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.
Periodic activation functions improve neural network reliability and interpretability.
Neural networks struggle with periodic functions, a new activation fixes this.
Debate over the existence of branches in the stellar activity-rotation diagrams continues. Application of modern time series analysis tools to study the mean cycle periods in chromospheric activity index is lacking. We develop such models, based on Gaussian processes, for one-dimensional time series and apply it to the…
Model explains periodic trading in financial markets through game theory.
AMSAs adaptively manage crypto-currency trading by selecting multiple strategies based on market conditions.
Bayesian model predicts online activity participation.
New method uses trainable activations to make BNNs behave like GPs.
Binary encoding enables neural networks to extrapolate periodic functions.
Power spectrum densities for the number of tick quotes per minute (market activity) on three currency markets (USD/JPY, EUR/USD, and JPY/EUR) for periods from January 1999 to December 2000 are analyzed. We find some peaks on the power spectrum densities at a few minutes. We develop the double-threshold agent model and …
The Stock Market is a complex self-interacting system, characterized by an intermittent behaviour. Periods of high activity alternate with periods of relative calm. In the present work we investigate empirically about the possibility that the market is in a self-organized critical state (SOC). A wavelet transform metho…
Bayesian nonparametric model predicts user activity and intervention success.
Using the new data from the OECD-WTO world network of economic activities we construct the Google matrix of this directed network and perform its detailed analysis. The network contains 58 countries and 37 activity sectors for years 1995, 2000, 2005, 2008, 2009. The construction of , based on Markov chain transi…
Analysis of log data generated by online educational systems is an essential task to better the educational systems and increase our understanding of how students learn. In this study we investigate previously unseen data from Clio Online, the largest provider of digital learning content for primary schools in Denmark.…
Covid lockdown increased interest in Italian stock market, leading to new investors.
The study examines cross-border lending behavior from G7 countries, showing changes in driving factors after the 2008 financial crisis.
We consider the optimal trade execution strategies for a large portfolio of single stocks proposed by Almgren (2003). This framework accounts for a nonlinear impact of trades on average market prices. The results of Almgren (2003) are based on the assumption that no shares of assets per unit of time are trade at the be…
Proposes a new model for time series that considers smooth transitions between states.
We determine the price of digital double barrier options with an arbitrary number of barrier periods in the Black-Scholes model. This means that the barriers are active during some time intervals, but are switched off in between. As an application, we calculate the value of a structure floor for structured notes whose …
Cryptocurrency market activity is decomposed into recurring and noise components, revealing patterns tied to macroeconomic reports.
FNN approximates functions and solves PDEs with periodic BCs.
Meta-learning improves event prediction from short sequences.
There are non-vanishing price responses across different stocks in correlated financial markets. We further study this issue by performing different averages, which identify active and passive cross-responses. The two average cross-responses show different characteristic dependences on the time lag. The passive cross-r…
Develops RL algorithm for non-Markovian, non-stationary reward streams.
Complex non-linear interactions between banks and assets we model by two time-dependent Erdős Renyi network models where each node, representing bank, can invest either to a single asset (model I) or multiple assets (model II). We use dynamical network approach to evaluate the collective financial failure---systemic ri…
Two machine learning methods detect insider trading from investor activity data.
Previous studies of the stock price response to individual trades focused on single stocks. We empirically investigate the price response of one stock to the trades of other stocks. How large is the impact of one stock on others and vice versa? -- This impact of trades on the price change across stocks appears to be tr…
Neural networks plateau during training, identified and quantified.
The study finds significant financial sector volatility and tail risk spillovers to real economy sectors.
Paper uses RL to optimize daily step distribution for better health biomarkers.
Firms having similar business activities are correlated. We analyze two different cross-correlation matrices C constructed from (i) 30-min price fluctuations of 1000 US stocks for the 2-year period 1994-95 and (ii) 1-day price fluctuations of 422 US stocks for the 35-year period 1962-96. We find that the eigenvectors o…
Quarter-hour market bursts predict algorithmic trading and returns in crypto futures.
The study reveals distinct patterns in retail investors' holding periods affecting stock returns.
Novel periodic momentum SGD method for decentralized training with linear speedup.
Bitcoin mining costs remain stable despite increased activity.
Study compares information flow between Chinese and US stock sectors.
We analyse a period spanning 35 years of activity in the Sao Paulo Stock Exchange Index (IBOVESPA) and show that the Heston model with stochastic volatility is capable of explaining price fluctuations for time scales ranging from 5 minutes to 100 days with a single set of parameters. We also show that the Heston model …
Understanding urban growth is one with understanding how society evolves to satisfy the needs of its individuals in sharing a common space and adapting to the territory. We propose here a quantitative analysis of the historical development of a large urban area by investigating the spatial distribution and the age of c…
ESOP uses Bayesian optimization to find optimal lock-down schedules.
The study examines cryptocurrency market activity, revealing multifractal inter-transaction times and challenging traditional statistical models.
By studying all the trades and best bids/asks of ultra high frequency snapshots recorded from the order books of a basket of 10 futures assets, we bring qualitative empirical evidence that the impact of a single trade depends on the intertrade time lags. We find that when the trading rate becomes faster, the return var…
The expressivity of neural networks as a function of their depth, width and type of activation units has been an important question in deep learning theory. Recently, depth separation results for ReLU networks were obtained via a new connection with dynamical systems, using a generalized notion of fixed points of a con…
Brain source imaging is an important method for noninvasively characterizing brain activity using Electroencephalogram (EEG) or Magnetoencephalography (MEG) recordings. Traditional EEG/MEG Source Imaging (ESI) methods usually assume that either source activity at different time points is unrelated, or that similar spat…
This study examines the presence of the day-of-the-week effect on daily returns of biotechnology stocks over a 16-year period from January 2002 to December 2015. Using daily returns from the NASDAQ Biotechnology Index (NBI), we find that the stock returns were the lowest on Mondays, and compared to the Mondays the stoc…
BEKAN uses RBFs and evolutionary methods to solve PDEs with boundary conditions.
Automatic music transcription (AMT) aims to infer a latent symbolic representation of a piece of music (piano-roll), given a corresponding observed audio recording. Transcribing polyphonic music (when multiple notes are played simultaneously) is a challenging problem, due to highly structured overlapping between harmon…
Active authentication is the problem of continuously verifying the identity of a person based on behavioral aspects of their interaction with a computing device. In this study, we collect and analyze behavioral biometrics data from 200subjects, each using their personal Android mobile device for a period of at least 30…
MRC improves credit assignment in multi-agent LLM systems, achieving high returns and transparency.
We propose an algorithm to actively estimate the parameters of a linear dynamical system. Given complete control over the system's input, our algorithm adaptively chooses the inputs to accelerate estimation. We show a finite time bound quantifying the estimation rate our algorithm attains and prove matching upper and l…