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.
This paper develops a valuation model for private companies.
Analyzes how inclusion/exclusion from STOXX Europe 600 Index affects company prices.
Study examines stock price correlations between Indonesian holding companies and their subsidiaries.
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…
To understand the relationship between news sentiment and company stock price movements, and to better understand connectivity among companies, we define an algorithm for measuring sentiment-based network risk. The algorithm ranks companies in networks of co-occurrences, and measures sentiment-based risk, by calculatin…
The aim of this study is to investigate quantitatively whether share prices deviated from company fundamentals in the stock market crash of 2008. For this purpose, we use a large database containing the balance sheets and share prices of 7,796 worldwide companies for the period 2004 through 2013. We develop a panel reg…
Study finds companies react negatively to material cybersecurity incident disclosures.
Study compares LSTM models with sentiment analysis for stock price prediction.
Study examines how COVID-19 vaccine companies' popularity affects their stock prices.
Research shows franchised fast food companies' stock prices decline more during recessions.
We consider a price-maker company which generates electricity and sells it in the spot market. The company can increase its level of installed power by irreversible installations of solar panels. In absence of the company's economic activities, the spot electricity price evolves as an Ornstein-Uhlenbeck process, and th…
In this paper we consider a utility maximization problem with defaultable stocks and looping contagion risk. We assume that the default intensity of one company depends on the stock prices of itself and other companies, and the default of the company induces immediate drops in the stock prices of the surviving companie…
The paper analyzes how news sentiment of companies can affect market movements.
The paper analyzes market risk factors for a mining company using a VAR model with stable distribution.
Anti-ELAB protests affected Hong Kong firms' stock prices, especially those linked to pan-democrats.
StonkBERT predicts stock price movements using company text data.
A price-maker company extracts an exhaustible commodity from a reservoir, and sells it instantaneously in the spot market. In absence of any actions of the company, the commodity's spot price evolves either as a drifted Brownian motion or as an Ornstein-Uhlenbeck process. While extracting, the company affects the marke…
We statistically investigate the distribution of share price and the distributions of three common financial indicators using data from approximately 8,000 companies publicly listed worldwide for the period 2004-2013. We find that the distribution of share price follows Zipf's law; that is, it can be approximated by a …
Study evaluates if LLMs have company-specific biases in financial sentiment analysis.
This paper analyzes stock market data to predict share prices using regression models.
Model earnings call transcripts for better stock price prediction.
A pairwise clustering approach is applied to the analysis of the Dow Jones index companies, in order to identify similar temporal behavior of the traded stock prices. To this end, the chaotic map clustering algorithm is used, where a map is associated to each company and the correlation coefficients of the financial ti…
Study shows adding correlated features doesn't improve LSTM model interpretability for oil stocks.
We present a phenomenological study of stock price fluctuations of individual companies. We systematically analyze two different databases covering securities from the three major US stock markets: (a) the New York Stock Exchange, (b) the American Stock Exchange, and (c) the National Association of Securities Dealers A…
We describe and document three mechanisms by which corporations can influence or even control stock prices. (i) Parent and holding companies wield control over other publicly traded companies. (ii) Through clever management of treasury stock based on buyback programs and stock issuance, stock price fluctuations can be …
We analyze the sequence of time intervals between consecutive stock trades of thirty companies representing eight sectors of the U. S. economy over a period of four years. For all companies we find that: (i) the probability density function of intertrade times may be fit by a Weibull distribution; (ii) when appropriate…
Long term investment is one of the major investment strategies. However, calculating intrinsic value of some company and evaluating shares for long term investment is not easy, since analyst have to care about a large number of financial indicators and evaluate them in a right manner. So far, little help in predicting …
Paper presents LSTM models for short-term stock price prediction.
We uncover a new anomaly in asset pricing that is linked to the remuneration: the more a company spends on salaries and benefits per employee, the better its stock performs, on average. Moreover, the companies adopting similar remuneration policies share a common risk, which is comparable to that of the value premium. …
Taureau uses Twitter sentiment analysis to predict stock market movement.
Large language models learn company embeddings from SEC filings.
In a stock market, the price fluctuations are interactive, that is, one listed company can influence others. In this paper, we seek to study the influence relationships among listed companies by constructing a directed network on the basis of Chinese stock market. This influence network shows distinct topological prope…
Machine learning models outperform traditional actuarial methods in predicting health insurance costs.
Catastrophe risk is a major threat faced by individuals, companies, and entire economies. Catastrophe (CAT) bonds have emerged as a method to offset this risk and a corresponding literature has developed that attempts to provide a market-consistent pricing methodology for these and other long-dated, insurance-type cont…
Predicts stock price changes based on clinical trial announcements.
This paper introduces a non-parametric framework to statistically examine how news events, such as company or macroeconomic announcements, contribute to the pre- and post-event jump dynamics of stock prices under the intraday seasonality of the news and jumps. We demonstrate our framework, which has several advantages …
Weak predictability of stock price movement 2 days after annual report disclosure.
Unfair pricing policies have been shown to be one of the most negative perceptions customers can have concerning pricing, and may result in long-term losses for a company. Despite the fact that dynamic pricing models help companies maximize revenue, fairness and equality should be taken into account in order to avoid u…
The NYSE and NASDAQ stock markets have very different structures and there is continuing controversy over whether differences in stock price behaviour are due to market structure or company characteristics. As the influence of market structure on stock prices may be obscured by exogenous factors such as demand and supp…
Recently, there has been a surge of interest in the use of machine learning to help aid in the accurate predictions of financial markets. Despite the exciting advances in this cross-section of finance and AI, many of the current approaches are limited to using technical analysis to capture historical trends of each sto…
Employee stock options (ESOs) are American-style call options that can be terminated early due to employment shock. This paper studies an ESO valuation framework that accounts for job termination risk and jumps in the company stock price. Under general Lévy stock price dynamics, we show that a higher job termination ri…
Predict stock prices using HMMs trained on fractional price changes and intraday highs/ lows.
New model uses financial news to predict stock returns.
The paper proposes a demand prediction model for e-commerce sites using machine learning and stacking.
Neural model learns company embeddings from data and news.
Hybrid model combines PCA and RNN for better aerospace stock price prediction.
Predict stock price movements using financial data and news articles with LLMs.
Model predicts stock prices using Twitter sentiment data.