The paper analyzes optimal statistical arbitrage strategies for co-integrated stocks.
problem Finding optimal portfolio weights for co-integrated stocks.
method Solving a Hamilton-Jacobi-Bellman (HJB) partial differential equation for optimal portfolio weights.
result The proposed co-integrated model with eigenportfolios can generate stable growth rates over a long time horizon.
Improved S&P stock prediction by integrating related stocks' data.
problem Lack of comprehensive data in stock prediction models.
method Enriched stock data with related stocks, tested five similarity functions, and used co-integration similarity for best results.
result Prediction model on similar stocks had significantly better accuracy and profit.
The study models market price movement based on investors' expectations.
problem Understanding the dynamics of investors' expectations and market price movement.
method Developed a non-linear evolutionary equation linking investors' expectations and market asset price movement.
result Model predictions co-integrated with asset time series, suggesting potential for price movement forecasting.
Study examines downsizing impact on Indian construction firms' profitability.
problem Impact of downsizing layoffs on construction firms' profitability in India.
method Used Co-integration test, OLS, and VAR models on secondary data of 15 companies.
result Employee Expenses and Number of Employees have significant impact on profitability.
A new model disentangles long-term and short-term sentiment components in stock returns.
problem Identifying distinct components of sentiment data in stock markets.
method Dynamic factor model with random walk and stationary VAR(1) components, estimated via Kalman filtering and EM.
result The long-term sentiment component co-integrates with market principal factor, while the short-term captures market swings.
Optimal trading strategy with unobservable pricing errors for co-integrated assets.
problem Dynamic portfolio optimization of convergence trading with unobservable pricing errors.
method Modeling of convergence trading strategy with unobservable Markov-modulated pricing errors, extending Liu and Timmermann (2013) model.
result Characterization of optimal portfolio strategies in full and partial information settings.
Algorithm combines ESG ratings with pairs trading for sustainable investing.
problem Lack of socially responsible investment solutions.
method Integrates ESG data with pairs trading strategy using technical indicators.
result Model generates positive returns while adhering to ESG principles.
Modeling precious metals market making using nested Ornstein-Uhlenbeck processes.
problem Navigating liquidity provided by futures contracts in spot precious metals.
method Nested Ornstein-Uhlenbeck process for EFP spread modeling, Hamilton-Jacobi-Bellman equation approximation.
result Maximizing expected P&L while minimizing inventory risk in near real-time.
Executing a basket of co-integrated assets is an important task facing investors. Here, we show how to do this accounting for the informational advantage gained from assets within and outside the basket, as well as for the permanent price impact of market orders (MOs) from all market participants, and the temporary imp…
A pairs trading model with time-varying volatility using stochastic control.
problem Optimizing pairs trading strategies with fluctuating asset volatilities.
method Stochastic control techniques, Finite Difference method, Generalized Method of Moments.
result Optimal trading strategies maximizing expected power utility from terminal wealth.
This paper examines the implementation of a statistical arbitrage trading strategy based on co-integration relationships where we discover candidate portfolios using multiple factors rather than just price data. The portfolio selection methodologies include K-means clustering, graphical lasso and a combination of the t…
Study optimal asset allocation for insurers with multiple lines of business and constraints.
problem Maximize expected utility from dividends and wealth for insurers with multivariate insurance risk.
method Lagrangian convex duality techniques for continuous-time asset-allocation problem.
result Explicit characterization of optimal strategies under CRRA preferences.
The purpose of this study is to estimate the production function and examine the structure of production in the mining sector of Iran. Several studies have already been conducted in estimating production functions of various economic sectors; however, less attention has been paid to mining sectors. After examining the …
This paper discusses a novel explanation for asymmetric volatility based on the anchoring behavioral pattern. Anchoring as a heuristic bias causes investors focusing on recent price changes and price levels, which two lead to a belief in continuing trend and mean-reversion respectively. The empirical results support ou…
Statistical arbitrage strategies, such as pairs trading and its generalizations, rely on the construction of mean-reverting spreads enjoying a certain degree of predictability. Gaussian linear state-space processes have recently been proposed as a model for such spreads under the assumption that the observed process is…
Paper proposes a new method for finding sparse mean reverting portfolios efficiently.
problem Finding sparse mean reverting portfolios from a large number of assets.
method Leverages H-SGDLM data to formulate a quasi-convex minimization problem with a normalisation constraint, solving it with a cyclical coordinate descent algorithm.
result Efficiently computes exact sparse solutions for large asset universes, demonstrating flexibility, speed, and scalability.
This paper reviews and analyzes various modeling approaches for financial index tracking.
problem Efficient replication of market index performance in financial markets.
method Categorization into three frameworks: optimization, statistical, and machine learning; empirical study on S&P 500 dataset.
result Optimization-based models deliver the most precise index tracking, statistical-based models achieve the strongest return-risk balance, and data-driven models provide competitive performance.
REST framework predicts stock trends by considering stock-specific and related-stock events.
problem Predicting stock trends using event information from news, social media, and discussion boards.
method REST framework addresses two main shortcomings of existing event-driven methods: stock-specific event influence and related-stock event influence.
result REST framework achieves higher investment returns compared to baselines.
