This study presents an agent-based computational cross-market model for Chinese equity market structure, which includes both stocks and CSI 300 index futures. In this model, we design several stocks and one index futures to simulate this structure. This model allows heterogeneous investors to make investment decisions …
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,181 papers · 148 categories
Trend · papers per month
48 results for “Chinese Index Futures”
Study on profitability of technical trading rules using high-frequency data of Chinese Index Futures.
problem Investigating the profitability of technical trading rules with high-frequency data of Chinese Index Futures.
method Converted MA, KDJ, and Bollinger bands into stationary processes and used ADF-test and SPA test to verify stationarity and assess trading rules' performance.
result Significant combinations of parameters for each indicator were found, but trading profits were eliminated with transaction costs included.
This study examines lead-lag relationships in Chinese futures markets using high-frequency data.
problem Understanding high-frequency trading dynamics and information flow in futures markets.
method High-frequency tick-by-tick data analysis of lead-lag relationships between different maturity futures contracts.
result The near-month futures lead longer-dated contracts by one tick, with a negative feedback effect on the leading asset.
Study reveals multifractal nature in Chinese stock markets and predicts future returns.
problem Predicting future stock market returns using multifractal characteristics.
method Multifractal detrended fluctuation analysis (MF-DFA) on high-frequency stock data.
result Spectral width Δα is a significant predictor of future stock returns.
Stochastic Price Dynamics in Response to Order Flow Imbalance: Evidence from CSI 300 Index Futuresq-fin.MF
Modeling price dynamics in response to order flow imbalance in Chinese futures markets.
problem Understanding price dynamics in markets with order flow imbalance.
method Modeling order flow imbalance as an Ornstein-Uhlenbeck process with memory and mean-reverting characteristics.
result Horizon-dependent heterogeneity in conventional metrics' interaction with order flow imbalance.
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.
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.
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).
Study reveals dynamic linkage between Peanut and Soybean Oil futures markets.
problem Exploring interdependence between Peanut and other agricultural commodities in Chinese futures market.
method Constructed multivariate linear regression models and used VAR and DCC-EGARCH models for dynamic relationships. Applied MLP, CNN, and LSTM neural networks for price prediction.
result Significant dynamic linkage between Peanut and Soybean Oil futures markets through DCC-EGARCH, limited influence from other futures markets through VAR model.
Study shows post-COVID commodity futures returns and volatility changed for different products.
problem Analyzing how the pandemic affected Chinese commodity futures markets.
method Empirical analysis of commodity futures returns and cointegration before and after the pandemic.
result Post-COVID, some commodity futures returns increased significantly, while others saw higher volatility.
This paper models CSI 300 index volatility using machine learning and addresses jump prediction.
problem Volatility modeling and jump prediction for high-frequency CSI 300 index data.
method Generalized Barndorff-Nielsen and Shephard model with machine learning algorithms for parameter estimation and forecast evaluation.
result Deterministic component of stochastic volatility processes can be captured over short and longer-term windows.
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.
By combining (i) the economic theory of rational expectation bubbles, (ii) behavioral finance on imitation and herding of investors and traders and (iii) the mathematical and statistical physics of bifurcations and phase transitions, the log-periodic power law model has been developed as a flexible tool to detect bubbl…
The study finds that Chinese internet users have different search behaviors and attention patterns.
problem Heterogeneity in search behavior and attention among Chinese internet users.
method Data extraction technology to analyze Baidu Index keyword search volume data.
result Chinese internet users exhibit different search behaviors and attention patterns.
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.
We have performed detailed multifractal analysis on the minutely volatility of two indexes and 1139 stocks in the Chinese stock markets based on the partition function approach. The partition function scales as a power law with respect to box size . The scaling exponents form a nonlinear function of …
Study detects Chinese stock market bubbles using LPPLS confidence indicator.
problem Early detection of stock market bubbles in China.
method LPPLS confidence indicator applied to CSI 300 index data.
result LPPLS detects positive and negative bubbles with high accuracy.
The paper analyzes return distribution of Chinese stock market indices over various time scales.
problem Understanding return distribution properties of Chinese stock markets.
method Systematic analysis of 1-min to 4000-min composite index datasets from 2005-2021.
result Return distribution properties are similar to mature markets, with distinct behavior at different time scales.
Although technical trading rules have been widely used by practitioners in financial markets, their profitability still remains controversial. We here investigate the profitability of moving average (MA) and trading range break (TRB) rules by using the Shanghai Stock Exchange Composite Index (SHCI) from May 21, 1992 th…
Pricing commodity index optionsq-fin.PR
Model prices commodity futures and index options.
problem Deriving accurate prices for derivative contracts on commodity futures and indices.
method Stochastic local volatility model for commodity futures.
result Model accurately recovers prices of derivative claims.
Paper proposes a framework for precise daily default risk prediction of Chinese credit bonds.
problem Inadequate and inaccurate bond information disclosure creates risk of default for investors.
method Framework includes summarizing factors impacting defaults, constructing a risk index system, and using ConvLSTM neural network for prediction.
result The model provides more responsive and accurate daily default risk predictions than authoritative ratings.
The study finds solar terms significantly impact China's stock market returns and volatility.
problem Investigating the effect of solar terms on China's stock market.
method Regression framework, analyzing multiple solar terms and their impact on return and volatility.
result Solar terms 1, 3, and 4 cause significant positive returns, while 8, 11, and 14 bring high volatility.
