Quantitative trading and investment decision making are intricate financial tasks that rely on accurate stock selection. Despite advances in deep learning that have made significant progress in the complex and highly stochastic stock prediction problem, modern solutions face two significant limitations. They do not directly optimize the target of investment in terms of profit, and treat each stock as independent from the others, ignoring the rich signals between related stocks' temporal price movements. Building on these limitations, we reformulate stock prediction as a learning to rank problem and propose STHAN-SR, a neural hypergraph architecture for stock selection. The key novelty of our work is the proposal of modeling the complex relations between stocks through a hypergraph and a temporal Hawkes attention mechanism to tailor a new spatiotemporal attention hypergraph network architecture to rank stocks based on profit by jointly modeling stock interdependence and the temporal evolution of their prices. Through experiments on three markets spanning over six years of data, we show that STHAN-SR significantly outperforms state-of-the-art neural stock forecasting methods. We validate our design choices through ablative and exploratory analyses over STHAN-SR's spatial and temporal components and demonstrate its practical applicability.

Designing profitable trading strategies is complex as stock movements are highly stochastic; the market is influenced by large volumes of noisy data across diverse information sources like news and social media. Prior work mostly treats stock movement prediction as a regression or classification task and is not directly optimized towards profit-making. Further, they do not model the fine-grain temporal irregularities in the release of vast volumes of text that the market responds to quickly. Building on these limitations, we propose a novel hierarchical, learning to rank approach that uses textual data to make time-aware predictions for ranking stocks based on expected profit. Our approach outperforms state-of-the-art methods by over 8% in terms of cumulative profit and risk-adjusted returns in trading simulations on two benchmarks: English tweets and Chinese financial news spanning two major stock indexes and four global markets. Through ablative and qualitative analyses, we build the case for our method as a tool for daily stock trading.

Political discourses provide a forum for representatives to express their opinions and contribute towards policy making. Analyzing these discussions is crucial for recognizing possible delegates and making better voting choices in an independent nation. A politician's vote on a proposition is usually associated with their past discourses and impacted by cohesion forces in political parties. We focus on predicting a speaker's vote on a bill by augmenting linguistic models with temporal and cohesion contexts. We propose TEC, a time evolving graph based model that jointly employs links between motions, speakers, and temporal politician states. TEC outperforms competitive models, illustrating the benefit of temporal and contextual signals for predicting a politician's stance.

Ramit Sawhney, Arnav Wadhwa, Shivam Agarwal, Rajiv Ratn Shah. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

The rapidly rising ubiquity and dissemination of online information such as social media text and news improve user accessibility towards financial markets, however, modeling these vast streams of irregular, temporal data poses a challenge. Such temporal streams of information show power-law dynamics, scale-free characteristics, and time irregularities that sequential models are unable to accurately model. In this work, we propose the first Hierarchical Time-Aware Hyperbolic LSTM (HTLSTM), which leverages the Riemannian manifold for encoding the scale-free nature of a sequence of text in a time-aware fashion. Through experiments on three financial tasks: stock trading, equity price movement prediction, and financial risk prediction, we demonstrate HTLSTM's applicability for modeling temporal sequences of online information. On real-world data from four global stock markets and three stock indices spanning data in English and Chinese, we make a step towards time-aware text modeling via hyperbolic geometry.

In the financial realm, profit generation greatly relies on the complicated task of stock prediction. Lately, neural methods have shown success in exploiting stock affecting signals from textual data across news and tweets to forecast stock performance. However, the dynamic, stochastic, and variably influential nature of text and prices makes it difficult to train neural stock trading models, limiting predictive performance and profits. To transcend this limitation, we propose a novel multi-modal curriculum learning approach: FinCLASS, which evaluates stock affecting signals via entropy-based heuristics and measures their linguistic and price-based complexities in a time-aware, hierarchical fashion. We show that training financial models can benefit by exposing neural networks to easier examples of stock affecting signals early during the training phase, before introducing samples having more complex linguistic and price-based temporal variations. Through experiments on benchmark English tweets and Chinese financial news spanning two major indexes and four global markets, we show how FinCLASS outperforms state-of-the-art across financial tasks of stock movement prediction, volatility regression, and profit generation. Through ablative and qualitative experiments, we set the case for FinCLASS as a generalizable framework for developing natural language-centric neural models for financial tasks.

