Paper tackles natural language generation using GANs, achieving state-of-the-art results.
problem Discrepancy in progress between image and natural language generation using GANs.
method Introduces a simple baseline for generating natural language from noise without gradient estimators.
result Achieves state-of-the-art results on a Chinese poem generation dataset.
Study classifies poetry by poet using text categorization techniques.
problem Classifying poetry based on the poet who wrote it.
method Constructed a data set of English poetry, applied text categorization techniques, used Chi-Square for feature selection, and tested five classification algorithms.
result Sequential minimal optimization achieved a 70% classification success rate.
In this work, we employ quantitative methods from the realm of statistics and machine learning to develop novel methodologies for author attribution and textual analysis. In particular, we develop techniques and software suitable for applications to Classical study, and we illustrate the efficacy of our approach in sev…
Paper improves Chinese word segmentation using dictionary knowledge.
problem Chinese word segmentation with limited labeled data.
method Two methods: pseudo labeled data generation and multi-task learning.
result Improves Chinese word segmentation performance, especially with scarce training data.
Paper proposes SA-VAE for generating stylized Chinese characters.
problem Automatic generation of stylized Chinese characters is challenging.
method Proposes Style-Aware Variational Auto-Encoder (SA-VAE) to capture content and style components.
result Shows powerful one-shot/low-shot generalization ability.
Paper uses PCA to analyze Chinese sovereign bonds and discusses bond immunization.
problem Analyzing factors affecting Chinese sovereign bond yield changes.
method Applied Principal Component Analysis (PCA) on bond yield data.
result Identified principal factors influencing Chinese sovereign bond yield changes.
Paper proposes neural approach for Chinese named entity recognition.
problem Challenges in Chinese named entity recognition due to context-dependency and lack of word delimiters.
method Introduces a CNN-LSTM-CRF neural architecture and a unified framework for joint training with word segmentation.
result Improves Chinese named entity recognition performance, especially with limited training data.
Study reveals risk transmission channels among Chinese sectors.
problem Understanding risk transmission within Chinese economic sectors.
method Volatility spillovers analysis using VAR model and rolling window approach.
result 17 sectors are risk transmitters and 11 are risk takers.
FGN improves Chinese NER by integrating glyph information and interactive context.
problem Chinese named entity recognition is challenging due to the complexity of characters and their glyphs.
method FGN uses a novel CGS-CNN structure to capture glyph and interactive information, and a sliding window method to fuse BERT and glyph representations.
result FGN achieves state-of-the-art performance on four NER datasets, improving over previous methods.
Deep learning system generates new Chinese fonts via style variables.
problem Efficiently design new Chinese fonts.
method End-to-end deep learning system generating new style fonts via interpolation of latent style-related embedding variables.
result Smooth transition between different font styles achieved.
L. Kauffman conjectured that a particular solution of the Chinese Rings puzzle is the simplest possible. We prove his conjecture by using low-dimensional topology and group theory. We notice also a surprising connection between the Chinese Rings and Habiro moves (related to Vassiliev invariants).
Study finds common poetic themes across languages over time.
problem Understanding thematic evolution in different poetic traditions.
method Applied Latent Dirichlet Allocation (LDA) to poetry corpora of four languages.
result Identified common themes and their temporal trends across poetic traditions.
Study shows Chinese stock market returns are predictable over time, especially during market turbulence.
problem Predicting returns in the Chinese stock market is challenging due to market inefficiency.
method Used wild bootstrap automatic variance ratio test and generalized spectral test.
result Return predictability varies over time, with significant predictability during market turmoils.
The paper constructs financial sentiment factors using NLP for the Chinese market.
problem Evaluating sentiment in the Chinese financial market.
method Crawling news and comments, applying NLP techniques, building a finance-specific lexicon, and adjusting the sentiment factor.
result The adjusted sentimental factor has a strong correlation with the Chinese market, especially during crises.
Analysis shows preference for Chinese yuan in global trade network.
problem Analyzing the preference of countries to trade in US dollars or Chinese yuan.
method Mathematical analysis of world trade network using Ising spin interactions.
result Majority of countries prefer trading in Chinese yuan due to network structure.
Study finds key subgraphs in Chinese guarantee networks.
problem Understanding the structure of Chinese guarantee networks.
method Analysis of 2- and 3-node subgraphs considering financial heterogeneity.
result Mutual, 2-out-stars, and triangle sub-patterns are common motifs.
Model predicts Chinese stock market liquidity and customer order behavior.
problem Understanding market liquidity and customer order behavior in the Chinese stock market.
method Dual state-space model using Fourier transform to connect volume-at-price buckets to correlations.
result Customer orders are correlated with market sentiment and stock returns, not with bond returns.
