Research Projects on FinTech

Dynamic Graph on Asset Pricing

We propose a dynamic graph framework designed to seamlessly integrate asset additions and deletions along with corresponding correlation changes. This not only enhances predictive accuracy but also supports informed investment strategies in an ever-changing market environment. In our framework, assets at various historical time steps are structured as a sequence of dynamic graphs, where connections between assets reflect their long-term relationship. Our model leverages state-of-the-art techniques enriched with financial insights for price prediction on all existing nodes in future time steps, capturing both topological and temporal patterns. Extensive experiments on both dynamic and static asset data demonstrate that our framework surpasses popular benchmarks on price prediction, and can offer profitable investment strategies in real-world scenarios.

signal_control

Margin Trader: A Reinforcement Learning Framework for Portfolio Management with Margin and Constraints

Margin Trader is proposed as a foundational and flexible Reinforcement Learning (RL) framework tailored for margin trading in the stock market. Our framework goes beyond the cash-only trading approach by incorporating margin account and constraints, thereby allowing traders to leverage their positions in both long and short directions. This integration provides a more realistic trading environment. Its primary objective is to strike a balance between profit maximization and risk management. To achieve it, two key modules are implemented: the Margin Adjustment Module timely updates the buying power to ensure traders’ maximum potential returns, and the Maintenance Detection Module protects the stability of the portfolio by prompt alerting traders when the margin level approaches critical points. Margin Trader supports various Deep Reinforcement Learning (DRL) algorithms and offers traders the flexibility to customize crucial settings for the trading environment. Traders can adapt their strategies to ever changing market conditions by fine-tuning equity allocation to long and short positions, manage risk tolerance levels by adjusting maintenance requirements, and cater to either conservative or aggressive trading styles based on their individual preferences by control- ling margin ratios. Margin Trader is versatile and can be extended to various financial markets, such as futures and cryptocurrencies.

margin_trader

RAGIC: Risk-Aware Generative Adversarial Model for Stock Interval Construction

We aim to bridge the gap that many existing prediction approaches focus on single-point predictions, lacking the depth needed for effective decision-making and often overlooking market risk. Therefore, we propose RAGIC, a Risk-Aware Generative model for Interval Construction to quantify uncertainty more effectively. Specifically, RAGIC has two phases: sequence generation and interval construction. In the first phase, we employ a Generative Adversarial Network (GAN) to learn the historical stock features and simulate the future price sequences. The generator incorporates a risk module, which captures risk using a risk attention score derived from volatility index, and a temporal module, which captures the multi-scale trend expressed by historical prices. The well-trained generator can produce an adequate set of future price sequences with artificial randomness learned from the financial market. In the second phase, a horizon-wise strategy is designed to gather simulated sequences with different prediction horizons, and statistical inference is utilized to construct a risk-sensitive interval to reflect uncertainty, where the interval width is adaptively determined by the volatility index. In practice, RAGIC relies solely on publicly available data and incurs only low computational overhead. Extensive experiments on multiple stock indices world-wide illustrate that RAGIC achieves consistently over 95% coverage with reasonable interval width in a balanced manner.

ragic

Prediction with Time-Series Mixer for the S&P500 Index

Time-Series Mixer (TS- Mixer) is the first attempt at an MLP-Mixer-based design for sequence modeling in stock forecasting. The central architecture of the TS-Mixer is inspired by MLP-Mixer, a competitive but conceptually and technically simple alternative that does not use gating structure, convolutions, or self-attention. MLPs in TS-Mixer are repeatedly applied to either per-location features or temporal features, which can be considered a novel method to capture temporal correlations. Like MLP-Mixers, TS-Mixer relies only on basic matrix multiplication routines, changes to data layout (reshapes and transpositions), and scalar nonlinearities. It contains two types of MLP layers: feature mixer and temporal mixer. The feature mixer is applied independently to each data point to capture the correlation among features. In contrast, the temporal mixer extracts temporal dependency (trend, seasonal, cyclical, or random characteristics) of each feature across the whole input sequence. The experiments on SPX in a high-volatility period demonstrate that our proposed TS-Mixer outperforms multiple benchmarks, including tradi- tional indicators, RNN-based models, and popular time-series models.

ts_mixer