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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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6491,2971,9462,594 · Jun 202019922001200920172026
48 results for internal rate of return

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 study reveals the hierarchical structure of the international FOREX market using currency fluctuation distribution similarities.

problem Understanding the hierarchical structure of the international FOREX market.
method Using Jensen-Shannon divergence to quantify the similarity between normalized logarithmic return distributions of currencies.
result Clusters of currencies are consistent with the nature of underlying economies but diverge during crises.

EXFormer predicts foreign exchange returns with high accuracy using a multi-scale self-attention mechanism and dynamic variable selection.

problem Accurately forecasting daily exchange rate returns in international finance.
method EXFormer uses a multi-scale trend-aware self-attention mechanism with dynamic variable selection and embedded squeeze-and-excitation blocks.
result EXFormer outperforms other models in forecasting daily exchange rate returns, achieving statistically significant improvements in directional accuracy.

Clarifies interest rate cap rules for loans with unconventional cash flows.

problem Ambiguity in applying interest rate caps to loans with non-conventional internal rate of return (IRR).
method Clarified conventional IRR definition, axiomatized, and extended to all loans.
result Unique extension of interest rate cap rule for all loans, based on net present value test.

This paper uses SampEn to measure and predict oil price volatility.

problem Measuring and predicting volatility in international oil prices.
method Sample Entropy (SampEn) compared with standard deviation; machine learning algorithms used.
result SampEn effectively predicts traditional volatility measures, especially during financial crises.

This paper examines pricing and hedging strategies for cross-currency equity protection swaps.

problem Dynamic requirements from EPS buyers in cross-currency equity protection swaps.
method Detailed analysis of two hedging paradigms, including separate and aggregated returns, with consideration of different types of returns.
result Proposes various hedging strategies with practical implications for EPS providers and investors.

Defines an implied CO2-price to cover climate change costs, finding it significantly higher than the SCC.

problem The social cost of carbon (SCC) does not fully cover climate change costs.
method Defines an implied CO2-price as a 'polluter pays principle' and calculates its value using a DICE model.
result The cost-implied CO2 price is around 500/tCO2,comparedto50/tCO2, compared to 50/tCO2 for SCC.

AMSAs adaptively manage crypto-currency trading by selecting multiple strategies based on market conditions.

problem Maximizing gains in volatile crypto-currency markets with high uncertainty.
method AMSAs use multiple sub-agents with different strategies, dynamically selecting them based on market conditions.
result AMSAs can achieve high positive alpha in long-term crypto-currency trading.

In their activity, the traders approximate the rate of return by integer multiples of a minimal one. Therefore, it can be regarded as a quantized variable. On the other hand, there is the impossibility of observing the rate of return and its instantaneous forward time derivative, even if we consider it as a continuous …

2012-11-08abs ↗pdf ↗

A model explains why 4% is a safe retirement withdrawal rate.

problem Determining a safe withdrawal rate for American retirees.
method Discrete-time model of stochastic returns on assets and their moments.
result The 4% rule emerges from adjusting high expected rates of return for various risks.

In this paper, we use replica analysis to investigate the influence of correlation among the return rates of assets on the solution of the portfolio optimization problem. We consider the behavior of the optimal solution for the case where the return rate is described with a single-factor model and compare the findings …

2017-04-05abs ↗pdf ↗

In this paper we give definitions of matrix rates of return which do not depend on the choice of basis describing baskets. We give their economic interpretation. The matrix rate of return describes baskets of arbitrary type and extends portfolio analysis to the complex variable domain. This allows us for simultaneous a…

2006-07-19abs ↗pdf ↗

This paper tackles post-trade allocation inefficiencies and presents a uniform return allocation method.

problem Return divergence among accounts after trade allocation.
method Systematic treatment of trade allocation risk, presenting a uniform return allocation method.
result Uniform allocation of returns irrespective of the number of accounts and trade sizes.

