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.
Study finds short-term trading signals can enhance alpha in U.S. S&P 500 portfolios.
Model combines long-term and short-term memory using conceptors.
The paper uses data science to predict stock trends of Amazon, Apple, Google, and Microsoft.
HFformer outperforms LSTM in high-frequency trading with multiple signals.
Paper presents methods to create stock price confidence intervals using LSTM models.
Unified deep learning approach for time series forecasting using VMD-CNN-LSTM.
Short-term trend-following has stopped delivering profits since 2009, especially on smaller market ticks.
Study causal financial signals for non-stationary markets, improving short-term forecasts.
LSTMs improve bond yield forecasting with unique signals.
Comparative study of neural networks for short-term FOREX forecasting.
The standard LSTM recurrent neural networks while very powerful in long-range dependency sequence applications have highly complex structure and relatively large (adaptive) parameters. In this work, we present empirical comparison between the standard LSTM recurrent neural network architecture and three new parameter-r…
Improved crypto market forecasting using historical price reactions to tweets.
We compare optimal static and dynamic solutions in trade execution. An optimal trade execution problem is considered where a trader is looking at a short-term price predictive signal while trading. When the trader creates an instantaneous market impact, it is shown that transaction costs of optimal adaptive strategies …
This paper uses Bayesian models to analyze CTA returns across short and long-term trends.
Optimal trading is a recent field of research which was initiated by Almgren, Chriss, Bertsimas and Lo in the late 90's. Its main application is slicing large trading orders, in the interest of minimizing trading costs and potential perturbations of price dynamics due to liquidity shocks. The initial optimization frame…
YC Bench forecasts startup success in Y Combinator batches with a short-term metric.
Deep neural networks improve sEMG-based hand gesture classification.
A method for inferring ground-truth signals from degraded sensor data.
The paper proposes a method to identify high-quality financial patterns using entropy.
FinRLlama wins FinRL Challenge 2024 by fine-tuning LLMs with market data.
High-performing equity factor with Sharpe ratio above 13 out-of-sample.
PHASE predicts surgical complications from physiological signals.
RL agent learns to place limit orders for trading signals in financial markets.
Attention mechanism combines bottom-up and top-down signals in neural networks.
Multivariate techniques based on engineered features have found wide adoption in the identification of jets resulting from hadronic top decays at the Large Hadron Collider (LHC). Recent Deep Learning developments in this area include the treatment of the calorimeter activation as an image or supplying a list of jet con…
Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, hig…
Bayesian Neural Networks detect gravitational wave events with high accuracy and real-time potential.
A new method forecasts hourly electricity prices considering product dynamics and limit order book signals.
This report first provides a brief overview of a number of supervised learning algorithms for regression tasks. Among those are neural networks, regression trees, and the recently introduced Nexting. Nexting has been presented in the context of reinforcement learning where it was used to predict a large number of signa…
We apply the Zipf power law to financial time series of WIG20 index daily changes (open-close). Thanks to the mapping of time series signal into the sequence of 2k+1 'spin-like' states, where k=0, 1/2, 1, 3/2, ..., we are able to describe any time series increments, with almost arbitrary accuracy, as the one of such 's…
Integrating wind power into the grid is challenging because of its random nature. Integration is facilitated with accurate short-term forecasts of wind power. The paper presents a spatio-temporal wind speed forecasting algorithm that incorporates the time series data of a target station and data of surrounding stations…
Being able to predict the occurrence of extreme returns is important in financial risk management. Using the distribution of recurrence intervals---the waiting time between consecutive extremes---we show that these extreme returns are predictable on the short term. Examining a range of different types of returns and th…
Financial trading is at the forefront of time-series analysis, and has grown hand-in-hand with it. The advent of electronic trading has allowed complex machine learning solutions to enter the field of financial trading. Financial markets have both long term and short term signals and thus a good predictive model in fin…
The study finds cash productivity predicts stock performance in a specific subset of firms.
In this paper, we have proposed a brain signal classification method, which uses eigenvalues of the covariance matrix as features to classify images (topomaps) created from the brain signals. The signals are recorded during the answering of 2D and 3D questions. The system is used to classify the correct and incorrect a…
Effective and powerful methods for denoising real electrocardiogram (ECG) signals are important for wearable sensors and devices. Deep Learning (DL) models have been used extensively in image processing and other domains with great success but only very recently have been used in processing ECG signals. This paper pres…
Improved LSTM cell for high-frequency trading forecasts.
In this article, we discuss various implementation of L1 filtering in order to detect some properties of noisy signals. This filter consists of using a L1 penalty condition in order to obtain the filtered signal composed by a set of straight trends or steps. This penalty condition, which determines the number of breaks…
Social media signals have been successfully used to develop large-scale predictive and anticipatory analytics. For example, forecasting stock market prices and influenza outbreaks. Recently, social data has been explored to forecast price fluctuations of cryptocurrencies, which are a novel disruptive technology with si…
Sleep disorders are implicated in a growing number of health problems. In this paper, we present a signal-processing/machine learning approach to detecting arousals in the multi-channel polysomnographic recordings of the Physionet/CinC Challenge2018 dataset. Methods: Our network architecture consists of two components.…
Modeling price impacts and trading signals for optimal execution and speculation.
Earthquake signal detection is at the core of observational seismology. A good detection algorithm should be sensitive to small and weak events with a variety of waveform shapes, robust to background noise and non-earthquake signals, and efficient for processing large data volumes. Here, we introduce the Cnn-Rnn Earthq…
Quaternion neural networks improve distant speech recognition.
Machine learning models outperform traditional technical analysis in Bitcoin trading.
This paper addresses the energy disaggregation problem, i.e. decomposing the electricity signal of a whole home to its operating devices. First, we cast the problem as a dictionary learning (DL) problem where the key electricity patterns representing consumption behaviors are extracted for each device and stored in a d…
Recurrent Neural Networks (RNNs) are extensively used for time-series modeling and prediction. We propose an approach for automatic construction of a binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for detection of a vehicle passage through a checkpoint. As an input to the classifier we use multidime…
Paper proposes novel hedging strategies using LSTM models for diversified investment portfolios.
New neural network extracts signal components and their IFs from non-uniform samples.