nep-fmk New Economics Papers
on Financial Markets
Issue of 2022‒05‒16
seven papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Valuing Financial Data By Maryam Farboodi; Dhruv Singal; Laura Veldkamp; Venky Venkateswaran
  2. A Stock Return Decomposition Using Observables By Benjamin Knox; Annette Vissing-Jorgensen
  3. Idiosyncratic Equity Risk Two Decades Later By John Y. Campbell; Martin Lettau; Burton G. Malkiel; Yexiao Xu
  4. Outliers and Momentum in the Corporate Bond Market By Galvani, Valentina; Li, Lifang
  5. Household Portfolios and Retirement Saving over the Life Cycle By Jonathan A. Parker; Antoinette Schoar; Allison T. Cole; Duncan Simester
  6. Robust Portfolio Design and Stock Price Prediction Using an Optimized LSTM Model By Jaydip Sen; Saikat Mondal; Gourab Nath
  7. The expected returns of ESG excluded stocks. The case of exclusions from Norway's Oil Fund By Berle, Erika; He, Wanwei; Odegaard, Bernt Arne

  1. By: Maryam Farboodi; Dhruv Singal; Laura Veldkamp; Venky Venkateswaran
    Abstract: How should an investor value financial data? The answer is complicated because it depends on the characteristics of all investors. We develop a sufficient statistics approach that uses equilibrium asset return moments to summarize all relevant information about others' characteristics. It can value data that is public or private, about one or many assets, relevant for dividends or for sentiment. While different data types have different valuations, heterogeneous investors value the same data very differently, which suggests a low price elasticity for data demand. Heterogeneous investors' data valuations are also affected very differentially by market illiquidity.
    JEL: G0 G11 G12 G14
    Date: 2022–03
  2. By: Benjamin Knox; Annette Vissing-Jorgensen
    Abstract: We propose a method to decompose stock returns period by period. First, we argue that one can directly estimate expected stock returns from securities available in modern financial markets (using the real yield curve and the Martin (2017) equity risk premium). Second, we derive a return decomposition which is based on stock price elasticities with respect to expected returns and expected dividends. We calculate elasticities from dividend futures. Our decomposition is an alternative to the Campbell-Shiller log-linearization which relies on an assumption about the log-linearization constant. An application to the COVID crisis in 2020 reveals that risk premium changes drove much of the crash and rebound in the SP500 while a fall in long-term real yields drove a strong positive return for 2020 as a whole.
    Keywords: Asset pricing; Duration; Return decomposition; Stock Market
    JEL: G10 G12 G14
    Date: 2022–03–23
  3. By: John Y. Campbell; Martin Lettau; Burton G. Malkiel; Yexiao Xu
    Abstract: This paper reviews the literature on idiosyncratic equity volatility since the publication of “Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk” in 2001. We respond to replication studies by Chiah, Gharghori, and Zhong and by Leippold and Svaton, and we present volatility estimates through the end of 2021, significantly extending the period covered in our original paper as well as the two replication studies. After spiking in the 1999- 2000 period, idiosyncratic volatility declined thereafter; but sharp increases in market, industry, and idiosyncratic volatility occurred during the global financial crisis of 2008-2009 and the COVID-19 pandemic of 2020-2021. We argue that market microstructure effects are not of first-order importance for volatility measurement, and we discuss the roles of fundamental factors and investor sentiment in driving the observed fluctuations in volatility.
    JEL: G10 G12
    Date: 2022–04
  4. By: Galvani, Valentina (University of Alberta, Department of Economics); Li, Lifang (Xi’an Jiaotong University)
    Abstract: How we filter outliers matters in empirical research. As a demonstration, we analyze how momentum returns respond to different outlier treatments in the corporate bond market TRACE database. We find that momentum profitability depends crucially on return outliers. Specifically, outlier trimming vanishes momentum returns, whereas winsorization yields a robust but conservative assessment of the momentum effect. Price filters show that momentum is generated by low-priced bonds and volume filters reveal that momentum profits during the 2007-2009 crisis were due to the activities of small investors. Lastly, finer partitions of the bond cross-section are shown to deliver superior momentum gains without sacrificing portfolio diversification over bonds and issuers.
    Keywords: momentum; outliers; winsorization; corporate bonds; TRACE
    JEL: G01 G10
    Date: 2022–03–24
  5. By: Jonathan A. Parker; Antoinette Schoar; Allison T. Cole; Duncan Simester
    Abstract: This paper documents the share of investable wealth that middle-class U.S. investors hold in the stock market over their working lives. This share rises modestly early in life and falls significantly as people approach retirement. Prior to 2000, the average investor held less of their investable wealth in the stock market and did not adjust this share over their working life. These changes in portfolio allocation were accelerated by the Pension Protection Act (PPA) of 2006, which allowed employers to adopt target date funds (TDFs) as default options in retirement saving plans. Young retail investors who start at an employer shortly after it adopts TDFs have higher equity shares than those who start at that same employer shortly before the change in defaults. Older investors rebalance more to safe assets. We also study retirement contribution rates over the life-cycle and find that average retirement saving rates increase steadily over the working life. In contrast to what we find for investment in the stock market, contribution rates have been stable over time and across cohorts and were not increased by the PPA.
    JEL: D14 E21 G11 G23 G28 G51
    Date: 2022–03
  6. By: Jaydip Sen; Saikat Mondal; Gourab Nath
    Abstract: Accurate prediction of future prices of stocks is a difficult task to perform. Even more challenging is to design an optimized portfolio with weights allocated to the stocks in a way that optimizes its return and the risk. This paper presents a systematic approach towards building two types of portfolios, optimum risk, and eigen, for four critical economic sectors of India. The prices of the stocks are extracted from the web from Jan 1, 2016, to Dec 31, 2020. Sector-wise portfolios are built based on their ten most significant stocks. An LSTM model is also designed for predicting future stock prices. Six months after the construction of the portfolios, i.e., on Jul 1, 2021, the actual returns and the LSTM-predicted returns for the portfolios are computed. A comparison of the predicted and the actual returns indicate a high accuracy level of the LSTM model.
    Date: 2022–03
  7. By: Berle, Erika (University of Stavanger); He, Wanwei (University of Stavanger); Odegaard, Bernt Arne (University of Stavanger)
    Abstract: What are the consequences of widespread ESG-based portfolio exclusions on the expected returns of firms subject to exclusion? We consider two possible theoretical explanations. 1) Short-term price pressure around the exclusions leading to correction of mispricing going forward. 2) Long term changes in required returns. We use the exclusions of Norwegian Government Pension Fund Global (GPFG -`The Oil Fund') to investigate. GPFG is the world's largest SWF, and its ESG decisions are used as a model for many institutional investors. We construct various portfolios representing the GPFG exclusions. We find that these portfolios have significant superior performance (alpha) relative to a Fama-French five factor model. The sheer magnitude of these excess returns (5% in annual terms) leads us to conclude that short-term price pressure can not be the only explanation for our results, the excluded firms expected returns must be higher in the longer term.
    Keywords: ESG investing; Exclusion; Oil Fund
    JEL: G10 G20
    Date: 2022–04–27

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