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Results for “earnings yield” · papers 18 · wiki 3
Academic Papers · 18arXiv q-fin live 4 · desk corpus 117
arXiv · arXiv q-fin · 2015

Optimal strategies of investment in a linear stochastic model of market

We study the continuous time portfolio optimization model on the market where the mean returns of individual securities or asset categories are linearly dependent on underlying economic factors. We introduce the functional $Q_γ$ featuring the expected earnings yield of portfolio minus a penalty term proportional with a coefficient $γ$ to the variance when we keep the value of the factor levels fixed. The coefficient

O. S. Rozanova, G. S. Kambarbaeva
arXiv · arXiv q-fin · 2026

Optimal Control of the Ethena Yield-Bearing Stablecoin

We formulate and solve stochastic control problems that model the core yield-generating strategy of the Ethena protocol, a decentralized finance (DeFi) stablecoin that earns yield by combining a long position in staked Ethereum (stETH) with an equal-sized short position in ETH perpetual futures. The combined position is delta-neutral with respect to the ETH spot price, yet earns carry from two sources: staking reward

Matthew Lorig
arXiv · arXiv q-fin · 2019

A Stock Selection Method Based on Earning Yield Forecast Using Sequence Prediction Models

Long-term investors, different from short-term traders, focus on examining the underlying forces that affect the well-being of a company. They rely on fundamental analysis which attempts to measure the intrinsic value an equity. Quantitative investment researchers have identified some value factors to determine the cost of investment for a stock and compare different stocks. This paper proposes using sequence predict

Jessie Sun
arXiv · arXiv · 2026

Corporate Bond Yield Curve Modeling: A Rating-Based Regime-Switching Generalized CIR Approach

Persistent shifts in term-structure dynamics undermine the stability of single-regime models in long samples. We develop an arbitrage-free regime-switching generalized CIR (RS-GCIR) model that jointly prices the Chinese government bond (CGB) curve and corporate bond curves. To capture the systematic transmission from interest-rate conditions to credit spreads, we structure the model into two blocks and price corporat

Maochun Xu, Yunqi Liang, Yi Hong
arXiv · arXiv q-fin · 2023

The inverse Cox-Ingersoll-Ross process for parsimonious financial price modeling

We propose a formulation to construct new classes of financial price processes based on the insight that the key variable driving prices $P$ is the earning-over-price ratio $γ\simeq 1/P$, which we refer to as the earning yield and is analogous to the yield-to-maturity of an equivalent perpetual bond. This modeling strategy is illustrated with the choice for real-time $γ$ in the form of the Cox-Ingersoll-Ross (CIR) pr

Li Lin, Didier Sornette
OpenAlex · The Journal of Finance · 2004 · cites 390

Price Discovery in the U.S. Treasury Market: The Impact of Orderflow and Liquidity on the Yield Curve

ABSTRACT We examine the role of price discovery in the U.S. Treasury market through the empirical relationship between orderflow, liquidity, and the yield curve. We find that orderflow imbalances (excess buying or selling pressure) account for up to 26% of the day‐to‐day variation in yields on days without major macroeconomic announcements. The effect of orderflow on yields is permanent and strongest when liquidity i

Michael W. Brandt, Kenneth A. Kavajecz
arXiv · arXiv · 2025

Supervised Similarity for High-Yield Corporate Bonds with Quantum Cognition Machine Learning

We investigate the application of quantum cognition machine learning (QCML), a novel paradigm for both supervised and unsupervised learning tasks rooted in the mathematical formalism of quantum theory, to distance metric learning in corporate bond markets. Compared to equities, corporate bonds are relatively illiquid and both trade and quote data in these securities are relatively sparse. Thus, a measure of distance/

Joshua Rosaler, Luca Candelori, Vahagn Kirakosyan, Kharen Musaelian, Ryan Samson
arXiv · arXiv · 2010

GDP Trend Deviations and the Yield Spread: the Case of Five E.U. Countries

Several studies have established the predictive power of the yield curve in terms of real economic activity. In this paper we use data for a variety of E.U. countries: both EMU (Germany, France, Italy) and non-EMU members (Sweden and the U.K.). The data used range from 1991:Q1 to 2009:Q1. For each country, we extract the long run trend and the cyclical component of real economic activity, while the corresponding inte

Periklis Gogas, Ioannis Pragidis
arXiv · arXiv · 2024

Credit Spreads' Term Structure: Stochastic Modeling with CIR++ Intensity

This paper introduces a novel stochastic model for credit spreads. The stochastic approach leverages the diffusion of default intensities via a CIR++ model and is formulated within a risk-neutral probability space. Our research primarily addresses two gaps in the literature. The first is the lack of credit spread models founded on a stochastic basis that enables continuous modeling, as many existing models rely on fa