Chinese stock market shows time series momentum and contrarian effects over different periods.
problem Analyzing momentum and contrarian effects in Chinese stock market performance.
method Examined time series momentum and contrarian strategies applied to major indices in China.
result Time series momentum effect in short run, contrarian effect in long run, performance dependent on look-back and holding periods.
BERT improves Chinese word segmentation performance.
problem Chinese word segmentation task.
method Applying BERT to CWS task using benchmark datasets.
result BERT can improve performance even with inconsistent labels.
Study examines volatility-based strategy for Chinese ETF options, improving returns in volatile markets.
problem Lack of effective trading strategies in volatile Chinese equity markets.
method Volatility forecasting using GARCH models to dynamically adjust positions and exposures.
result Dynamic adjustment of positions and exposures enhances returns in volatile markets.
Neural approach for Chinese word segmentation using lexicon and unlabeled data.
problem Efficiently segment Chinese words with limited labeled data.
method Posterior regularization algorithm with indirect supervision from lexicon and unlabeled data.
result Validation of approach on multiple benchmark datasets in in-domain and cross-domain scenarios.
Study evaluates LLMs for predicting Chinese stock movements using financial news sentiments.
problem Evaluating LLMs' ability to predict stock price movements using financial news sentiments.
method Standardized experimental procedure with three LLMs, each with unique performance enhancement methods.
result Developed quantitative trading strategies and conducted back-tests to assess LLMs' performance.
This study uses NLP to predict stock performance based on analyst reports.
problem Predicting stock performance using textual information from analyst reports.
method Natural language processing (NLP) and a customized BERT deep learning model for Chinese text.
result Strong positive sentiment in analyst reports increases excess return and intraday volatility, while strong negative sentiment increases volatility and trading volume but decreases excess return.
This paper analyzes the quantitative relations between stock prices and quantities of tradable stock shares in Chinese stock markets at six time points by means of Exploratory Data Analysis (EDA) method. It is found the resulting formulae have the same structure but different parameters. This paper also uses these rela…
Modeling equity market impact with Chinese data, improving on existing models.
problem Understanding and quantifying market impact in Chinese equity markets.
method Developed a price impact model considering heteroscedasticity and dependency between permanent and temporary impacts using large tick data.
result The model outperforms existing models and suggests a constant impact exponent of around 0.7 across all stocks.
Study predicts 2015 Chinese stock market bubble using LPPLS model.
problem Detecting and predicting the 2015 Chinese stock market bubble.
method Calibrated Log Periodic Power Law Singularity (LPPLS) model, Lomb spectral analysis, Unit-root tests, CMA-ES optimization.
result The LPPLS model can predict the actual critical day (tc) two months before the bubble crash.
Paper develops neural network for Mandarin polyphone disambiguation.
problem Homograph problem in Mandarin Chinese text-to-speech.
method Bidirectional RNN for context, prediction network for mapping embeddings to pronunciations.
result Achieves 94.69% accuracy on polyphonic character dataset.
A new Chinese Checkers agent combines heuristics, MCTS, and deep RL.
problem Challenging state-space and unbounded depth in Chinese Checkers.
method Combines heuristics, Monte Carlo Tree Search, and deep reinforcement learning.
result Competent agent reaches human player level.
To investigate the universal structure of interactions in financial dynamics, we analyze the cross-correlation matrix C of price returns of the Chinese stock market, in comparison with those of the American and Indian stock markets. As an important emerging market, the Chinese market exhibits much stronger correlations…
Study replicates reference-dependent preferences impact on risk-return trade-off in Chinese stock market.
problem Impact of reference-dependent preferences on risk-return trade-off in Chinese stock market.
method Utilized CGO proxy, econometric techniques (Dependent Double Sorting, Fama-MacBeth regressions), and data from 1995-2024.
result Reference-dependent preferences have a weaker or absent positive risk-return relationship in the Chinese market.
The paper examines how contrarian and momentum effects in Chinese stock markets fluctuate over time.
problem Investigating the time-varying risk-premium relation of Chinese stock markets.
method Using the Capital Asset Pricing Model and French-Fama three factor model, the paper studies the evolving arbitrage opportunities and contrarian profitability in Chinese stock markets.
result Contrarian and momentum effects in Chinese stock markets vary over time, with higher profitability in certain market conditions.
Article proposes a profitable intraday trading strategy for Chinese stocks.
problem Intraday trading opportunities in Chinese stock market.
method Markowitz optimization and Multilayer Perceptron (MLP) for stock price prediction.
result Validation of Markowitz portfolio optimization and MLP for intraday stock price prediction.
