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.
Proves accuracy guarantees for self-supervised learning with correlated positive pairs.
We carry out a large-scale empirical data analysis to examine the efficiency of the so-called pairs trading. On the basis of relevant three thresholds, namely, starting, profit-taking, and stop-loss for the `first-passage process' of the spread (gap) between two highly-correlated stocks, we construct an effective strat…
Sampling a fraction of pairs can match full evaluation in machine learning losses.
The paper introduces a new pairs trading model using nonlinear and non-Gaussian state-space models.
Pairs Trading is carried out in the financial market to earn huge profits from known equilibrium relation between pairs of stock. In financial markets, seldom it is seen that stock pairs are correlated at particular lead or lag. This lead-lag relationship has been empirically studied in various financial markets. Earli…
This paper unifies three regularization methods in batch reinforcement learning.
We propose to use nonparametric Bernstein copulas as bivariate pair-copulas in high-dimensional vine models. The resulting smooth and nonparametric vine copulas completely obviate the error-prone need for choosing the pair-copulas from parametric copula families. By means of a simulation study and an empirical analysis…
Measures three types of noise in LLM evaluations.
Paper explains contrastive learning using cosine similarity and proposes mitigations for batch size effects.
MTRGL learns temporal correlations from multi-modal data for improved pair trading.
A novel graphical matching approach improves pairs trading by reducing portfolio variance and risk-adjusted returns.
Deep learning predicts currency volatility accurately.
In this paper, we establish a fluid limit for a two--sided Markov order book model. Our main result states that in a certain asymptotic regime, a pair of measure-valued processes representing the "sell-side shape" and "buy-side shape" of an order book converges to a pair of deterministic measure-valued processes in a c…
A new measure scales MMD to assess distribution closeness.
New method recovers diverse policies from expert data using state-action pair weighting.
In this on-going work, I explore certain theoretical and empirical implications of data transformations under the PCA. In particular, I state and prove three theorems about PCA, which I paraphrase as follows: 1). PCA without discarding eigenvector rows is injective, but looses this injectivity when eigenvector rows are…
We present explicit formulas - that are also computer code - for 101 real-life quantitative trading alphas. Their average holding period approximately ranges 0.6-6.4 days. The average pair-wise correlation of these alphas is low, 15.9%. The returns are strongly correlated with volatility, but have no significant depend…
Enhances pairs trading with neural networks and Kalman Filters.
Optimal trading strategies for pairs trading have been studied by models that try to find either optimal shares of stocks by assuming no transaction costs or optimal timing of trading fixed numbers of shares of stocks with transaction costs. To find optimal strategies which determine optimally both trade times and numb…
Unified framework for comparing clusterings from information-theoretic and pair-counting perspectives.
We extend the framework of trading strategies of Gatheral [2010] from single stocks to a pair of stocks. Our trading strategy with the executions of two round-trip trades can be described by the trading rates of the paired stocks and the ratio of their trading periods. By minimizing the potential cost arising from cros…
The paper proves impossibilities and positive results for universal machine translation.
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…
Study shows post-COVID commodity futures returns and volatility changed for different products.
Recent state-of-the-art natural language understanding models, such as BERT and XLNet, score a pair of sentences (A and B) using multiple cross-attention operations - a process in which each word in sentence A attends to all words in sentence B and vice versa. As a result, computing the similarity between a query sente…
Diffusion models learn simple statistics before complex ones, revealing a sample complexity exponent.
This paper extends Median-of-Means to new learning problems involving pairwise comparisons.
Deep metric learning (DML) has received much attention in deep learning due to its wide applications in computer vision. Previous studies have focused on designing complicated losses and hard example mining methods, which are mostly heuristic and lack of theoretical understanding. In this paper, we cast DML as a simple…
We study the long memory of order flow for each of three liquid currency pairs on a large electronic trading platform in the foreign exchange (FX) spot market. Due to the extremely high levels of market activity on the platform, and in contrast to existing empirical studies of other markets, our data enables us to perf…
When stock prices are observed at high frequencies, more information can be utilized in estimation of parameters of the price process. However, high-frequency data are contaminated by the market microstructure noise which causes significant bias in parameter estimation when not taken into account. We propose an estimat…
Augmented bridge matching preserves coupling information between distributions.
Learning disentangled representations that correspond to factors of variation in real-world data is critical to interpretable and human-controllable machine learning. Recently, concerns about the viability of learning disentangled representations in a purely unsupervised manner has spurred a shift toward the incorporat…
Study shows AMM liquidity providers lose more than they earn, with varying profitability across pairs.
PAIR optimizes machine learning models to generalize better to out-of-distribution data.
This work compares human feedback methods for reward learning in bandits.
Deep neural networks identify robust arbitrage strategies in financial markets.
ALPINE predicts links in networks by querying the most informative pairs.
New method learns useful disentangled representations from weakly labeled data.
New estimators improve Rasch model item parameter estimation for sparse data.
Networks are ubiquitous in biology and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used to complete a partially known network by integrating various measurements. Two main supervised frameworks have been proposed: the local appro…
Geometric approach for unsupervised word embedding alignment.
In prior research, a statistically cheap method was developed to monitor transportation network performance by using only a few groups of agents without having to forecast the population flows. The current study validates this "multi-agent inverse optimization" method using taxi GPS probe data from the city of Wuhan, C…
Multi-armed bandit(MAB) problem is a reinforcement learning framework where an agent tries to maximise her profit by proper selection of actions through absolute feedback for each action. The dueling bandits problem is a variation of MAB problem in which an agent chooses a pair of actions and receives relative feedback…
New findings reveal discount regularization can be seen as a strong prior, leading to poor performance in unevenly sampled data.
Deep learning algorithms have increasingly been shown to lack robustness to simple adversarial examples (AdvX). An equally troubling observation is that these adversarial examples transfer between different architectures trained on different datasets. We investigate the transferability of adversarial examples between m…
Bayesian Empirical Bayes extends EB to complex structures using probabilistic symmetry.
The Bellman error is a poor proxy for value function accuracy, even with all state-action pairs.
The paper proves probabilistic alignment between unseen modalities using contrastive learning.