SEM217: Michael Ohlrogge (New York University), Mike Klausner (Stanford University), and Emily Ruan (Stanford University): A Sober Look at SPACs
Tuesday, March 2 @ 11:00 - 12:30 PM (ONLINE)
ABSTRACT: Special Purpose Acquisition Companies (“SPACs”)— touted as a better alternative to an IPO for taking a company public—have become the next big thing in the securities markets. This paper analyzes the structure of SPACs and the costs embedded in that structure.
Tuesday, March 9 @ 11:00 - 12:30 PM (ONLINE)
ABSTRACT: We will discuss how to quantify the benefit from selling a muni at a loss, and how to determine the optimal time to sell. Calculating the benefit from tax-loss harvesting is more complex for munis than for stocks. Muni prices below par are depressed by the tax cost payable by the marginal buyer. But the ‘hold’ value to the seller depends on the price at which the bond was acquired.
SEM217: Roger Ibbotson (Yale), Thomas Idzorek (Morningstar), and Paul Kaplan (Morningstar): The Popularity Asset Pricing Model
Tuesday, March 16 @ 11:00 - 12:30 PM (ONLINE)
ABSTRACT: In “Disagreement, Tastes, and Asset Prices,” Fama and French argue that the assumptions of standard asset pricing models, such as the Capital Asset Pricing Model (CAPM), are unrealistic and that both ‘disagreement’ and ‘tastes’ affect asset pricing. The Popularity Asset Pricing Model (PAPM) is a generalized equilibrium model that builds on the familiar CAPM but relaxes these two unrealistic assumptions, not only subsuming the CAPM but a range of newer ESG asset pricing models.
Tuesday, April 6 @ 11:00 - 12:30 PM (ONLINE)
ABSTRACT: Using yearly KLD-MSCI data from January 1992 to December 2019, we explore the alpha and beta generated by long and long/short positions on the "good" and "bad" companies with respect to the CAPM model, the Fama-French three- and five-factor models, and the Carhart four-factor model.
SEM217: Steven Thorley, Brigham Young University: Risk Management and the Optimal Combination of Equity Market Factors
Tuesday, February 2 @ 11:00 - 12:30 PM (ONLINE)
ABSTRACT: Managing the intertemporal risk of optimally constructed multifactor portfolios adds to performance. The increases in Sharpe ratios are in addition to the utility that investors gain from controlling how much active risk they are exposed to over time. We derive a simple closed-form formula for security weights in optimal multifactor portfolios with an active-risk target. We test the risk control of five well-known factors—value, momentum, small size, low beta, and profitability—and the optimal multifactor portfolio.
SEM217: David Ritzwoller, Stanford University: Uncertainty in the Hot Hand Fallacy: Detecting Streaky Alternatives to Random Bernoulli Sequences
Tuesday, February 9 @ 11:00 - 12:30 PM (ONLINE)
ABSTRACT: We study a class of permutation tests of the randomness of a collection of Bernoulli sequences and their application to analyses of the human tendency to perceive streaks of consecutive successes as overly representative of positive dependence - the hot hand fallacy. In particular, we study permutation tests of the null hypothesis of randomness (i.e., that trials are i.i.d.) based on test statistics that compare the proportion of successes that directly follow k consecutive successes with either the overall proportion of successes or the proportion of successes that directly follow k consecutive failures.
SEM217: Petter Kolm, New York University: Greedy online classification of persistent market states using realized intraday volatility features
Tuesday, February 16 @ 11:00 - 12:30 PM (ONLINE)
ABSTRACT: In many financial applications it is important to classify time series data without any latency while maintaining persistence in the identified states. We propose a greedy online classifier that contemporaneously determines which hidden state a new observation belongs to without the need to parse historical observations and without compromising persistence.
Tuesday, February 23 @ 11:00 - 12:30 PM (ONLINE)
ABSTRACT: Investors seeking low cost, automated, and tax aware solutions are increasingly turning to model portfolios that use exchange-traded funds (ETFs) as building blocks to achieve specific investment objectives. There is no regulatory requirement to track ETF model portfolio assets, flows, and performance. We use data science to identify models based on ownership data looking for clusters of individuals whose proportional portfolio holdings are virtually identical.