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.
The paper proposes a hybrid approach using MODWT and machine learning for stock index prediction.
Analyzed Indian stock market data to find stylized facts with deviations.
One of the principal statistical features characterizing the activity in financial markets is the distribution of fluctuations in market indicators such as the index. While the developed stock markets, e.g., the New York Stock Exchange (NYSE) have been found to show heavy-tailed return distribution with a characteristi…
Study examines how BRICS nations' economies respond to COVID-19.
No significant cointegration found between Indian stock index, gold, and crude prices.
Study examines the impact of employment benefit costs on firm profitability.
The nature of fluctuations in the Indian financial market is analyzed in this paper. We have looked at the price returns of individual stocks, with tick-by-tick data from the National Stock Exchange (NSE) and daily closing price data from both NSE and the Bombay Stock Exchange (BSE), the two largest exchanges in India.…
Study reveals multifractal nature in Chinese stock markets and predicts future returns.
In this paper we study BSE Index financial time series for fractal and multifractal behaviour. We show that Bombay stock Exchange (BSE)Index time series is mono-fractal and can be represented by a fractional Brownian motion.
This paper is trying to unveil general statistical characteristic of financial; time series data that is subjected to several financial time series data present in Indonesia, e.g. individual index such as stock price of PT. TELKOM, stock price of PT HM SAMPOERNA, and compiled stock price index (Jakarta Stock Exchange I…
Investment risk on a regulated market is influenced by gold prices and oil trading.
Paper presents a machine learning algorithm for hedging ETF options, outperforming static hedging methods.
GARCH models predict stock volatility in Indian sectors.
CNN improves stock price prediction accuracy.
Study shows demonetization strengthened Indian currency and stock market.
Simple technical trading rules like MA and TRB do not outperform buy-and-hold for Chinese stock indexes.
Study examines how COVID-19 affected India's exchange rates and stock market.
Study of oil price, stock index, and exchange rate co-movements in Mexico.
This paper uses SARIMA models to forecast Nifty 50 index.
The paper compares advanced deep learning models for Indian stock price forecasting.
We analyze the constituents stocks of the Dow Jones Industrial Average (DJIA30) and the Standard & Poor's 100 index (S&P100) of the NYSE stock exchange market. Surprisingly, we discover the data collapse of the histograms of the DJIA30 price fluctuations and of the S&P100 price fluctuations to the universal non-paramet…
This paper presents deep learning models for NIFTY 50 stock price prediction.
A time series that represents daily values of the WIG index (the main index of Warsaw Stock Exchange) over last 5 years is examined. Non-Gaussian features of distributions of fluctuations, namely returns, over a time scale are considered. Some general properties like exponents of the long range correlation estimated by…
A method based on wavelet transform and genetic programming is proposed for characterizing and modeling variations at multiple scales in non-stationary time series. The cyclic variations, extracted by wavelets and smoothened by cubic splines, are well captured by genetic programming in the form of dynamical equations. …
Study reveals complex, multi-scale relationships between NYSE and BSE indexes.
Study finds power-law tails in order imbalance distributions of Chinese stocks.
Paper ranks stocks by compression risk, not volatility.
Scaling properties of the BUX index are similar to those observed in other parts of the world. The main difference is that the traditional quantities like volatility, growth and autocorrelation of returns follows more closely the assumptions of the traditional stock market theory developed by Bachelier and by Black and…
Paper introduces CSIE for estimating stock market volatility.
We use insight from a model of earth tectonic plate movement to obtain a new understanding of the build up and release of stress in the price dynamics of the worlds stock exchanges. Nonlinearity enters the model due to a behavioral attribute of humans reacting disproportionately to big changes. This nonlinear response …
Classic studies of the probability density of price fluctuations for stocks and foreign exchanges of several highly developed economies have been interpreted using a {\it power-law} probability density function with exponent values , which are outside the Lévy-stable regime . …
This study predicts stock prices using hybrid machine learning and LSTM models.
To investigate the universality of the structure of interactions in different markets, we analyze the cross-correlation matrix C of stock price fluctuations in the National Stock Exchange (NSE) of India. We find that this emerging market exhibits strong correlations in the movement of stock prices compared to developed…
In this paper we attempt to introduce an econophysics approach to evaluate some aspects of the risks in financial markets. For this purpose, the thermodynamical methods and statistical physics results about entropy and equilibrium states in the physical systems are used. Some considerations on economic value and financ…
We analyse the dynamics of the Warsaw Stock Exchange index WIG at a daily time horizon before and after its well defined local maxima of the cusp-like shape decorated with oscillations. The rising and falling paths of the index peaks can be described by the Mittag-Leffler function superposed with various types of oscil…
An original method, assuming potential and kinetic energy for prices and conservation of their sum is developed for forecasting exchanges. Connections with power law are shown. Semiempirical applications on S&P500, DJIA, and NASDAQ predict a coming recession in them. An emerging market, Istanbul Stock Exchange index IS…
The paper uses LSTM to predict stock prices and analyzes sector profitability.
A new stock index model simplifies high-dimensional stock data.
This study examines the interaction between CDS and stock indices, revealing significant short and long-term impacts.
Using the tools developed for statistical physics, we simultaneously analyze statistical properties of the Jakarta and Kuala Lumpur Stock Exchange indices. In spite of the small number of data used in the analysis, the result shows the universal behavior of complex systems previously found in the leading stock indices.…
Deep learning model predicts stock market direction.
We present some stylized facts exhibited by the time series of returns of the Mexican Stock Exchange Index (IPC) and compare them to a sample of both developed (USA, UK and Japan) and emerging markets (Brazil and India). The period of study is 1997-2011. The stylized facts are related mostly to the probability distribu…
This study uses deep learning to analyze stock market sentiment from financial forums.
It is usually assumed that stock prices reflect a balance between large numbers of small individual sellers and buyers. However, over the past fifty years mutual funds and other institutional shareholders have assumed an ever increasing part of stock transactions: their assets, as a percentage of GDP, have been multipl…
Study finds stock search trends correlate with developing economies' stock indices.
We investigate the local fractal properties of the financial time series based on the evolution of the Warsaw Stock Exchange Index (WIG) connected with the largest developing financial market in Europe. Calculating the local Hurst exponent for the WIG time series we find an interesting dependence between the behavior o…
Study lead-lag relationships in foreign exchange markets using three approaches.
Investigates the relationship between US money supply and asset indices over 2001-2019.