Basem Suleiman

Simulating financial time series (FTS) data consistent with non-stationary, empirical market behaviour is difficult, but it has valuable applications for financial risk management. A better risk estimation can improve returns on capital and capital efficiency in investment decision making. Challenges to modelling financial risk in market crisis environments are anomalous asset price behaviour and a lack of historical data to learn from. This paper proposes a novel semi-supervised approach for generating regime-specific ‘deep fakes’ of FTS data using generative adversarial networks (GANs). The proposed architecture, a regime-specific Quant GAN (RSQGAN), is a conditional GAN (cGAN) that generates class-conditional synthetic asset return data. Conditional class labels correspond to distinct market regimes that have been detected using a structural breakpoint algorithm to segment FTS into regime classes for simulation. Our RSQGAN approach accurately simulated univariate time series behaviour consistent with specific empirical regimes, outperforming equivalently configured unconditional GANs trained only on crisis regime data. To evaluate the RSQGAN performance for simulating asset return behaviour during crisis environments, we also propose four test metrics that are sensitive to path-dependent behaviour and are also actionable during a crisis environment. Our RSQGAN model design borrows from innovation in the image GAN domain by enabling a user-controlled hyperparameter for adjusting the fit of synthetic data fidelity to real-world data; however, this is at the cost of synthetic data variety. These model features suggest that RSQGAN could be a useful new tool for understanding risk and making investment decisions during a time of market crisis.

Recommender systems have been successfully used in many domains with the help of machine learning algorithms. However, such applications tend to use multi-dimensional user data, which has raised widespread concerns about the breach of users’ privacy. Meanwhile, wearable technologies have enabled users to collect fitness-related data through embedded sensors to monitor their conditions or achieve personalized fitness goals. In this article, we propose a novel privacy-aware personalized fitness recommender system. We introduce a multi-level deep learning framework that learns important features from a large-scale real fitness dataset that is collected from wearable Internet of Things (IoT) devices to derive intelligent fitness recommendations. Unlike most existing approaches, our approach achieves personalization by inferring the fitness characteristics of users from sensory data, minimizing the need for explicitly collecting user identity or biometric information, such as name, age, height, and weight. Our proposed models and algorithms predict (a) personalized exercise distance recommendations to help users to achieve target calories, (b) personalized speed sequence recommendations to adjust exercise speed given the nature of the exercise and the chosen route, and (c) personalized heart rate sequence to guide the user of the potential health status for future exercises. Our experimental evaluation on a real-world Fitbit dataset demonstrated high accuracy in predicting exercise distance, speed sequence, and heart rate sequence compared with similar studies. 1 Furthermore, our approach is novel compared with existing studies, as it does not require collecting and using users’ sensitive information. Thus, it preserves the users’ privacy.