Designing algorithmic trading strategies targeting volume-weighted average price (VWAP) for long-duration orders is a critical concern for brokers. Traditional rule-based strategies are explicitly predetermined, lacking effective adaptability to achieve lower transaction costs in dynamic markets. Numerous studies have attempted to minimize transaction costs through reinforcement learning. However, the improvement for long-duration order trading strategies, such as VWAP strategy, remains limited due to intraday liquidity pattern changes and sparse reward signals. To address this issue, we propose a jointed model called Macro-Meta-Micro Trader, which combines deep learning and hierarchical reinforcement learning. This model aims to optimize parent order allocation and child order execution in the VWAP strategy, thereby reducing transaction costs for long-duration orders. It effectively captures market patterns and executes orders across different temporal scales. Our experiments on stocks listed on the Shanghai Stock Exchange demonstrated that our approach outperforms optimal baselines in terms of VWAP slippage by saving up to 2.22 base points, verifying that further splitting tranches into several subgoals can effectively reduce transaction costs.

Exposure to massive information in daily lives makes it necessary for people to obtain major points efficiently, promoting the development of text summarization technology. However, existing sentiment-based text summarization methods only pay attention to the sentiment polarity of either a single sentence or a whole document, ignoring changes of sentiments along with sentences or sentiment flow across the whole document. To incorporate the above two aspects into the summarization process to generate high-quality summaries, we propose a topic sentiment summarization framework based on reaching definition (TSSRD). In the framework, we first use topic models to model documents and calculate topic sentiment embeddings. Then, we analyze document structures from different perspectives to design data flow diagrams, in which improved reaching definition is used to analyze sentiment changes and sentiment flow. Finally, topic sentiment summaries are generated based on sentiments in steady states of the reaching definition. To evaluate our summarization framework, we introduce an extrinsic evaluation method. In this method, a sentiment classifier is trained by the topic sentiment summaries, and accuracy of the sentiment classification is used as a quality score. Experimental results demonstrate that our summarization framework is at least 2.32% better than baselines on IMDb and Amazon datasets.

As a classical optimal trade execution algorithm, Volume Weighted Average Price (VWAP) strategy is favored by brokers. Since it is schedule-based and cannot perform well in a dynamic stock market, optimizing the traditional VWAP strategy via reinforcement learning is worth investigating. Most of the existing reinforcement learning-based execution strategies focus on formulating trading volumes or trading prices separately, ignoring the cooperation between trading volumes and trading prices. To address this issue, we propose a Multi-Agent Deep Q-Network (MADQN) trading framework to optimize the traditional VWAP strategy, which can dynamically adapt to the complex stock market and simultaneously formulate trading prices and volumes at each transaction period. Specifically, we design two different types of agents: 1) volume-driven agent for determining trading volumes at each transaction period and 2) price-driven agent for deciding trading prices. We model our stock market environment in which multiple agents participate as a fully cooperative stochastic game. The volume-driven agent and the price-driven agent take joint actions to interact with the stock market environment and then update their network respectively. We use 9-month level-2 data of eight stocks from different sectors on Shanghai Stock Exchange as experimental data. Experimental results demonstrate that our MADQN trading framework outperforms baselines in terms of several evaluation metrics.

The dramatic increase in the use of smartphones has allowed people to comment on various products at any time. The analysis of the sentiment of users’ product reviews largely depends on the quality of sentiment lexicons. Thus, the generation of high‐quality sentiment lexicons is a critical topic. In this paper, we propose an automatic approach for constructing a domain‐specific sentiment lexicon by considering the relationship between sentiment words and product features in mobile shopping reviews. The approach first selects sentiment words and product features from original reviews and mines the relationship between them using an improved pointwise mutual information algorithm. Second, sentiment words that are related to mobile shopping are clustered into categories to form sentiment dimensions. At each sentiment dimension, each sentiment word can take the value of 0 or 1, where 1 indicates that the word belongs to a particular category whereas 0 indicates that it does not belong to that category. The generated lexicon is evaluated by constructing a sentiment classification task using several product reviews written in both Chinese and English. Two popular non‐domain‐specific sentiment lexicons as well as state‐of‐the‐art machine‐learning and deep‐learning models are chosen as benchmarks, and the experimental results show that our sentiment lexicons outperform the benchmarks with statistically significant differences, thus proving the effectiveness of the proposed approach.

Stock price volatility prediction is regarded as one of the most attractive and meaningful research issues in financial market. Some existing researches have pointed out that both the prediction accuracy and the prediction speed are the most important facts in the process of stock prediction. In this paper, we focus on the problem of how to design a methodology which can improve prediction accuracy as well speed up prediction process, and propose a multi-kernel learning based extreme learning machine (MKL-ELM) model to enhance the prediction performance. ELM is a fast learning model and has been successfully applied in many research fields. Based on ELM, this MKL-ELM has the benefits of both multiple kernel learning and ELM, which can well balanced the requirements of both prediction accuracy and prediction speed. To validate the performance of the proposed MKL-ELM, we take experiments on HKEx 2001 stock market datasets. The market historical price and the market news are implemented in our MKL-ELM. We Compare our proposed MKL-ELM with Back-Propagation Neural Network(BP-NN), Support Vector Machine(SVM), Basic ELM and K-ELM. Experimental results show that, 1) MKL-ELM, K-ELM and SVM get higher prediction accuracy than BP-NN and B-ELM; 2) Both MKL-ELM and K-ELM can achieve faster prediction speed than SVM in most cases; 3) MKL-ELM has higher prediction accuracy in some cases than K-ELM and SVM.

In recent years, there has been a proliferation of collaborative tagging systems in Web 2.0 communities. With the increasingly large amount of social data, how to manage and organize them becomes an important and crucial problem for folksonomy applications. To better understand and meet users’ needs, multimedia resources can be organized or indexed from these user perspectives; it is thus important to find latent user communities for social media applications. In this paper, we propose the mechanism of augmented folksonomy graph (AFG) to incorporate multi-faceted relations in social media, along with a novel density-based clustering method to discover latent user community from AFG by combining contents and tags of multimedia resources. To evaluate the proposed method, we conduct experiments on a public dataset, the empirical results of which show that our approach outperforms baseline ones in terms of tag-based and content-based personalized search.

With the development of the Internet, user-generated data has been growing tremendously in Web 2.0 era. Facing such a big volume of resources in folksonomy, people need a method of fast exploration and indexing to find their demanded data. To achieve this goal, contextual information is indispensable and valuable to understand user preference and purpose. In sociolinguistics, context can be mainly categorized as verbal context and social context. Comparing with verbal context, social context not only requires domain knowledge to pre-define contextual attributes but also acquires additional data from users. However, there is no research of addressing irrelevant contextual factors for verbal context model so far. The dominating set from verbal context proposed in this paper is to fill this blank. We present the verbal context in folksonomy to capture the user intention, and propose a dominating set discovering method for this verbal context model to prune the irrelevant contextual factors and keep the major characteristics at the same time. Furthermore, the experiments, which are conducted on a public data set, show that the proposed method gives convincing results.