Geography effect is investigated for the Chinese stock market including the Shanghai and Shenzhen stock markets, based on the daily data of individual stocks. The Shanghai city and the Guangdong province can be identified in the stock geographical sector. By investigating a geographical correlation on a geographical pa…
EarnMore uses masked stock representations to train RL agents for customizable stock pools efficiently.
problem Training RL agents for customizable stock pools (CSPs) is computationally expensive and unstable.
method EarnMore introduces a mechanism to mask out stocks outside the target pool, learns meaningful stock representations, and uses a re-weighting mechanism to focus on favorable stocks.
result EarnMore significantly outperforms state-of-the-art baselines in profit metrics with over 40% improvement.
New deep learning method predicts stock rankings better than existing models.
problem Predicting stock trends and prices with deep learning models.
method Tailored deep learning for stock ranking, capturing temporal and relational stock data.
result RSR method outperforms existing solutions, achieving high return ratios on NYSE and NASDAQ.
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…
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…
Graham's formula simplifies stock valuation for growth stocks.
problem Valuing growth stocks using a simple yet effective formula.
method Presenting a practical methodology to calculate and compare growth stocks.
result Demonstrates a scoring system to compare growth stocks.
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 …
Paper uses HGNN to predict stock types from relationships and temporal data.
problem Predicting stock types from complex market data.
method Integrates stock relationships and temporal data using HGNN.
result Effective prediction of stock types with HGNN model.
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.
Green stocks show less factor exposure heterogeneity compared to brown stocks.
problem Exploring differences in factor exposure between green and brown stocks.
method Examined S&P 500 firms grouped by greenhouse gas emissions, analyzing factor exposure over 2014-2020.
result Green stocks have less factor exposure heterogeneity than brown stocks, except for the value factor.
We investigated the topological properties of stock networks through a comparison of the original stock network with the estimated stock network from the correlation matrix created by the random matrix theory (RMT). We used individual stocks traded on the market indices of Korea, Japan, Canada, the USA, Italy, and the …
A new framework forecasts stock trends by mining shared information from concepts.
problem Forecasting stock trends using static concept information limits accuracy.
method Proposes a graph-based framework that mines concept-oriented shared information from both predefined and hidden concepts.
result Improves stock trend forecasting performance through dynamic concept relevance and hidden concept information.
We propose improved methods to identify stock groups using the correlation matrix of stock price changes. By filtering out the marketwide effect and the random noise, we construct the correlation matrix of stock groups in which nontrivial high correlations between stocks are found. Using the filtered correlation matrix…
GRU-PFG model extracts inter-stock correlations from stock factors using graph neural networks.
problem Limited effectiveness of models relying solely on stock factors for capturing stock correlations.
method Project stock factors into a graph and use graph neural networks to extract inter-stock correlations.
result Achieves better prediction results than models relying solely on stock factors and comparable to second category models.
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 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.
In this paper, we study the determinants of expected returns on the listed penny stocks from two perspectives. Traditionally financial economics literature has been devoted to study the macro and micro determinants of expected returns on stocks (Subrahmanyam, 2010). Very few research has been carried out on penny stock…
Deep learning predicts stock prices using CNN and NALUs.
problem Predicting future stock prices accurately.
method Convolutional Neural Network (CNN) for feature extraction and Neural Arithmetic Logic Units (NALUs) for arithmetic operations.
result Improved accuracy in predicting stock prices.
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.
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.
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.
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.
The stock market has been known to form homogeneous stock groups with a higher correlation among different stocks according to common economic factors that influence individual stocks. We investigate the role of common economic factors in the market in the formation of stock networks, using the arbitrage pricing model …
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.
Meta-learning predicts stock trading volumes by learning from each stock's unique patterns.
problem Predicting trading volumes for different stocks using a universal model.
method Dual-process meta-learning framework that learns common patterns with a meta-learner and specific patterns with stock-dependent parameters.
result Improves performance of various baseline models in volume predictions.
The high-frequency cross-correlation existing between pairs of stocks traded in a financial market are investigated in a set of 100 stocks traded in US equity markets. A hierarchical organization of the investigated stocks is obtained by determining a metric distance between stocks and by investigating the properties o…
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…
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.
A machine learning approach for dynamic stock recommendation outperforms traditional strategies.
problem Lack of time for analysts to check all S&P 500 stocks and the need for a reliable stock selection strategy.
method Selecting representative stock indicators, using five machine learning methods, and choosing the model with the lowest Mean Square Error to rank stocks.
result The proposed scheme outperforms the long-only strategy on the S&P 500 index in terms of Sharpe ratio and cumulative returns.
Predict stock movement by considering cross effects among stocks.
problem Challenges in predicting stock price movement due to cross effects among stocks.
method Multi-GCGRU framework combining GCN and GRU, encoding cross effects from financial domain knowledge and data-driven relationships.
result Our model outperforms other baselines in predicting stock movement.