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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.

169,051 papers · 148 categories

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48 results for Global Stock Market

Deep Q-Network predicts global stock market returns from chart images.

problem Predicting global stock market returns using chart images.
method Deep Q-Network with CNN approximator, trained on US stock market, tested on 31 countries.
result Artificial intelligence can predict stock prices in small markets.

Model forecasts global stock market volatility using dynamic graphs and all trading days.

problem Enhance forecasting accuracy and practical utility in global stock market volatility.
method Spatial-temporal graph neural network architecture to capture volatility spillover effect.
result Forecasting performance surpasses baseline models in all scenarios.

Global stock markets exhibit exponential growth and Gaussian fluctuations with self-similar monthly patterns.

problem Understanding regularities in stock market fluctuations across different countries.
method Analysis of daily and monthly stock indices from six countries.
result Monthly stock growth is statistically self-similar to daily growth and follows a Wiener process.

With the random matrix theory, we study the spatial structure of the Chinese stock market, American stock market and global market indices. After taking into account the signs of the components in the eigenvectors of the cross-correlation matrix, we detect the subsector structure of the financial systems. The positive …

2012-01-31abs ↗pdf ↗

Study reveals 2020 stock crashes were mostly endogenous, not exogenous.

problem Identifying the cause of the 2020 global stock market crash.
method Applied log-periodic power law singularity (LPPLS) methodology to analyze stock market indexes.
result The 2020 stock market crashes were mostly endogenous, driven by systemic instability.

In the age of globalization, it is natural that the stock market of each country is not independent form the other markets. In this case, collective behavior could be emerged form their dependency together. This article studies the collective behavior of a set of forty influential markets in the world economy with the …

2017-03-26abs ↗pdf ↗

The global financial system is highly complex, with cross-border interconnections and interdependencies. In this highly interconnected environment, local financial shocks and events can be easily amplified and turned into global events. This paper analyzes the dependencies among nearly 4,000 stocks from 15 countries. T…

2017-04-04abs ↗pdf ↗

Analyzes how Trump's tweets impact global stock markets.

problem Understanding the financial impact of presidential tweets on stock markets.
method Examined tweets from Donald Trump's presidency, collected from The Guardian and Bloomberg, and analyzed their effect on equity indices.
result Identified tweets that significantly influenced stock market indices.

Study examines short-term stress of COVID-19 on major global stock indices.

problem Short-term impact of COVID-19 on global stock markets.
method Secondary data from 41 stock exchanges in 32 countries, focusing on first reported cases.
result Volatility in stock markets increases with the rise of COVID-19 cases, and there is a significant negative correlation.

Study shows economic policy uncertainty increases stock market crash risk during pandemic.

problem Impact of economic policy uncertainty on stock market crashes during the pandemic.
method Used GARCH-S model to estimate daily skewness as a proxy for crash risk, analyzed data from US stock market.
result Significantly negative correlation between economic policy uncertainty and stock market crash risk, stronger during pandemic.

Hypothesis of Market Efficiency is an important concept for the investors across the globe holding diversified portfolios. With the world economy getting more integrated day by day, more people are investing in global emerging markets. This means that it is pertinent to understand the efficiency of these markets. This …

2017-09-12abs ↗pdf ↗

We study precursors to the global market crash that occurred on all main stock exchanges throughout the world in October 2008 about three weeks after the bankruptcy of Lehman Brothers Holdings Inc. on 15 September. We examine the collective behavior of stock returns and analyze the market mode, which is a market-wide c…

2011-11-20abs ↗pdf ↗

Study shows stock market efficiency varies over time and can be networked.

problem Understanding the dynamic and collective aspects of stock market efficiency.
method Defined and calculated time-varying efficiency using permutation entropy of log-returns.
result Major world stock markets can be hierarchically classified into groups with similar efficiency profiles, but these rankings are unstable.

The study aims to explore the strength of causal relationship between stock price search interest and real stock market outcomes on worldwide equity market indices. Such a phenomenon could also be mediated by investor behavior and extent of news coverage. The stock-specific internet search trends data and corresponding…

2018-04-05abs ↗pdf ↗

Informer improves financial market prediction accuracy with global time stamp features.

problem Intra-day minute-scale financial market prediction challenges.
method Informer network, a novel deep learning model with smaller computational complexity and global time stamp features.
result Informer achieves best performance on MAE, RMSE, and MAPE evaluation criteria across all datasets.

