Current State of the Art and Limitations 4 Conclusion and Questions? Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 2 / 61
  3. 3. Section 1 Introduction Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 3 / 61
  4. 4. Introduction (3 min. video) https://www.youtube.com/watch?v=97B8tuHwLY0 Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 4 / 61
  5. 5. Scope of the presentation In this talk, we will focus on doing a review of GANs success so far (mostly image generation), explaining how Generative Adversarial Networks (GANs) work. Then, we will present applications of these models for generating finance-related data, and the associated ’business’ use cases. Finally, we shall briefly discuss the current limitations and challenges to be overcome for a broader adoption of these models in the industry. Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 5 / 61
  6. 6. Section 2 GANs explained Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 6 / 61
  7. 7. Subsection 1 GANs Milestones & Major Achievements Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 7 / 61
  8. 8. GANs: A relatively new Deep Learning model (2014) Goodfellow, Ian, et al. “Generative adversarial nets.” Advances in neural information processing systems. 2014. Cited by 22354 papers as of 18 September 2020. Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 8 / 61
  9. 9. Text to Photo-realistic Image Synthesis (2016) https://arxiv.org/pdf/1612.03242.pdf Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 9 / 61
  10. 10. Deepfakes (2017) Ctrl Shift Face (YouTube channel) https://www.youtube.com/channel/UCKpH0CKltc73e4wh0_pgL3g Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 10 / 61
  11. 11. Edmond de Belamy (2018) Created by 3 students First artwork created using Artificial Intelligence to be featured in a Christie’s auction Sold for USD 432,500 Signed minG maxD Ex [log(D(x))] + Ez [log(1 − D(G(z)))] In French, “bel ami” means “good fellow”, a pun-tribute to Ian Goodfellow, the creator of GANs Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 11 / 61
  12. 12. StyleGAN (2019) & StyleGAN2 (2020) GANs can generate realistic fake human faces: Give it a try: https://thispersondoesnotexist.com/ NVIDIA StyleGAN paper: https://arxiv.org/pdf/1812.04948.pdf NVIDIA StyleGAN2 paper: https://arxiv.org/pdf/1912.04958.pdf Remark. In December 2019, Facebook took down a network of accounts with false identities, and mentioned that some of them had used profile pictures created with artificial intelligence. Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 12 / 61
  13. 13. Speech2Face & Wav2Pix (2019) GANs conditioned on speech of a person can output a realistic face with its correct gender, ethnicity, and approximate age: Relevant papers: Wav2Pix: https://arxiv.org/pdf/1903.10195.pdf Speech2Face: https://arxiv.org/pdf/1905.09773.pdf Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 13 / 61
  14. 14. Text to High Fidelity Speech Synthesis Generative adversarial networks have seen rapid development in recent years and have led to remarkable improvements in generative modelling of images. However, their application in the audio domain has received limited attention, and autoregressive models, such as WaveNet, remain the state of the art in generative modelling of audio signals such as human speech. To address this paucity, we introduce GAN-TTS, a Generative Adversarial Network for Text-to-Speech. Our architecture is composed of a conditional feed-forward generator producing raw speech audio, and an ensemble of discriminators which operate on random windows of different sizes. The discriminators analyse the audio both in terms of general realism, as well as how well the audio corresponds to the utterance that should be pronounced. To measure the performance of GAN-TTS, we employ both subjective human evaluation (MOS – Mean Opinion Score),as well as novel quantitative metrics (Fr´echet DeepSpeech Distance and Kernel DeepSpeech Distance), which we find to be well correlated with MOS. We show that GAN-TTS is capable of generating high-fidelity speech with naturalness comparable to the state-of-the-art models, and unlike autoregressive models, it is highly parallelisable thanks to an efficient feed-forward generator. Listen to GAN-TTS reading this abstract at https://storage.googleapis.com/deepmind-media/research/abstract.wav. Abstract from https://arxiv.org/pdf/1909.11646.pdf Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 14 / 61
  15. 15. GameGAN (2020) GameGAN is able to learn Pac-Man dynamics and produce a visually consistent simulation of the game: NVIDIA paper https://arxiv.org/pdf/2005.12126.pdf https://www.youtube.com/watch?v=4OzJUNsPx60 Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 15 / 61
  16. 16. Subsection 2 How do GANs work? Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 16 / 61
  17. 17. A GAN basic architecture Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 17 / 61
  18. 18. GAN training – step by step tutorial Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 18 / 61
  19. 19. GAN training – step by step tutorial Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 19 / 61
  20. 20. GAN training – step by step tutorial Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 20 / 61
  21. 21. GAN training – step by step tutorial Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 21 / 61
  22. 22. GAN training – step by step tutorial Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 22 / 61
  23. 23. GAN training – step by step tutorial Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 23 / 61
  24. 24. GAN training – step by step tutorial Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 24 / 61
  25. 25. GAN training – step by step tutorial Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 25 / 61
