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.

It is challenging to design profitable and practical trading strategies, as stock price movements are highly stochastic, and the market is heavily influenced by chaotic data across sources like news and social media. Existing NLP approaches largely treat stock prediction as a classification or regression problem and are not optimized to make profitable investment decisions. Further, they do not model the temporal dynamics of large volumes of diversely influential text to which the market responds quickly. Building on these shortcomings, we propose a deep reinforcement learning approach that makes time-aware decisions to trade stocks while optimizing profit using textual data. Our method outperforms state-of-the-art in terms of risk-adjusted returns in trading simulations on two benchmarks: Tweets (English) and financial news (Chinese) pertaining to two major indexes and four global stock markets. Through extensive experiments and studies, we build the case for our method as a tool for quantitative trading.

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.

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 sentiment analysis jointly exploiting both semantic language representations and relations between debate transcripts, motions, and political party members. Through experiments on real-world English data and by visualizing attention, we provide a use case of GPolS as a tool for political speech analysis and polarity prediction.