Study analyzes stock market dynamics using Tsallis statistics and GHE, revealing pre-bubble and post-bubble market characteristics.

problem Understanding stock market dynamics and predicting market bubbles.
method Non-linear analysis using time-dependent Tsallis statistics and Generalized Hurst Exponents.
result Temporal trends of q-triplet values differ before and after market bubbles, indicating significant market dynamics changes.

Although the threshold network is one of the most used tools to characterize the underlying structure of a stock market, the identification of the optimal threshold to construct a reliable stock network remains challenging. In this paper, the concept of dynamic consistence between the threshold network and the stock ma…

2018-03-06abs ↗pdf ↗

We investigated the temporally evolving network structures of the Japanese and Korean stock markets through the minimum spanning trees composed of listed stocks. We tested the validity of conventional grouping by industrial categories, and found a common trend of decrease for Japan and Korea. This phenomenon supports t…

2005-11-27abs ↗pdf ↗

SimStock learns stock similarities for better investment management.

problem Challenges in identifying similar stocks due to non-stationary financial markets.
method Temporal self-supervised learning framework combining SSL and temporal domain generalization.
result SimStock outperforms existing methods in finding similar stocks.

Study predicts US stock market will continue to fall post-COVID-19.

problem Analyzing the recovery trend of the US stock market post-COVID-19.
method Used Deep Learning, Neuro Network, and Time-series analysis on S&P 500, Nasdaq 100, and Dow Jones Industrial Average data.
result LSTM model predicts US stock market will continue to fall post-COVID-19.

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.

We investigated the network structures of the Japanese stock market through the minimum spanning tree. We defined grouping coefficient to test the validity of conventional grouping by industrial categories, and found a decreasing in trend for the coefficient. This phenomenon supports the increasing external influences …

2007-08-03abs ↗pdf ↗

LSTM model predicts stock prices with high accuracy in stable sectors but struggles with volatile ones.

problem Predicting stock prices in emerging markets with limited data.
method Developed and evaluated an LSTM network on historical OHLCV data and technical indicators.
result Strong predictive performance (R2>0.87R^2 > 0.87) for stable sectors, but challenges for volatile ones.

Develops a hybrid deep learning model for stock price prediction.

problem Predicting daily stock prices in the stock market.
method Representation learning with Stock2Vec embedding and temporal convolutional layers.
result Achieves better performance on stock price prediction than benchmarks.

Study evaluates financial anomaly detection methods on Canadian stock market.

problem Detecting financial anomalies in the Canadian stock market.
method Topological data analysis (TDA), principal component analysis (PCA), and neural network-based approaches.
result Neural network-based methods achieve the strongest performance in detecting financial anomalies.

Network analysis improves stock return forecasting.

problem Improving stock return forecasting using network properties.
method Network analysis of stock return correlations, using individual and global properties of stocks.
result 50% improvement in R2 score for long-term stock returns forecasting, 3% for short-term.

ChatGPT predicts stock market movements based on Bloomberg headlines, showing a positive correlation over short to medium terms.

problem Predicting stock market movements using news headlines.
method Used a two-stage prompt approach with a dataset of Bloomberg market summaries from 2010 to 2023.
result ChatGPT's sentiment scores correlate positively with future equity market returns over short to medium terms, with a negative correlation over longer horizons.

This paper analyses the behaviour of volatility for several international stock market indexes, namely the SP 500 (USA), the Nikkei (Japan), the PSI 20 (Portugal), the CAC 40 (France), the DAX 30 (Germany), the FTSE 100 (UK), the IBEX 35 (Spain) and the MIB 30 (Italy), in the context of non-stationarity. Our empirical …

2006-07-19abs ↗pdf ↗

Study shows adding correlated features doesn't improve LSTM model interpretability for oil stocks.

problem Improving interpretability of LSTM models for predicting oil company stocks.
method Designed and trained Standard LSTM networks using various correlated datasets.
result Adding correlated features does not enhance LSTM model interpretability.

RAGIC predicts stock intervals with risk considerations, improving prediction accuracy and coverage.

problem Limited success in predicting stock market outcomes due to stochastic nature and risk oversight.
method RAGIC uses a GAN with a risk module and temporal module to generate risk-sensitive stock intervals.
result RAGIC achieves a consistent 95% coverage with narrow interval widths, balancing accuracy and risk.

AlphaMLDigger predicts excess returns in fluctuating markets.

problem Mining effective information for investment decisions in a volatile market.
method Two-phase approach using deep NLP for sentiment analysis and ensemble ML models.
result Ensemble models achieve 0.984 accuracy, significantly outperforming baseline.