Study finds stock prices deviated from company fundamentals in 2008 crash.
problem Deviation of stock prices from company fundamentals during the 2008 financial crisis.
method Used a large database of 7,796 companies to develop a panel regression model with three financial indicators.
result Share prices were overvalued before 2008 and undervalued in 2008, indicating market anomalies.
The distribution of share prices follows Zipf's law, similar to financial indicators.
problem Investigating the distribution of share prices and financial indicators.
method Statistical analysis of data from 8,000 companies over 10 years.
result Share price and financial indicators follow Zipf's law, indicating that corporate value follows Zipf's law.
Financial economic models often assume that investors know (or agree on) the fundamental value of the shares of the firm, easing the passage from the individual to the collective dimension of the financial system generated by the Share Exchange over time. Our model relaxes that heroic assumption of one unique "true val…
Hybrid model combines PCA and RNN for better aerospace stock price prediction.
problem Challenges in predicting stock prices of aerospace companies due to market uncertainty and complexity.
method Combination of Principal Component Analysis (PCA) and Recurrent Neural Networks (RNN).
result PCA improves both accuracy and efficiency of stock price prediction.
Biondi et al. (2012) develop an analytical model to examine the emergent dynamic properties of share market price formation over time, capable to capture important stylized facts. These latter properties prove to be sensitive to regulatory regimes for fundamental information provision, as well as to market confidence c…
Share prices of financial companies from the S&P 500 list have been modeled by a linear function of consumer price indices in the USA. The Johansen and Engle-Granger tests for cointegration both demonstrated the presence of an equilibrium long-term relation between observed and predicted time series. Econometrically, t…
Stochastic Gradient Descent (SGD) is a fundamental algorithm in machine learning, representing the optimization backbone for training several classic models, from regression to neural networks. Given the recent practical focus on distributed machine learning, significant work has been dedicated to the convergence prope…
Short sales allow tax deferral by offsetting gains from ordinary sales.
problem Tax deferral opportunity in short sales not regulated in the Philippine tax system.
method Short selling to offset gains from ordinary sales of identical stocks.
result Tax deferral opportunity exists but is unregulated in the Philippine tax system.
Study examines reasons for Nutek India's share price drop.
problem Deep fall in Nutek India Limited's share price.
method Analyzed transactions and market forces to identify manipulation.
result Identified intentional interference in supply and demand.
This paper analyzes stock market data to predict share prices using regression models.
problem Predicting stock prices in the share market of Bangladesh.
method Thorough linear regression analysis on Dhaka Stock Exchange data, compared with random forest.
result Random forest model performs better than linear regression for predicting stock prices.
A framework for fair derivative contract pricing and risk-sharing between parties with funding differences.
problem Price asymmetry due to funding differences in bilateral contracts.
method Defines a negotiation problem that maximizes the sum of utilities for two parties, deriving optimal prices and collateral.
result Optimal negotiation price and collateral can be used to interpret margin requirements.
A new method is proposed to obtain the risk neutral probability of share prices without stochastic calculus and price modeling, via an embedding of the price return modeling problem in Le Cam's statistical experiments framework. Strategies-probabilities Pt0,n and PT,n are thus determined and used, respective…
Model examines how margin trading affects stock market stability.
problem Margin trading's impact on stock market stability.
method Cascading failure model based on bipartite graph of investors and shares.
result Margin trading increases share price vulnerability to external shocks.
New method uses execution flow to predict market direction.
problem Predicting market direction based on trading data.
method Defining scalp-price based on high execution flow events.
result Market trend changes indicated by scalp-price changes.
Symbolic dynamics applied to share prices reveals complex, non-Markovian patterns.
problem Analyzing complex systems like share prices using symbolic dynamics.
method Symbolic dynamics applied to time series of share price returns.
result Nontrivial spectrum of Renyi entropies found, indicating non-Markovian behavior.
Model resolves asset pricing puzzles with price-impact.
problem Asset pricing puzzles like interest rate, stock-price volatility, and equity premium.
method Closed-form equilibrium model with exponential investors trading continuously and experiencing price-impact.
result Price-impact amplifies risk-sharing distortions, resolving puzzles.
Study compares LSTM models with sentiment analysis for stock price prediction.
problem Efficient stock price prediction models using LSTM with sentiment analysis.
method Various types of LSTM models combined with sentiment analysis.
result Identifies the most effective model for short and long-term stock price prediction.
