Study shows stock prices influence news more than the other way around.
problem Understanding the interdependency between stock market and financial news.
method Time series analysis using five classification models.
result Stock prices have a greater impact on news contents than the other way around.
Hybrid model predicts stock prices using online forum sentiments and popularity.
problem Predicting stock prices accurately considering investor sentiment.
method XLNET for sentiment analysis, BiLSTM-highway model integration, combining post popularity.
result Hybrid model outperforms traditional methods in stock price prediction.
Deep learning models predict stock prices with high accuracy.
problem Accurate prediction of stock prices despite market unpredictability.
method CNN and LSTM-based deep learning models trained on historical stock data.
result Models achieve high accuracy in forecasting future stock prices.
The study introduces a new stickiness parameter for stock prices using a non-linear model.
problem Understanding how closely individual stocks follow a stock index's price movements.
method Developed a non-linear pricing model inspired by tectonic plate movements to measure stickiness.
result Defined a stickiness parameter for stock price returns using a novel model.
The trade of a fixed stock can be regarded as the basic process that measures its momentary price. The stock price is exactly known only at the time of sale when the stock is between traders, that is, only in the case when the owner is unknown. We show that the stock price can be better described by a function indicati…
The paper defines the time function of stock prices using a mathematical model.
problem Understanding the movement and predictability of stock prices over time.
method Empirical evidence and mathematical modeling of white noise.
result Derives auto-correlation function, displacement formula, and power spectral density of stock price movement.
Warrants with stock price dependent threshold conditions give the right to buy specially issued stocks, if the performance of the stock price satisfies some requirements. Existence of these derivatives changes the price process of the underlying. We show that in the presence of such warrants one cannot assume that the …
The paper explains stock predictability by integrating rational finance without behavioral finance assumptions.
problem The predictability of stock returns observed in the stock market.
method Developed a statistical model within rational finance to incorporate stock predictability into the Black-Scholes formula.
result Empirical analysis shows asymmetric predictability by spot and option traders, and potential stock return predictors.
Quantum algorithms improve stock price prediction accuracy.
problem Improving stock price prediction accuracy using quantum techniques.
method Extracted stock price indicators, used QA and PCA for feature selection and dimensionality reduction, trained QSVM for binary classification.
result Quantum Support Vector Machine (QSVM) outperformed classical models in stock price prediction accuracy.
Deep learning predicts cross-sectional stock prices for practical investment.
problem Predicting stock prices using cross-sectional factors.
method Deep learning model for daily stock price prediction.
result Profitable investment framework demonstrated in Japanese stock market.
Game-theoretic model captures investor interactions for stock price forecasting.
problem Complex market dynamics driving stock price movements.
method Game-theoretic modeling of heterogeneous investor interactions in a dynamic graph structure.
result Our method outperforms state-of-the-art stock price forecasting methods.
Transformer model predicts stock prices in Bangladesh's stock market.
problem Predicting volatile stock prices in the Bangladesh stock market.
method Transformer model applied to time series data for stock price prediction.
result Transformer model shows promising results in predicting stock price movements.
This paper predicts significant stock price changes using neural networks.
problem Predicting significant stock price changes.
method Three neural network models (MLP, CNN, LSTM) and two benchmark models (Random Forest, Relative Strength Index) were tested on 10-year daily stock price data of four major US companies.
result Neural network models significantly outperform traditional methods in predicting significant stock price changes.
Predict stock prices using HMMs trained on fractional price changes and intraday highs/ lows.
problem Forecasting stock prices considering time dependency and volatility.
method Hidden Markov Models (HMMs) trained on fractional price changes and intraday highs/ lows.
result The MAP estimate of stock prices for the next day was produced using the trained HMM.
Study finds GBM model accurately predicts stock prices on Ghana Stock Exchange.
problem Investigating the suitability of GBM for modeling stock price dynamics.
method Geometric Brownian Motion model applied to weekly and monthly returns of equities listed on the Ghana Stock Exchange.
result GBM model accurately forecasts stock prices with minimal deviations, as evidenced by MSE evaluations.
