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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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336699132 · May 202619922001200920172026
48 results for Price Reporting

Weak predictability of stock price movement 2 days after annual report disclosure.

problem Predicting stock price movement after annual report disclosure.
method Used various models including decision tree, logistic regression, random forest, neural network, prototypical networks; used financial indicators from EastMoney.
result Maximum accuracy and precision of stock price movement prediction is around 59.6% and 0.56 respectively, with random forest performing best.

Paper fine-tunes a language model to predict long-term stock buy signals.

problem Predicting long-term stock price movements with narrative text.
method Fine-tuning a small language model on 10-K reports for buy/sell decisions.
result Buy signals generated from 10-K text are most precise at 6 and 9 months, providing 4.8-9% improvement over random selection.

Paper uses LLMs to analyze annual reports for stock investment, improving efficiency.

problem Manual analysis of annual reports is time-consuming and requires expertise.
method Leverages Large Language Models to extract and analyze annual reports.
result Machine Learning model trained on LLM outputs outperforms S&P500 returns.

The analysis of markets with indivisible goods and fixed exogenous prices has played an important role in economic models, especially in relation to wage rigidity and unemployment. This research report provides a mathematical and computational details associated to the mathematical programming based approaches proposed…

2014-01-14abs ↗pdf ↗

Employing probabilistic techniques we compute best possible upper and lower bounds on the price of an option on one or two assets with continuous piecewise linear payoff function based on prices of simple call options of possibly distinct maturities and the no-arbitrage condition, but without any assumption on the pric…

2006-12-03abs ↗pdf ↗

Study earnings calls to predict stock price movements, finding them more predictive than traditional data.

problem Improving investment decisions by analyzing earnings calls for stock price predictions.
method Graph Neural Network based approach to process and analyze earnings call transcripts.
result Earnings call transcripts are more predictive of stock price movements than traditional hard data.

Report presents analysis of empirical distribution of future returns of bitcoin (BTC) from BTUSD inverse option prices. Logistic pdf is chosen as underlying distribution to fit option prices. The result is satisfactory and suggests that these prices can be described with just three or even one parameter. Fitted Logisti…

2019-01-15abs ↗pdf ↗

We develop a pricing model for Sovereign Contingent Convertible bonds (S-CoCo) with payment standstills triggered by a sovereign's Credit Default Swap (CDS) spread. We model CDS spread regime switching, which is prevalent during crises, as a hidden Markov process, coupled with a mean-reverting stochastic process of spr…

2018-04-04abs ↗pdf ↗

We first investigate the evolution of opening and closing auctions volumes of US equities along the years. We then report dynamical properties of pre-auction periods: the indicative match price is strongly mean-reverting because the imbalance is; the final auction price reacts to a single auction order placement or can…

2018-02-06abs ↗pdf ↗

In an observed generalized semi-Markov regime, estimation of transition rate of regime switching leads towards calculation of locally risk minimizing option price. Despite the uniform convergence of estimated step function of transition rate, to meet the existence of classical solution of the modified price equation, t…

2015-06-11abs ↗pdf ↗

An elementary arbitrage principle and the existence of trends in financial time series, which is based on a theorem published in 1995 by P. Cartier and Y. Perrin, lead to a new understanding of option pricing and dynamic hedging. Intricate problems related to violent behaviors of the underlying, like the existence of j…

2012-06-07abs ↗pdf ↗

The financial rogue waves are reported analytically in the nonlinear option pricing model due to Ivancevic, which is nonlinear wave alternative of the Black-Scholes model. These solutions may be used to describe the possible physical mechanisms for rogue wave phenomenon in financial markets and related fields.

2009-11-22abs ↗pdf ↗

Predict stock price movements using financial data and news articles with LLMs.

problem Predicting stock price movements using financial data and news articles.
method Combining financial data and news articles, employing pre-trained LLMs, and using retrieval augmentation techniques.
result Predicted stock price movements with a weighted F1-score of 58.5% and 59.1%.

Study shows oil prices but not COVID-19 cases affect US economic policy uncertainty.

problem Effect of COVID-19 and crude oil prices on US economic policy uncertainty.
method Used ARDL model with daily data from January 21-March 13, 2020.
result Crude oil price dynamics increase US economic policy uncertainty, while COVID-19 cases have mixed effects.

