nep-fmk New Economics Papers
on Financial Markets
Issue of 2023‒06‒12
thirteen papers chosen by

  1. One Asset Does Not Fit All: Inflation Hedging by Index and Horizon By Stefania D'Amico; Thomas B. King
  2. Does Private Equity Over-Lever Portfolio Companies? By Sharjil M. Haque
  3. Generative AI and Firm Values By Andrea L. Eisfeldt; Gregor Schubert; Miao Ben Zhang
  4. Credit Ratings and Investments By Anna Bayona; Oana Peia; Razvan Vlahu
  5. FinTech, Investor Sophistication and Financial Portfolio Choices By Leonardo Gambacorta; Romina Gambacorta; Roxana Mihet
  6. Asset Pricing in a Low Rate Environment By Marlon Azinovic; Harold L. Cole; Felix Kübler
  7. Investor-driven corporate finance: evidence from insurance markets By Kubitza, Christian
  8. Systematic Review on Reinforcement Learning in the Field of Fintech By Nadeem Malibari; Iyad Katib; Rashid Mehmood
  9. Impact of size and volume on cryptocurrency momentum and reversal By Milan Fičura
  10. Bitcoin: A life in crises By Jevgeni Tarassov; Nicolas Houli\'e
  11. Financial Risk-Taking under Health Risk By Björn Bos; Moritz A. Drupp; Jasper N. Meya; Martin F. Quaas
  12. War Discourse and Disaster Premia: 160 Years of Evidence from Stock and Bond Markets By David Hirshleifer; Dat Y. Mai; Kuntara Pukthuanthong
  13. Range Volatility Spillover across Sectoral Stock Indices during COVID-19 Pandemic: Evidence from Indian Stock Market By Datta, Susanta; Hatekar, Neeraj

  1. By: Stefania D'Amico; Thomas B. King
    Abstract: We examine the inflation-hedging properties of various financial assets and portfolios by estimating simple time-series models of the joint dynamics of each asset-inflation pair, for multiple inflation indices and at horizons from one month to 30 years. There is no one-size-fits-all approach to inflation hedging: the optimal hedge depends on the particular types of prices that an investor is exposed to and at which horizons. For example, food and energy prices are easy to hedge with commodities and certain stock portfolios, while non-housing service prices and wages are not highly correlated with any financial asset. Inflation-protected bonds are good hedges for headline consumer inflation at horizons matching their maturities but can perform quite poorly at shorter horizons and for other price indices. During the inflationary period of 2020-2022, many historical hedging relationships failed, as monetary policy tightening lagged inflation.
    Keywords: Inflation; real assets; Treasury Inflation-Protected Securities (TIPS); Hedging
    Date: 2023–04–14
  2. By: Sharjil M. Haque
    Abstract: Detractors have warned that Private Equity (PE) funds tend to over-lever their portfolio companies because of an option-like payoff, building up default risk and debt overhang. This paper argues PE-ownership leads to substantially higher levels of optimal (value-maximizing) leverage, by reducing the expected cost of financial distress. Using data from a large sample of PE buyouts, I estimate a dynamic trade-off model where leverage is chosen by the PE investor. The model is able to explain both the level and change in leverage documented empirically following buyouts. The increase in optimal leverage is driven primarily by a reduction in the portfolio company's asset volatility and, to a lesser extent, an increase in asset return. Counterfactual analysis shows significant loss in firmvalue if PE sub-optimally chose lower leverage. Consistent with lower asset volatility, additional tests show PE-backed firms experience lower volatility of sales and receive greater equity injections for distress resolution, compared to non PE-backed firms. Overall, my findings broaden our understanding of factors that drive buyout leverage.
    Keywords: Private Equity; Capital Structure; Default Risk; Trade-off Theory
    JEL: G23 G30 G32 G33
    Date: 2023–02–03
  3. By: Andrea L. Eisfeldt; Gregor Schubert; Miao Ben Zhang
    Abstract: What are the effects of recent advances in Generative AI on the value of firms? Our study offers a quantitative answer to this question for U.S. publicly traded companies based on the exposures of their workforce to Generative AI. Our novel firm-level measure of workforce exposure to Generative AI is validated by data from earnings calls, and has intuitive relationships with firm and industry-level characteristics. Using Artificial Minus Human portfolios that are long firms with higher exposures and short firms with lower exposures, we show that higher-exposure firms earned excess returns that are 0.4% higher on a daily basis than returns of firms with lower exposures following the release of ChatGPT. Although this release was generally received by investors as good news for more exposed firms, there is wide variation across and within industries, consistent with the substantive disruptive potential of Generative AI technologies.