  26. 26. GAN training – step by step tutorial Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 26 / 61
  27. 27. GAN training – step by step tutorial Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 27 / 61
  28. 28. GAN training – step by step tutorial Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 28 / 61
  29. 29. GAN training – step by step tutorial Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 29 / 61
  30. 30. GAN training – step by step tutorial Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 30 / 61
  31. 31. GAN training – step by step tutorial Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 31 / 61
  32. 32. GAN training – step by step tutorial Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 32 / 61
  33. 33. GAN training – step by step tutorial Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 33 / 61
  34. 34. GAN training – step by step tutorial Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 34 / 61
  35. 35. GAN training – step by step tutorial Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 35 / 61
  36. 36. GAN training – step by step tutorial Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 36 / 61
  37. 37. GAN training – step by step tutorial Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 37 / 61
  38. 38. GAN training – step by step tutorial Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 38 / 61
  39. 39. GAN training – step by step tutorial Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 39 / 61
  40. 40. Conditional Generative Adversarial Nets Seminal paper: https://arxiv.org/pdf/1411.1784.pdf Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 40 / 61
  41. 41. Conditional Generative Adversarial Nets Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 41 / 61
  42. 42. Section 3 Applications of GANs in Finance Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 42 / 61
  43. 43. Subsection 1 Applications Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 43 / 61
  44. 44. Strategy Overfitting Scenario In September 2020, a naive or unscrupulous strat presents to his manager a new strategy: The strategy would have initiated a massive short in February 2020, and would have bet big on a rally starting late March 2020. The strat sells the strategy to his manager as able to pick-up early signals of market sell-offs and bounce-backs thanks to advanced machine learning and alternative data (obviously, what else?). The manager, excited, cannot wait but to deploy capital to it. How to fight against this industry-wide fallacy? Well-thought and carefully designed incentives schemes (e.g. reward a sound research framework rather than impressive in-sample backtests) Counterfactual thinking, realistic simulations of alternative paths Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 44 / 61
  45. 45. Strategy Overfitting – Simulations of Alternative Paths (1) Some alternative data: Second-hand car market in Hong Kong. A tabular dataset X Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 45 / 61
  46. 46. Strategy Overfitting – Simulations of Alternative Paths (2) Strat idea: When inventory builds up (more people are selling their cars than buying), then the Hang Seng Index plummets over the next quarter. Using GANs, we can sample new datasets ˆX ∼ X, or even ( ˆX, ˆy) ∼ (X, y), where y can be the HSI performance or any variable we aim at predicting (e.g. market+industry residualized returns of the respective carmakers). Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 46 / 61
  47. 47. Strategy Overfitting – Simulations of Alternative Paths (3) Method 1: Strategy stability Use a GAN to learn the distribution (X, y) of the tabular data (cf. https://arxiv.org/pdf/1907.00503.pdf) From the calibrated GAN, generate ˆX(n) , ˆy(n) N n=1 synthetic datasets Backtest the strategy on the N datasets, and collect perf. metrics Analyze the perf. metrics; Discard the strategy if not stable. Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 47 / 61
  48. 48. Strategy Overfitting – Simulations of Alternative Paths (4) In Koshiyama et al. (https://arxiv.org/pdf/1901.01751.pdf): Generate N synthetic datasets: Method 2: Strategy fine-tuning Find the parameters which maximize the average perf. over the N datasets Method 2bis: Strategy combination (ensemble models) Fit a model on each of the N datasets Predict using an ensemble of the N trained models GAN vs. Stationary Bootstrap (for generating the N synthetic datasets) Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 48 / 61
  49. 49. Risk-based Portfolio Allocation (1) Scenario You want to systematically allocate capital to your strategies. Literature claims that such or such method works better than all others by providing a dubious backtest. When you horse race the various allocation methods, results are not stable across periods and universes. How can we conclude anything useful? Realistic simulations! Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 49 / 61
  50. 50. Risk-based Portfolio Allocation (2) Method 3: Testing Portfolio Allocation on Alternative Historical Paths Use a GAN to generate realistic synthetic correlation matrices (cf. https://arxiv.org/pdf/1910.09504.pdf) Generate time series verifying the correlation structure Estimate portfolio weights (in-sample), measure out-of-sample risk Analyze performance of the portfolio allocation methods Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 50 / 61
  51. 51. Risk-based Portfolio Allocation (3) Question: Can we predict when a method (say the Hierarchical Risk Parity (HRP)) outperforms another one (say naive risk parity)? And why? Method 4: Understanding the outperformance of a method over another Extract features from the underlying correlation matrix Fit a ML model features → outperformance (using train, validation, test datasets) Verify if there is any good predictability Explain the predictions as a function of features Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 51 / 61
