Ali Choubdaran

Ali Choubdaran

PhD in Finance

London School of Economics

PhD candidate in Finance (LSE) focused on quantitative finance and financial economics. I study how information and incentives shape prices and corporate decisions—drawing on market microstructure, asset pricing, causal inference, and applied ML/NLP. I build end-to-end pipelines that turn scattered, unstructured sources—from corporate filings and news to venue seating charts—into structured, decision-ready datasets and tools.

I'm pursuing roles in quantitative research, data science, and economist/ML teams. I bring experience designing robust empirical studies, shipping reproducible analyses, and communicating results clearly to both technical and non-technical audiences.

Interests

  • Quantitative Finance
  • Financial Economics
  • Market Microstructure
  • AI/ML for Finance

Education

  • PhD in Finance, 2021–25
    London School of Economics (LSE)
  • MRes in Finance, 2019–21
    London School of Economics (LSE)
  • MSc in Economics, 2016–19
    Sharif University of Technology
  • BSc in Electrical Engineering, 2011–16
    Sharif University of Technology

CV

You can download my CV here.

Research

Job Market Paper

  • Disagreement Resolution Horizon and Open Market Repurchase Program Completion

    This paper examines the puzzling heterogeneity in completion rates of open market repurchase programs, where some announcing firms execute zero repurchases while some complete their programs rapidly. I propose the disagreement resolution horizon hypothesis (DRHH), which argues that completion rates reflect managers’ expectations about when their disagreement with the market will naturally resolve. Using hand-collected data from SEC filings (2004-2022), I document three key findings. First, low-completion firms significantly outperform analyst expectations in years one and two post-announcement, while high-completion firms excel in years three and four. Second, this pattern is reflected in market reactions, with significant positive returns around earnings announcements occurring in corresponding periods. Third, while all announcing firms earn significant long-run abnormal returns, the timing of return realization systematically varies with completion rates. These results suggest that managers strategically balance duration-dependent costs of undervaluation against immediate costs of share repurchases, with their completion decisions signaling the expected timeline of information asymmetry resolution. The findings extend traditional signaling theories by highlighting how the temporal dimension of information asymmetry influences corporate payout policy.

Other Working Papers

  • Open Market Repurchase Programs and Systematic Liquidity

    This study examines how open market repurchase (OMR) programs affect firms’ exposure to systematic liquidity shocks and liquidity risk. Consistent with the view of repurchasing firms as buyers of last resort, I find: (1) firms experience a significant decline in liquidity commonality during OMR programs; (2) this decline is temporary, with liquidity commonality reverting to pre-program levels once repurchases end; (3) during these programs, firms stabilize against both variation in the demand for liquidity by institutional investors and variation in the supply of liquidity by market makers; and (4) the temporary reduction in liquidity commonality is accompanied by a temporary reduction in firms’ liquidity risk. Together, these results highlight a less emphasized aspect of OMR programs: the role of firms’ trading activity in shaping their liquidity dynamics and risk exposures.

  • Mispricing, Mutual Fund Flows, and Corporate Buybacks

    Using price pressure induced by mutual fund flows, I show that firms significantly adjust their repurchase activity in response to undervaluation. Repurchase behavior is captured both by the likelihood of announcing open market repurchase (OMR) programs and the quarterly amount repurchased. Leveraging the 2003 mutual fund trading scandal as a natural experiment, I provide causal evidence that flow-induced valuation shocks drive repurchase decisions, with instrumental variable estimates revealing stronger effects than standard regressions imply. Further analysis of long-run stock performance reveals that the well-documented buyback anomaly is primarily driven by repurchase announcements following periods of negative fund flows. These findings point to limits to arbitrage as a key explanation for the slow correction of undervaluation in repurchasing firms and demonstrate how fund flows influence both corporate repurchase decisions and the market’s price response to those announcements.

Teaching

I’ve taught various courses at LSE, consistently earning teaching evaluations well above the course, department, and LSE averages.

  • FM360 Options, Futures & Other Derivatives (Evaluation: 4.8/5) Summer 2025
  • FM202 Analysis and Management of Financial Risk (Evaluation: 4.8/5) Summer 2024
  • FM202 Analysis and Management of Financial Risk (Evaluation: 4.6/5) Summer 2023
  • FM215 Principles of Finance II (Evaluation: 4.9/5) 2024–2025
  • FM214 Principles of Finance I (Evaluation: 4.8/5) 2023–2024
  • FM213 Principles of Finance (Evaluation: 4.7/5) 2022–2023

Projects

Quantitative finance meets machine learning

LLM-Driven Market Reaction Prediction

Using financial markets as a natural labeling machine to train reasoning-based LLMs.

Built a full-stack pipeline that scrapes and structures corporate press releases from thousands of U.S.-listed company websites.

Used the market's own reaction — abnormal returns following each release — as a free, high-quality labeling signal, eliminating the need for manual annotation.

Queried an 8B-parameter LLM to predict market reaction (positive, negative, neutral) and provide an accompanying reasoning chain.

Selected reasoning chains that matched actual market outcomes and fine-tuned the model on those tokens to improve predictive accuracy.

Demonstrates a scalable ML paradigm: turning market behavior into labeled data for continuous model improvement.

  • NLP
  • LLM Fine-Tuning
  • Self-Supervision
  • Data Engineering
  • Signal Extraction
  • Finance
  • Python
  • Scraping

Time-Varying Pricing Kernels

Recovering real probability distributions from option data to solve the monotonicity puzzle and improve asset allocation.

Extracts the risk-neutral distribution of market returns from S&P 500 option prices.

Models and estimates a time-varying pricing kernel as a function of real moments, allowing its shape to evolve with market conditions.

Shows that the monotonicity puzzle arises from assuming a static kernel; introducing time variation restores theoretical consistency.

Uses a fixed-point + MLE estimation procedure to recover the pricing kernel and the implied real probabilities.

Develops a dynamic S&P 500 vs Treasury timing strategy based on the inferred distributions. Backtests show a Sharpe ratio about 1.5× the market benchmark.

  • Asset Pricing
  • Options
  • Pricing Kernel
  • Risk-Neutral Distribution
  • MLE
  • Time-Varying Models
  • Trading Strategy
  • Quant Research

Repurchase Data Pipeline for SEC Filings

Linking program announcements and execution to build the most comprehensive buyback dataset available.

Built a multi-threaded pipeline to parse both text and tables in 10-Q (Part II Item 2) and 10-K (Part II Item 5) filings.

Extracts monthly repurchase figures (e.g., number of shares repurchased) as well as program details (e.g., program size).

Linked monthly repurchases to their corresponding programs, enabling precise tracking of program completion over time — a capability not possible before.

Processed 300 k+ filings across 4 k+ firms and 20 years, producing the most comprehensive buyback dataset to date.

Provides the empirical foundation for multiple research papers on buybacks, liquidity, and corporate trading behavior.

  • Data Engineering
  • Information Extraction
  • Text Parsing
  • SEC Filings
  • Finance
  • Event Linking
  • Repurchases
  • Large-Scale Processing

Contact