    JEL: E0 G0
    Date: 2023–05
  4. By: Anna Bayona; Oana Peia; Razvan Vlahu
    Abstract: We study how inflated credit ratings affect investment decisions in bond markets using experimental coordination games. Theoretical models that feature a feedback effect between capital markets and the real economy suggest that inflated ratings can have both positive and negative real effects. We compare markets with and without a credit rating agency and find that ratings significantly impact investor behaviour and capital allocation to firms. We show that the main mechanism through which these real effects materialize is a shift in investors’ beliefs about the behaviour of other investors rather than firms’ underlying fundamentals. Our experimental results sug- gest that the positive impact of inflated ratings is likely to dominate in the presence of feedback effects since ratings act as a strong coordination mechanism resulting in enhanced market outcomes.
    Keywords: Credit ratings; Imperfect information; Investor beliefs; Firm financing
    JEL: D81 D82 D83 G24
    Date: 2023–05
  5. By: Leonardo Gambacorta (Bank for International Settlements (BIS); Centre for Economic Policy Research (CEPR)); Romina Gambacorta (Bank of Italy); Roxana Mihet (Swiss Finance Institute - HEC Lausanne)
    Abstract: This paper analyses the links between advances in financial technology, investors’ sophistication, and the composition and returns of their financial portfolios. We develop a simple portfolio choice model under asymmetric information and derive some theoretical predictions. Using detailed microdata from Banca d’Italia, we test these predictions for Italian households over the period 2004- 20. In general, heterogeneity in portfolio composition and in returns between sophisticated and unsophisticated investors grows with improvements in financial technology. This heterogeneity is reduced only if financial technology is accessible to everyone and if investors have a similar capacity to use it.
    Keywords: Inequality, Inclusion, FinTech, Innovation, Matthew Effect.
    JEL: G1 G5 G4 D83 L8 O3
    Date: 2023–04
  6. By: Marlon Azinovic (University of Pennsylvania); Harold L. Cole (University of Pennsylvania; National Bureau of Economic Research); Felix Kübler (University of Zurich; Swiss Finance Institute)
    Abstract: We examine asset prices in environments where the risk-free rate lies considerably below the growth rate. To do so, we introduce a tractable model of a production economy featuring heterogeneous trading technologies, as well as idiosyncratic and aggregate risk. We show that allowing for the possibility of firms exiting is crucial for matching key macroeconomic moments and, simultaneously, the risk-free rate, the market price of risk, and price-earnings ratios. In particular, our model allows us to consider calibrations that match the high observed market price of risk and average interest rates as low as 2-3.5 percent below the average growth rate. High values for risk aversion or non-standard preferences are not necessary for this. We use the model to examine the wealth distribution and asset prices in economies with very low real rates. We also examine under which conditions realistic calibrations allow for an infinite rollover of government debt. For our benchmark calibration, rollover is impossible even if the average risk-free rate lies 3.5 percent below the average growth rate.
