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.
Framework analyzes stock price co-movement with fundamentals using big data.
In this chapter we studied the nonlinear co-movements between the Mexican Crude Oil price, the Mexican Stock Market Index and the USD/MXN Exchange Rate, for the sample period from 1994 to date. We used a battery of nonlinear tests, cf. (Patterson & Ashley, 2000) and one multivariate test, in order to determine the dyna…
Study examines oil and US stock market interactions during coronavirus crisis.
As described in this paper, we study market-wide price co-movements around crashes by analyzing a dataset of high-frequency stock returns of the constituent issues of Nikkei 225 Index listed on the Tokyo Stock Exchange for the three years during 2007--2009. Results of day-to-day principal component analysis of the time…
Graph auto-encoders predict stock market instability by measuring graph structure changes.
We revisit the problem of predicting directional movements of stock prices based on news articles: here our algorithm uses daily articles from The Wall Street Journal to predict the closing stock prices on the same day. We propose a unified latent space model to characterize the "co-movements" between stock prices and …
This paper analyzes the process of long-run co-movements and stock market globalization on the basis of cointegration tests and vector error correction (VEC) models. The cointegration tests used here allow for structural breaks to be explicitly modeled and breakpoints to be computed on a relative-time basis. The data u…
The paper explains stock market predictability through a model of heterogeneous beliefs.
This non-linear relationship in the joint time-frequency domain has been studied for the Indian National Stock Exchange (NSE) with the international Gold price and WTI Crude Price being converted from Dollar to Indian National Rupee based on that week's closing exchange rate. Though a good correlation was obtained duri…
This paper uses cointegration to identify profitable pair-trading strategies for Indian stocks.
On the fifth of February, 2018, the Dow Jones Industrial Average dropped 1,175.21 points, the largest single-day fall in history in raw point terms. This followed a 666-point loss on the second, and another drop of over a thousand points occurred three days later. It is natural to ask whether these events indicate a tr…
In this paper we use the Brooks and Hinich cross-bicorrelation test in order to uncover nonlinear dependence periods between USA Standard and Poor 500 (SP500), used as benchmark, and six Latin American stock markets indexes: Mexico (BMV), Brazil (BOVESPA), Chile (IPSA), Colombia (COLCAP), Peru (IGBVL) and Argentina (ME…
Since the beginning of the new millennium, stock markets went through every state from long-time troughs, trade suspensions to all-time highs. The literature on asset pricing hence assumes random processes to be underlying the movement of stock returns. Observed procyclicality and time-varying correlation of stock retu…
In this article we review several techniques to extract information from stock market data. We discuss recurrence analysis of time series, decomposition of aggregate correlation matrices to study co-movements in financial data, stock level partial correlations with market indices, multidimensional scaling and minimum s…
We demonstrate that future market correlation structure can be predicted with high out-of-sample accuracy using a multiplex network approach that combines information from social media and financial data. Market structure is measured by quantifying the co-movement of asset prices returns, while social structure is meas…
The Moscow Stock Exchange was inefficient for most of 2012-2021.
Develops a new model to better estimate cryptocurrency and stock volatility.
Co-trading networks reveal dynamic market structures and improve covariance estimation.
The assessment of co-movement among metals is crucial to better understand the behaviors of the metal prices and the interactions with others that affect the changes in prices. In this study, both Wavelet Analysis and VARMA (Vector Autoregressive Moving Average) models are utilized. First, Multiple Wavelet Coherence (M…
Based on a recent theorem due to the authors, it is shown how the extreme tail dependence between an asset and a factor or index or between two assets can be easily calibrated. Portfolios constructed with stocks with minimal tail dependence with the market exhibit a remarkable degree of decorrelation with the market at…
As financial instruments grow in complexity more and more information is neglected by risk optimization practices. This brings down a curtain of opacity on the origination of risk, that has been one of the main culprits in the 2007-2008 global financial crisis. We discuss how the loss of transparency may be quantified …
This paper examines momentum spillover across multiple asset classes using only pricing data.
Study finds significant BTC co-movements with equity markets, highlighting dynamic risk management needs.
The aim of this article is to briefly review and make new studies of correlations and co-movements of stocks, so as to understand the "seasonalities" and market evolution. Using the intraday data of the CAC40, we begin by reasserting the findings of Allez and Bouchaud [New J. Phys. 13, 025010 (2011)]: the average corre…
Trading strategy uses analyst coverage network to outperform markets.
Model forecasts market structure from financial networks using machine learning.
The Autoencoder Reconstruction Ratio detects increased asset co-movements.
This study examines local co-movements in energy, agriculture, and metal markets using copulas.
In order to figure out and to forecast the emergence phenomena of social systems, we propose several probabilistic models for the analysis of financial markets, especially around a crisis. We first attempt to visualize the collective behaviour of markets during a financial crisis through cross-correlations between typi…
Hybrid model predicts stock prices using online forum sentiments and popularity.
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…
Deep learning models predict stock prices with high accuracy.
The study introduces a new stickiness parameter for stock prices using a non-linear model.
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…
The paper defines the time function of stock prices using a mathematical model.
Warrants with stock price dependent threshold conditions give the right to buy specially issued stocks, if the performance of the stock price satisfies some requirements. Existence of these derivatives changes the price process of the underlying. We show that in the presence of such warrants one cannot assume that the …
The paper identifies a mesoscopic market structure and uses it to improve portfolio optimization.
The paper explains stock predictability by integrating rational finance without behavioral finance assumptions.
Quantum algorithms improve stock price prediction accuracy.
Deep learning predicts cross-sectional stock prices for practical investment.
Game-theoretic model captures investor interactions for stock price forecasting.
Transformer model predicts stock prices in Bangladesh's stock market.
This paper predicts significant stock price changes using neural networks.
Study finds GBM model accurately predicts stock prices on Ghana Stock Exchange.
Predict stock prices using HMMs trained on fractional price changes and intraday highs/ lows.
Study finds stock prices rarely appreciate during capital inflows but often appreciate during normal flows.
The paper presents an evolutionary economic model for the price evolution of stocks. Treating a stock market as a self-organized system governed by a fast purchase process and slow variations of demand and supply the model suggests that the short term price distribution has the form a logistic (Laplace) distribution. T…
Deep learning models predict stock prices with high accuracy.