Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach
Independent Researcher（独立研究者）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Southern Methodist University（南卫理公会大学）；Shanghai University of Finance and Economics（上海财经大学）
Shun Liu, Kexin Wu, Chufeng Jiang, Bin Huang, Danqing Ma
In the realm of cryptocurrency, the prediction of Bitcoin prices has garnered substantial attention due to its potential impact on financial markets and investment strategies. This paper propose a comparative study on hybrid machine learning algorithms and leverage on enhancing model interpretability. Specifically, linear regression(OLS, LASSO), long-short term memory(LSTM), decision tree regressors are introduced. Through the grounded experiments, we observe linear regressor achieves the best performance among candidate models. For the interpretability, we carry out a systematic overview on the preprocessing techniques of time-series statistics, including decomposition, auto-correlational function, exponential triple forecasting, which aim to excavate latent relations and complex patterns appeared in the financial time-series forecasting. We believe this work may derive more attention and inspire more researches in the realm of time-series analysis and its realistic applications.
Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods
Computer Science Department, College of Computers and Information Technology, Taif University（计算机科学系，计算机与信息科技学院，泰夫大学）；Department of Computer Science, Taif University（计算机科学系，泰夫大学）
Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review
Georgia Institute of Technology, College of Computing（佐治亚理工学院计算机学院）；Harvard University, School of Engineering and Applied Sciences（哈佛大学工程与应用科学学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Columbia University, The Fu Foundation School of Engineering and Applied Science（哥伦比亚大学傅以文工程与应用科学学院）
Enhancing Profitability and Investor Confidence through Interpretable AI Models for Investment Decisions
School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST)（国家科技大学电气工程与计算机科学学院）；Department of Computer Science, Bahria University（巴里亚大学计算机科学系）；College of Computer and Information Sciences (CCIS), Prince Sultan University（苏丹王子大学计算机与信息科学学院）
Exploring the Interpretability of Forecasting Models for Energy Balancing Market
Department of Informatics, University of Oslo（信息学院，奥斯陆大学）；Department of Electric Energy, Norwegian University of Science and Technology（电力能源系，挪威科学与技术大学）
Recurrent Neural Networks (RNNs) represent the de facto standard machine learning tool for sequence modelling, owing to their expressive power and memory. However, when dealing with large dimensional data, the corresponding exponential increase in the number of parameters imposes a computational bottleneck. The necessity to equip RNNs with the ability to deal with the curse of dimensionality, such as through the parameter compression ability inherent to tensors, has led to the development of the Tensor-Train RNN (TT-RNN). Despite achieving promising results in many applications, the full poten…
History Rhymes: Macro-Contextual Retrieval for Robust Financial Forecasting
Fraunhofer IAIS - Department of Media Engineering（弗劳恩霍夫研究所-媒体工程部门）；University of Bonn - Department of Computer Science（波恩大学-计算机科学系）；Lamarr Institute for Machine Learning（拉马尔机器学习研究所）
University of Toronto（多伦多大学）；Vector Institute（向量研究所）；University of Birmingham（伯明翰大学）
Sophisticated machine learning (ML) models to inform trading in the financial sector create problems of interpretability and risk management. Seemingly robust forecasting models may behave erroneously in out of distribution settings. In 2020, some of the world's most sophisticated quant hedge funds suffered losses as their ML models were first underhedged, and then overcompensated. We implement a gradient-based approach for precisely stress-testing how a trading model's forecasts can be manipulated, and their effects on downstream tasks at the trading execution level. We construct inputs -- wh…
Bayesian Modeling for Uncertainty Management in Financial Risk Forecasting and Compliance
iLynx Inc.（iLynx公司）；Cognitive Links；United International University（联合国际大学）；University of Maryland, Baltimore County（马里兰大学巴尔的摩分校）；Washington University of Science and Technology（科学与技术华盛顿大学）