Empirical study of CAPM and Fama-French model in Chinese A-share market.
problem Testing and validating CAPM and Fama-French model in Chinese A-share market.
method Used Fama-MacBeth regression and Fama-French three-factor model to analyze Chinese A-share trading data from 2000 to 2019, adjusting for IPO shell value contamination.
result Fama-French model captures most of A-share market returns, with adjusted R-squared > 0.88.
FinTech negatively impacts Chinese banks' financial sustainability.
problem Impact of FinTech on financial sustainability of Chinese commercial banks.
method Three-stage network DEA-Malmquist model and two-way fixed effects model.
result FinTech primarily undermines financial sustainability by eroding loan efficiency and profitability.
Study optimizes market making in Chinese stock market with stochastic control and scenario analysis.
problem Limited research on market making in Chinese stock market.
method Optimal market making framework with exponential CARA utility function, accounting for market conditions and risks.
result Impact of volatility and stamp duty on market maker's profit and liquidity.
Paper builds a supervised learning model for Chinese futures price prediction.
problem Predicting the trend of Chinese futures prices accurately.
method Supervised learning model designed for futures price movement classification.
result The model meets accuracy requirements for classifying futures price movements.
Paper finds political networks reduce bond issuance costs in China.
problem The financial value of within-government political networks in China.
method Using municipal leaders' working experience to measure political networks, the study examines the effect on bond issuance yield spreads.
result Political networks reduce bond issuance yield spreads by improving issuer credit ratings, especially in less developed financial markets.
Improved AI patent classifier measures U.S. and China's AI patenting.
problem Measuring AI patents with high precision and generalization.
method Fine-tuning PatentSBERTa on manually labeled data from USPTO's AI Patent Dataset.
result Rapid growth in AI patenting in both countries, but different organizational patterns.
Directly proves CRP from stick-breaking process without measure theory.
problem Indirect proof of CRP from stick-breaking process is complex.
method Direct proof using stick-breaking process to CRP, avoiding measure theory.
result Direct proof connects stick-breaking process to CRP.
Improved CNN for HCCR with new loss function and ranking method.
problem Loss of inter-class information in traditional CNN models for HCCR.
method Combining cross entropy with a new similarity ranking function (Average variance similarity) as loss function.
result New loss function (SoftMax cross entropy with Average variance similarity) achieves highest accuracy in HCCR.
A new method limits over-clustering in DPM models.
problem Over-clustering in Dirichlet process mixture models.
method Powered Chinese restaurant process to penalize over-clustering.
result Reduces the number of clusters in large datasets.
Amid the current financial crisis, there has been one equity index beating all others: the Shanghai Composite. Our analysis of this main Chinese equity index shows clear signatures of a bubble build up and we go on to predict its most likely crash date: July 17-27, 2009 (20%/80% quantile confidence interval).
The study compares A and N shares returns of Chinese firms cross-listed in U.S. markets.
problem Analyzing the impact of cross-listing on Chinese stock returns.
method Used CAPM for expected returns, GARCH model for volatility, and event study method.
result Cross-listing significantly affected N shares negatively, with increased volatility for some companies.
Pricing Chinese convertible bonds using Monte Carlo simulation and dynamic programming.
problem Pricing Chinese convertible bonds accurately.
method Monte Carlo simulation and dynamic programming with regression and backward induction.
result An underpriced strategy significantly outperforms benchmarks.
The latest global financial tsunami and its follow-up global economic recession has uncovered the crucial impact of housing markets on financial and economic systems. The Chinese stock market experienced a markedly fall during the global financial tsunami and China's economy has also slowed down by about 2\%-3\% when m…
Bar-Natan used Chinese characters to show that finite type invariants classify string links up to homotopy. In this paper, I construct the correct spaces of chord diagrams and Chinese characters for links up to homotopy. I use these spaces to show that the only rational finite type invariants of link homotopy are the p…
Study reveals resilience of Chinese guarantee network during financial crisis and stimulus.
problem Limited knowledge about guarantee network dynamics during financial downturn.
method Analyzed comprehensive bank loan dataset covering 80% of total loans in China.
result Guarantee network became smaller, less connected, and more stable during financial crisis.
Bayesian model clusters, forecasts, and imputes multivariate time series data.
problem Jointly modeling and forecasting sparsely observed multivariate time series with varying dynamics.
method Chinese restaurant process prior for clustering, temporally-reweighted extension for joint modeling within clusters, MCMC for inference.
result Superior forecasting and competitive imputation accuracy compared to baselines, interpretable clusters discovered in macroeconomic data.