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arXiv research

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.

168,695 papers · 148 categories

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255075100 · Jun 202619922001200920172026
48 results for accumulated returns

Study of historic stock returns distributions, highlighting asymmetry and outliers.

problem Understanding the asymmetry in accumulated gains and losses in stock returns over time.
method Analyzing decades-long historic distributions of S&P500 returns, comparing gains and losses, using statistical U-tests and fitting log-log scale linearly.
result The mean of de-trended distributions increases linearly with the number of days of accumulation, and the overall skew is negative, indicating heavier tails of losses.

Analyzes multi-day stock returns, showing linear volatility and mean dependence.

problem Linear dependence of volatility and mean in accumulated stock returns.
method Modified Jones-Faddy skew t-distribution analysis.
result Linear dependence of volatility and mean on the number of days of accumulation.

Wealth inequality is an important matter for economic theory and policy. Ongoing debates have been discussing recent rise in wealth inequality in connection with recent development of active financial markets around the world. Existing literature on wealth distribution connects the origins of wealth inequality with a v…

2018-09-23abs ↗pdf ↗

The paper models financial markets and real economy interactions using a large agent framework.

problem Understanding capital allocation and accumulation in financial markets and real economy interactions.
method Developed a field-formalism model to analyze interactions between financial markets and real economy with a large number of heterogeneous agents.
result The number of firms in each sector depends on the aggregate financial capital invested and expected long-term returns.

Statistical fields model explains capital allocation and accumulation among firms and investors.

problem Understanding capital allocation and accumulation dynamics among firms and investors.
method Applied statistical fields formalism to heterogeneous agents divided into firms and investors.
result Capital accumulation depends on various factors including long-term returns, competition, and stock price volatility.

Study of homeomorphisms on infinite type surfaces with a classification theorem.

problem Classifying homeomorphisms on surfaces of infinite type.
method Introduce tame homeomorphisms and prove a Nielsen-Thurston type classification theorem.
result For tame homeomorphisms, surfaces decompose into invariant subsurfaces with canonical decompositions.

We develop an axiomatic theory of balance functions (future value functions) in the theory of interest that is derived from financial considerations and which applies to general regulated payment streams, including continuous payment streams. Balance functions exist and are unique up to an initial choice of deposit and…

2012-08-05abs ↗pdf ↗

The paper translates economic models into a field formalism to study capital accumulation and its fluctuations.

problem Understanding capital accumulation and its fluctuations in a complex economic system.
method Developed a field formalism to preserve interactions and microeconomic features, applying it to a microeconomic framework of investors and firms.
result Capital accumulation patterns can emerge at the macro-scale and affect neighboring sectors, leading to permanent fluctuations.

Study analyzes cryptocurrency pump-and-dump dynamics using minute-level data.

problem Identifying and quantifying insider trading in cryptocurrency markets.
method Algorithmic identification of insider volume spikes, conservative profit bounds calculation, social-media verification.
result Median returns above 100%, upper-quartile returns exceeding 2000% for insider profits.

Topological anomaly scores predict return curves in S&P 500 stocks

problem Detecting anomalies in financial time series
method BallMapper, decoder-conditional VAE, Function-on-Function regression
result Anomaly history carries predictive content for return curves

Currently, pension providers are running into trouble mainly due to the ultra-low interest rates and the guarantees associated to some pension benefits. With the aim of reducing the pension volatility and providing adequate pension levels with no guarantees, we carry out mathematical analysis of a new pension design in…

2019-12-26abs ↗pdf ↗

Study compares deep learning stock trading strategies in adverse market conditions.

problem Comparing deep learning models for stock trading performance in extreme market downturns.
method Reconstructed three deep learning models and compared their strategies through trading simulations.
result Deep learning models, especially LSTM, can mitigate losses in severe market downturns.

Variable annuities (VA) are popular insurance products. VAs provides the insured with a guaranteed accumulation rate on their premium at maturity. In addition, the insured may receive extra benefit if returns of underlying funds are high enough. Here we consider a special case of VA with high-water mark feature and Gua…

2011-08-22abs ↗pdf ↗

A declining CVaR glidepath framework for TDF design with Chilean pension system application

problem Designing Target-Date Funds around an explicit return objective while controlling risk
method Propose a framework for designing TDFs with a declining CVaR constraint
result Key feature: conservative evaluation of each glidepath

CryptoRLPM uses on-chain data to improve crypto portfolio management performance.

problem Lack of effective use of on-chain data in RL-based crypto portfolio management.
method Developed CryptoRLPM, an RL-based system that incorporates on-chain data for crypto PM, consisting of five units.
result CryptoRLPM outperforms baselines in ARR, DRR, and SR, especially for Bitcoin.

New data accumulation prevents model collapse in generative models.

problem Model collapse in generative models trained on their own outputs.
method Empirical study of language models, diffusion models, and variational autoencoders; analytically tractable framework for linear models.
result Accumulating synthetic data alongside real data avoids model collapse, preventing performance degradation.