This paper investigates the effects of the "uptick rule" (a short selling regulation formally known as rule 10a-1) by means of a simple stock market model, based on the ARED (adaptive rational equilibrium dynamics) modeling framework, where heterogeneous and adaptive beliefs on the future prices of a risky asset were f…
Paper evaluates whether AI is a bubble or a productivity revolution.
problem Determining if AI investments are a bubble or a sustainable technology.
method Hybrid review and diagnostic framework combining asset pricing foundations and modern econometric methods.
result AI investments show both genuine fundamentals and bubble-like fragilities.
New method optimizes share buyback contracts without optimal control's limitations.
problem High-dimensional state spaces and risk penalty selection issues in traditional methods.
method Applies optimized heuristic strategies and classical pricing methods.
result Maximizes contract value and disentangles repurchase from hedging.
In this article, we consider the optimal execution problem associated to accelerated share repurchase contracts. When firms want to repurchase their own shares, they often enter such a contract with a bank. The bank buys the shares for the firm and is paid the average market price over the execution period, the length …
Develops a machine-learning framework for optimal share repurchase hedging.
problem Challenges in hedging share repurchase programs due to market regulations and trading activity.
method Machine-learning framework that optimizes execution and hedging of share repurchase programs.
result Substantial performance improvements and an optimized hedging approach.
Study examines pricing strategies in competitive supply chains with discrete prices.
problem Inaccurate assumptions in traditional SC models for pricing decisions.
method Examines a SC model with one supplier and two manufacturers, considering customer demand segmentation and discrete price setting.
result Nash equilibria among manufacturers are not unique, and low denomination factors can lead to instability.
SHHK Stock Connect increases A-H share price premium, more for less efficient markets.
problem Impact of financial liberalization on cross-market pricing efficiency.
method Monthly data for 67 A-H dual-listed firms, system GMM dynamic models.
result Heterogeneous effect of SHHK Stock Connect on A-H price premium, more pronounced for less efficient markets.
Study examines value relevance of oil and gas reserve disclosures in London Stock Exchange.
problem Uncertainty in oil and gas reserves poses accounting challenges for investors.
method Empirical analysis using archival data and multifactor framework.
result Changes in reserves and their components are associated with share returns, but insignificantly due to oil price and longitudinal effects. Quality of disclosures positively impacts share returns.
This paper develops a theoretical model for SPPC prices and improves accounting standards.
problem Lack of theoretical price for SPPC and issues in current accounting standards.
method Developed a theoretical SPPC price model with a marginal utility-based approach, explicitly indicated stochastic processes, and proposed a convenient model.
result Improved theoretical price model and addressed problems in current accounting standards.
NFT royalties boost creator earnings by sharing risk, reducing info asymmetry, and enabling price discrimination.
problem NFTs' royalties are criticized for being neutralized by speculators.
method Analyzes NFTs' royalties in various market conditions and their effects on creators.
result Royalties enable creators to capitalize on speculators' presence through risk sharing, info reduction, and price discrimination.
The paper studies an oligopolistic equilibrium model of financial agents who aim to share their random endowments. The risk-sharing securities and their prices are endogenously determined as the outcome of a strategic game played among all the participating agents. In the complete-market setting, each agent's set of st…
In an incomplete market setting, we consider two financial agents, who wish to price and trade a non-replicable contingent claim. Assuming that the agents are utility maximizers, we propose a transaction price which is a result of the minimization of a convex combination of their utility differences. We call this price…
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.
Developed policy gradient methods for stochastic control with exit time, outperforming traditional techniques in share repurchase pricing.
problem Optimal control with exit time in stochastic models.
method Two types of algorithms: direct policy learning and alternately learning value function and control.
result Policy gradient methods outperform PDE or neural networks in share repurchase pricing.
