Model predicts risk-adjusted returns across various financial markets.
problem Stationary models fail in predicting risk-adjusted returns due to market regime changes.
method Asset-independent regime-switching model using hidden Markov models.
result Accurately detects bull, bear, and high volatility periods for improved risk-adjusted returns.
We calculate the realized volatility in the spin model of financial markets and examine the returns standardized by the realized volatility. We find that moments of the standardized returns agree with the theoretical values of standard normal variables. This is the first evidence that the return dynamics of the spin fi…
The study explains stock return distributions using reaction functions.
problem Stock return distributions often deviate from normal distributions.
method Assumes normal event/information effects, financial over/underreaction, proposes reaction function model.
result Financial markets often underreact to minor events, overreact to significant ones, and react stronger to positive events.
We present a nonlinear stochastic differential equation (SDE) which mimics the probability density function (PDF) of the return and the power spectrum of the absolute return in financial markets. Absolute return as a measure of market volatility is considered in the proposed model as a long-range memory stochastic vari…
A spin model is used for simulations of financial markets. To determine return volatility in the spin financial market we use the GARCH model often used for volatility estimation in empirical finance. We apply the Bayesian inference performed by the Markov Chain Monte Carlo method to the parameter estimation of the GAR…
Study finds investor sentiment has a significant positive relationship with stock returns in Moroccan and Tunisian markets.
problem Investor sentiment and stock returns relationship in Moroccan and Tunisian markets.
method Used indirect measures of investor sentiment (SENT and ARMS) and Granger causality tests.
result Sentiment has a significant positive relationship with stock returns, but not the other way around.
New financial dataset and model detect claims affecting market returns.
problem Detecting analyst claims' impact on financial markets.
method Constructed new dataset, used weak-supervision model with SME knowledge.
result Outperformed existing models in claim detection and market analysis.
Modeling financial market dynamics with noise and fundamentalist agents.
problem Understanding opinion formation and market behavior in financial markets.
method Agent-based model with Erdös-Rényi random graph structure, incorporating anxiety parameter.
result Model accurately reproduces key market features like fat-tailed returns and volatility clustering.
A three-state model based on the Potts model is proposed to simulate financial markets. The three states are assigned to "buy", "sell" and "inactive" states. The model shows the main stylized facts observed in the financial market: fat-tailed distributions of returns and long time correlations in the absolute returns. …
The paper examines Indian market bubbles using financial ratios.
problem Detecting bubbles in emerging markets like India is challenging.
method Utilizes financial ratios and the Philips et al 2015 right-tailed unit test.
result Identifies various bubble episodes in the Indian market.
The SV-GARCH-EVT model improves risk assessment in financial markets.
problem Inaccurate risk assessment in financial markets due to fat-tailed and leverage effects.
method Enhanced SV model with EVT for tail distribution, MCMC for parameter estimation.
result SV-EVT models outperform other models in backtesting and out-of-sample analysis.
The FCA improved insider trading regulation after 2012, reducing abnormal returns.
problem Regulation of insider trading before and after the UK Financial Services Act 2012.
method Event study methodology using abnormal returns analysis.
result Abnormal returns were reduced after the FCA took over from the FSA.
Study uses OT to simulate markets, revealing power-law returns are driven by informational effect.
problem Reproduce power-law returns in financial markets using realistic simulations.
method Constructed artificial markets, used optimal transport (OT) to measure similarity, incrementally introduced behavioral components.
result Informational effect of prices is dominant in reproducing power-law returns, and multiple components interact synergistically.
Cryptocurrencies are becoming more linked in their returns and volatilities.
problem Understanding the increasing interconnectivity of cryptocurrencies.
method Examined market linkages using returns and volatilities, applied various methodologies.
result Significant increase in market linkages for both returns and volatilities.
New study finds day-of-the-week effects in stock market returns using multifractal analysis.
problem Exploring calendar anomalies in stock markets, particularly day-of-the-week effects.
method Multifractal Detrended Fluctuation Analysis (MF-DFA) applied to daily returns of market indices.
result Monday returns exhibit more persistent behavior and richer multifractal structures than other days.
Quantum walks model financial returns with flexibility and asymmetry.
problem Accurate modeling of financial asset price dynamics.
method Discrete-time quantum walks to model asset price evolution.
result Quantum walk models can generate asymmetric return distributions and higher probabilities for extreme events.
