Geography and distance impact financial dynamics in Chinese stock markets.
problem Investigate the impact of geography and distance on financial dynamics in Chinese stock markets.
method Daily data analysis of individual stocks in Shanghai and Shenzhen stock markets, focusing on geographical correlation and distance effect.
result Stock location impacts financial dynamics, except during financial crises. Short distance has higher probability than long distance, and correlation weakly decays with distance in Shanghai but remains stable in Shenzhen.
Study reveals the 2020 U.S. stock crash was endogenous, not caused by COVID.
problem Understanding the cause of the 2020 U.S. stock market crash.
method Applied log-periodic power law singularity (LPPLS) methodology to analyze four major U.S. stock market indexes.
result The 2020 U.S. stock market crash was endogenous, stemming from systemic instability, not COVID.
We study numeraire markets in open stock markets.
problem Understanding the numeraire portfolio in open stock markets.
method Constructed an example of a numeraire market that is asymptotically stable.
result Found an asymptotically stable numeraire market in open stock markets.
Study finds stock markets follow nonextensive statistical mechanics.
problem Understanding nonextensivity in stock market volatilities.
method Analysis of 34 major stock market indices over 10 years.
result Stock markets exhibit nonextensive behavior, distinguishing between developed and developing countries.
It seems to be very unlikely that all relevant information in the stock market could be fully encoded in a geometrical shape. Still,the present paper will reveal the geometry behind the stock market transactions. The prices of market index (DJIA) stock components are arranged in ascending order from the smallest one in…
A simple and elegant arrangement of stock components of a portfolio (market index-DJIA) in a recent paper [1], has led to the construction of crossing of stocks diagram. The crossing stocks method revealed hidden remarkable algebraic and geometrical aspects of stock market. The present paper continues to uncover new ma…
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.
Survey on deep learning methods for stock market prediction.
problem Lack of comprehensive survey on deep learning methods for stock market prediction.
method Propose a novel taxonomy summarizing state-of-the-art models based on deep neural networks.
result Provide detailed statistics on datasets and evaluation metrics.
Investor expectations shifted pessimistically during the 2020 stock market crash and recovery.
problem Analyzing changes in investor expectations during the 2020 stock market crash and recovery.
method Surveying Vanguard clients at three points: before, during, and after the crash.
result Investor pessimism increased following the crash, with significant disagreement about future outcomes.
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 study examines network analysis for predicting stock market performance.
problem Understanding lead-lag relationships in the NYSE.
method Network analysis of the NYSE to identify lead-lag effects.
result Network analysis reveals valuable insights for investors and analysts.
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.
Study examines how economic policy uncertainty impacts stock markets.
problem Dynamic relationship between economic policy uncertainty and stock markets.
method Used symmetric thermal optimal path (TOPS) method.
result Different interaction patterns observed in emerging and developed markets.
We study the temporal evolution of the market efficiency in the stock markets using the complexity, entropy density, standard deviation, autocorrelation function, and probability distribution of the log return for Standard and Poor's 500 (S&P 500), Nikkei stock average index, and Korean composition stock price index (K…
The paper reports the construction of artificial stock market that emerges the similar statistical facts with real data in Indonesian stock market. We use the individual but dominant data, i.e.: PT TELKOM in hourly interval. The artificial stock market shows standard statistical facts, e.g.: volatility clustering, the …
Stock market price fluctuations follow Lévy's stable distribution over long term.
problem Understanding the stability of stock market price fluctuations over different time scales.
method Estimated Lévy's stable parameters from four stock markets over long and short term.
result Stable parameters from different stock markets showed a unique value over long term, but fluctuated with correlation in short term.
Study finds long memory in some emerging Asian stocks but not in developed markets.
problem Evaluating stock market efficiency in emerging vs developed markets.
method Improved wavelet estimator of long range dependence.
result Emerging Asian markets show more long memory in stock returns than developed markets.
Deep learning predicts stock market trends using candlestick charts.
problem Predicting stock market prices with multiple influencing factors.
method Used Deep Convolutional Neural Networks and candlestick charts.
result 92.2% and 92.1% accuracy for Taiwan and Indonesian stock markets.
This paper surveys NLP techniques for predicting stock market movements.
problem Predicting stock market movements with volatility, seasonality, and time dependence.
method Survey of recent literature in NLP and ML for stock market prediction.
result Illustrates recent trends in stock market prediction research.
How an investor invests in the market is largely influenced by the market efficiency because if a market is efficient, it is extremely difficult to make excessive returns because in an efficient market there will be no undervalued securities i.e. securities whose value is less than its assumed intrinsic value, which of…
A surprising image of the stock market arises if the price time series of all Dow Jones Industrial Average stock components are represented in one chart at once. The chart evolves into a braid representation of the stock market by taking into account only the crossing of stocks and fixing a convention defining overcros…
Model shows stock markets can be inefficiently mispriced.
problem Limits of informationally efficient stock markets.
method Chartist-fundamentalist model with chartists and fundamentalists trading conditions.
result Stock markets can exhibit constant or oscillatory mispricing.
We investigate the strength and the direction of information transfer in the U.S. stock market between the composite stock price index of stock market and prices of individual stocks using the transfer entropy. Through the directionality of the information transfer, we find that individual stocks are influenced by the …
The paper analyzes the crash of stock and commodity markets during COVID-19 using Topological Data Analysis.
problem Identifying and understanding the dynamics and interdependence of stock and commodity markets during the COVID-19 crash.
method Topological Data Analysis (TDA) and Wasserstein Distance (WD) to identify crashes and compare market dynamics.
result Significant topological differences and interdependence between stock and commodity markets during the crash period.
