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.
A declining CVaR glidepath framework for TDF design with Chilean pension system application
This paper studies a continuous-time market where an agent, having specified an investment horizon and a targeted terminal mean return, seeks to minimize the variance of the return. The optimal portfolio of such a problem is called mean-variance efficient à la Markowitz. It is shown that, when the market coefficients a…
Raising statistical hurdles may not be justified due to data bias.
Optimizes renewable energy mix to meet carbon-free targets at lowest cost.
In this paper, we propose a novel investment strategy for portfolio optimization problems. The proposed strategy maximizes the expected portfolio value bounded within a targeted range, composed of a conservative lower target representing a need for capital protection and a desired upper target representing an investmen…
We determine the optimal strategy for investing in a Black-Scholes market in order to maximize the probability that wealth at death meets a bequest goal , a type of goal-seeking problem, as pioneered by Dubins and Savage (1965, 1976). The individual consumes at a constant rate , so the level of wealth required fo…
Wireless systems perform rate adaptation to transmit at highest possible instantaneous rates. Rate adaptation has been increasingly granular over generations of wireless systems. The base-station uses SINR and packet decode feedback called acknowledgement/no acknowledgement (ACK/NACK) to perform rate adaptation. SINR i…
The paper analyzes bank decisions in a three-step model, focusing on equity and debt raising.
The paper evaluates the probability distributions of analog-to-target distances for multiple analogs.
This paper applies quantum probability theory to model asset returns, avoiding assumptions about quantum effects.
We propose and analyze an alternate approach to off-policy multi-step temporal difference learning, in which off-policy returns are corrected with the current Q-function in terms of rewards, rather than with the target policy in terms of transition probabilities. We prove that such approximate corrections are sufficien…
A theorem divides hyperplanes evenly with a line through the origin.
Investor aims to meet financial goals with deadlines and target amounts, considering stock trading costs.
Leveraged ETFs can outperform their targets in certain market conditions, contrary to the volatility drag hypothesis.
Algorithm approximates target distribution using weight queries.
The paper explores how market-based returns depend on past trade values.
Study finds 'happiness' search data predicts stock returns, suggesting utility needs impact firm performance.
This paper tackles post-trade allocation inefficiencies and presents a uniform return allocation method.
We consider returns of two Korean stock market indices, KOSPI and KOSDAQ index. Central parts of the probability distribution function of returns are well fitted by the Lorentzian distribution function. However, tail parts of the probability distribution function follow a power law behavior well. We found that the prob…
New measure corrects news bias in NLP stock return forecasting.
Hybrid approach combines Markowitz's theory with reinforcement learning for optimal portfolio management.
Enhances RL in target domains with limited data using augmented return.
MB-DQN uses different backup lengths for improved reinforcement learning.
Deep neural networks forecast financial return distributions accurately.
Paper examines trade/no trade patterns in illiquid stocks, highlighting effects of varying zero returns probabilities.
The herd behavior of returns is investigated in Korean futures exchange market. It is obtained that the probability distribution of returns for three types of herding parameter scales as a power law with the exponents (KTB203) and 2.9(KTB209) in two kinds of Korean treasury bond. For our case since the…
With the rapid growth in fashion e-commerce and customer-friendly product return policies, the cost to handle returned products has become a significant challenge. E-tailers incur huge losses in terms of reverse logistics costs, liquidation cost due to damaged returns or fraudulent behavior. Accurate prediction of prod…
With the daily and minutely data of the German DAX and Chinese indices, we investigate how the return-volatility correlation originates in financial dynamics. Based on a retarded volatility model, we may eliminate or generate the return-volatility correlation of the time series, while other characteristics, such as the…
Risk hedging can reduce operational costs by adjusting prices and production levels in response to asset price movements.
Proposes a risk parity portfolio optimization method that accounts for uncertainty in asset returns.
Method learns statistics of return distributions via neural networks and maximum mean discrepancy.
We study the Heston model, where the stock price dynamics is governed by a geometrical (multiplicative) Brownian motion with stochastic variance. We solve the corresponding Fokker-Planck equation exactly and, after integrating out the variance, find an analytic formula for the time-dependent probability distribution of…
We consider the tail probabilities of stock returns for a general class of stochastic volatility models. In these models, the stochastic differential equation for volatility is autonomous, time-homogeneous and dependent on only a finite number of dimensional parameters. Three bounds on the high-volatility limits of the…
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 (-…
Market makers face a trade-off between fill probability and post-fill returns, requiring contrarian strategies.
Simple model uses time series momentum to outperform benchmarks in equity and bond markets.
Research examines how foreign direct investment in Vietnam affects stock returns.
Price and return predictions are limited by economic complexity, not just volatility.
We explore how to improve machine translation systems by adding more translation data in situations where we already have substantial resources. The main challenge is how to buck the trend of diminishing returns that is commonly encountered. We present an active learning-style data solicitation algorithm to meet this c…
New algorithms ensure generated objects evolve and fill a distribution, unlike static neural networks.
Method optimizes diffusion model generation to meet user preferences.
Unified framework for portfolio optimization using gain PDF.
We study the rank distribution, the cumulative probability, and the probability density of returns of stock prices of listed firms traded in four stock markets. We find that the rank distribution and the cumulative probability of stock prices traded in are consistent approximately with the Zipf's law or a power law. It…
We present a simple approach to forecasting conditional probability distributions of asset returns. We work with a parsimonious specification of ordered binary choice regression that imposes a connection on sign predictability across different quantiles. The model forecasts the future conditional probability distributi…
Market timing is an investment technique that tries to continuously switch investment into assets forecast to have better returns. What is the likelihood of having a successful market timing strategy? With an emphasis on modeling simplicity, I calculate the feasible set of market timing portfolios using index mutual fu…
Risk-controlled post-processing optimizes decision policies under risk constraints.
Using a rolling windows analysis of filtered and aligned stock index returns from 40 countries during the period 2006-2014, we construct Granger causality networks and investigate the ensuing structure of the relationships by studying network properties and fitting spatial probit models. We provide evidence that stock …
In order to protect brokers from customer defaults in a volatile market, an active margin system is proposed for the transactions of margin lending in China. The probability of negative return under the condition that collaterals are liquidated in a falling market is used to measure the risk associated with margin loan…