This paper analyzes the quantitative relations between stock prices and quantities of tradable stock shares in Chinese stock markets at six time points by means of Exploratory Data Analysis (EDA) method. It is found the resulting formulae have the same structure but different parameters. This paper also uses these rela…
The large majority of risk-sharing transactions involve few agents, each of whom can heavily influence the structure and the prices of securities. This paper proposes a game where agents' strategic sets consist of all possible sharing securities and pricing kernels that are consistent with Arrow-Debreu sharing rules. F…
The balance property is crucial for insurance pricing, ensuring total actuarial price equals loss. Maximum likelihood GLMs fulfill it, but Lindholm-Wüthrich suggests three methods, with constrained GLM being superior.
problem Ensuring the balance property in insurance pricing models
method Using constrained GLM fitting
result Constrained GLM fitting is superior to the two previously discussed balance correction methods
Fundamental portfolio beats market portfolio under certain conditions.
problem Empirical evidence of fundamental portfolio outperformance.
method Theoretical foundation based on stock price reversion to fundamental values.
result Fundamental portfolio outperforms market portfolio under strong reversion conditions.
Study asset pricing with transaction costs, showing unique equilibrium exists.
problem Risk-sharing economies with heterogeneous agents trading under quadratic transaction costs.
method Characterizes equilibrium asset prices and strategies via nonlinear, fully-coupled equations.
result Unique solution exists when agents' preferences are sufficiently similar, and empirical liquidity premia and discounts match transaction costs and volatility.
Optimal trading strategy with Poisson process times and price impact.
problem Maximizing net present value in algorithm trading with multiplicative price impact.
method Modelled optimal strategy with Poisson inter-selling times and barrier form.
result An optimal strategy has a barrier form depending on remaining shares and asset price.
This work presents the results of an empirical research with the target of modeling the stylized facts of the daily expost System Marginal Price (SMP) of the Greek wholesale electricity market, using data from January 2004 to December of 2011. SMP is considered here as the footprint of an underline stochastic and nonli…
Be it for taking advantage of stock undervaluation or in order to distribute part of their profits to shareholders, firms may buy back their own shares. One of the way they proceed is by including Accelerated Share Repurchases (ASR) as part of their repurchase programs. In this article, we study the pricing and optimal…
Empirical study of CAPM and Fama-French model in Chinese A-share market.
problem Testing and validating CAPM and Fama-French model in Chinese A-share market.
method Used Fama-MacBeth regression and Fama-French three-factor model to analyze Chinese A-share trading data from 2000 to 2019, adjusting for IPO shell value contamination.
result Fama-French model captures most of A-share market returns, with adjusted R-squared > 0.88.
Model predicts stock prices using Twitter sentiment data.
problem Predicting stock prices using social media sentiment.
method Extracted 19 sentiment features from Twitter posts about Petrobras, trained XBoot models, and simulated trading.
result Simulated trading gained R$88,82 (net) over 250 days.
The paper models liquidity in financial markets with a continuous fundamental price.
problem Liquidity and price formation in financial markets with discrete order books.
method Adapting Madhavan et al. (1997) model to realistic order books with quote discretization and liquidity rebates.
result The fundamental price is continuous, efficient, and outside the quote interval, and can be estimated from volume imbalance.
The study extracts market direction from transaction data.
problem Extracting market direction from transaction data.
method Dynamic equation with time scale selection from past transactions.
result Automatic determination of time scale for price calculation.
This research predicts stock market movements using Vision-Language models.
problem Predicting future stock market direction using historical data.
method Utilizing image and byte-based representations of stock data processed with Vision-Language models.
result The proposed approach significantly outperforms deep learning baselines.
Pareto optimal centralized risk sharing with multiple agents
problem Centralized risk sharing with endogenous prices
method Inclusive and fair Pareto optimality
result Equivalence between inclusive and fair Pareto optimality and balanced sequential optimization
Study finds short-term trading signals can enhance alpha in U.S. S&P 500 portfolios.
problem Traditional factor investing misses real-time market dislocations.
method Double-selection LASSO framework to control for fundamental factors and isolate trading signals.
result 17 distinct trading signals capture significant risk premiums and enhance portfolio diversification.
The study models productivity growth and cost shares in Japan and Korea.
problem Estimating productivity growth and cost shares in multifactor CES models.
method Regression of cost shares on factor prices using linked input-output tables.
result Economy-wide propagation of productivity stimuli evaluated in a multi-sectoral model.
Algorithm learns shared demand structure across dynamic pricing experiments.
problem Learning shared demand parameters across multiple dynamic pricing experiments.
method Meta dynamic pricing algorithm that learns prior online while solving Thompson sampling experiments.
result Algorithm achieves sublinear meta regret in experiment-rich environments.