主题：Fully Closed: Individual
Responses to Paper Versus Realized Capital Gains and Losses（完全闭合：理论和实际的资本收益及损失的个人反应）
主讲人：Michaela Pagel，哥伦比亚大学商德州扑克大小
助理教授
日期：2018年3月14日（周三）
时间：上午10:00-11:30
地点：德州扑克大小
金融德州扑克大小
4号楼101教室
语言：英文
摘要：
We use
transaction-level data for portfolio holdings and trades as well as account
balances, income, and spending of a large sample of retail investors to explore
how individuals respond to paper versus realized capital gains and losses. To
identify the effects of realized gains and losses, we exploit plausibly
exogenous mutual fund liquidations. Specifically, we estimate the marginal
propensity to reinvest out of one dollar received from a forced sale event when
the investor either achieved a capital gain or a loss relative to his or her initial
investment. Theoretically, if individuals held optimized portfolios, the marginal
propensity to reinvest out of forced liquidations should be 100% independent of
realizing a gain or a loss. Individuals should just reinvest all of their
liquidity immediately into a fund with similar characteristics. Empirically,
individuals keep a share of their newly found liquidity in cash, save it,
consume it, or reinvest it into different funds, stocks, or bonds. Moreover,
individuals reinvest 89% if the forced sale resulted in a capital gain, but
only 46% in the event of a loss. Such differential treatment of gains and
losses is inconsistent with active rebalancing or tax considerations but consistent
with mental accounting and the idea that individuals treat realized losses
differently from paper losses.
主讲人简介:
Michaela
Pagel is an Assistant Professor at Columbia Business School. She received her
Ph.D. from the Economics Department at UC Berkeley and works on topics in
behavioral economics, household finance, and macroeconomics. Her dissertation
focused on the consumption and investment implications of non-standard
preferences. More specifically, she theoretically studied how decision-making
is affected by people's beliefs about their consumption. Her current work
analyzes transaction-level data on income, spending, balances, credit limits,
and logins stemming from a financial aggregation app. Furthermore, she is
working with bank account data linked to individual investors' security trades
and portfolios.