Mohamed Ben Alaya, Ahmed Kebaier, Djibril Sarr
arXiv · arXiv · 2026

Liquidity-Based Audit of Algorithmic Trading Strategies

We show that net demand for liquidity by algo strategies is identifiable from its trade and price history alone, with no knowledge of its signal or optimization problem. An exact multi-period regret decomposition implies that the sign of this statistic classifies a linear strategy as a net liquidity consumer or provider, recovering the Kyle (1985) informed-trader/market-maker dichotomy from observables alone. Under a

Irene Aldridge
arXiv · arXiv · 2026

Data-Driven Duration Management -- Term Structure Forecasting Using Machine Learning

This paper compares different methods for forecasting the term structure of U.S. and European zero-coupon government bonds using both traditional econometric and Machine Learning (ML) approaches. We compare classical models (e.g., Dynamic Nelson-Siegel (DNS) and Principal Component Analysis (PCA)) with different Neural Network (NN) architectures, including those inspired by the classical models, on the U.S. Treasury

Tobias Lausser, Joao Eduardo Vuolo, Rudi Zagst
arXiv · arXiv · 2026

Replication-Consistent Liquidity Forecasting for Derivatives -- Forward Funding Sensitivities and a Liquidity Valuation Adjustment for Settlement Lags

We study cash-flow forecasting for derivatives used in liquidity management and clarify its relation to risk-neutral valuation and replication. While it is well known that expectations under different measures (e.g., $\mathbb{P}$ vs. $\mathbb{Q}$) can yield different undiscounted cash-flows, further inconsistencies arise when payment times are stochastic. We show that using discounting sensitivities (funding-curve he

Christian P. Fries
arXiv · arXiv · 2026

A unified theory of order flow, market impact, and volatility

We propose a microstructural model for the order flow in financial markets that distinguishes between {\it core orders} and {\it reaction flow}, both modeled as Hawkes processes. This model has a natural scaling limit that reconciles a number of salient empirical properties: persistent signed order flow, rough trading volume and volatility, and power-law market impact. In our framework, all these quantities are pinne

Johannes Muhle-Karbe, Youssef Ouazzani Chahdi, Mathieu Rosenbaum, Grégoire Szymanski
arXiv · arXiv · 2025

Interpretable Hypothesis-Driven Trading:A Rigorous Walk-Forward Validation Framework for Market Microstructure Signals

We develop a rigorous walk-forward validation framework for algorithmic trading designed to mitigate overfitting and lookahead bias. Our methodology combines interpretable hypothesis-driven signal generation with reinforcement learning and strict out-of-sample testing. The framework enforces strict information set discipline, employs rolling window validation across 34 independent test periods, maintains complete int

Gagan Deep, Akash Deep, William Lamptey
arXiv · arXiv · 2025

PEARL: Private Equity Accessibility Reimagined with Liquidity

In this work, we introduce PEARL (Private Equity Accessibility Reimagined with Liquidity), an AI-powered framework designed to replicate and decode private equity funds using liquid, cost-effective assets. Relying on previous research methods such as Erik Stafford's single stock selection (Stafford) and Thomson Reuters - Refinitiv's sector approach (TR), our approach incorporates an additional asymmetry to capture th

E. Benhamou, JJ. Ohana, B. Guez, E. Setrouk, T. Jacquot
arXiv · arXiv · 2025

FR-LUX: Friction-Aware, Regime-Conditioned Policy Optimization for Implementable Portfolio Management

Transaction costs and regime shifts are major reasons why paper portfolios fail in live trading. We introduce FR-LUX (Friction-aware, Regime-conditioned Learning under eXecution costs), a reinforcement learning framework that learns after-cost trading policies and remains robust across volatility-liquidity regimes. FR-LUX integrates three ingredients: (i) a microstructure-consistent execution model combining proporti

Jian'an Zhang
arXiv · arXiv · 2025

Do Mutual Funds Make Active and Skilled Liquidity Choices in Portfolio Management? Evidence from India

This study examines active liquidity management by Indian open-ended equity mutual funds. We find that fund managers respond to inflows by increasing cash holdings, which are later used to purchase less-liquid stocks at favourable valuations. Funds with less liquid portfolios tend to maintain larger cash reserves to manage flows. Funds that make active liquidity choices yield statistically and economically significan

Pankaj K Agarwal, H K Pradhan, Konark Saxena
arXiv · arXiv · 2025

Myopic Optimality: why reinforcement learning portfolio management strategies lose money

Myopic optimization (MO) outperforms reinforcement learning (RL) in portfolio management: RL yields lower or negative returns, higher variance, larger costs, heavier CVaR, lower profitability, and greater model risk. We model execution/liquidation frictions with mark-to-market accounting. Using Malliavin calculus (Clark-Ocone/BEL), we derive policy gradients and risk shadow price, unifying HJB and KKT. This gives dua

Yuming Ma
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