Research Interests
Empirical Asset Pricing, Behavioral Finance, Climate Finance, Investments
Finance Research
Job Market Paper
Rethinking Measures of Sentiment: A New Approach (Working Paper) [PDF] [SSRN]
solo-authored
Abstract: I argue that valid measures of investor sentiment must satisfy additional conditions beyond conventional return predictability tests to imply sentiment-induced misvaluation. Specifically, both positive and negative sentiments should explain returns and volatility contemporaneously, and forecast returns. I show that several well-known sentiment indexes fail to fully meet these necessary conditions and introduce three new empirical indexes that perform better. These proposed measures demonstrate superior predictive power for returns both in-sample and out-of-sample, particularly over longer horizons; and survive the inclusion of non-sentiment variables known to predict returns. Further evidence shows that their robust forecasting ability extends to broader financial outcomes, including flows into actively managed equity mutual funds, implied market volatility (VIX), and aggregate credit spreads.
The Role of Sentiment in Fundamentals-Driven Returns (Working Paper) [PDF] [SSRN]
w/ Seth Pruitt and Christos Makridis
Abstract: Using high-frequency sentiment data from 1998–2022 at both the firm and aggregate levels, we show how investor sentiment affects market reactions to fundamental news. Abnormal returns following earnings surprises are higher when pre-announcement sentiment is low and lower when sentiment is high, indicating that earnings announcements can serve as a channel for correcting prior misvaluations. This effect is stronger for firms that are harder to value, such as growth, non-dividend-paying, and high-beta firms, as well as when analyst forecasts are noisier. Firm-level and aggregate sentiment have distinct effects and can compound one another. A simple Bayesian updating framework explains these empirical patterns.
Dividend Habitants and the Closed-End Fund Puzzle (Working Paper) [PDF] [SSRN]
solo-authored
Abstract: I hypothesize that income-oriented investors, who prioritize stable, high income from their portfolios, extrapolate past dividend distribution patterns to assess the reliability of future payouts. Consistent with this view, I find that closed-end funds with a history of dividend cuts, high variability in distributions, and infrequent payments trade at deeper discounts to NAV than those with a record of dividend increases and more stable, frequent distributions. This finding offers a novel explanation for the long-standing closed-end fund puzzle. Importantly, dividend yields explain more of the cross-sectional variation in discount rates once past distribution patterns are accounted for, suggesting that investors do not consider yields in isolation.
Dynamic Validation of Climate Change Risk (Working Paper) [PDF]
Abstract: There is little agreement about which, if any, climate change risk measures effectively capture the potential consequences of climate change. We address this issue by showing that identifying priced climate change risk critically depends upon estimating the dynamic amplification of climate change effects specifically related to transition risk. We find statistically significant, dynamically augmented risk premia associated with firms’ carbon emissions, green patent production, and aggregate climate change awareness exposure beginning in 2009. We incorporate the dynamic variation from these three distinct risk channels to optimally construct a new climate change risk measure.
Pricing the Closed-End Funds with Instrumented PCA (Work in Progress)
solo-authored
Abstract: I apply the Instrumented Principal Component Analysis (IPCA) approach of Kelly et al. (2019) to estimate an asset pricing model that explains the temporal and cross-sectional variations in closed-end fund (CEF) mispricings, defined as the difference between market and NAV returns. I find that the IPCA framework, which accommodates dynamic loadings on latent factors, is particularly well-suited for modeling the highly dynamic nature of these returns. I show that individual CEF characteristics—including past performance, dividend yield and frequency, market liquidity, and prior dividend distribution patterns—shape the dynamic loadings on latent sentiment factors. The findings are robust to the inclusion of observable factors and hold across different fund types, extending prior work that has focused primarily on equity CEFs.