Study finds stock prices rarely appreciate during capital inflows but often appreciate during normal flows.
problem Understanding stock price behavior during capital inflows and outflows.
method Identified capital flow episodes using threshold and k-means clustering; detected stock index changepoints using PELT method; combined results over identified capital flows.
result Stock prices rarely appreciate during capital inflows but often appreciate during normal flows.
Deep learning models predict stock prices with high accuracy.
problem Accurate prediction of future stock prices in an efficient market.
method Robust deep learning models using historical stock data.
result Models achieve high precision in predicting stock prices.
The paper presents an evolutionary economic model for the price evolution of stocks. Treating a stock market as a self-organized system governed by a fast purchase process and slow variations of demand and supply the model suggests that the short term price distribution has the form a logistic (Laplace) distribution. T…
Crowded trades cluster investors, affecting stock price stability.
problem Crowded trades lead to price instability and systemic risk.
method Market clustering measure using granular trading data.
result Market clustering has a causal effect on stock return distribution tails, especially positive tail.
This paper predicts stock prices using LLMs and news embeddings.
problem Predicting stock prices with high accuracy and relevance.
method Integrates LLMs with stock name embeddings and attention mechanisms for news filtering.
result Reduces MAE by 7.11% compared to baseline.
The price of a stock will rarely follow the assumed model and a curious investor or a Regulatory Authority may wish to obtain a probability model the prices support. A risk neutral probability P∗ for the stock's price at time T is determined in closed form from the prices before T without assuming a price…
The paper compares advanced deep learning models for Indian stock price forecasting.
problem Complexity of stock price forecasting due to numerous influencing factors.
method Utilizes historical data from national banks in India, combines deep learning models and sentiment analysis.
result Achieved higher accuracy in stock price forecasting compared to traditional methods.
Cross-shareholding improves stock price synchronicity in China.
problem Measuring price informativeness in Chinese stock market firms.
method Analyzing cross-shareholding networks and centrality measures.
result Cross-shareholding reduces price delay and enhances price synchronicity.
We investigate the behavior of stocks in daily price-limited stock markets by purposing a quantum spatial-periodic harmonic model. The stock price is presumed to oscillate and damp in a quantum spatial-periodic harmonic oscillator potential well. Complicated non-linear relations including inter-band positive correlatio…
Paper compares stock price prediction models using Heston and Geometric Brownian Motion.
problem Predicting stock prices accurately.
method Developed Heston and Geometric Brownian Motion models using Ito's lemma and Euler-Maruyama methods.
result Models outperform statistical indicators in predicting stock prices.
Proposes a new model for stock and dividend derivatives pricing.
problem Pricing stock and dividend derivatives with positive stock prices and non-negative dividends.
method Jointly specifies dynamics for stock price and dividend rate, using mean-reverting dividend rate.
result Closed-form expressions for stock and dividend futures prices, accurate option approximations.
Study proposes a machine learning method to predict stock price crashes based on investor sentiment.
problem Predicting stock price crashes due to investor sentiment.
method Minimum covariance determinant methodology and cross-sectional regression analysis.
result The proposed method effectively captures stock price crash risk and is robust across different firm sizes.
Improved stock prediction using news features and RNN.
problem Predicting stock prices with high accuracy.
method Extracted news features, optimized seed words, calculated positive polar, constructed news features, proposed RNN model.
result Our method improves stock prediction accuracy by over 5%.
The paper extends option pricing theory for markets with informed traders.
problem Discontinuity in option pricing for markets with informed traders.
method New models for option pricing in complete markets considering informed traders' information on stock price direction and return mean.
result The discontinuity puzzle in option pricing is resolved using continuous diffusion price processes.
Study models illiquid stock prices and finds low correlation due to constant prices.
problem Modeling illiquid stock prices and measuring correlation accurately.
method Combined Markov model with Ornstein Uhlenbeck and geometric Brownian motion.
result Low correlation in USE stocks due to constant prices and illiquidity.
