Finance - Research Publications
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ItemMeasuring Financial Wellbeing with Self-Reported and Bank Record Data(WILEY, 2022-06)We develop scales of the financial well‐being of customers of a major Australian bank using self‐reported survey data matched to customer financial records. Using item response theory (IRT) models, we develop: (1) a Reported Financial Wellbeing Scale from information about people’s experiences and perceptions of financial outcomes; and (2) an Observed Financial Wellbeing Scale from financial record measures of customers’ account balances, net spending and payment problems. Each scale reliably differentiates between a wide range of outcomes, and the scale components have similar power to discriminate. We confirm the validity of the scales by estimating predictive models using other measurable characteristics.
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ItemComment on: Price Discovery in High Resolution(OXFORD UNIV PRESS, 2021-01-01)The microstructure literature comprises a rich set of papers that seek to understand pricing dynamics at a granular level, commonly exploring the joint dynamics of bids, asks and last sale prices. Its focus is on identifying innovations in prices and separating permanent price impacts from transient effects. Hasbrouck (1995) provides a tool that has been extensively utilized in the literature to examine these dynamics in many different market contexts over the last two decades.1 However, the evolution of markets over this period, most notably the exponential growth in the volume of data and the increasing importance of trading speed has made the application of Hasbrouck’s (1995) method and other related tools discussed in Hasbrouck (2018) more computationally and econometrically challenging. Hasbrouck (2018) offers a new approach to help overcome these challenges. In this comment, we briefly describe the evolution of markets and detail the challenges that these changes create for microstructure researchers and highlight the solution that Hasbrouck (2018) offers for these problems. We survey the literature that uses linear multivariate time-series models to understand high-frequency markets. We focus on three examples from the literature to discuss how estimation constraints have affected their modelling choices, describe the potential drawbacks of these choices and how Hasbrouck’s (2018) method can alleviate these constraints. We deliberately select papers that cover different asset classes: cash equities, fixed income and equity options. We hope that our discussion will help provide guidance about the costs and benefits of different modelling choices for future researchers confronted with a variety of methods to answer related research questions. We conclude by considering the implications of Hasbrouck’s 2018 paper for the current policy debate on market data costs.
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ItemDoes Financial Market Structure Affect the Cost of Raising Capital?(CAMBRIDGE UNIV PRESS, 2021-08)Abstract We provide evidence on market structure and the cost of raising capital by examining changes in market structure in U.S. equity markets. Only the Order Handling Rules (OHR) of the Nasdaq, the one reform that reduced institutional trading costs, lowered the cost of raising capital. Using a difference-in-differences framework relative to the New York Stock Exchange (NYSE) that exploits the OHR’s staggered implementation, we find that the OHR reduced the underpricing of seasoned equity offerings by 1–2 percentage points compared with a pre-OHR average of 3.6%. The effect is the largest in stocks with the largest reduction in institutional trading costs after the OHR.