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 shows OAT decomposition generates unexplained profit and loss, while SU decompositions depend on risk factor order.
This paper finds a new method for decomposing insurer profits and losses.
An algorithm was recently introduced by INTECH for the purposes of estimating the trading-profit contribution of systematic rebalancing to the relative return of rules-based investment strategies. We apply this methodology to analyze the size factor through the use of equal-weighted portfolios. These strategies combine…
Equations track profits and losses in trading algorithms.
Online trading platforms manipulate profits and losses, causing 82% of retail traders to lose money.
Study analyzes factors affecting profits in crypto liquidity provision.
Study shows AMM liquidity providers lose more than they earn, with varying profitability across pairs.
Study improves trading decisions by predicting profit and loss outcomes.
Optimizes liquidity provision intervals for profitable AMM participation.
The paper defines fair profit sharing ratios in Islamic PL contracts.
Paper presents a machine learning algorithm for hedging ETF options, outperforming static hedging methods.
New method decomposes profits and losses continuously, avoiding discrete reporting issues.
Corporate transparency reduces investors' disposition effect by increasing confidence in holding profitable and losing stocks.
Modeling fees impacts on arbitrage profits and LP losses in AMMs.
This paper introduces a high frequency trade execution model to evaluate the economic impact of supervised machine learners. Extending the concept of a confusion matrix, we present a 'trade information matrix' to attribute the expected profit and loss of the high frequency strategy under execution constraints, such as …
Algorithmic trading is well studied in traditional financial markets. However, it has received less attention in centralized cryptocurrency exchanges. The Commodity Futures Trading Commission (CFTC) attributed the flash crash, one of the most turbulent periods in the history of financial markets that saw the Dow…
In this work, we address the problem of modifying textual attributes of sentences. Given an input sentence and a set of attribute labels, we attempt to generate sentences that are compatible with the conditioning information. To ensure that the model generates content compatible sentences, we introduce a reconstruction…
Real-Time Bidding is nowadays one of the most promising systems in the online advertising ecosystem. In the presented study, the performance of RTB campaigns is improved by optimising the parameters of the users' profiles and the publishers' websites. Most studies about optimising RTB campaigns are focused on the biddi…
Credit risk may be warehoused by choice, or because of limited hedging possibilities. Credit risk warehousing increases capital requirements and leaves open risk. Open risk must be priced in the physical measure, rather than the risk neutral measure, and implies profits and losses. Furthermore the rate of return on cap…
Attributing forecast gaps to component models in complex model suites
The paper tackles attributing forecast gaps in complex model suites.
A new method, VIF, calculates influence for non-decomposable losses efficiently.
A method for predicting profit and loss distributions of complex financial portfolios using neural networks.
Traders buy and sell financial instruments in hopes of making profit, and brokers are responsible for the transaction. There are several hypotheses and conspiracy theories arguing that in some situations, brokers want their traders to lose money. For instance, a broker may want to protect the positions of a privileged …
Paper estimates differences in multi-attribute Gaussian graphical models using non-convex penalties.
The purpose of this study was to build a customer selection model based on 20 dimensions, including customer codes, total contribution, assets, deposit, profit, profit rate, trading volume, trading amount, turnover rate, order amount, withdraw amount, withdraw rate, process fee, process fee submitted, process fee retai…
Proposes VCLANC for attributed network clustering using node and attribute embeddings.
New method optimizes fairness in predictive models for continuous sensitive attributes.
Agricultural research has been profited by technical advances such as automation, data mining. Today, data mining is used in a vast areas and many off-the-shelf data mining system products and domain specific data mining application soft wares are available, but data mining in agricultural soil datasets is a relatively…
We consider the problem of learning linear classifiers when both features and labels are binary. In addition, the features are noisy, i.e., they could be flipped with an unknown probability. In Sy-De attribute noise model, where all features could be noisy together with same probability, we show that - loss ($l_{…
We use a continuous-time random walk (CTRW) to model market fluctuation data from times when traders experience excessive losses or excessive profits. We analytically derive "superstatistics" that accurately model empirical market activity data (supplied by Bogachev, Ludescher, Tsallis, and Bunde)that exhibit transitio…
This paper introduces a Bayesian framework for optimizing online experiments to maximize profit.
Traditionally, most of the existing attribute learning methods are trained based on the consensus of annotations aggregated from a limited number of annotators. However, the consensus might fail in settings, especially when a wide spectrum of annotators with different interests and comprehension about the attribute wor…
Developing an Agent-Based Model to Mitigate Adverse Selection in Uniswap v3 Liquidity Providers
The paper optimizes stock portfolios with constraints based on performance attribution.
Bayesian approach scores influential training examples for model predictions.
Network embedding is the process of learning low-dimensional representations for nodes in a network, while preserving node features. Existing studies only leverage network structure information and focus on preserving structural features. However, nodes in real-world networks often have a rich set of attributes providi…
We present an analytical study of an insurance company. We model the company's performance on a statistical basis and evaluate the predicted annual income of the company in terms of insurance parameters namely the premium, total number of the insured, average loss claims etc. We restrict ourselves to a single insurance…
The paper proves ADL mechanisms face a trilemma and optimizes them for fairness, revenue, and exchange solvency.
Paper proposes a deep hedging method for Bermudan swaptions to manage residual profit and loss.
A new method for disentangled latent spaces in VAEs that can manipulate attributes.
We introduce a modular framework for market making. It combines cost-function based automated market makers with bandit algorithms. We obtain worst-case profits guarantee's relative to the best in hindsight within a class of natural "overround" cost functions . This combination allow us to have distribution-free guaran…
We introduce a quantitative approach to comparative statics that allows to bound the maximum effect of an exogenous parameter change on a system's equilibrium. The motivation for this approach is a well known paradox in multimarket Cournot competition, where a positive price shock on a monopoly market may actually redu…
We investigate how price variations of a stock are transformed into profits and losses (P&Ls) of a trend following strategy. In the frame of a Gaussian model, we derive the probability distribution of P&Ls and analyze its moments (mean, variance, skewness and kurtosis) and asymptotic behavior (quantiles). We show that …
DRSVM uses deep learning to rank relative attributes between image pairs.
Winterization of Texas power system profitable but risky, estimated at $11.74bn over 30 years.
Users in various web and mobile applications are vulnerable to attribute inference attacks, in which an attacker leverages a machine learning classifier to infer a target user's private attributes (e.g., location, sexual orientation, political view) from its public data (e.g., rating scores, page likes). Existing defen…
This paper evaluates various loss functions for Transformer models in stock ranking.