Interpretability in deep learning for finance: a case study for the Heston model

• URL: http://arxiv.org/abs/2104.09476v1
• Date: Mon, 19 Apr 2021 17:37:17 GMT
• Title: Interpretability in deep learning for finance: a case study for the Heston model
• Authors: Damiano Brigo, Xiaoshan Huang, Andrea Pallavicini, Haitz Saez de Ocariz Borde
• Abstract summary: We focus on the calibration process of the volatility model, a subject recently tackled by deep learning algorithms. We investigate the capability of local strategies and global strategies coming from cooperative game theory to explain the trained neural networks. We find that fully-connected neural networks perform better than convolutional neural networks in predicting and interpreting the Heston model prices to relationship parameters.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning is a powerful tool whose applications in quantitative finance are growing every day. Yet, artificial neural networks behave as black boxes and this hinders validation and accountability processes. Being able to interpret the inner functioning and the input-output relationship of these networks has become key for the acceptance of such tools. In this paper we focus on the calibration process of a stochastic volatility model, a subject recently tackled by deep learning algorithms. We analyze the Heston model in particular, as this model's properties are well known, resulting in an ideal benchmark case. We investigate the capability of local strategies and global strategies coming from cooperative game theory to explain the trained neural networks, and we find that global strategies such as Shapley values can be effectively used in practice. Our analysis also highlights that Shapley values may help choose the network architecture, as we find that fully-connected neural networks perform better than convolutional neural networks in predicting and interpreting the Heston model prices to parameters relationship.

Related papers

• Transferable Post-training via Inverse Value Learning [83.75002867411263]
  We propose modeling changes at the logits level during post-training using a separate neural network (i.e., the value network) After training this network on a small base model using demonstrations, this network can be seamlessly integrated with other pre-trained models during inference. We demonstrate that the resulting value network has broad transferability across pre-trained models of different parameter sizes.
  arXiv  Detail & Related papers  (2024-10-28T13:48:43Z)
• Convergence Analysis for Deep Sparse Coding via Convolutional Neural Networks [7.956678963695681]
  We introduce a novel class of Deep Sparse Coding (DSC) models. We derive convergence rates for CNNs in their ability to extract sparse features. Inspired by the strong connection between sparse coding and CNNs, we explore training strategies to encourage neural networks to learn more sparse features.
  arXiv  Detail & Related papers  (2024-08-10T12:43:55Z)
• Coding schemes in neural networks learning classification tasks [52.22978725954347]
  We investigate fully-connected, wide neural networks learning classification tasks. We show that the networks acquire strong, data-dependent features. Surprisingly, the nature of the internal representations depends crucially on the neuronal nonlinearity.
  arXiv  Detail & Related papers  (2024-06-24T14:50:05Z)
• How neural networks learn to classify chaotic time series [77.34726150561087]
  We study the inner workings of neural networks trained to classify regular-versus-chaotic time series. We find that the relation between input periodicity and activation periodicity is key for the performance of LKCNN models.
  arXiv  Detail & Related papers  (2023-06-04T08:53:27Z)
• Creating Powerful and Interpretable Models withRegression Networks [2.2049183478692584]
  We propose a novel architecture, Regression Networks, which combines the power of neural networks with the understandability of regression analysis. We demonstrate that the models exceed the state-of-the-art performance of interpretable models on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-07-30T03:37:00Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)
• Deep Neural Networks and Neuro-Fuzzy Networks for Intellectual Analysis of Economic Systems [0.0]
  We consider approaches for time series forecasting based on deep neural networks and neuro-fuzzy nets. This paper presents also an overview of approaches for incorporating rule-based methodology into deep learning neural networks.
  arXiv  Detail & Related papers  (2020-11-11T06:21:08Z)
• Learning Connectivity of Neural Networks from a Topological Perspective [80.35103711638548]
  We propose a topological perspective to represent a network into a complete graph for analysis. By assigning learnable parameters to the edges which reflect the magnitude of connections, the learning process can be performed in a differentiable manner. This learning process is compatible with existing networks and owns adaptability to larger search spaces and different tasks.
  arXiv  Detail & Related papers  (2020-08-19T04:53:31Z)
• Learning Queuing Networks by Recurrent Neural Networks [0.0]
  We propose a machine-learning approach to derive performance models from data. We exploit a deterministic approximation of their average dynamics in terms of a compact system of ordinary differential equations. This allows for an interpretable structure of the neural network, which can be trained from system measurements to yield a white-box parameterized model.
  arXiv  Detail & Related papers  (2020-02-25T10:56:47Z)
• A deep learning framework for solution and discovery in solid mechanics [1.4699455652461721]
  We present the application of a class of deep learning, known as Physics Informed Neural Networks (PINN), to learning and discovery in solid mechanics. We explain how to incorporate the momentum balance and elasticity relations into PINN, and explore in detail the application to linear elasticity.
  arXiv  Detail & Related papers  (2020-02-14T08:24:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.