nep-fmk New Economics Papers
on Financial Markets
Issue of 2021‒11‒08
nine papers chosen by

  1. Structural Breaks in Interactive Effects Panels and the Stock Market Reaction to COVID-19 By Yiannis Karavias; Paresh Narayan; Joakim Westerlund
  2. Expectations Concordance and Stock Market Volatility: Knightian Uncertainty in the Year of the Pandemic By Roman Frydman; Nicholas Mangee
  3. FinTech Lending By Tobias Berg; Andreas Fuster; Manju Puri
  4. Flow-Driven ESG Returns By Philippe van der Beck
  5. Dividend Momentum and Stock Return Predictability: A Bayesian Approach By Juan Antolín-Díaz; Ivan Petrella; Juan F. Rubio-Ramírez
  6. Corporate Transactions in Hard-to-Value Stocks By Ben-David, Itzhak; Kim, Byungwook; Moussawi, Hala; Roulstone, Darren T.
  7. The anatomy of government bond yields synchronization in the Eurozone By Claudio Barbieri; Mattia Guerini; Mauro Napoletano
  8. Why the CHIPS Are Down: Stock Buybacks and Subsidies in the U.S. Semiconductor Industry By William Lazonick; Matt Hopkins
  9. The Treasury market in spring 2020 and the response of the Federal Reserve By Annette Vissing-Jørgensen

