nep-fmk New Economics Papers
on Financial Markets
Issue of 2023‒09‒11
eleven papers chosen by
Kwang Soo Cheong, Johns Hopkins University

  1. Analyzing the Impact of the Covid-19 Pandemic on Stock Market Investments By S, Ramya
  2. Crisis Risk and Risk Management By Stulz, Rene M.
  3. FinTech Lending with LowTech Pricing By Johnson, Mark J.; Ben-David, Itzhak; Lee, Jason; Yao, Vincent
  4. Reinforcement Learning for Financial Index Tracking By Xianhua Peng; Chenyin Gong; Xue Dong He
  5. Deep Learning from Implied Volatility Surfaces By Bryan T. Kelly; Boris Kuznetsov; Semyon Malamud; Teng Andrea Xu
  6. The Subjective Risk and Return Expectations of Institutional Investors By Couts, Spencer J.; Goncalves, Andrei S.; Loudis, Johnathan
  7. Systematic Default and Return Predictability in the Stock and Bond Markets By Bao, Jack; Hou, Kewei; Zhang, Shaojun
  8. Why Are Bank Holdings of Liquid Assets So High? By Stulz, Rene M.; Taboada, Alvaro G.; van Dijk, Mathijs A.
  9. End of an Era: The Coming Long-Run Slowdown in Corporate Profit Growth and Stock Returns By Michael Smolyansky
  10. Regulatory Reforms and Price Heterogeneity in an OTC Derivative Market By Daisuke Miyakawa; Takemasa Oda; Taihei Sone
  11. Which is Worse: Heavy Tails or Volatility Clusters? By Joshua Traut; Wolfgang Schadner

  1. By: S, Ramya
    Abstract: The Covid-19 pandemic has had a notable impact on financial markets, prompting individuals to reevaluate their risk and return objectives and modify their investment portfolios. This study aims to examine the pandemic's influence on the portfolio allocation decisions of individual investors. It investigates investors' perceptions and preferences concerning different investment strategies both before and during the period of heightened anxiety caused by the Covid-19 pandemic. The data for the study was collected from individual investors residing in Delhi and Mumbai. The Analytical Hierarchy Process (AHP) was utilized to rank the investment choices of the participants. The findings indicate that, owing to the current economic uncertainty associated with the pandemic, investors have started realigning their portfolios. As the predictability of returns on risky assets has decreased, investors are shifting towards more conservative portfolios. Nevertheless, the extent of the transition from high-risk to low-risk assets varies among individual investors.
    Date: 2023–07–31
  2. By: Stulz, Rene M. (Ohio State U)
    Abstract: This paper assesses the current state of knowledge about crisis risk and its implications for risk management. Better data that became available since the Global Financial Crisis (GFC) has improved our understanding of crisis risk. These data have been used to show that some types of crises become predictable when one accounts for interactions between risks. Specifically, a financial crisis is much more likely in the years following both high credit growth and high asset valuations. However, some other types of crises do not seem predictable. There is no evidence that the frequency of economic and financial crises is increasing. The existing data show that political crises make economic crises more likely, so that, as suggested by the concept of polycrisis, feedback between non-economic crises and economic crises can be important, but there is no comparable evidence for climate events. Strategies that increase firm operational and financial flexibility appear successful at reducing the adverse impact of crises on firms.
    JEL: G01 G21 G32
    Date: 2023–05
  3. By: Johnson, Mark J. (Brigham Young U); Ben-David, Itzhak (Ohio State U); Lee, Jason (US Securities and Exchange Commission); Yao, Vincent (Georgia State U)
    Abstract: FinTech lending—known for using big data and advanced technologies—promised to break away from the traditional credit scoring and pricing models. Using a comprehensive dataset of FinTech personal loans, our study shows that loan rates continue to rely heavily on conventional credit scores, including 45% higher rates for nonprime borrowers. Other known default predictors are often neglected. Within each segment (prime/nonprime) loan rates are not very responsive to default risk, resulting in realized loan-level returns decreasing with risk. The pricing distortions result in substantial transfers from nonprime to prime borrowers and from low- to high-risk borrowers within segment.
