Capital Structure, ETF Flows, and Performance

Abstract

The aim of the dissertation is to study the impact of distinct financial variables such as ETF flows and capital structure on the performance of ETFs and firms. We investigate the heterogeneous effects of decomposed ETF flows, demand-driven, arbitrage-driven, and unexpected on abnormal returns across six ETF categories. Using a sample of 424 U.S. equity ETFs from 2000 to 2023 we run panel regressions, quantile models, and two-stage least squares (2SLS) estimations. The findings of this paper are in line with return-chasing behavior and crowding dynamics, demand flows are significantly associated with underperformance in index and Smart Beta ETFs, while active ETFs show less consistent effects. These effects persist across lag structures and are most pronounced in higher-performing quantiles. Under high volatility conditions, arbitrage flows improve alpha in most ETF classes. Unexpected flows generally lack predictive power, underscoring their idiosyncratic nature but their impact is more pronounced in sector active ETFs. Our findings challenge the one-size-fits-all approach of ETF flow analysis, suggesting the importance of ETF classification when evaluating flow-performance relationships

We also study the relationship between capital structure and firm performance in the U.S. information technology (IT) sector during 2010–2022. Using panel data from 32 publicly listed IT firms (401 firm-year observations), we apply a comprehensive econometric framework including pooled OLS, fixed and random effects, quantile regression, 2SLS, and Pooled Mean Group ARDL. Return on equity and market capitalization are the two main performance measures in this paper, while capital structure is captured through total liabilities to total assets (debt ratio), cost of debt, and cost of capital. Results show a consistently negative impact of the cost of debt on both accounting- and market-based performance. At high leverage levels, debt ratio has a nonlinear and distribution-sensitive effect and is positive in long-run models. The capital structure outcomes confirm heterogeneity across firm types by using threshold and quantile regressions. Robustness checks validate the findings. Both papers highlight the distinct link between the impact of financial variables such as ETF flows and capital structure to the variation in performance level at both fund and firm level.