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.
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.
Optimizes trading co-integrated assets considering price impact and informational advantage.
problem Executing a basket of co-integrated assets with price impact.
method Optimal stochastic control problem, closed-form solution, verification theorem.
result Improves trading strategy by including additional assets, outperforming existing models.
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.
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.
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.
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.
Estimates production function of Iran's mining sector, finding capital and labor intensive.
problem Estimating production function of Iran's mining sector.
method Used co-integration method and time-series data for 1976-2006, augmented Dickey-Fuller and Phillips-Perron tests for stationarity.
result Elasticity of production with respect to capital and labor are 0.44 and 0.41, respectively; technological progress positively affects output.
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.
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.
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.
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.
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.
The study explains asymmetric volatility using anchoring bias in investor behavior.
problem Understanding the cause of asymmetric volatility in financial markets.
method Empirical analysis of anchoring bias in S&P 500 price fluctuations.
result Anchoring bias explains the asymmetry in volatility responses to shocks.
Enhances pairs trading with neural networks and Kalman Filters.
problem Inaccurate linear models in pairs trading lead to suboptimal performance.
method Augments Kalman Filter with Neural Networks to improve financial indicator extraction.
result Empirically shows improved trading performance compared to benchmarks.