Journal of Behavioral and Experimental Finance · 2026 · Vol. 51, 101212

Household Interest in Artificial Intelligence for Financial Advice: Evidence from the United States [Link]

Ayush Jha, Ali Jaffri, and Hassan A. Butt

Examines U.S. households’ interest in AI-based financial advice using nationally representative 2024 NFCS data. Finds that digital readiness, risk preferences, and behavioral frictions are key drivers of AI advice adoption.

Frontiers of Mathematical Finance · 2026 · Vol. 9, 41–66

Option Pricing under Stochastic Volatility and Jumps: A PIDE Framework with Empirical Evidence [Link]

Ayush Jha, Abigail Mensah, Hongwei Mei, Rui Wang, Svetlozar Rachev, and Frank J. Fabozzi

Develops a PIDE framework for option pricing with stochastic volatility and jumps, applied to S&P 500 index options across maturities. Finds that stochastic volatility drives most pricing gains, while jumps provide modest improvements mainly for short-maturity, deep OTM options.

Journal of Financial Data Science · 2026

Reinforcement Learning for Strategic Asset Allocation: Why the Objective Function Dominates the Agent Architecture [Link]

Ayush Jha

Examines what drives reinforcement-learning asset allocation performance across agent design, investment objectives, and rebalancing frequency. Finds that objective choice dominates performance, while frequent rebalancing helps preserve the value of forward-looking signals.

The Journal of Fixed Income · 2026 · Vol. 35(3), 6–17

Option-Implied Probabilities and Bond Valuation [Link]

Ayush Jha, Ali Jaffri, Svetlozar T. Rachev, and Frank J. Fabozzi

Examines risk-neutral asymmetries in short-rate lattices using option-implied probabilities across maturities and market conditions. Finds that imposing symmetric probabilities leads to bond mispricing, biased return forecasts, and less effective hedging, while option-implied probabilities improve valuation and risk management.

The Journal of Fixed Income · 2026 · Vol. 35(4), 7–24

Equity-Imposed Tilts in Affine Term Structure Models: Evidence from Option-Implied Asymmetries [Link]

Ayush Jha, Ali Jaffri, Svetlozar T. Rachev, and Frank J. Fabozzi

Develops a tilted AFNS model that incorporates option-implied asymmetries into yield-curve dynamics. Finds that these asymmetries improve yield-curve forecasts and capture economically meaningful variation in term premiums.

The Journal of Portfolio Management · 2025 · Vol. 52(2), 212

Demystifying FinBERT: How Transformer Models Turn Financial Text into Market Insights [Link]

Ayush Jha, Ali Jaffri, and Hassan A. Butt

Demonstrates how FinBERT transforms financial news into actionable sentiment signals for portfolio and risk analysis. Provides a practical workflow for linking headline sentiment to intraday stock-price movements.

Journal of Risk and Financial Management · 2025 · Vol. 18(1):11

Beyond the Traditional VIX: A Novel Approach to Identifying Uncertainty Shocks in Financial Markets [Link]

Ayush Jha, Abootaleb Shirvani, Svetlozar T. Rachev, and Frank J. Fabozzi

Develops a heavy-tail-aware measure of financial uncertainty using a double-subordinated Normal Inverse Gaussian model of S&P 500 returns. Introduces risk–reward measures that improve the identification of uncertainty shocks in financial markets.

Journal of Risk and Financial Management · 2025 · Vol. 18(3):158

Optimizing Portfolios with Pakistan-Exposed Exchange-Traded Funds: Risk and Performance Insight [Link]

Ali Jaffri, Abootaleb Shirvani, Ayush Jha, Svetlozar T. Rachev, and Frank J. Fabozzi

Examines Pakistan-exposed ETFs from 2016–2024, assessing their risks, performance, and diversification benefits for international investors. Compares historical and dynamic portfolio optimization to evaluate how adaptive strategies perform in an emerging-market setting.

September (2026)

Agentic AI and Real-Time Market Disequilibrium [Link]

Ayush Jha

Coming Soon.

September (2026)

Robust Efficient Frontiers under Fixed-Point Variance Selection Rule [Link]

Ayush Jha, Ali Jaffri, Svetlozar Rachev, and Frank J. Fabozzi

Coming Soon.

September (2026)

Attention Traps [Link]

Ayush Jha

Develops a model of misspecified learning with endogenous information acquisition, in which rationally inattentive agents choose what evidence to process based on their maintained model. Shows that attention constraints can sustain persistent mispricing—including in AI-related valuation—because additional capacity may reduce inattention without resolving model misspecification.