MSPM uses modular agents to manage financial portfolios efficiently.

problem Scalability and reusability issues in RL-based financial portfolio management.
method Modular design with Evolving Agent Module (EAM) and Strategic Agent Module (SAM).
result MSPM improves profit accumulation by at least 186.5% compared to CRP.

This research combines DRL with BL model for better portfolio optimization.

problem Lack of dynamic correlation knowledge in DRL for optimal portfolio optimization.
method Hybrid model combining DRL and Black-Litterman model.
result DRL agent significantly outperforms other strategies in terms of return and risk.

In this paper, finite type domains with hyperbolic orbit accumulation points are studied. We prove, in case of C2\mathbb{C}^2, it has to be a (global) pseudoconvex domain, after an assumption of boundary regularity. Moreover, one of the applications will realize the classification of domains within this class, precisel…

2013-04-30abs ↗pdf ↗

DSPO optimizes portfolio construction from raw stock data efficiently.

problem Manual design and misalignment in traditional portfolio construction methods.
method End-to-end neural network framework with Monotonical Logistic Regression loss.
result DSPO constructs optimal sorted portfolios with high performance metrics.

Using an artificial neural network (ANN), a fixed universe of approximately 1500 equities from the Value Line index are rank-ordered by their predicted price changes over the next quarter. Inputs to the network consist only of the ten prior quarterly percentage changes in price and in earnings for each equity (by quart…

2008-06-16abs ↗pdf ↗

The paper improves QD policy ensembles using distribution ratio estimators.

problem Training diverse and high-quality reinforcement learning agents.
method Using Stein variational gradient descent and distribution ratio estimators.
result The method generates diverse and high-quality reinforcement learning agents.

ReCAP adapts to dynamic financial markets by segmenting and combining policy vectors.

problem Inefficient traditional PM approaches in non-stationary financial markets.
method Integrates continual learning into PM, segmenting regimes and adapting policies.
result Consistently outperforms baselines in real-world financial datasets.

Study shows trust and trustworthiness emerge through reinforcement learning.

problem Trust and trustworthiness are universal but not predicted by traditional economic models.
method Used Q-learning algorithm to simulate trust and trustworthiness dynamics in a trust game.
result High levels of trust and trustworthiness emerge when individuals consider both past and future experiences.

This paper presents a model of capital accumulation for a large number of heterogenous producer-consumers in an exchange space in which interactions depend on agents' positions. Each agent is described by his production, consumption, stock of capital, as well as the position he occupies in this abstract space. Each age…

2019-09-09abs ↗pdf ↗

Model analyzes debt recycling strategies under various fiscal regimes and jurisdictions.

problem Understanding debt recycling dynamics and their impact on repayment times and equity growth.
method Developed a calibrated model incorporating mortgage interest rates, borrowing costs, and tax shields.
result Introducing positive interest rates without tax shields contracts success regions and lengthens repayment times, but tax shields partially reverse these effects.

Investment decisions can benefit from incorporating an accumulated knowledge of the past to drive future decision making. We introduce Continual Learning Augmentation (CLA) which is based on an explicit memory structure and a feed forward neural network (FFNN) base model and used to drive long term financial investment…

2018-12-06abs ↗pdf ↗

WrapNet optimizes inference for low-resolution neural networks by using 8-bit additions.

problem Reducing multiplication complexity in low-resolution neural networks.
method Adapting neural networks to use low-resolution (8-bit) additions in accumulators, with a cyclic activation layer and overflow penalty regularizer.
result Achieves comparable classification accuracy to 32-bit counterparts using low-resolution additions.

Improves likelihood-free inference by using a new sampling approach to avoid biased data collection.

problem Efficient Bayesian inference without likelihood evaluation for real-world datasets.
method Introduces Neural Proposal (NP) to sample simulation inputs i.i.d. for unbiased posterior inference.
result Demonstrates improved performance, especially for multi-modal posteriors, through experiments.

Study reveals patterns in trader clusters over time, improving investment predictions.

problem Managing diverse trader risk in financial services.
method Clustered trader data analyzed using Ewens' Sampling Distribution and Aggregating Algorithm (AA). Statistically Validated Networks (SVN) applied for improved results.
result Temporal distributions of trader clusters follow Ewens' Sampling Distribution, and AA can be improved with SVN.

For any nonorientable closed surface, we determine the minimal dilatation among pseudo-Anosov mapping classes arising from Penner's construction. We deduce that the sequence of minimal Penner dilatations has exactly two accumulation points, in contrast to the case of orientable surfaces where there is only one accumula…

2018-07-24abs ↗pdf ↗

The study improves Monte Carlo simulations for long-term investments using advanced financial models.

problem Improving the accuracy of long-term investment simulations.
method Developed a multivariate process incorporating recent financial models and probabilistic forecasts.
result Increased accuracy in predicting portfolio values over decades.