    JEL: G21 G23 G50
    Date: 2023–04
  4. By: Xianhua Peng; Chenyin Gong; Xue Dong He
    Abstract: We propose the first discrete-time infinite-horizon dynamic formulation of the financial index tracking problem under both return-based tracking error and value-based tracking error. The formulation overcomes the limitations of existing models by incorporating the intertemporal dynamics of market information variables not limited to prices, allowing exact calculation of transaction costs, accounting for the tradeoff between overall tracking error and transaction costs, allowing effective use of data in a long time period, etc. The formulation also allows novel decision variables of cash injection or withdraw. We propose to solve the portfolio rebalancing equation using a Banach fixed point iteration, which allows to accurately calculate the transaction costs specified as nonlinear functions of trading volumes in practice. We propose an extension of deep reinforcement learning (RL) method to solve the dynamic formulation. Our RL method resolves the issue of data limitation resulting from the availability of a single sample path of financial data by a novel training scheme. A comprehensive empirical study based on a 17-year-long testing set demonstrates that the proposed method outperforms a benchmark method in terms of tracking accuracy and has the potential for earning extra profit through cash withdraw strategy.
    Date: 2023–08
  5. By: Bryan T. Kelly (Yale School of Management; AQR Capital Management; NBER); Boris Kuznetsov (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute); Semyon Malamud (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute; and CEPR); Teng Andrea Xu (Ecole Polytechnique Fédérale de Lausanne)
    Abstract: We develop a novel methodology for extracting information from option implied volatility (IV) surfaces for the cross-section of stock returns, using image recognition techniques from machine learning (ML). The predictive information we identify is essentially uncorrelated with most of the existing option-implied characteristics, delivers a higher Sharpe ratio, and has a significant alpha relative to a battery of standard and option-implied factors. We show the virtue of ensemble complexity: Best results are achieved with a large ensemble of ML models, with the out-of-sample performance increasing in the ensemble size, saturating when the number of model parameters significantly exceeds the number of observations. We introduce principal linear features, an analog of principal components for ML and use them to show IV feature complexity: A low-rank rotation of the IV surface cannot explain the model performance. Our results are robust to short-sale constraints and transaction costs.
    Date: 2023–08
  6. By: Couts, Spencer J. (U of Southern California); Goncalves, Andrei S. (Ohio State U); Loudis, Johnathan (U of Notre Dame)
    Abstract: We use the long-term Capital Market Assumptions of major asset managers and institutional investor consultants from 1987 to 2022 to provide three stylized facts about their subjective risk and return expectations on 19 asset classes. First, the subjective distribution of asset class returns is well described by a 1-factor structure, with this single risk factor typically explaining more than 65% of the subjective variability in asset class returns. Second, at least 80% of the variability in subjective expected returns is due to variability in subjective risk premia (compensation for beta) as opposed to subjective mispricing (alpha). And third, subjective risk and return expectations vary much more across asset classes than across institutions. Our findings imply that models with subjective beliefs should reflect a risk-return tradeoff. Additionally, accounting for this risk-return trade-off is even more important than incorporating belief heterogeneity across institutional investors when modeling multiple asset classes.
    JEL: G11 G12 G23
    Date: 2023–05
  7. By: Bao, Jack (U of Delaware); Hou, Kewei (Ohio State U); Zhang, Shaojun (Ohio State U)
    Abstract: We construct a measure of systematic default defined as the probability that many firms default at the same time. We account for correlations in defaults between firms through exposures to common shocks. Systematic default spikes during recessions, is correlated with macroeconomic indicators, and predicts future realized defaults. More importantly, it predicts future equity and corporate bond index returns both in- and out-of-sample. Finally, we find that the cross-section of average stock returns is related to firm-level exposures to systematic default risk.