New model explains price, volume, and waiting times in financial markets.
problem Understanding price, volume, and waiting times in financial markets.
method Generalized semi-Markov chains with endogenous index process and copulae for dependence.
result Model accurately reproduces empirical evidence from Italian stock market data.
Complex network analysis reveals dominant stocks in financial stock returns correlations.
problem Inferring financial stock returns correlations from complex network analysis.
method Simulated geometric Brownian motion for stocks, complex network analysis, eigenvector centrality, clustering.
result Returns correlation matrix is dominated by stocks with high eigenvector centrality and clustering.
Study shows cryptocurrency market impact on DeFi returns stronger than other drivers.
problem Understanding drivers of DeFi returns and their relative importance.
method Investigated four drivers: cryptocurrency market exposure, network effect, investor attention, and valuation ratio. Designed a new market index, DeFiX.
result Cryptocurrency market impact on DeFi returns is stronger than other drivers and provides superior explanatory power.
This work models financial market returns with asymmetric Tsallis distributions, improving fit over symmetric q-Gaussians.
problem Non-symmetric behavior of stock market returns over time scales.
method Linear combination of two independent normalized half q-Gaussians with different parameters.
result Asymmetric distributions provide better fits to stock market returns than symmetric q-Gaussians, especially over longer time scales.
New method uses asymmetric Tsallis relative entropy for better risk assessment in financial portfolios.
problem Improving risk assessment for financial portfolios using asymmetric data.
method Generalized Tsallis relative entropy (ATRE) for asymmetric distributions of returns.
result ATRE shows better risk-return profiles, especially during market crashes.
Study tests 11 stylized facts for modern stock markets, finding support for 8.
problem Whether stylized facts from 2001 still hold for modern markets.
method Replicated 11 stylized facts for intraday returns of Dow 30 stocks using authoritative data.
result 8 of 11 stylized facts supported, 3 not supported.
The paper examines how randomness in forex returns increases during financial crises.
problem Measuring randomness in forex returns during financial crises.
method Approximate Entropy and Sample Entropy statistics.
result Randomness in forex returns decreases during financial crises, as evidenced by lower entropy values.
We construct a financial "Turing test" to determine whether human subjects can differentiate between actual vs. randomized financial returns. The experiment consists of an online video-game (http://arora.ccs.neu.edu) where players are challenged to distinguish actual financial market returns from random temporal permut…
Analyzes financial return distributions over various time scales.
problem Understanding the changing nature of financial return distributions over time.
method Modeling return distributions using power-law, stretched exponential, and q-Gaussian functions.
result The 'inverse-cubic power-law' is still a good fit for short-term returns, but market dynamics are more complex.
Paper presents a deep learning method for estimating asset return precision matrices in noisy financial markets.
problem Estimating precision matrices of asset returns in low signal-to-noise ratio environments.
method Non-linear factor model within deep learning framework, consistent estimator with error covariance estimator.
result Superior accuracy in simulations and empirical data.
With the daily and minutely data of the German DAX and Chinese indices, we investigate how the return-volatility correlation originates in financial dynamics. Based on a retarded volatility model, we may eliminate or generate the return-volatility correlation of the time series, while other characteristics, such as the…
Bid-ask spread is taken as an important measure of the financial market liquidity. In this article, we study the dynamics of the spread return and the spread volatility of four liquid stocks in the Chinese stock market, including the memory effect and the multifractal nature. By investigating the autocorrelation functi…
Stylized facts can be regarded as constraints for any modeling attempt of price dynamics on a financial market, in that an empirically reasonable model has to reproduce these stylized facts at least qualitatively. The dynamics of market prices is modeled on a macro-level as the result of the dynamic coupling of two dyn…
ChatGPT predicts stock market reactions from news headlines without financial training.
problem Predicting stock price movements using non-financial data.
method Used post-knowledge-cutoff headlines to train ChatGPT-4, which forecasts stock market reactions.
result ChatGPT-4 can predict stock market reactions with high accuracy, especially for small stocks and negative news.
Cryptocurrencies show mature market characteristics but vary by size.
problem Understanding maturity in cryptocurrency markets.
method Quantitative analysis of return distributions, volatility, and correlations.
result Smaller cryptocurrencies lack mature market characteristics.