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…
The paper adjusts stock and strike prices for dividends after maturity in stock call pricing.
problem Inconsistent pricing of European calls with dividends after maturity.
method Extension of the Black-Scholes formula to include dividends after maturity.
result Model-consistent pricing of calls over all maturities with dividends after maturity.
This study improves stock price forecasting by analyzing daily news sentiment.
problem Improving stock price forecasting accuracy using news sentiment.
method Data collection, preprocessing, and sentiment analysis of NITY50 stocks' news.
result LSTM models with sentiment scores outperform without them in forecasting stock prices.
Deep learning LSTM predicts stock prices for portfolio design in Indian sectors.
problem Predicting stock prices in Indian stock market.
method Long Short-Term Memory (LSTM) model for historical stock price prediction.
result Efficacy of LSTM model in predicting stock prices and informing investment decisions.
Study shows stock price interactions increase during crises due to external stimulus.
problem Understanding stock price interactions during economic crises.
method Granger Causality and recurrence analysis on stock price series.
result External stimulus drives stock price interactions during crises.
This paper uses neural networks to predict stock prices more accurately.
problem Current stock analysis methods are inaccurate.
method Dynamic neural networks to identify stock price patterns.
result Neural networks outperform traditional stock analysis methods.
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…
Deep learning model forecasts stock prices for portfolio optimization.
problem Precise stock price prediction and portfolio optimization.
method LSTM network for web-scraped historical data, automated stock price forecasting.
result Model demonstrates profitability of sectors for investors.
Paper proposes integrating wavelet transform, channel attention, and LSTM for better stock price prediction.
problem Inherently difficult stock price prediction due to low signal-to-noise ratio.
method Wavelet transform convolution, channel attention, and LSTM integration.
result Robust performance in post-pandemic market conditions.
The paper uses LSTM to predict stock prices and analyzes sector profitability.
problem Predicting future stock prices in a volatile market.
method LSTM architecture for predicting stock prices from historical data.
result The model accurately predicts stock prices and analyzes sector profitability.
This study analyzes stock trading networks to quantify price impacts based on trader positions.
problem Quantifying the immediate price impact of trades in stock markets.
method Constructed stock trading networks using k-shell decomposition to classify traders and compare different market segments. result Institutional traders have lower price impacts compared to individuals at the same positions in the trading network.
It is well known how to determine the price of perpetual American options if the underlying stock price is a time-homogeneous diffusion. In the present paper we consider the inverse problem, that is, given prices of perpetual American options for different strikes, we show how to construct a time-homogeneous stock pric…
Recent academic work has developed a method to determine, in real time, if a given stock is exhibiting a price bubble. Currently there is speculation in the financial press concerning the existence of a price bubble in the aftermath of the recent IPO of LinkedIn. We analyze stock price tick data from the short lifetime…
This study improves stock price prediction by incorporating anticipated macroeconomic policy changes.
problem Improving accuracy in stock price prediction.
method Incorporates future expected macroeconomic policy changes and historical stock prices.
result Our method outperforms conventional approaches with an RMSE of 1.61 compared to 1.75.
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.
Deep learning models predict stock prices with high accuracy and speed.
problem Precise prediction of stock prices in an efficient market.
method Design and training of ten deep learning regression models.
result Models achieve high accuracy in forecasting stock prices of an auto sector company.
The study evaluates nine machine learning regressors for predicting NASDAQ stock opening prices.
problem Predicting stock market opening prices for profitable trading strategies.
method Nine different machine learning regressors were applied to NASDAQ stock market data.
result The study found that certain regressors outperform others in predicting stock opening prices.
Hybrid model predicts stock prices using ML, DL, and NLP.
problem Improving prediction accuracy of stock price movement.
method Machine learning, deep learning, natural language processing, sentiment analysis.
result LSTM model outperforms traditional machine learning models.