Quantitative trading and investment decision making are intricate financial tasks in the ever-increasing sixty trillion dollars global stock market. Despite advances in stock forecasting, a limitation of most existing neural methods is that they treat stocks independent of each other, ignoring the valuable rich signals between related stocks’ movements. Motivated by financial literature that shows stock markets and inter-stock correlations show scale-free network characteristics, we leverage domain knowledge on the Web to model inter-stock relations as a graph in four major global stock markets and formulate stock selection as a scale-free graph-based learning to rank problem. To capture the scale-free spatial and temporal dependencies in stock prices, we propose HyperStockGAT: Hyperbolic Stock Graph Attention Network, the first model on the Riemannian Manifolds for stock selection. Our work’s key novelty is the proposal of modeling the complex, scale-free nature of inter-stock relations through temporal hyperbolic graph learning on Riemannian manifolds that can represent the spatial correlations between stocks more accurately. Through extensive experiments on long-term real-world data spanning over six years on four of the world’s biggest markets: NASDAQ, NYSE, TSE, and China exchanges, we show that HyperStockGAT significantly outperforms state-of-the-art stock forecasting methods in terms of profitability by over 12%, and risk-adjusted Sharpe Ratio by over 4%. We analyze HyperStockGAT’s components’ contributions through a series of exploratory and ablative experiments to demonstrate its practical applicability to real-world trading. Furthermore, we propose a novel hyperbolic architecture that can be applied across various spatiotemporal problems on the Web’s commonly occurring scale-free networks.

Parliamentary debates present a valuable language resource for analyzing comprehensive options in electing representatives under a functional, free society.However, the esoteric nature of political speech coupled with non-linguistic aspects such as political cohesion between party members presents a complex and underexplored task of contextual parliamentary debate analysis.We introduce GPolS, a neural model for political speech stance analysis jointly exploiting both semantic language representations and relations between debate transcripts, motions, and political party members.Through experiments on real-world English data, we provide a use case of GPolS as a tool for political speech analysis and polarity prediction.

In the financial domain, risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task.Stock forecasting is complex, given the stochastic dynamics and non-stationary behavior of the market.Stock movements are influenced by varied factors beyond the conventionally studied historical prices, such as social media and correlations among stocks.The rising ubiquity of online content and knowledge mandates an exploration of models that factor in such multimodal signals for accurate stock forecasting.We introduce an architecture that achieves a potent blend of chaotic temporal signals from financial data, social media, and inter-stock relationships via a graph neural network in a hierarchical temporal fashion.Through experiments on real-world S&P 500 index data and English tweets, we show the practical applicability of our model as a tool for investment decision making and trading.

Stock movement prediction, a widely addressed research avenue in the world of computer science and finance, it finds fundamental applications in quantitative trading and investment decision making. Predicting future trends in stock prices is a complex problem, fundamentally due to the highly stochastic and dynamic nature of the market. Advances in neural stock forecasting through deep learning models have made improvements in stock movement prediction. However, a majority of existing research treats stocks independent of each other or simplifies the complex higher-order relations between stocks in a pairwise fashion through graphs. Another limitation of recent graph-based approaches for stock movement prediction is the lack of time-aware modeling of the temporal evolution of stock prices jointly while modeling inter stock relations. To this end, we propose STHGCN: Spatio-Temporal Hypergraph Convolution Network, the first neural hypergraph model for stock trend forecasting. At the core of STHGCN, we devise a gated temporal convolution over hypergraphs for learning stock price evolution over stock relations in a time-aware manner. STHGCN significantly outperforms state-of-the-art stock forecasting methods over extensive experiments on long term realworld S&P500 index data of stocks traded in the NASDAQ and NYSE markets over 12 diverse phases. We highlight STHGCN's practical applicability through a market simulation and a latency analysis with competitive models. Furthermore, we propose a novel architecture for stock trend forecasting that can be applied across various problems in the spatiotemporal domain.