New framework models stock relationships and investor expectations for better financial market predictions.
problem Limited by predefined stock relationships and immediate effects, current financial market analysis methods need improvement.
method Jointly models investor expectations and automatically mines latent stock relationships.
result Annual return exceeds 10%, surpassing existing benchmarks.
We perform a large-scale simulation of an Ising-based financial market model that includes 300 asset time series. The financial system simulated by the model shows a fat-tailed return distribution and volatility clustering and exhibits unstable periods indicated by the volatility index measured as the average of absolu…
The paper predicts financial markets using news text and semantic network analysis.
problem Predicting financial markets with news data.
method Semantic network analysis of news text to assess economic keywords' importance.
result The index captures financial market phases and predicts returns and volatilities.
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.
This paper extends liquidity returns in geometric mean markets to time-varying weights.
problem Understanding returns and no-arbitrage prices in geometric mean markets with time-varying weights.
method Extending known results for constant-weight G3Ms to the general case of G3Ms with time-varying and potentially stochastic weights.
result LP shares can replicate the payoffs of financial derivatives and various trading strategies.
We investigate the volatility return intervals in the NYSE and FOREX markets. We explain previous empirical findings using a model based on the interacting agent hypothesis instead of the widely-used efficient market hypothesis. We derive macroscopic equations based on the microscopic herding interactions of agents and…
We present a simple model of a stock market where a random communication structure between agents gives rise to a heavy tails in the distribution of stock price variations in the form of an exponentially truncated power-law, similar to distributions observed in recent empirical studies of high frequency market data. Ou…
An average instantaneous cross-correlation function is introduced to quantify the interaction of the financial market of a specific time. Based on the daily data of the American and Chinese stock markets, memory effect of the average instantaneous cross-correlations is investigated over different price return time inte…
Study links public concern in Italy to financial markets worldwide.
problem Understanding public concern's impact on financial markets during pandemics.
method Used Google Trends data from YouTube, News, and Search to measure public concern and correlate it with stock index returns.
result Public concern in Italy drives concerns in other countries and explains stock index returns of multiple nations.
Market inefficiencies arise from density-dependent returns in a noisy environment.
problem Market inefficiencies and excess volatility.
method Developed a market model using ecological concepts.
result Market dynamics are density-dependent, leading to inefficiencies.
DSVM model predicts financial market volatility with better accuracy.
problem Predicting financial market volatility accurately.
method Deep latent variable models with variational inference.
result DSVM outperforms GARCH models in predicting volatility.
Model uses statistical physics principles to predict financial market volatility and returns.
problem Predicting price volatility and expected returns in financial markets.
method Inspired by statistical physics, the study introduces a physical model using Level 3 order book data to measure kinetic energy and momentum.
result The model outperforms traditional and machine learning approaches in forecasting volatility and expected returns.
Develops a new flexible grid trading model using ANN and SSO.
problem Improving automated trading strategies in financial markets.
method Combines SSO algorithm with ANN for optimizing trading parameters.
result Provides a robust and efficient trading model with better returns.
Financial market created for wellbeing indices to mitigate socioeconomic risks.
problem Risk mitigation in financial indices of socioeconomic wellbeing.
method Developed new quantitative measure, created financial market, and implemented insurance instruments.
result Optimal portfolio weights and efficient frontiers for wellbeing indices.
Study financial crises using mathematical techniques to compare equity performance.
problem Comparing financial crises to understand market dynamics and investor strategies.
method New mathematical techniques including portfolio diversification, linear operator method, and combinatorial portfolio optimisation.
result New methods to quantify and compare equity returns during different market crises.
TradeFM learns market microstructure from trade events, improving financial model accuracy.
problem Lack of generalizable models for market microstructure.
method Generative Transformer model trained on billions of trade events, using scale-invariant features and universal tokenization.
result TradeFM generates rollouts that match key stylized facts of financial returns and outperforms existing models.
This paper uses rank correlation methods to construct MSTs from financial returns, finding them more stable and robust.
problem Stability and robustness of MSTs constructed from financial correlation matrices.
method Pearson, Spearman, and Kendall's τ rank correlation methods applied to daily financial returns. result Rank MSTs are more stable and robust than MSTs constructed using Pearson correlation.