  52. 52. Risk-based Portfolio Allocation (4) Concrete example: HRP vs. naive risk parity High values for the cophenetic correlation coefficient are characteristic of a strong hierarchical structure. Thus, HRP outperforms naive risk parity when the underlying DGP has a strong hierarchical structure (nested clusters). cf. Jochen Papenbrock recent work for similar applications of Explainable AI (XAI) in finance (https://firamis.de/) Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 52 / 61
  53. 53. Training Machine Learning Models in the Cloud on Synthetic Data Scenario Your company forbids the transfer of sensitive data (e.g. trades & positions) to the Cloud It would be more relevant and cost-effective to train large and recent ML models in the Cloud (e.g. Amazon SageMaker) Method 5: Training in the Cloud on synthetic data Generate anonymized synthetic versions of the sensitive data Send the GAN-generated non-sensitive synthetic datasets to the Cloud Train Machine Learning models on these datasets in the Cloud Download the Machine Learning models on premise Fine-tune and apply the ML models on the original data Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 53 / 61
  54. 54. A Larger Data Market: Synthetic Datasets, A New Product As a data vendor: Use Case 1 You may gather interests from research labs and startups which cannot afford the price tag a hedge fund can for a dataset You cannot sell them the original premium dataset at a hard discount But you could sell them anonymized synthetic datasets based on the original one at a fraction of the price In some cases, realistic synthetic datasets may be sufficient, e.g. a researcher studying the structural properties of a supply-chain network rather than trying to predict markets from it cf. this paper https://arxiv.org/pdf/2002.02271.pdf (Feb 2020) from American Express researchers =⇒ A broader and more diverse base of clients Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 54 / 61
  55. 55. A Larger Data Market: Synthetic Datasets, A New Product As a data vendor, you should be aware that: Use Case 2 Quantitative trading firms are afraid of over-fitting Besides the original dataset, they may be interested in buying realistic synthetic versions of it: 1 Original dataset will be used in production for trading 2 Realistic synthetic versions can be used at the research stage Managers can distribute the synthetic datasets to their strat quants, then they can check for consistent results across the synthetic datasets and the original one (cf. Methods 1, 2 and 2bis previously discussed). =⇒ A new product offered at a premium on top of the original dataset Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 55 / 61
  56. 56. A Larger Data Market: Synthetic Datasets, A New Product As a data vendor, you know that: Use Case 3 Getting the client’s legal and compliance departments approval can be a long process, even for a simple trial In some cases, it can result in the loss of business to a competitor So that the prospect has a quick first overview, you may be able to send over a synthetic dataset. This should not raise the scrutiny of legal (e.g. no contractual terms to check) or compliance (e.g. no material non-public information in anonymized synthetic data by construction). =⇒ Easier to maintain customer engagement and pitch new datasets Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 56 / 61
  57. 57. Deepfakes of Financial Statements and Tools to Find Them An an auditor: Scenario You have to assess journal entries comprising millions of transactions You use ’Computer Assisted Audit Techniques’ which range from rule-based tests designed according to past frauds to basic statistical methods for detecting accounting anomalies Fraudsters may adapt deepfakes to business accounting cf. this paper https://arxiv.org/pdf/1910.03810.pdf (Oct 2019) about deepfakes in accounting =⇒ Auditors and regulators should learn the techniques to uncover these special types of deepfakes. Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 57 / 61
  58. 58. Subsection 2 Current State of the Art and Limitations Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 58 / 61
  59. 59. Current State of the Art and Limitations This discussion is by nature technical, but we can highlight the following: GANs can generate realistic tabular datasets (2019), but models trained on synthetic data only were shown inferior to the ones trained on the original data GANs can offer a high degree of anonymization, but not all of them are built to be differentially private meaning they might leak information about the original data We know how to GAN-generate realistic synthetic financial time series (e.g. S&P 500 returns); We know how to GAN-generate realistic synthetic financial correlation matrices (of the S&P 500 constituents, for example); However, we do not know yet how to GAN-generate realistic multivariate financial time series, i.e. verifying both the time series and the cross-sectional stylized facts Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 59 / 61
  60. 60. Section 4 Conclusion and Questions? Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 60 / 61
  61. 61. Conclusion and Questions? Deep generative models and GANs in particular are an exciting new technology. In finance, they are actively researched in a few places but results are not widely advertised. We have to rely on top tech companies and academic labs to drive the fundamental understanding and improvements of these models. contact@hkml-research.com Gautier Marti (HKML Research) Applications of GANs in Finance 15 October 2020 61 / 61

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