Civil Engineering Research
Publications
Li, S., Kazemi, H., Rockaway, T. D. (2021). Statistical modelling of hydrological performance in a suite of green infrastructure practices. Water Science and Technology, 84(12), 3663-3675. https://doi.org/10.2166/wst.2021.447
Li, S., Kazemi, H., Rockaway, T.D. (2019). Performance Assessment of Stormwater GI Practices Using Artificial Neural Networks, Science of The Total Environment 651, 2811-2819. https://doi.org/10.1016/j.scitotenv.2018.10.155
Ebrahimi, M., Kazemi, H., Mirbagheri, S. A., Rockaway, T. D. (2019). Quality Appraisal of Groundwater in Arid Regions Using Probabilistic and Deterministic Approaches, Environmental Engineering Geoscience 25 (4), 331-344. https://doi.org/10.2113/EEG-2240
Abdollahian, S., Kazemi, H., Rockaway, T.D., Gullapalli, V. (2018) Stormwater Quality Benefits of Permeable Pavement Systems with Deep Aggregate Layers, Environments 5 (68). https://doi.org/10.3390/environments5060068
Ebrahimi, M.,Kazemi, H., Mirbagheri, S. A., Rockaway, T. D. (2017). Integrated Approach to Treatment of High-Strength Organic Wastewater by Using Anaerobic Rotating Biological Contactor. Journal of Environmental Engineering, 144(2), 04017102. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001312
Kazemi, H., Rockaway, T.D., and Rivard, J.A., Abdollahian, S., (2017) Assessment of Surface Infiltration Performance and Maintenance of Two Permeable Pavement Systems in Louisville, Kentucky, Journal of Sustainable Water in Built Environment, 3(4). https://doi.org/10.1061/JSWBAY.0000830
Ebrahimi, M., Kazemi, H., Mirbagheri, S.A., Rockaway, T.D., (2016) An optimized biological approach for treatment of petroleum refinery wastewater, Journal of Environmental Chemical Engineering, 4 (3). https://doi.org/10.1016/j.jece.2016.06.030
Ebrahimi, M., Kazemi, H., Ehteshami, M., Rockaway, T.D., (2016) Assessment of groundwater quantity and quality and saltwater intrusion in the Damghan basin, Iran, Chemie der Erde - Geochemistry, 76 (2). https://doi.org/10.1016/j.chemer.2016.04.003
Kazemi, H., Abdollahian, S., Rockaway, T.D., Rivard, J., (2015) The Water Quality and Quantity Performance of a Permeable Pavement System in Louisville, KY, Proceedings of the WEFTEC Congress. https://doi.org/10.2175/193864715819555472
Kazemi, H., (2014) Evaluating the effectiveness and hydrological performance of green infrastructure stormwater control measures. Department of Civil and Environmental Engineering. Louisville, Kentucky, University of Louisville. Ph.D. Dissertation. https://doi.org/10.18297/etd/1744
Kazemi, H., Rockaway, T.D., and Rivard, J.A., (2013) Monitoring Hydrological Performance of Green Infrastructure Stormwater Controls, Proceedings of ASCE-EWRI World Env. and Water Resources Congress, ASCE. https://doi.org/10.1061/9780784412947.022
Externally-Funded Projects
LG&E-KU Restoration and Prediction Study
Developed a machine learning (ML) model to enhance power outage response for LG&E-KU by predicting more accurate estimated repair times (ERT). The implemented random forest model outperformed the utility’s internal heuristic, demonstrating the operational value of ML for infrastructure resilience and customer satisfaction in utility service delivery.
Performance Assessment of Louisville MSD’s Integrated Overflow Abatement Plan
Analyzed stormwater infrastructure data across multiple sites; participated in developing a rainfall tool and custom evaluation methods to support sewer overflow mitigation and regulatory planning.
Green Infrastructure Evaluation
Assessed hydrologic and maintenance performance of Green Infrastructure (GI) Stormwater Measures (permeable pavements and treeboxes); supported field sensor installations and contributed to data-driven GI evaluations.
Research Support on Monitoring and QA/QC of Green Infrastructure Performance
Assisted the U.S. EPA’s ORD (Edison, NJ) in evaluating the performance of GI Stormwater Measures through data analysis, laboratory tests, and Quality Assurance Project Plans development.