  1. By: Yiannis Karavias (University of Birmingham); Paresh Narayan (Monash University); Joakim Westerlund (Lund University; Deakin University)
    Abstract: Dealing with structural breaks is an important step in most, if not all, empirical economic research. This is particularly true in panel data comprised of many cross-sectional units, such as individuals, firms or countries, which are all affected by major events. The COVID-19 pandemic has affected most sectors of the global economy, and there is by now plenty of evidence to support this. The impact on stock markets is, however, still unclear. The fact that most markets seem to have partly recovered while the pandemic is still ongoing suggests that the relationship between stock returns and COVID-19 has been subject to structural change. It is therefore important to know if a structural break has occurred and, if it has, to infer the date of the break. In the present paper we take this last observation as a source of motivation to develop a new break detection toolbox that is applicable to different sized panels, easy to implement and robust to general forms of unobserved heterogeneity. The toolbox, which is the first of its kind, includes a test for structural change, a break date estimator, and a break date confidence interval. Application to a panel covering 61 countries from January 3 to September 25, 2020, leads to the detection of a structural break that is dated to the first week of April. The effect of COVID-19 is negative before the break and zero thereafter, implying that while markets did react, the reaction was short-lived. A possible explanation for this is the quantitative easing programs announced by central banks all over the world in the second half of March.
    Date: 2021–11
  2. By: Roman Frydman (New York University); Nicholas Mangee (Georgia Southern University)
    Abstract: This study introduces a novel index based on expectations concordance for explaining stock-price volatility when historically unique events cause unforeseeable change and Knightian uncertainty in the process driving outcomes. Expectations concordance measures the degree to which non-repetitive events are associated with directionally similar expectations of future returns. Narrative analytics of daily news reports allow for assessment of bullish versus bearish views in the stock market. Increases in expectations concordance across all KU events leads to reinforcing effects and an increase in stock market volatility. Lower expectations concordance produces a stabilizing effect wherein the offsetting views reduce market volatility. The empirical findings hold for ex post and ex ante measures of volatility and for OLS and GARCH estimates.
    Keywords: expectations concordance, narrative analytics, volatility, Knightian uncertainty
    JEL: D81 D84 G12 G14
    Date: 2021–09–05
  3. By: Tobias Berg (Frankfurt School of Finance & Management); Andreas Fuster (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute; Centre for Economic Policy Research (CEPR)); Manju Puri (Duke University - Fuqua School of Business; NBER)
    Abstract: In this paper, we review the growing literature on FinTech lending – the provision of credit facilitated by technology that improves the customer-lender interaction or lenders’ screening and monitoring of borrowers. FinTech lending has grown rapidly, though in developed economies like the U.S. it still only accounts for a small share of total credit. An increase in convenience and speed appears to have been more central to FinTech lending’s growth than improved screening or monitoring, though there is certainly potential for the latter, as is the case for increased financial inclusion. The COVID 19 pandemic has shown potential vulnerabilities of FinTech lenders, although in certain segments they have displayed rapid growth.
    Keywords: FinTech, lending, COVID-19
    JEL: G21 G23 G51
    Date: 2021–10
  4. By: Philippe van der Beck (Ecole Polytechnique Fédérale de Lausanne and Swiss Finance Institute)
    Abstract: I show that the performance of ESG investments is strongly driven by price-pressure arising from flows towards sustainable funds, causing high realized returns that do not reflect high expected returns. The coefficient linking ESG flows and realized returns is the product of two factors: The deviation of green funds' portfolios from the market portfolio and a flow multiplier matrix that is the inverse of the market's demand elasticity of substitution between stocks. Empirically, withdrawing 1 dollar from the market portfolio and investing it in the representative ESG fund increases the aggregate value of high ESG-taste stocks by 2-2.5 dollars. Under the absence of flow-driven price pressure, the aggregate ESG industry would have strongly underperformed the market from 2016 to 2021. Furthermore, the positive alpha of a long-short ESG taste portfolio becomes significantly negative.
    Keywords: sustainable investing, ESG, price pressure, flows, demand elasticity
    JEL: G11 G12 G23
    Date: 2021–10
  5. By: Juan Antolín-Díaz; Ivan Petrella; Juan F. Rubio-Ramírez
    Abstract: A long tradition in macro-finance studies the joint dynamics of aggregate stock returns and dividends using vector autoregressions (VARs), imposing the cross-equation restrictions implied by the Campbell-Shiller (CS) identity to sharpen inference. We take a Bayesian perspective and develop methods to draw from any posterior distribution of a VAR that encodes a priori skepticism about large amounts of return predictability while imposing the CS restrictions. In doing so, we show how a common empirical practice of omitting dividend growth from the system amounts to imposing the extra restriction that dividend growth is not persistent. We highlight that persistence in dividend growth induces a previously overlooked channel for return predictability, which we label “dividend momentum.” Compared to estimation based on OLS, our restricted informative prior leads to a much more moderate, but still significant, degree of return predictability, with forecasts that are helpful out-of-sample and realistic asset allocation prescriptions with Sharpe ratios that out-perform common benchmarks.
    Date: 2021–11
  6. By: Ben-David, Itzhak (Ohio State University and NBER); Kim, Byungwook (Ohio State University); Moussawi, Hala (Stanford Graduate School of Business); Roulstone, Darren T. (Ohio State University)
    Abstract: Hard-to-value stocks provide opportunities for managers to exploit their informational advantage through trading on their firms' and their own personal accounts. In contrast to the prediction that such transactions reflect private information about future events, they are contrarian and heavily depend on past returns. Corporate transactions in hard-to-value stocks outperform those in easy-to-value stocks in the early part of our sample, but this difference disappears after 2002, coinciding with a general decline in the profitability of stock market anomalies. Our evidence is consistent with managers' perception of mispricing, rather than private information, being a key motivator of their transactions.
    JEL: G12 G14 G23 G32
    Date: 2021–09
  7. By: Claudio Barbieri; Mattia Guerini; Mauro Napoletano (OFCE - Observatoire français des conjonctures économiques - Sciences Po - Sciences Po)
    Abstract: We investigate the synchronization of Eurozone's government bond yields at different maturities. For this purpose, we combine principal component analysis with random matrix theory. We find that synchronization depends upon yields maturity. Short-term yields are not synchronized. Medium- and long-term yields, instead, were highly synchronized early after the introduction of the Euro. Synchronization then decreased significantly during the Great Recession and the European Debt Crisis, to partially recover after 2015. We show the existence of a duality between our empirical results and portfolio theory and we point to divergence trades and flight-to-quality effects as a source of the self-sustained yield asynchronous dynamics. Our results envisage synchronization as a requirement for the smooth transmission of conventional monet ary policy in the Eurozone.
    Keywords: synchronization,bond yields,factor models,random matrix theory,monetary policy
    Date: 2021
  8. By: William Lazonick (Academic-Industry Research Network); Matt Hopkins (Academic-Industry Research Network)
    Abstract: The Semiconductor Industry Association (SIA) is promoting the Creating Helpful Incentives to Produce Semiconductors (CHIPS) for America Act, introduced in Congress in June 2020. An SIA press release describes the bill as `bipartisan legislation that would invest tens of billions of dollars in semiconductor manufacturing incentives and research initiatives over the next 5-10 years to strengthen and sustain American leadership in chip technology, which is essential to our country`s economy and national security.`` On June 8, 2021, the Senate approved $52 billion for the CHIPS for America Act, dedicated to supporting the U.S. semiconductor industry over the next decade. This paper highlights a curious paradox: Most of the SIA corporate members now lobbying for the CHIPS for America Act have squandered past support that the U.S. semiconductor industry has received from the U.S. government for decades by using their corporate cash to do buybacks to boost their own companies` stock prices. Among the SIA corporate signatories of a letter to President Biden in February 2021, the five largest stock repurchasers - Intel, IBM, Qualcomm, Texas Instruments, and Broadcom — did a combined $249 billion in buybacks over the decade 2011-2020, equal to 71 percent of their profits and almost five times the subsidies over the next decade for which the SIA is lobbying. In addition, among the members of the Semiconductors in America Coalition (SIAC), formed specifically in May 2021 to lobby Congress for the passage of the CHIPS for America Act, are Apple, Microsoft, Cisco, and Google. These firms spent a combined $633 billion on buybacks during 2011-2020. That is about 12 times the government subsidies provided under the CHIPS for America Act to support semiconductor fabrication in the United States in the upcoming decade. If the Congress wants to achieve the legislation`s stated purpose of promoting major new investments in semiconductors, it needs to deal with this paradox. It could, for example, require the SIA and SIAC to extract pledges from its member corporations that they will cease doing stock buybacks as open-market repurchases over the next ten years. Such regulation could be a first step in rescinding Securities and Exchange Commission Rule 10b-18, which has since 1982 been a major cause of extreme income inequality and loss of global industrial competitiveness in the United States.
    Keywords: Semiconductor fabrication, nanometer technology, global competition, CHIPS for America Act, Semiconductor Industry Association, Semiconductors in America Coalition, stock buybacks, Intel, TSMC, Samsung Electronics, IBM, GlobalFoundries, Apple, semiconductor fabrication equipment, Huawei.
    JEL: D01 D21 D25 D40 G22 G28 G35 G38 L21 L52 L63
    Date: 2021–09–27
  9. By: Annette Vissing-Jørgensen
    Abstract: Treasury yields spiked during the initial phase of COVID. The 10-year yield increased by 64 bps from March 9 to 18, 2020, leading the Federal Reserve to purchase $1T of Treasuries in 2020Q1. Fed Treasury purchases were causal for reducing Treasury yields based on (1) the timing of purchases (which increased on March 19), (2) evidence against confounding factors, and (3) the timing of yield reversal and Fed purchases in the MBS market. Treasury-QE worked more via purchases than announcements. The yield spike was driven by liquidity needs of mutual funds, foreign official agencies, and hedge funds that were unaffected by the March 15, 2020 Treasury-QE announcement.
    JEL: E5 G1
    Date: 2021–10

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