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.
Alpha2 discovers logical formulaic alphas using deep reinforcement learning.
AlphaCFG discovers alpha factors using grammar-guided search.
Enhances genetic programming for stock alpha discovery with warm start and structural constraints.
AlphaLogics mines market logic to generate interpretable alpha factors.
In the online multiple testing problem, p-values corresponding to different null hypotheses are observed one by one, and the decision of whether or not to reject the current hypothesis must be made immediately, after which the next p-value is observed. Alpha-investing algorithms to control the false discovery rate (FDR…
New method controls false discoveries in real-time data streams.
Alpha-GPT 2.0 integrates human insights into AI-driven investment research.
FactorMiner discovers financial alpha factors with low redundancy.
New findings control FDR for online testing methods under positive dependence.
AlphaEval evaluates alpha mining models efficiently and comprehensively.
ADNN uses prior knowledge to construct financial features.
Multiple hypothesis testing is a core problem in statistical inference and arises in almost every scientific field. Given a set of null hypotheses , Benjamini and Hochberg introduced the false discovery rate (FDR), which is the expected proportion of false positives among rejected nu…
AI-driven investment strategies self-defeat at scale due to signal crowding and erosion.
Study uses LLMs to categorize financial tweets, revealing useful sentiment signals.
Alpha-GPT mines new trading signals with human-AI interaction.
We give an explicit algorithm and source code for extracting expected returns for stocks from expected returns for alphas. Our algorithm altogether bypasses combining alphas with weights into "alpha combos". Simply put, we have developed a new method for trading alphas which does not involve combining them. This yields…
Paper proposes a new framework to mine synergistic formulaic alphas for better stock trend forecasting.
Alpha-R1 uses LLMs to reason about economic factors and news for better alpha screening.
We propose a framework for constructing factor models for alpha streams. Our motivation is threefold. 1) When the number of alphas is large, the sample covariance matrix is singular. 2) Its out-of-sample stability is challenging. 3) Optimization of investment allocation into alpha streams can be tractable for a factor …
We compute the analytic expression of the probability distributions F{FTSE100,+} and F{FTSE100,-} of the normalized positive and negative FTSE100 (UK) index daily returns r(t). Furthermore, we define the alpha re-scaled FTSE100 daily index positive returns r(t)^alpha and negative returns (-r(t))^alpha that we call, aft…
In terms of the stock exchange returns, we compute the analytic expression of the probability distributions F{DAX,+} and F{DAX,-} of the normalized positive and negative DAX (Germany) index daily returns r(t). Furthermore, we define the alpha re-scaled DAX daily index positive returns r(t)^alpha and negative returns (-…
It is well known that combining multiple hedge fund alpha streams yields diversification benefits to the resultant portfolio. Additionally, crossing trades between different alpha streams reduces transaction costs. As the number of alpha streams increases, the relative turnover of the portfolio decreases as more trades…
RiskMiner discovers formulaic alphas using MCTS for better performance.
Internal crossing of trades between multiple alpha streams results in portfolio turnover reduction. Turnover reduction can be modeled using the correlation structure of the alpha streams. As more and more alphas are added, generally turnover reduces. In this note we use a factor model approach to address the question o…
We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications typically there is insufficient history to compute a sample covariance matrix (SCM) for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted) regression ove…
AlphaForgeBench evaluates LLMs as quantitative researchers, not trading agents, to address instability in financial decision-making.
Unified convergence analysis of alpha-SVRG under strong convexity.
New methods for tuning alpha in Gibbs posteriors improve speed and accuracy.
An explicit expression is obtained for the sectional curvature in the plane spanned by two stationary flows, cos(k, x) and cos(l, x). It is shown that for certain values of the wave vectors k and l the curvature becomes positive for alpha > alpha_0, where 0 < alpha_0 < 1 is of the order 1/k. This suggests that the flow…
The weak variance-alpha-gamma process is a multivariate Lévy process constructed by weakly subordinating Brownian motion, possibly with correlated components with an alpha-gamma subordinator. It generalises the variance-alpha-gamma process of Semeraro constructed by traditional subordination. We compare three calibrati…
Researchers calculate alpha invariant for certain complex projective spaces.
We give a simple explicit formula for turnover reduction when a large number of alphas are traded on the same execution platform and trades are crossed internally. We model turnover reduction via alpha correlations. Then, for a large number of alphas, turnover reduction is related to the largest eigenvalue and the corr…
AlphaSAGE mines diverse alphas via GFlowNets, overcoming RL issues.
We give an explicit algorithm and source code for extracting equity risk factors from dead (a.k.a. "flatlined" or "hockey-stick") alphas and using them to improve performance characteristics of good (tradable) alphas. In a nutshell, we use dead alphas to extract directions in the space of stock returns along which ther…
PPO optimizes LLM-generated alpha weights for better trading performance.
The paper presents methods to improve uncertainty calibration in Bayesian Neural Networks.
AlphaForge mines and dynamically combines alpha factors for better investment performance.
AutoAlpha efficiently discovers effective alpha factors for quantitative investment.
GAIF enhances online multiple testing with feedback, improving statistical power.
In these notes we discuss investment allocation to multiple alpha streams traded on the same execution platform, including when trades are crossed internally resulting in turnover reduction. We discuss approaches to alpha weight optimization where one maximizes P&L subject to bounds on volatility (or Sharpe ratio). The…
Mannheim curves are defined for immersed curves in 3-dimensional sphere S^3 . The definition is given by considering the geodesics of S^3. First, two special geodesics, called principal normal geodesic and binormal geodesic, of S^3 are defined by using Frenet vectors of a curve immersed in S^3. Later, the curve alpha i…
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…
The paper evaluates biased methods for alpha-divergence minimization.
Given a smooth, closed, oriented 4-manifold X and alpha in H_2(X,Z) such that alpha.alpha > 0, a closed 2-form w is constructed, Poincare dual to alpha, which is symplectic on the complement of a finite set of unknotted circles. The number of circles, counted with sign, is given by d = (c_1(s)^2 -3sigma(X) -2chi(X))/4,…
Unbiased methods for alpha-divergence minimization struggle in high dimensions.
Paper proposes a new REINFORCE algorithm for mining formulaic alpha factors with reduced variance.
We present conditions under which positive alpha exists in the realm of active portfolio management- in contrast to the controversial result in Jarrow (2010, pg. 20) which implicates delegated portfolio management by surmising that positive alphas are illusionary. Specifically, we show that the critical assumption used…
DCMIX learns channel importance for high content imaging.