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.
New geometrical method optimizes portfolio with minimal risk.
In this paper we perform a statistical analysis over the returns and relative prices of the CAC and the S\&P with the purpose of analyzing the intra-day seasonalities of single and cross-sectional stock dynamics. In order to do that, we characterized the dynamics of a stock (or a set of stocks) by the evolut…
This paper analyses the behaviour of volatility for several international stock market indexes, namely the SP 500 (USA), the Nikkei (Japan), the PSI 20 (Portugal), the CAC 40 (France), the DAX 30 (Germany), the FTSE 100 (UK), the IBEX 35 (Spain) and the MIB 30 (Italy), in the context of non-stationarity. Our empirical …
We present some indications of inefficiency of the Brazilian stock market based on the existence of strong long-time cross-correlations with foreign markets and indices. Our results show a strong dependence on foreign markets indices as the S\&P 500 and CAC 40, but not to the Shanghai SSE 180, indicating an intricate i…
The paper introduces a new model selection criterion for various time series models.
Recent studies show that a negative shock in stock prices will generate more volatility than a positive shock of similar magnitude. The aim of this paper is to appraise the hypothesis under which the conditional mean and the conditional variance of stock returns are asymmetric functions of past information. We compare …
One of the major issues studied in finance that has always intrigued, both scholars and practitioners, and to which no unified theory has yet been discovered, is the reason why prices move over time. Since there are several well-known traditional techniques in the literature to measure stock market volatility, a centra…
Derives a new formula for measuring risk aversion in markets.
Study the link between entropy and market efficiency using fractal properties.
Quantum computer helps optimize stock portfolios.
The majority of recommender systems are designed to recommend items (such as movies and products) to users. We focus on the problem of recommending buyers to sellers which comes with new challenges: (1) constraints on the number of recommendations buyers are part of before they become overwhelmed, (2) constraints on th…
The probability distribution of stock price changes is studied by analyzing a database (the Trades and Quotes Database) documenting every trade for all stocks in three major US stock markets, for the two year period Jan 1994 -- Dec 1995. A sample of 40 million data points is extracted, which is substantially larger tha…
Study forecasts stock returns on JSE using SGDLMs capturing cross-series dependencies.
We have performed detailed multifractal analysis on the minutely volatility of two indexes and 1139 stocks in the Chinese stock markets based on the partition function approach. The partition function scales as a power law with respect to box size . The scaling exponents form a nonlinear function of …
Cardiovascular disease (CVD) is the global leading cause of death. A strong risk factor for CVD events is the amount of coronary artery calcium (CAC). To meet demands of the increasing interest in quantification of CAC, i.e. coronary calcium scoring, especially as an unrequested finding for screening and research, auto…
Investigates quantum vs classical portfolio optimization of 60 stocks.
Hybrid LSTM-ARIMA model outperforms other algorithms in algorithmic investment strategies.
Using a recently developed method of noise level estimation that makes use of properties of the coarse grained-entropy we have analyzed the noise level for the Dow Jones index and a few stocks from the New York Stock Exchange. We have found that the noise level ranges from 40 to 80 percent of the signal variance. The c…
Motivated by the literature on investment flows and optimal trading, we examine intraday predictability in the cross-section of stock returns. We find a striking pattern of return continuation at half-hour intervals that are exact multiples of a trading day, and this effect lasts for at least 40 trading days. Volume, o…
We show that the Snake on a square is homotopy equivalent to the space which was investigated in the previous work by Eda, Karimov and Repov\vs. We also introduce related constructions and and investigate homotopical differences between these four constructions. Finally, we explici…
The paper uses news headlines to predict stock prices using embeddings.
EarnMore uses masked stock representations to train RL agents for customizable stock pools efficiently.
Data augmentation improves financial prediction models, especially for small datasets.
A non-Bayesian time-varying model is developed by introducing the concept of the degree of market efficiency that varies over time. This model may be seen as a reflection of the idea that continuous technological progress alters the trading environment over time. With new methodologies and a new measure of the degree o…
In this paper, we describe a newly discovered statistical property of time series data for daily price changes. We conducted quantitative investigation of the {\it calm-time intervals} of price changes for 800 companies listed in the Tokyo Stock Exchange, and for the Nikkei 225 index over a 27-year period from January …
We investigate multifractality in the Korean stock-market index KOSPI. The generalized th order height-height correlation function shows multiscaling properties. There are two scaling regimes with a crossover time around min. We consider the original data sets and the modified data sets obtained by removin…
Study analyzes stock performance before, during, and after the pandemic.
A model-free hedging method using stock crowding scores.
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…
Study uses deep learning to predict stock trends with superior performance.
The aim of this paper is to compare statistical properties of a bubble period with those of the anti-bubble period in stock markets. We investigate the statistical properties of daily data for the Nikkei 225 index in the 28-year period from January 1975 to April 2003, corresponded to the periods of bubbles and anti-bub…
Enhances portfolio performance using deep reinforcement learning and future rewards.
In an Ultrafast Extreme Event (or Mini Flash Crash), the price of a traded stock increases or decreases strongly within milliseconds. We present a detailed study of Ultrafast Extreme Events in stock market data. In contrast to popular belief, our analysis suggests that most of the Ultrafast Extreme Events are not prima…
Using a rolling windows analysis of filtered and aligned stock index returns from 40 countries during the period 2006-2014, we construct Granger causality networks and investigate the ensuing structure of the relationships by studying network properties and fitting spatial probit models. We provide evidence that stock …
A novel CAB-XDE framework predicts speculative stock prices with high accuracy.
This paper extends Heston model to fractional Brownian motion for option pricing.
Author discusses the Poincaré conjecture from 40 years ago.
Model forecasts market structure from financial networks using machine learning.
Suppose you look at today's stock prices and bet on the value of the first digit. One could guess that a fair bet should correspond to the frequency of for each digit from 1 to 9. This is by no means the case, and one can easily observe a strong prevalence of the small values over the large ones. The fir…
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…
This article proposes a new method for the estimation of the parameters of a simple linear regression model which accounts for the role of co-moments in non-Gaussian distributions being based on the minimization of a quartic loss function. Although the proposed method is very general, we examine its application to fina…
Market timing is an investment technique that tries to continuously switch investment into assets forecast to have better returns. What is the likelihood of having a successful market timing strategy? With an emphasis on modeling simplicity, I calculate the feasible set of market timing portfolios using index mutual fu…
We use Random Matrix Theory (RMT) and information theory to analyze the correlations and flow of information between 64,939 news from The New York Times and 40 world financial indices during 10 months along the period 2015-2016. The set of news was quantified and transformed into daily polarity time series using tools …
Long short-term memory network outperforms seasonal model in JSE Top 40 forecasting.
Study finds 'Dragon Kings' in stock market volatility during major economic crises.
Study on diversifying equity portfolios during financial crises and stability.
Study on knots formed by Coxeter galleries, finding bounds and symmetric trefoils.
We examine how the structure of the world trade network has been shaped by globalization and recessions over the last 40 years. We show that by treating the world trade network as an evolving system, theory predicts the trade network is more sensitive to evolutionary shocks and recovers more slowly from them now than i…