    JEL: E32 G12 G13 G17
    Date: 2023–05
  8. By: Stulz, Rene M. (Ohio State U); Taboada, Alvaro G. (Mississippi State U); van Dijk, Mathijs A. (Erasmus U Rotterdam)
    Abstract: Aggregate bank liquid asset holdings (reserves and liquid securities) increased from 13% to 33% of assets from before the Global Financial Crisis (GFC) to 2020. If banks allocate their balance sheet by equalizing the marginal risk-adjusted expected return across asset classes, they hold more liquid assets when they have less advantageous lending opportunities. We show that, indeed, holdings of liquid assets are negatively related to lending opportunities. Our findings indicate that bank liquid asset holdings grew since the GFC because of weak lending opportunities, though regulatory changes help explain the higher liquid asset holdings of the largest banks before COVID.
    JEL: G21 G28
    Date: 2023–05
  9. By: Michael Smolyansky
    Abstract: I show that the decline in interest rates and corporate tax rates over the past three decades accounts for the majority of the period’s exceptional stock market performance. Lower interest expenses and corporate tax rates mechanically explain over 40 percent of the real growth in corporate profits from 1989 to 2019. In addition, the decline in risk-free rates alone accounts for all of the expansion in price-to-earnings multiples. I argue, however, that the boost to profits and valuations from ever-declining interest and corporate tax rates is unlikely to continue, indicating significantly lower profit growth and stock returns in the future.
    Keywords: long-run prediction; stock returns; equity premium; corporate profits; interest rates; corporate taxes
    JEL: G10 G12 G17
    Date: 2023–06–26
  10. By: Daisuke Miyakawa (Waseda University); Takemasa Oda (Bank of Japan); Taihei Sone (Bank of Japan)
    Abstract: After the great financial crisis in the late 2000s, the over-the-counter (OTC) derivative markets started to face a set of new regulatory reforms. In this study, we empirically examine how and whether these reforms have achieved the transparent OTC derivative market accompanied by homogeneous prices as one of its intended goals. To do so, we use data from the universe of JPY-denominated interest rate swap (IRS) contracts that were executed in the period from April 2013 to October 2021 and involved at least one Japan-based entity. First, as reported in Cenedese et al. (2020), we observe a higher fixed rate for bilateral clearing than for central clearing even after the introduction of a quantity-based measure: the central counterparty (CCP) mandate. Second, such price heterogeneity temporarily increased but eventually diminished after the introduction of new margin rules for bilateral clearing. These results indicate that the ultimate source of price heterogeneity had been the insufficient margin provision in the bilateral clearing that the reforms effectively resolved.
    Keywords: Interest rate swap; regulatory reforms; over-the-counter; central clearing; margin
    JEL: G12 G15 G18 G20 G28
    Date: 2023–08–18
  11. By: Joshua Traut (University of St. Gallen); Wolfgang Schadner (University of St. Gallen)
    Abstract: Heavy tails and volatility clusters are both stylized facts of financial returns that destabilize markets and are often neglected using the simplifying assumptions of normally distributed and iid returns respectively. This work disentangles the two sources and is the first to assess which one does the greater damage to financial stability and whether the threat can be reduced via diversification. As such, it also quantifies the potential shortfalls of the two commonly used simplifying assumptions. The analysis is carried out for index return series representing seven different asset classes and for individual stock portfolios. The stylized facts are isolated using recent developments in surrogate analysis (IAAFT, IAAWT). Our analysis shows that volatility clusters have a greater impact on maximum drawdowns and aggregate losses across all markets and that diversification does not yield any protection from those risks. In fact, diversification amplifies the translation of the two stylized facts into drawdowns, exacerbating their potential negative effects. We further demonstrate the practical relevance of our findings as we can replicate the results of our surrogate analysis using real portfolios. Moreover, we show that regulators should consider the impact of volatility clusters and discard the simplifying assumption of iid returns in order to enhance the accuracy of capital buffers.
    Keywords: Financial Stability, Tail Risk, Autocorrelation, Volatility Clustering, Heavy Tails, Risk Management
    JEL: G12 G18 G15 G01
    Date: 2023–08

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