Reinforcement-Guided Hyper-Heuristic Hyperparameter Optimization for Fair and Explainable Spiking Neural Network-Based Financial Fraud Detection
organization= Department of Computer Science \& Engineering, Khulna University of Engineering \& Technology (KUET) , city= Khulna , postcode= 9203 , country= Bangladesh；organization= Thomas Lord Department of Computer Science, Viterbi School of Engineering, University of Southern California , city= Los Angeles , state= CA , postcode= 90089 , country= USA；organization= Department of Computer Science, American International University-Bangladesh (AIUB) , city= Dhaka , postcode= 1229 , country= Bangladesh
Building a machine learning solution in real-life applications often involves the decomposition of the problem into multiple models of various complexity. This has advantages in terms of overall performance, better interpretability of the outcomes, and easier model maintenance. In this work we propose a Bayesian framework to model the interaction amongst models in such a hierarchy. We show that the framework can facilitate stress testing of the overall solution, giving more confidence in its expected performance prior to active deployment. Finally, we test the proposed framework on a toy probl…
Posterior Regimes and Latent Deception: Variational Bayesian Inference in Hidden Markov Models for Sequential Fraud Detection in Financial Transactions
University of Stirling（斯特灵大学）；University of Nottingham（诺丁汉大学）
University of Maine（缅因大学）；University of Illinois, Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Duke University（杜克大学）；University of Rochester（罗切斯特大学）；University of Iowa（爱荷华大学）
Lending decisions are usually made with proprietary models that provide minimally acceptable explanations to users. In a future world without such secrecy, what decision support tools would one want to use for justified lending decisions? This question is timely, since the economy has dramatically shifted due to a pandemic, and a massive number of new loans will be necessary in the short term. We propose a framework for such decisions, including a globally interpretable machine learning model, an interactive visualization of it, and several types of summaries and explanations for any given dec…
DeepKoopFormer: A Koopman Enhanced Transformer Based Architecture for Time Series Forecasting
Helmholtz Center for Environmental Research-UFZ（海德堡环境研究中心-UFZ）；Delft Center for Systems and Control（代尔夫特系统与控制中心）；School of Engineering, University of Pittsburgh（匹兹堡大学工程学院）
School of Economics and Finance, Shanghai International Studies University（经济金融学院，上海国际问题研究大学）；School of AI and Advanced Computing, Xi’an Jiaotong-Liverpool University（人工智能与先进计算学院，西安交通大学利物浦大学）；School of Foreign Studies, Shanghai University of Finance and Economics（外国语言学院，上海金融学院）
Enhancing ML Models Interpretability for Credit Scoring
Dept. of Computer Science, Delft University of Technology（代尔夫特理工大学计算机科学系）；Dept. of Applied Mathematics, Delft University of Technology（代尔夫特理工大学应用数学系）；FF Quant Advisory B.V.（FF量化咨询公司）
Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models
Central University of Finance and Economics（中央财经大学）；New York University（纽约大学）；Harvard University（哈佛大学）；Columbia University（哥伦比亚大学）；University of Michigan（密歇根大学）
We introduce a family of interpretable machine learning models, with two broad additions: Linearised Additive Models (LAMs) which replace the ubiquitous logistic link function in General Additive Models (GAMs); and SubscaleHedge, an expert advice algorithm for combining base models trained on subsets of features called subscales. LAMs can augment any additive binary classification model equipped with a sigmoid link function. Moreover, they afford direct global and local attributions of additive components to the model output in probability space. We argue that LAMs and SubscaleHedge improve th…
KASPER: Kolmogorov Arnold Networks for Stock Prediction and Explainable Regimes
Vishwakarma Government Engineering College（维斯瓦卡罗马工程学院）；QuantumAI Lab, Fractal Analytics（量子AI实验室，Fractal Analytics）；eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD)（eBRAIN实验室，工程系，纽约大学阿布扎比分校（NYUAD））；Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute（量子与拓扑系统中心（CQTS），NYUAD研究机构）