MiFID II impacts European stock liquidity and price formation.

problem Impact of MiFID II on European stock liquidity and price formation.
method Analyzed effects of MiFID II on European stock markets, focusing on intraday and closing auction liquidity and tick size changes.
result Closing auction volumes increased and price formation became more efficient after MiFID II.

Many studies assume stock prices follow a random process known as geometric Brownian motion. Although approximately correct, this model fails to explain the frequent occurrence of extreme price movements, such as stock market crashes. Using a large collection of data from three different stock markets, we present evide…

2009-12-30abs ↗pdf ↗

We report an empirical study of Tehran Price Index (TEPIX). To analyze our data we use various methods like as, rescaled range analysis (R/SR/S), modified rescaled range analysis (Lo's method), Detrended Fluctuation Analysis (DFA) and generalized Hurst exponents analysis. Based on numerical results, the scaling range of…

2004-12-11abs ↗pdf ↗

We consider the problem of computing the Credit Value Adjustment ({CVA}) of a European option in presence of the Wrong Way Risk ({WWR}) in a default intensity setting. Namely we model the asset price evolution as solution to a linear equation that might depend on different stochastic factors and we provide an approxima…

2018-11-18abs ↗pdf ↗

Cryptocurrency market activity is decomposed into recurring and noise components, revealing patterns tied to macroeconomic reports.

problem Investigating temporal patterns of cryptocurrency market activity.
method Decomposition of market activity measures into recurring and noise components via correlation matrix formalism.
result Recurring market activity bursts coincide with significant U.S. macroeconomic reports, indicating their influence.

We consider a fundamental dynamic allocation problem motivated by the problem of securities lending\textit{securities lending} in financial markets, the mechanism underlying the short selling of stocks. A lender would like to distribute a finite number of identical copies of some scarce resource to nn clients, each of whom has a priv…

2019-12-12abs ↗pdf ↗

StonkBERT predicts stock price movements using company text data.

problem Can language models predict medium-run stock price movements?
method Fine-tuning transformer-based language models (BERT) on company text data (news articles, blogs, annual reports) for stock price performance classification.
result StonkBERT shows substantial improvement in predictive accuracy compared to traditional models, with news articles providing the best results.

Study finds on-chain data can proxy off-chain cryptocurrency pricing.

problem Develop methods to proxy off-chain cryptocurrency pricing using on-chain data.
method Graphical models, mutual information, and ensemble machine learning.
result A significant amount of pricing information is contained in on-chain data, but precise prices are hard to recover except on short time scales.
Ponzi Fundsq-fin.GN

Funds inflate their returns due to price pressure, leading to wealth reallocation and market crashes.

problem Funds inflate their returns due to price pressure, leading to wealth reallocation and market crashes.
method Decomposed fund returns into price pressure and fundamental components, and identified the impact of price chasing on fund flows.
result Funds' self-inflated returns lead to wealth reallocation and market crashes, and can be predicted by fund illiquidity.

Algorithm learns to bid in auctions with shilling, masking real bids.

problem Learning to bid in auctions manipulated by shilling.
method Combines interval-elimination and optimistic branches, debiases losing-side reports.
result Achieves dynamic-pricing rate ildeO(T2/3) ilde{\mathcal{O}}(T^{2/3}) and first-price auctions rate ildeO(T) ilde{\mathcal{O}}(\sqrt{T}).

Study finds companies react negatively to material cybersecurity incident disclosures.

problem Understanding market reactions to cybersecurity incidents.
method Examined daily stock price movements of companies disclosing material cybersecurity incidents.
result Companies tend to experience negative price reactions after disclosing material cybersecurity incidents.

Study simulates Variance Gamma processes for energy derivatives pricing.

problem Simulating Variance Gamma processes for accurate energy derivative pricing.
method Three-step procedure to relate self-decomposability to increments, derived from Qu et al. (2019). Exact simulation of skeleton of Variance Gamma and symmetric Variance Gamma driven Ornstein-Uhlenbeck processes.
result Exact simulation of Variance Gamma and related processes without numerical inversion.