We present an extension of the Johansen-Ledoit-Sornette (JLS) model to include an additional pricing factor called the "Zipf factor", which describes the diversification risk of the stock market portfolio. Keeping all the dynamical characteristics of a bubble described in the JLS model, the new model provides additiona…
Large language models improve futures market factor models in China.
problem Designing effective factor models for Chinese futures markets.
method Used large language models (GPT) to generate 40 factors for single and multi-factor portfolios.
result GPT-generated factors outperform benchmarks with high Sharpe ratios and alphas.
The paper examines market efficiency in China and India post financial crises.
problem Testing market efficiency in emerging markets post crises.
method Examined daily returns using auto correlation tests, runs tests, and unit root tests.
result Both Chinese and Indian stock markets do not exhibit weak form of market efficiency.
We investigate the behavior of the Shanghai Stock Exchange Composite (SSEC) index for the period from 1990:12 to 2007:06 using an unconstrained two-regime threshold autoregressive (TAR) model with an unit root developed by Caner and Hansen. The method allows us to simultaneously consider non-stationarity and nonlineari…
Stock index futures trading impact on spot price volatility. The CSI 300 studied with a TGARCH modelq-fin.ST
TGARCH model shows CSI-300 futures reduce spot price volatility.
problem Impact of CSI-300 futures trading on spot price volatility.
method TGARCH model applied to CSI-300 index data.
result CSI-300 futures trading significantly reduces spot price volatility.
Hidden Markov model predicts profitable statistical arbitrage in Shanghai crude oil futures.
problem Statistical arbitrage opportunities in international crude oil futures markets.
method Hidden Markov model for cointegration spread, mean-reverting regime-switching process.
result Statistical arbitrage strategies involving Shanghai crude oil futures are profitable.
This study analyzes factors affecting China's stock market volatility.
problem Investigating factors influencing China's stock market volatility.
method Used ARDL model and principal component analysis on daily data from 2010-2024.
result Exchange rate and bond yields significantly impact stock market volatility.
Paper generates personalized fonts from a few characters.
problem Creating personalized fonts from a limited set of characters.
method Designs a network framework to extract and recombine character content and style using various neural networks.
result Generated characters are structurally similar to real characters.
We investigate the probability distribution of order imbalance calculated from the order flow data of 43 Chinese stocks traded on the Shenzhen Stock Exchange. Two definitions of order imbalance are considered based on the order number and the order size. We find that the order imbalance distributions of individual stoc…
Based on our "finance-prediction-oriented" methodology which involves such elements as log-periodic self-similarity, the universal preferred scaling factor lambda=2, and allows a phenomenon of the "super-bubble" we analyze the 2009 world stock market (here represented by the SP500, Hang Seng and WIG) development. We id…
Model estimates corporate credibility using NLP and neural networks.
problem Estimating corporate credibility in Chinese listed companies.
method Latent Dirichlet Allocation + Residual Convolutional Neural Network.
result Model ranks companies based on transparency.
The study analyzes macroeconomic factors affecting copper futures volatility and long-term correlation with S&P 500.
problem Understanding the impact of macroeconomic variables on copper futures volatility and long-term correlation.
method Employed GARCH-MIDAS and DCC-MIDAS modeling frameworks to examine the influence of low-frequency macroeconomic variables on copper futures returns and long-term correlation with S&P 500.
result PPI is the most efficient macroeconomic variable impacting copper futures returns, and MIDAS filter improves model fitness and long-run relationship.
Machine learning reveals inventory effects on VSTOXX futures pricing.
problem Understanding how inventory affects VSTOXX futures pricing.
method Combining stochastic processes and machine learning, we formulate and calibrate a Heston model for VSTOXX futures pricing.
result Machine learning models show that inventory significantly impacts VSTOXX futures prices.
Since 2002 when China first introduced QFII (Qualified Foreign Institutional Investors) system, QFII has been developing in China for 14 years, during when RQFII, Shanghai-Hongkong Stock Connect Program, Shanghai-London Stock Connect Program furthur broadened the avenue for foreign capital to invest in Chinese Security…
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.
Study on VIX futures portfolios to track VIX index, finding dynamic strategy superior.
problem Tracking VIX index with VIX futures lagging.
method Optimization methods for static portfolios, dynamic trading strategy for VIX futures.
result Dynamic trading strategy outperforms static portfolios and VXX in tracking VIX.
The diagonal effect of orders is well documented in different markets, which states that orders are more likely to be followed by orders of the same aggressiveness and implies the presence of short-term correlations in order flows. Based on the order flow data of 43 Chinese stocks, we investigate if there are long-rang…
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.
Neural Chinese Named Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentationcs.CL
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.
This study examines investor sentiment's impact on stock market liquidity and volatility using deep learning and TVP-VAR models.
problem Investor sentiment's impact on stock market liquidity and volatility.
method Deep learning BERT model for sentiment extraction and TVP-VAR model for time-varying analysis.
result Investor sentiment has a stronger impact on stock market liquidity and volatility, with more pronounced effects in short-term shocks.
The real estate is a pillar industry of China's national economy. Due to changes in policy and market conditions, the real estate companies are facing greater pressures to survive in a competitive environment. They must improve their financial competitiveness. Based on the conceptual framework of financial competitiven…
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).