Ali Jaffri

We develop a structurally grounded measure of financial uncertainty that originates in the price discovery process itself rather than in realized transaction returns. Modeling bid and ask quotes as distinct stochastic processes evolving under side-specific stochastic business clocks, we show that pre-trade uncertainty has a dual character: it arises from both the magnitude of potential price fluctuations (the diffusion component) and the intensity of information arrival (the stochastic clock). We construct a Subordinated Uncertainty Index (SUI) that integrates diffusion risk, stochastic clock dynamics, and structural dependence into a unified, economically interpretable measure. The index is estimated from high-frequency S&P 500 limit-order-book data using a hybrid GMM-empirical characteristic function procedure applied across financial crisis episodes, monetary policy announcements, and geopolitical event windows. Three substantive findings emerge. First, spikes in the SUI are frequently driven by changes in the stochastic clock rather than by conventional volatility, indicating that the timing channel of uncertainty is empirically important and is missed by standard realized or implied volatility measures. Second, the bid-ask decomposition reveals systematic asymmetries between demand and supply-side uncertainty that vary predictably with event type: during the Bear Stearns episode, supply-side uncertainty dominates, consistent with an abrupt withdrawal of liquidity providers, whereas the Lehman episode exhibits broadly symmetric responses. Third, a formal first-order decomposition of the bid-ask uncertainty spread shows that even when volatility is similar across sides, differences in stochastic clock precision and drift-clock coupling can generate substantial divergence in perceived uncertainty. These results establish the SUI as a structurally motivated, pre-trade measure of uncertainty that captures dimensions of market risk not subsumed by existing volatility-based proxies.

This paper examines how social media operates as an investment-information channel and whether platform-specific usage is associated with speculative retail trading. Using nationally representative household data from the 2024 U.S. National Financial Capability Study, we link investor-reported use of individual social media platforms for investment information to self-reported participation in meme stocks. We estimate platform-by-platform logit models and report average marginal effects to measure the economic impact of social media-based information acquisition. The results reveal a pronounced platform gradient in speculative participation. Engagement with community-driven, discussion-oriented platforms-most notably Reddit, Discord, and Telegram-is consistently associated with a higher probability of memestock purchasing. In contrast, broadcast-style or professionally oriented platforms such as YouTube and LinkedIn exhibit economically small and statistically insignificant effects, while several mainstream platforms show weaker and imprecise associations. Reliance on financial influencers does not predict speculative participation once platform exposure is accounted for. These findings highlight the central role of peer interaction and collective narratives, rather than one-to-many persuasion, in driving speculative retail trading and inform debates on investor protection and platform design.

This paper provides the first micro-level evidence from a nationally representative U.S. household survey on individuals' reliance on artificial intelligence for financial advice. Using weighted binomial logit models, we examine how digital readiness, risk preferences, investment sophistication, and behavioral frictions shape AI advice adoption. Mobile banking usage and risk tolerance significantly increase reliance on AI advice, while ownership of securities and cryptocurrencies reduces it. Behavioral factors-financial anxiety, time burden, and perceived financial constraint-emerge as the strongest and most robust predictors. The results indicate that AI financial advice primarily functions as a behavioral and cognitive support technology rather than a substitute for financial expertise, with important implications for financial inclusion and consumer protection.

This study examines the investment landscape of Pakistan as an emerging and frontier market, focusing on implications for international investors, particularly those in the United States, through exchange-traded funds (ETFs) with exposure to Pakistan. The analysis encompasses 30 ETFs with varying degrees of exposure to Pakistan, covering the period from 1 January 2016 to February 2024. This research highlights the potential benefits and risks associated with investing in these ETFs, emphasizing the importance of thorough risk assessments and portfolio performance comparisons. By providing descriptive statistics and performance metrics based on historical optimization, this paper aims to equip investors with the necessary insights to make informed decisions when optimizing their portfolios with Pakistan-exposed ETFs. The second part of the paper introduces and assesses dynamic optimization methodologies. This section is designed to explore the adaptability and performance metrics of dynamic optimization techniques in comparison with conventional historical optimization methods. By integrating dynamic optimization into the investigation, this research aims to offer insights into the efficacy of these contrasting methodologies in the context of Pakistan-exposed ETFs. The findings underscore the significance of Pakistan’s market dynamics within the broader context of emerging markets, offering a pathway for diversification and potential growth in investment strategies.