The Basel II Accords have sparked increased interest in the development of approaches based on internal ratings systems and have initiated the elaboration of models for remote ratings forecasts based on external ones as part of Risk Management and Early Warning Systems. This article evaluates the peculiarities of curre…

2016-07-05abs ↗pdf ↗

The paper limits the profitability of technical trading rules and finds they are not better than random trading.

problem The profitability of technical trading rules in stock markets is controversial.
method Proves the upper bound of cumulative return and investigates the profitability of technical trading rules using bootstrap methodology.
result Technical trading rules are not better than random trading and less profitable than the market.

Study examines grain futures connectedness during Russia-Ukraine conflict.

problem Quantile return connectedness of grain futures markets during geopolitical instability.
method Dynamic quantile VAR combined with frequency-domain decomposition.
result Heterogeneous spillovers across quantiles, with strong transmitters and persistent receivers.

This study improves tail risk forecasting by integrating overnight information into semi-parametric models.

problem Improving tail risk forecasting in financial markets.
method Proposes RES-CAViaR-oc models combining overnight return and realized volatility, using Bayesian estimation.
result Realized volatility and overnight return significantly improve tail risk forecasting.

Currency volatility shocks predict lower excess returns, and buying weak transmitters outperforms selling strong ones.

problem Predicting currency returns using volatility shocks.
method Constructed a dynamic, directed network of volatility connections using option-implied volatilities.
result Currencies that transmit more volatility shocks earn lower excess returns.

MILLION framework optimizes portfolio risk and return efficiently.

problem Optimizing risk and return in AI for FinTech portfolio management.
method Two phases: return maximization with auxiliary objectives and risk control with portfolio interpolation and improvement.
result Framework achieves fine-grained risk control and improved return rates.

The paper explores how market trade values and volumes affect price and return statistics.

problem Understanding the statistical properties of market trade, price, and return.
method Introduces secondary averaging procedure to describe statistical moments of market trades, price, and return.
result Predictions of market-based probabilities of price and return are limited by Gaussian distributions.

In this paper we investigate the scaling behavior of the average daily exchange rate returns of the Indian Rupee against four foreign currencies namely US Dollar, Euro, Great Britain Pound and Japanese Yen. Average daily exchange rate return of the Indian Rupee against US Dollar is found to exhibit a persistent scaling…

2005-04-06abs ↗pdf ↗

The paper introduces a new method for forecasting financial risk using quantile-based modeling.

problem Forecasting Value-at-Risk (VaR) and Expected Shortfall (ES) for financial returns.
method Semiparametric approach using restricted quantile regression to model the conditional scale of financial returns.
result The method provides robust, distribution-free estimates of extreme losses and captures risk dynamics.

Study resolves the Korean LVRP puzzle by showing HVRP exists but is masked by investor heterogeneity and improper intensity normalization.

problem Puzzling Low Volume Return Premium (LVRP) in Korea, contradicting global High Volume Return Premium (HVRP) evidence.
method Used Korean market data (2020-2024) to demonstrate HVRP exists but is masked by investor heterogeneity and improper intensity normalization. Normalized institutional buying intensity by market capitalization rather than trading value.
result Demonstrated a perfect monotonic relationship between highest-conviction institutional buying and positive cumulative abnormal returns, while lowest-intensity trades yield modest returns.

New algorithms for hierarchical classification using conformal prediction.

problem Valid prediction sets in hierarchical classification tasks.
method Extended split conformal prediction framework with two inference algorithms.
result Empirical evaluations show effectiveness in achieving nominal coverage.

Study uses VIX for zero-coupon Treasury rates, proving long-term stability and returns.

problem Modeling zero-coupon Treasury rates with VIX for volatility.
method Multivariate autoregressive stochastic volatility model, proving stability and Law of Large Numbers.
result VIX accurately models zero-coupon Treasury rates and returns.

Motivated by the need for effectively summarising, modelling, and forecasting the distributional characteristics of intra-daily returns, as well as the recent work on forecasting histogram-valued time-series in the area of symbolic data analysis, we develop a time-series model for forecasting quantile-function-valued (…

2017-07-09abs ↗pdf ↗

Study uses ML to analyze how interest rates affect fund returns, finding gradient boosting is effective.

problem Understanding how interest rate changes impact fund returns.
method Combines Machine Learning and causal inference, using Double Machine Learning framework.
result Gradient boosting is useful for predicting fund returns, showing a significant negative effect of interest rate increases.