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.
Paper uses HGNN to predict stock types from relationships and temporal data.
Proposes LSR-IGRU for improved stock trend prediction.
New method evaluates financial graphs for stock trend forecasting.
Predict stock movement by considering cross effects among stocks.
Bayesian model reduces stock volatility by identifying key cointegrated relationships.
New framework models stock relationships and investor expectations for better financial market predictions.
Study examines how economic policy uncertainty impacts stock markets.
Study finds investor sentiment has a significant positive relationship with stock returns in Moroccan and Tunisian markets.
Study examines oil and US stock market interactions during coronavirus crisis.
The article presents calculations that prove practical importance of the earlier derived theoretical relationship between the interest rate on the interbank credit market, volume of investment and the quantity of securities tradable on the stock exchange.
NGAT predicts long-term stock trends using graph attention networks.
Development of stock networks is an important approach to explore the relationship between different stocks in the era of big-data. Although a number of methods have been designed to construct the stock correlation networks, it is still a challenge to balance the selection of prominent correlations and connectivity of …
Study shows how business cycle affects dividend payout based on managerial stock incentives.
This paper examines the short-run relationships between oil prices and GCC stock markets. Since GCC countries are major world energy market players, their stock markets may be susceptible to oil price shocks. To account for the fact that stock markets may respond nonlinearly to oil price shocks, we have examined both l…
Study examines stock price correlations between Indonesian holding companies and their subsidiaries.
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…
Study shows tweets about COVID-19 can predict stock market performance.
We test for the long-run relationship between stock prices, inflation and its uncertainty for different U.S. sector stock indexes, over the period 2002M7 to 2015M10. For this purpose we use a cointegration analysis with one structural break to capture the crisis effect, and we assess the inflation uncertainty based on …
The presence of significant cross-correlations between the synchronous time evolution of a pair of equity returns is a well-known empirical fact. The Pearson correlation is commonly used to indicate the level of similarity in the price changes for a given pair of stocks, but it does not measure whether other stocks inf…
We empirically investigated the effects of market factors on the information flow created from N(N-1)/2 linkage relationships among stocks. We also examined the possibility of employing the minimal spanning tree (MST) method, which is capable of reducing the number of links to N-1. We determined that market factors car…
We examine the relationship between trading volumes, number of transactions, and volatility using daily stock data of the Tokyo Stock Exchange. Following the mixture of distributions hypothesis, we use trading volumes and the number of transactions as proxy for the rate of information arrivals affecting stock volatilit…
Study examines dynamic relationship between BRICS stocks and cryptocurrencies.
VolTS uses stats & ML to forecast stock market trends based on volatility.
Study examines how social media sentiment impacts biotech stocks.
Pairs Trading is carried out in the financial market to earn huge profits from known equilibrium relation between pairs of stock. In financial markets, seldom it is seen that stock pairs are correlated at particular lead or lag. This lead-lag relationship has been empirically studied in various financial markets. Earli…
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…
We study historical correlations and lead-lag relationships between individual stock risk (volatility of daily stock returns) and market risk (volatility of daily returns of a market-representative portfolio) in the US stock market. We consider the cross-correlation functions averaged over all stocks, using 71 stock pr…
Study clusters Indian stocks using polyspectral means for nuanced market insights.
Deep learning model optimizes portfolios by integrating news sentiment, stock relationships, and price data.
The p-index improves investment performance for NYSE stocks but not for SSE stocks.
GAT-AGNN learns stock trends using graph and attention mechanisms.
Study finds super-efficiency correlates more strongly with stock market valuation than ROA in Chinese banks.
This paper analyzes the relationship between public disclosure, private information and stock liquidity in Tunisian context using a sample of 41 listed firms in the Tunis Stock Exchange in 2007. First, we find no evidence that there is a relation between public and private information. Second, Tunisian investors do not…
Enhances thematic investing with stock embeddings from textual data.
Stock prices are driven by various factors. In particular, many individual investors who have relatively little financial knowledge rely heavily on the information from news stories when making investment decisions in the stock market. However, these stories may not reflect future stock prices because of the subjectivi…
The study examines network analysis for predicting stock market performance.
Examines how central bank policies affect stock markets and asset prices.
FS-GCLSTM predicts stock returns by leveraging value-chain relationships.
This work tried to detect the existence of a relationship between the graphic signals - or patterns - observed day by day in the Brazilian stock market and the trends which happen after these signals, within a period of 8 years, for a number of securities. The results obtained from this study show evidence of the exist…
The study aims to explore the strength of causal relationship between stock price search interest and real stock market outcomes on worldwide equity market indices. Such a phenomenon could also be mediated by investor behavior and extent of news coverage. The stock-specific internet search trends data and corresponding…
The trade of a fixed stock can be regarded as the basic process that measures its momentary price. The stock price is exactly known only at the time of sale when the stock is between traders, that is, only in the case when the owner is unknown. We show that the stock price can be better described by a function indicati…
Simulation reveals relationships in stock market pyramid schemes.
We find a nonlinear dependence between an indicator of the degree of multiscaling of log-price time series of a stock and the average correlation of the stock with respect to the other stocks traded in the same market. This result is a robust stylized fact holding for different financial markets. We investigate this re…
Proposes neural model for stock embeddings to capture nuanced asset correlations.
The Capital Asset Pricing Model (CAPM) is one of the original models in explaining risk-return relationship in the financial market. However, when applying the CAPM into reality, it demonstrates a lot of shortcomings. While improving the performance of the model, many studies, on one hand, have attempted to apply diffe…
Develops a hybrid deep learning model for stock price prediction.
The paper defines the time function of stock prices using a mathematical model.
Lead-lag relationships among assets represent a useful tool for analyzing high frequency financial data. However, research on these relationships predominantly focuses on correlation analyses for the dynamics of stock prices, spots and futures on market indexes, whereas foreign exchange data have been less explored. To…