Study confirms Indian stock market is weak form inefficient.
problem Impact of stock market efficiency on investment returns.
method Runs test, Autocorrelation test, Autoregression test on daily stock indices.
result Indian stock market is weak form inefficient and can be outperformed.
Artificial Neural Networks predict stock returns, finding larger stocks less predictable.
problem Evaluating the validity of the Efficient Market Hypothesis.
method Backpropagation Artificial Neural Network analysis of Brazilian stock market.
result Predictability of stock returns is related to market capitalization, with larger stocks less predictable.
Study uses Kalman-Filter to assess market efficiency in major stock markets.
problem Assessing market efficiency in major stock markets.
method Utilizes Kalman-Filter in two stages, assuming a trendline representing true market value.
result Significant portfolio returns in emerging and developed markets.
Study finds varying market efficiency in prewar and wartime Japanese stock market.
problem Measuring market efficiency in prewar and wartime Japanese stock market.
method Using a new market capitalization-weighted stock price index, the study examines market efficiency over time and historical events.
result The adaptive market hypothesis is supported in the prewar and wartime Japanese stock market, with efficiency varying over time and with historical events.
This paper examines the short-run relationships between oil prices and GCC stock markets. Since GCC countries are major world energy market players, their stock markets may be susceptible to oil price shocks. To account for the fact that stock markets may respond nonlinearly to oil price shocks, we have examined both l…
Examines cross-stock price responses in correlated financial markets.
problem Understanding the impact of trades on prices across different stocks.
method Empirical investigation of cross-responses in a correlated market.
result Cross-stock price responses are transient, not permanent.
Paper applies fluid dynamics to stock market behavior.
problem Understanding stock market dynamics using physical principles.
method Uses Stokes law to model stock market as fluid system.
result Stock market dynamics can be explained by physical properties.
This study analyzes the collective behavior of world stock markets using RMT.
problem Understanding the collective behavior and structure of interconnected world stock markets.
method Analysis of cross-correlation matrix using Random Matrix Theory (RMT).
result Emergence of a world financial market structure with three communities.
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.
China's stock market reacts positively to PMI news but not to negative news.
problem Understanding asymmetric responses to PMI announcements in China's stock market.
method Empirical investigation of stock market reactions to PMI announcements.
result China's stock market reacts positively to positive PMI news but not to negative news.
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 gain-loss asymmetry in stock indices using a q-spin Potts model.
problem Understanding the dynamics of stock indices in complex markets.
method Developed a q-spin Potts model to represent stock market dynamics.
result Observed a self-organized gain-loss asymmetry in stock indices.
Advanced ML/DL models predict stock prices using technical analysis.
problem Accurately predicting stock prices in a complex market.
method Use of deep learning models for stock price prediction.
result Deep learning models can predict stock prices with high accuracy.
Study improves stock price prediction by integrating international markets using deep learning.
problem Complex cross-correlation between international stock markets.
method Multimodal deep learning to forecast stock prices.
result Early and intermediate fusion models outperform late fusion and single modality models.
Temporal network analysis reveals stock market instability and new portfolio optimization tools.
problem Detecting market instability in stock markets using temporal network analysis.
method Utilized temporal network framework to characterize stock market correlation networks and employed temporal centrality as a portfolio selection tool.
result Peripheral stocks with low temporal centrality scores perform better in portfolio optimization under different schemes.
BERTopic enhances stock market prediction by analyzing sentiment in topic models.
problem Improving stock price prediction accuracy using sentiment analysis.
method Employed BERTopic for sentiment analysis of stock market comments integrated with deep learning models.
result Enhanced model performance through topic sentiment integration.
Research shows SBP's tone impacts stock market returns positively or negatively.
problem Impact of State Bank of Pakistan's monetary policy communications on stock market.
method Sentiment analysis and high frequency stock market returns analysis.
result Positive or negative tone in SBP communications affects stock returns positively or negatively.
To investigate the universality of the structure of interactions in different markets, we analyze the cross-correlation matrix C of stock price fluctuations in the National Stock Exchange (NSE) of India. We find that this emerging market exhibits strong correlations in the movement of stock prices compared to developed…
Study finds financial YouTube channel 3PROTV predicts stock market performance and sentiment changes.
problem Determining the informational value of financial YouTube channels.
method Analyzing 3PROTV's content and its impact on stock market performance and sentiment.
result 3PROTV's content, particularly negative sentiment, predicts stock market performance and sentiment changes.
Mapping the economy to the some statistical physics models we get strong indications that, in contrary to the pure stock market, the stock market with derivatives could not self-regulate.
Study introduces TeMoP model for better stock market predictions.
problem Decreasing prediction errors and robustness across datasets in machine learning models.
method Probabilistic multiple lag order model based on trend encoding.
result TeMoP model outperforms machine learning models in accuracy and stability across different stock indexes.
The study analyzes co-movements in Indian stock market sectors using network techniques.
problem Understanding co-movements among Indian stock sectors.
method Mesoscopic network analysis, recurrence analysis, partial correlations, multidimensional scaling, minimum spanning tree.
result Minimum spanning tree effectively separates technologically related sectors.
This paper uses CNN-LSTM to predict stock market performance.
problem Predicting stock market performance is challenging due to changing prices and lack of advanced libraries.
method Developed a CNN-LSTM Neural Network model to track stock data patterns and predict future performance.
result The CNN-LSTM model outperformed other models in predicting stock market performance.
Price limit trading rules are adopted in some stock markets (especially emerging markets) trying to cool off traders' short-term trading mania on individual stocks and increase market efficiency. Under such a microstructure, stocks may hit their up-limits and down-limits from time to time. However, the behaviors of pri…