Investor Sophistication and Capital Income Inequality

Kacperczyk, Marcin; Nosal, Jaromir; Stevens, Luminita

doi:10.2139/ssrn.2648849

Cited by 27 publications

(33 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We develop a model whose purpose is to understand how the growth in big data technologies in finance affects firm size and gauge the size of that effect. The model builds on the information choice model in Kacperczyk et al (2016) and Kacperczyk et al (2015).…”

Section: Modelmentioning

confidence: 99%

Big data in finance and the growth of large firms

Begenau

Farboodi

Veldkamp

2018

Journal of Monetary Economics

222

View full text Add to dashboard Cite

This paper was prepared for the Carnegie-Rochester-NYU conference. We thank the conference committee for their support of this work. We also thank Nic Kozeniauskas for his valuable assistance with the data and Adam Lee for his outstanding research assistance. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. At least one co-author has disclosed a financial relationship of potential relevance for this research. Further information is available online at http://www.nber.org/papers/w24550.ack NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

show abstract

Section: Modelmentioning

confidence: 99%

Big data in finance and the growth of large firms

Begenau

Farboodi

Veldkamp

2018

Journal of Monetary Economics

222

View full text Add to dashboard Cite

show abstract

“…A growing literature in macroeconomics and finance explores how agents use information to form beliefs, with tools such as rational inattention (e.g., Maćkowiak and Wiederholt (2009), Matejka and McKay (2015), Kacperczyk, Nosal, and Stevens (2015)), inattentiveness (Reis, 2006), sentiments (Angeletos and La'O, 2013) or information diffusion (Amador and Weill, 2010). Our mechanism is not inconsistent with any of these frictions, all of which have Bayesian updating as a foundation.…”

mentioning

confidence: 99%

Understanding Uncertainty Shocks and the Role of Black Swans

Orlik

Veldkamp

2015

SSRN Journal

View full text Add to dashboard Cite

A fruitful emerging literature reveals that shocks to uncertainty can explain asset returns, business cycles and financial crises. The literature equates uncertainty shocks with changes in the variance of an innovation whose distribution is common knowledge. But how do such shocks arise? This paper argues that people do not know the true distribution of macroeconomic outcomes. Like Bayesian econometricians, they estimate a distribution. Using real-time GDP data, we measure uncertainty as the conditional standard deviation of GDP growth, which captures uncertainty about the distributions estimated parameters. When the forecasting model admits only normally-distributed outcomes, we find small, acyclical changes in uncertainty. But when agents can also estimate parameters that regulate skewness, uncertainty fluctuations become large and counter-cyclical. The reason is that small changes in estimated skewness whip around probabilities of unobserved tail events (black swans). The resulting forecasts resemble those of professional forecasters. Our uncertainty estimates reveal that revisions in parameter estimates, especially those that affect the risk of a black swan, explain most of the shocks to uncertainty. Anna Orlik Federal Reserve Board 20th Street and Constitution Avenue N.W.

show abstract

“… For example, from restricted access to the stock market as in Guvenen (). In the literature, differences in financial sophistication, access to information, or scale effects have been offered as alternative explanations for the existing differences in returns to wealth across individual investors (see Arrow (), Peress (), Kacperczyk, Nosal, and Stevens (), Jappelli and Padula (), Lusardi, Michaud, and Mitchell (), and Deuflhard, Georgarakos, and Inderst ()). …”

mentioning

confidence: 99%

Heterogeneity and Persistence in Returns to Wealth

Fagereng

Guiso

Malacrino

et al. 2020

ECTA

281

104

View full text Add to dashboard Cite

We provide a systematic analysis of the properties of individual returns to wealth using 12 years of population data from Norway's administrative tax records. We document a number of novel results. First, individuals earn markedly different average returns on their net worth (a standard deviation of 22.1%) and on its components. Second, heterogeneity in returns does not arise merely from differences in the allocation of wealth between safe and risky assets: returns are heterogeneous even within narrow asset classes. Third, returns are positively correlated with wealth: moving from the 10th to the 90th percentile of the net worth distribution increases the return by 18 percentage points (and 10 percentage points if looking at net-of-tax returns). Fourth, individual wealth returns exhibit substantial persistence over time. We argue that while this persistence partly arises from stable differences in risk exposure and assets scale, it also reflects heterogeneity in sophistication and financial information, as well as entrepreneurial talent. Finally, wealth returns are correlated across generations. We discuss the implications of these findings for several strands of the wealth inequality debate.

show abstract

Investor Sophistication and Capital Income Inequality

Cited by 27 publications

References 48 publications

Big data in finance and the growth of large firms

Big data in finance and the growth of large firms

Understanding Uncertainty Shocks and the Role of Black Swans

Heterogeneity and Persistence in Returns to Wealth

Contact Info

Product

Resources

About