nep-fmk New Economics Papers
on Financial Markets
Issue of 2022‒03‒21
six papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. The Federal Funds Market, Pre- and Post-2008 By Eric T. Swanson
  2. Exorbitant Privilege? Quantitative Easing and the Bond Market Subsidy of Prospective Fallen Angels By Viral V. Acharya; Ryan Banerjee; Matteo Crosignani; Tim Eisert; Renée Spigt
  3. On financial market correlation structures and diversification benefits across and within equity sectors By Nick James; Max Menzies; Georg Gottwald
  4. Managers versus Machines: Do Algorithms Replicate Human Intuition in Credit Ratings? By Matthew Harding; Gabriel F. R. Vasconcelos
  5. Local Journalism under Private Equity Ownership By Michael Ewens; Arpit Gupta; Sabrina T. Howell
  6. On Solving Robust Log-Optimal Portfolio: A Supporting Hyperplane Approximation Approach By Chung-Han Hsieh

  1. By: Eric T. Swanson
    Abstract: This chapter provides an overview of the federal funds market and how the equilibrium federal fund rate is determined. I devote particular attention to comparing and contrasting the federal funds market before and after 2008, since there were several dramatic changes around that time that completely changed the market and the way in which the equilibrium federal funds rate is determined. The size of this structural break is arguably as large and important as the period of reserves targeting under Fed Chairman Paul Volcker from 1979–82. Finally, I discuss the relationship between the federal funds rate and other short-term interest rates in the U.S. and the outlook for the federal funds market going forward.
    JEL: E43 E52 E58
    Date: 2022–02
  2. By: Viral V. Acharya; Ryan Banerjee; Matteo Crosignani; Tim Eisert; Renée Spigt
    Abstract: We document capital misallocation in the U.S. investment-grade (IG) corporate bond market, driven by quantitative easing (QE). Prospective fallen angels -- risky fi rms just above the IG rating cutoff -- enjoyed subsidized bond fi nancing since 2009, especially when the scale of QE purchases peaked and from IG-focused investors that held more securities purchased in QE programs. The bene fiting fi rms used this privilege to fund risky acquisitions and increase market share, exploiting the sluggish adjustment of credit ratings in downgrading after M&A and adversely affecting competitors' employment and investment. Eventually, these fi rms suffered more severe downgrades at the onset of the pandemic.
    JEL: E44 E52 E58 G01 G20
    Date: 2022–02
  3. By: Nick James; Max Menzies; Georg Gottwald
    Abstract: We study how to assess the potential benefit of diversifying an equity portfolio by investing within and across equity sectors. We analyse 20 years of US stock price data, which includes the global financial crisis (GFC) and the COVID-19 market crash, as well as periods of financial stability, to determine the `all weather' nature of equity portfolios. We establish that one may use the leading eigenvalue of the cross-correlation matrix of log returns as well as graph-theoretic diagnostics such as modularity to quantify the collective behaviour of the market or a subset of it. We confirm that financial crises are characterised by a high degree of collective behaviour of equities, whereas periods of financial stability exhibit less collective behaviour. We argue that during times of increased collective behaviour, risk reduction via sector-based portfolio diversification is ineffective. Using the degree of collectivity as a proxy for the benefit of diversification, we perform an extensive sampling of equity portfolios to confirm the old financial adage that 30-40 stocks provide sufficient diversification. Using hierarchical clustering, we discover a `best value' equity portfolio for diversification consisting of 36 equities sampled uniformly from 9 sectors. We further show that it is typically more beneficial to diversify across sectors rather than within. Our findings have implications for cost-conscious retail investors seeking broad diversification across equity markets.
    Date: 2022–02
  4. By: Matthew Harding; Gabriel F. R. Vasconcelos
    Abstract: We use machine learning techniques to investigate whether it is possible to replicate the behavior of bank managers who assess the risk of commercial loans made by a large commercial US bank. Even though a typical bank already relies on an algorithmic scorecard process to evaluate risk, bank managers are given significant latitude in adjusting the risk score in order to account for other holistic factors based on their intuition and experience. We show that it is possible to find machine learning algorithms that can replicate the behavior of the bank managers. The input to the algorithms consists of a combination of standard financials and soft information available to bank managers as part of the typical loan review process. We also document the presence of significant heterogeneity in the adjustment process that can be traced to differences across managers and industries. Our results highlight the effectiveness of machine learning based analytic approaches to banking and the potential challenges to high-skill jobs in the financial sector.
    Date: 2022–02
  5. By: Michael Ewens; Arpit Gupta; Sabrina T. Howell
    Abstract: Local daily newspapers historically played an important role in U.S. democracy by providing citizens with information about local policy issues. In recent decades, local newspapers have struggled to compete with new online platforms. In the first study of private equity (PE) in a struggling industry, we find nuanced effects. PE leads to higher digital circulation and lower chances of newspaper exit. However, the composition of news shifts away from local governance, the number of reporters and editors falls, and participation in local elections declines. The results have implications for knowledge about local policy issues and highlight trade-offs surrounding media ownership.
    JEL: G23 G32 H42 L82
    Date: 2022–02
  6. By: Chung-Han Hsieh
    Abstract: A {log-optimal} portfolio is any portfolio that maximizes the expected logarithmic growth (ELG) of an investor's wealth. This maximization problem typically assumes that the information of the true distribution of returns is known to the trader in advance. However, in practice, the return distributions are indeed {ambiguous}; i.e., the true distribution is unknown to the trader or it is partially known at best. To this end, a {distributional robust log-optimal portfolio problem} formulation arises naturally. While the problem formulation takes into account the ambiguity on return distributions, the problem needs not to be tractable in general. To address this, in this paper, we propose a {supporting hyperplane approximation} approach that allows us to reformulate a class of distributional robust log-optimal portfolio problems into a linear program, which can be solved very efficiently. Our framework is flexible enough to allow {transaction costs}, {leverage and shorting}, {survival trades}, and {diversification considerations}. In addition, given an acceptable approximation error, an efficient algorithm for rapidly calculating the optimal number of hyperplanes is provided. Some empirical studies using historical stock price data are also provided to support our theory.
    Date: 2022–02

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