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.
Sophisticated volatility models outperform naive portfolio strategies.
Naive investors make riskier choices than optimal strategies in continuous-time finance.
Pairs trading strategy improved using Ornstein-Uhlenbeck process.
We compare in this paper several feature selection methods for the Naive Bayes Classifier (NBC) when the data under study are described by a large number of redundant binary indicators. Wrapper approaches guided by the NBC estimation of the classification error probability out-perform filter approaches while retaining …
We introduce the concept of spontaneous symmetry breaking to arbitrage modeling. In the model, the arbitrage strategy is considered as being in the symmetry breaking phase and the phase transition between arbitrage mode and no-arbitrage mode is triggered by a control parameter. We estimate the control parameter for mom…
Study compares optimal vs. naive diversification in crypto markets, finds time-varying moments improve performance.
We consider the problem of stopping a diffusion process with a payoff functional that renders the problem time-inconsistent. We study stopping decisions of naive agents who reoptimize continuously in time, as well as equilibrium strategies of sophisticated agents who anticipate but lack control over their future selves…
Two strategies extend multi-label chaining for imprecise probability estimates.
Frequently, acquiring training data has an associated cost. We consider the situation where the learner may purchase data during training, subject TO a budget. IN particular, we examine the CASE WHERE each feature label has an associated cost, AND the total cost OF ALL feature labels acquired during training must NOT e…
Paper optimizes trading strategies by creating shadow prices for markets with transaction costs.
It has been widely observed that capitalization-weighted indexes can be beaten by surprisingly simple, systematic investment strategies. Indeed, in the U.S. stock market, equal-weighted portfolios, random-weighted portfolios, and other naive, non- optimized portfolios tend to outperform a capitalization-weighted index …
Random imputation is surprisingly effective for linear predictors in missing data scenarios.
Markov Chain Monte Carlo (MCMC) algorithms are a workhorse of probabilistic modeling and inference, but are difficult to debug, and are prone to silent failure if implemented naively. We outline several strategies for testing the correctness of MCMC algorithms. Specifically, we advocate writing code in a modular way, w…
New paper finds strategic trade centralization benefits firms, while naive centralization often harms them.
We consider an agent who needs to buy (or sell) a relatively small amount of asset over some fixed short time interval. We work at the highest frequency meaning that we wish to find the optimal tactic to execute our quantity using limit orders, market orders and cancellations. To solve the agent's control problem, we b…
MPM uses machine learning to switch between two portfolio strategies for better risk management.
Constant Proportion Portfolio Insurance (CPPI) is an investment strategy designed to give participation in the performance of a risky asset while protecting the invested capital. This protection is however not perfect and the gap risk must be quantified. CPPI strategies are path-dependent and may have American exercise…
We consider parallel asynchronous Markov Chain Monte Carlo (MCMC) sampling for problems where we can leverage (stochastic) gradients to define continuous dynamics which explore the target distribution. We outline a solution strategy for this setting based on stochastic gradient Hamiltonian Monte Carlo sampling (SGHMC) …
EB improves asset pricing by mining large strategies without lookahead bias.
Optimizes trading in CFMMs and exchanges using deep learning.
Dynamic econometric models improve trading signals in momentum strategies.
MPANF improves naive forecast by incorporating directional information.
In the past decade many researchers have proposed new optimal portfolio selection strategies to show that sophisticated diversification can outperform the naïve 1/N strategy in out-of-sample benchmarks. Providing an updated review of these models since DeMiguel et al. (2009b), I test sixteen strategies across six empir…
Despite its simplicity, the naive Bayes classifier has surprised machine learning researchers by exhibiting good performance on a variety of learning problems. Encouraged by these results, researchers have looked to overcome naive Bayes primary weakness - attribute independence - and improve the performance of the algo…
Optimized portfolio turnover strategies enhance wealth and reduce costs.
Develops a new bidding system to maximize advertiser profit.
Paper introduces Generalized Naive Bayes for better data fitting.
Improved Naive Bayes classifier with neural network models.
Naive Bayes estimator is widely used in text classification problems. However, it doesn't perform well with small-size training dataset. We propose a new method based on Naive Bayes estimator to solve this problem. A correlation factor is introduced to incorporate the correlation among different classes. Experimental r…
Study on investment strategy for agents with periodic preferences and discounting.
We investigate the problem of pricing and hedging derivatives of Electricity Futures contract when the underlying asset is not available. We propose to use a cross hedging strategy based on the Futures contract covering the larger delivery period. A quick overview of market data shows a basis risk for this market incom…
A widely applied diversification paradigm is the naive diversification choice heuristic. It stipulates that an economic agent allocates equal decision weights to given choice alternatives independent of their individual characteristics. This article provides mathematically and economically sound choice theoretic founda…
Investors face constraints in Heston's model; optimal allocation differs from naive capped strategy.
The paper compares one-hot encoding to Naïve Bayes for categorical variables.
A new method simulates large, diverse populations of learning agents evolving in games.
As the cornerstone of modern portfolio theory, Markowitz's mean-variance optimization is considered a major model adopted in portfolio management. However, due to the difficulty of estimating its parameters, it cannot be applied to all periods. In some cases, naive strategies such as Equally-weighted and Value-weighted…
Proposes a non-convex optimization method for a parsimonious weighted naive Bayes classifier.
Partner-aware algorithms improve AI collaboration in multi-agent settings.
Due to its linear complexity, naive Bayes classification remains an attractive supervised learning method, especially in very large-scale settings. We propose a sparse version of naive Bayes, which can be used for feature selection. This leads to a combinatorial maximum-likelihood problem, for which we provide an exact…
The study forecasts ETF return direction using machine learning models.
Proposes a sparse Naïve Bayes classifier to improve performance and interpretability.
We propose a microstructural modeling framework for studying optimal market making policies in a FIFO (first in first out) limit order book (LOB). In this context, the limit orders, market orders, and cancel orders arrivals in the LOB are modeled as Cox point processes with intensities that only depend on the state of …
Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training. An effective approach to this challenge is to pre-train a model on related tasks where data is abundant, and…
A simple algorithm improves model generalization in expert advice settings.
We study the (standard) cohomology of a Courant algebroid . We prove that if is transitive, the standard cohomology coincides with the naive cohomology as conjectured by Stienon and Xu. For a general Courant algebroid we define a spectral sequence converging to its stan…
Paper optimizes battery storage in multiple energy markets for better profits.
This paper proposes an efficient method for calculating Shapley values in Naive Bayes classifiers.
Naive Bayes can be used as a discriminative classifier, matching the definition of logistic regression.