Aesthetic Investments

How meaningful is the term Environmental, Social, and Corporate Governance (ESG) as a descriptor of firm valuation and investment performance? In his November 2021 paper entitled “ESG: Hyperboles and Reality”, George Serafeim assesses beliefs about ESG, including those involving firm valuation and ESG firm/fund investment performance. Drawing on more than a decade of research, he concludes that: Keep Reading

Does past performance of Environmental, Social, and Corporate Governance (ESG) stocks derive mostly from shift in demand from other stocks to ESG stocks? In his September 2021 paper entitled “Flow-Driven ESG Returns”, Philippe van der Beck examines whether flow of investor dollars toward ESG mutual funds explains aggregate performance of ESG stocks, as follows:

• Construct an ESG portfolio that aggregates quarterly holdings of U.S. equity mutual funds that assert sustainability mandates.
• Measure perceived sustainability of each stock by calculating the deviation of its ESG portfolio weight from its market portfolio weight.
• Estimate the price pressure due to a flow of dollars into ESG mutual funds.
• Combine perceived stock sustainability and price pressure to explore sensitivity of past ESG portfolio returns to level of dollar flow into ESG mutual funds.

Using mutual fund descriptions (with respect to importance of sustainability in investment decisions) and quarterly Form 13F mutual fund holdings data during 2000 through 2020, and underlying stock prices through the first quarter of 2021, he finds that:

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What returns should investors expect from Non-fungible Tokens (NFT) as an alternative asset class? In the October 2021 revision of their paper entitled “Alternative Investments in the Fintech Era: The Risk and Return of Non-fungible Token (NFT)”, De-Rong Kong and Tse-Chun Lin investigate returns of NFTs, which represent ownership of digital assets via blockchains. NFT markets let owners or collectors deal directly any time via crypto-assets such as Ethereum (ETH). Anyone can review historical bids, offers, sale prices, trading dates and changes of ownership for NFTs, facilitating analysis at the transaction level. They apply this data to construct an NFT price index using a regression model that accounts for both NFT characteristics and underlying network activity. They focus on CryptoPunks crypto-images (tokens) on the ETH blockchain as the earliest and largest collection of NFTs. Using data for 13,712 transactions involving 5,630 unique CryptoPunks tokens as recorded by Larva Labs during June 2017 through May 2021, they find that:

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Is it possible to isolate environmental, social and governance characteristics (ESG) effects on stock returns from those of other stock characteristics? In their July 2021 paper entitled “Chasing The ESG Factor”, Abraham Lioui and Andrea Tarelli specify a cross-sectional long-short ESG factor that neutralizes exposures to other firm characteristics, such as size and book-to-market ratio. By creating a pure ESG factor, they are able to isolate ESG alpha and estimate its separate E, S and G contributions. Their approach also suppresses effects of arbitrary ESG rating scales. They further construct an ESG sentiment index based on media attention to ESG-related topics and employ it to understand variations in pure ESG alpha. Using monthly firm ESG ratings from three sources as available during 1991-2019 and associated stock characteristics and returns during December 1992 through December 2020, with tests spanning December 2002 through December 2020, they find that: Keep Reading

How do investors react to different kinds of firm-level environmental, social and governance (ESG) news? In their April 2021 paper entitled “Which Corporate ESG News does the Market React to?”, George Serafeim and Aaron Yoon examine stock returns around ESG news from analysts, media, advocacy groups and government regulators (but not firms themselves) as compiled/evaluated by TruValue Labs. Evaluation includes degree to which each news item is positive or negative. They segment the news sample based on materiality classifications from the Sustainability Accounting Standards Board (SASB). Using daily market-adjusted and industry-adjusted stock returns associated with 111,020 firm-day ESG news items spanning 3,126 companies during January 2010 through June 2018, they find that: Keep Reading

Is outperformance of green (environmentally friendly) stocks relative to brown (not environmentally friendly) stocks due to firm performance or concern about the climate? In other words, do green stocks carry a climate concern premium? In their June 2021 paper entitled “Dissecting Green Returns”, Lubos Pastor, Robert Stambaugh and Lucian Taylor examine relative performance of green and brown stocks in the context of unexpectedly strong increases in environmental concerns (climate concern jumps). Specifically, they:

• Construct a green factor as the return on a portfolio that is each month long (short) green (brown) stocks weighted by greenness based on environment-focused elements of MSCI ESG Ratings.
• Devise and test a 2-factor (market and green) model of stock returns.
• Compute a monthly measure of public climate concern based on an associated media index, with focus on series jumps.

Using stock return and factor data during November 2012 (based on availability of ESG ratings) through December 2020 and climate concern data during November 2012 through June 2018, they find that: Keep Reading

Are non-fungible tokens (NFT) the future of music and art valuation? In his April 2021 early/incomplete draft entitled “Virtual Art and Non-fungible Tokens”, Lawrence Trautman describes the new market for digital art, explores the evolution of the digital world and virtual property, explores NFTs and offers a few thoughts about the future of digital property. Based on a survey of relevant technology commentaries and law, he concludes that: Keep Reading

What returns and risk should investors expect from private property (real estate, privately owned infrastructure, collectibles and non-corporate business equity), characterized by infrequent trading, inexact market values and noisy returns? In their March 2021 paper entitled “Real and Private-Value Assets”, William Goetzmann, Christophe Spaenjers and Stijn Van Nieuwerburgh survey current research on private property returns and risks. They provide a rough value of U.S. private property and summarize research findings from 11 papers, focusing on: measurement of risk, return and liquidity; and, (2) drivers of variation in valuations and investment behavior. Based on relevant government and association data and recent/current papers, they find that: Keep Reading

Are collectible (mint state, brilliant uncirculated or proof) U.S. coins attractive to investors as an alternative asset class? In their October 2019 paper entitled “U.S. Coins Market: Historical Performance and Anomalies”, Khaled Obaid, Kuntara Pukthuanthong and David Maslar measure historical returns within the multi-billion dollar collectible U. S. coins market and determine what investors should require and avoid when selecting coins. They also examine whether collectible coins are effective diversifiers of conventional asset classes and are useful as an inflation hedge. They construct their sample manually from bid and ask prices of U.S. penny, nickel, dime, quarter, half dollar and dollar coins as listed in the first CDN Publishing Greysheet of each calendar year. They assume that the average of bid and ask for a coin is a sale price. Using price data for 2,063 coins during 1967 through 2015, they find that:

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Given the uniqueness of fine art objects and uncertainties in demand (at auctions), can investors in paintings get accurate estimates of market values of holdings and potential acquisitions? In their March 2019 paper entitled “Machines and Masterpieces: Predicting Prices in the Art Auction Market”, Mathieu Aubry, Roman Kräussl, Gustavo Manso and Christophe Spaenjers compares accuracies of value estimates for paintings based on: (1) a linear hedonic regression (factor model), (2) neural network software and (3) auction houses. For the first two, they employ 985,188 auctions of paintings during 2008–2014 for in-sample training and 104,404 auctions of paintings during the first half of 2015 for out-of-sample testing. Neural network software inputs include information about artists and paintings (year of creation, materials, size, title and markings), and images of the paintings. Using information about artists/paintings and images and auction house estimates and sales prices for the specified 1,089,592 paintings by about 125,000 artists offered through 372 auction houses during January 2008 through June 2015, they find that:

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