August (2026)

The Horizon Dependence of the Equity Pricing Kernel [Link]

Ayush Jha, Freddie Papazyan, and Svetlozar Rachev

Studies how the equity pricing kernel changes with investment horizon using S&P 500 options and realized returns. Finds that kernel nonmonotonicity declines sharply from one month to one year, challenging standard preference and stochastic-volatility models and motivating horizon-dependent models of risk and learning.

July (2026)

Joint Identification of Beliefs and Preferences from Point and Density Forecasts [Link]

Ayush Jha and Frank Fabozzi

Examines what can be learned from jointly observing point and density forecasts, distinguishing beliefs, preferences, and forecast reporting behavior within a rank-dependent utility framework. Shows that joint forecasts provide additional identification, while some Survey of Professional Forecasters responses are inconsistent with standard preference and admissibility restrictions.

July (2026)

State-Dependent Portfolio Choice under Policy-Regime Switching: Evidence from Environmental, Social, and Governance Momentum Strategies [Link]

Ayush Jha, Abootaleb Shirvani, Ali Jaffri, Svetlozar T. Rachev, and Frank J. Fabozzi

Develops a discrete-time portfolio framework with latent policy regimes in the market price of risk and long-memory FIGARCH volatility, extending dynamic programming to non-Markov volatility states. Applied to Russell 3000 portfolios during U.S. ESG policy transitions, the model finds regime-dependent risk pricing and a sign reversal in the winners–losers spread, implying state-contingent optimal allocation.

June (2026)

How Professional Forecasters Compress Macroeconomic Tail Risks [Link]

Ayush Jha, Ali Jaffri, Hassan A. Butt, and Frank Fabozzi.

Examines how professional forecasters allocate probability across future outcomes using density forecasts from the Survey of Professional Forecasters. Finds systematic tail-risk compression for GDP growth and core inflation, while beliefs for other variables remain close to benchmark predictive distributions.

May (2026)

Distributional Granger Causality: Identification, Sequential Inference, and Adaptive Testing [Link]

Ayush Jha

Develops a framework for distributional Granger causality that captures predictive dependence through conditional means, volatility, tails, asymmetry, and other distributional channels. Introduces an adaptive sequential testing procedure with finite-sample error control and oracle-equivalent asymptotic power, providing a complete and computationally efficient approach to testing distributional predictive dependence.

February (2026)

Social Media as an Investment Information Channel: Evidence from U.S. Households [Link]

Ali Jaffri, Ayush Jha, and Hassan A. Butt

Examines how platform-specific social media use shapes speculative retail trading using nationally representative 2024 U.S. household data. Finds that community-driven platforms such as Reddit, Discord, and Telegram are associated with greater meme-stock participation, while broadcast-oriented platforms and financial influencers show little predictive power.

January (2026)

Forecasting Flash Crashes with Subordinated Lévy Processes [Link]

Ali Jaffri, Abootaleb Shirvani, Ayush Jha, Svetlozar Rachev, and Frank Fabozzi

Develops an intraday framework for forecasting S&P 500 flash crashes using one-minute returns and a double-subordinated Lévy process with stochastic trading intensity and heavy-tailed risk. Shows that model-based tail-risk forecasts, combined with structural-break, downside-risk, and distance-based indicators, can provide economically meaningful warning signals up to 30 minutes before crash onset.

October (2025)

The Ticketmaster and Live Nation Saga: A Robust Merger Analysis [Link]

Noah Liptack, Ali Jaffri, Ayush Jha, and Michael D. Noel

Examines the impact of the 2010 Live Nation–Ticketmaster merger on concert ticket prices using multiple empirical approaches. Finds substantial and persistent price increases, highlighting the need for stronger merger scrutiny and systematic post-merger evaluation.

Financial Innovation · Revise & Resubmit

Behavioral Probability Weighting and Portfolio Optimization under Semi-Heavy Tails [Link]

Ayush Jha, Abootaleb Shirvani, Ali Jaffri, Svetlozar T. Rachev, and Frank J. Fabozzi

Develops a portfolio optimization framework that extracts implied probability weighting functions from optimal portfolios under Gaussian and NIG return distributions. Shows that fat-tailed returns and changing risk-free rates amplify belief distortions, with implications for portfolio risk management under extreme market conditions.

Chapman and Hall / CRC · 2027 · 1st edition

Microstructure, Statistical Fluctuations, and Technical Signals in Rational Asset Pricing [Link]

Jha, A., Jaffri, A., Lindquist, W. B., Rachev, S., Boakye, R., Hettiachchi-Halpe-Kankanamalage, D. C. W., & Mensah, A.