    Keywords: Asset pricing, low rates, r-g, limited participation, market price of risk
    JEL: E6 E44 G12
    Date: 2023–05
  7. By: Kubitza, Christian
    Abstract: I study the causal effect of bond investor demand on the financing and investment decisions of nonfinancial firms using granular data on the bond transactions of U.S. insurance companies. Liquidity inflows from insurance premiums combined with insurers’ persistent investment preferences identify bond demand shifts, which raise bond prices and reduce firms’ financing costs. In response, firms issue more bonds, especially when they have well-connected bond underwriters. The proceeds are used for investment rather than shareholder payouts, particularly by financially constrained firms. The results emphasize that bond investors significantly affect corporate financing and investment decisions through their price impact. JEL Classification: G12, G22, G23, G3, G32
    Keywords: corporate bonds, corporate investment, institutional investors, insurance
    Date: 2023–05
  8. By: Nadeem Malibari; Iyad Katib; Rashid Mehmood
    Abstract: Applications of Reinforcement Learning in the Finance Technology (Fintech) have acquired a lot of admiration lately. Undoubtedly Reinforcement Learning, through its vast competence and proficiency, has aided remarkable results in the field of Fintech. The objective of this systematic survey is to perform an exploratory study on a correlation between reinforcement learning and Fintech to highlight the prediction accuracy, complexity, scalability, risks, profitability and performance. Major uses of reinforcement learning in finance or Fintech include portfolio optimization, credit risk reduction, investment capital management, profit maximization, effective recommendation systems, and better price setting strategies. Several studies have addressed the actual contribution of reinforcement learning to the performance of financial institutions. The latest studies included in this survey are publications from 2018 onward. The survey is conducted using PRISMA technique which focuses on the reporting of reviews and is based on a checklist and four-phase flow diagram. The conducted survey indicates that the performance of RL-based strategies in Fintech fields proves to perform considerably better than other state-of-the-art algorithms. The present work discusses the use of reinforcement learning algorithms in diverse decision-making challenges in Fintech and concludes that the organizations dealing with finance can benefit greatly from Robo-advising, smart order channelling, market making, hedging and options pricing, portfolio optimization, and optimal execution.
    Date: 2023–04
  9. By: Milan Fičura
    Abstract: We analyse how cryptocurrency size and trading volume impact the momentum and reversal dynamics of their returns. We show that the previously reported weekly return reversal occurs for small and illiquid coins only (t-stat = -7.31), while the large and liquid coins exhibit weekly momentum effect instead (t-stat = 2.33). Long-term returns exhibit reversal effects, which are, however, insignificant for the large and liquid coins. We further analyse the impact of high momentum on future cryptocurrency returns, measured as the distance of previous-week closing price from the k-week high. High momentum has not been analysed on cryptocurrency markets before, and we show it to be a superior predictor of future returns when compared to regular momentum. The distance from the 1-week high predicts negatively future returns of small and illiquid coins (t-stat = -9.03) and positively future returns of large and liquid coins (t-stat = 4.93). The results are highly robust to different settings of the size and liquidity thresholds. We further show that the short-term reversal of small and illiquid coins is driven mostly by their low trading volumes, while the short-term momentum of large and liquid coins is driven mostly by high market capitalizations and to a lower degree by high trading volumes.
    Keywords: Cryptocurrency, momentum, reversal, high-momentum, size, liquidity, asset pricing
    JEL: G11 G12 G17
    Date: 2023–04–05
  10. By: Jevgeni Tarassov; Nicolas Houli\'e
    Abstract: In this study, we investigate the BTC price time-series (17 August 2010-27 June 2021) and show that the 2017 pricing episode is not unique. We describe at least ten new events, which occurred since 2010-2011 and span more than five orders of price magnitudes ($US 1-$US 60k). We find that those events have a similar duration of approx. 50-100 days. Although we are not able to predict times of a price peak, we however succeed to approximate the BTC price evolution using a function that is similar to a Fibonacci sequence. Finally, we complete a comparison with other types of financial instruments (equities, currencies, gold) which suggests that BTC may be classified as an illiquid asset.
    Date: 2023–04
  11. By: Björn Bos; Moritz A. Drupp; Jasper N. Meya; Martin F. Quaas
    Abstract: We study how background health risk affects financial risk-taking. We elicit financial risk-taking behavior of a representative sample of more than 5, 000 Germans in five panel waves during the COVID-19 pandemic. Exploiting variation in local infections across time and space, we find that an increase in infections affecting background health risk translates into higher levels of self-reported fear and decreases financial investments in a risky asset. Once vaccines become available as a self-insurance device, the tempering effect on investments ceases. Our results provide evidence that non-financial background risks affect financial risk-taking, and for the alleviating effect of self-insurance devices.
    JEL: D14 D91 G11 G41 G51
    Date: 2023
  12. By: David Hirshleifer; Dat Y. Mai; Kuntara Pukthuanthong
    Abstract: Using a semi-supervised topic model on 7, 000, 000 New York Times articles spanning 160 years, we test whether topics of media discourse predict future stock and bond market returns to test rational and behavioral hypotheses about market valuation of disaster risk. Focusing on media discourse addresses the challenge of sample size even when major disasters are rare. Our methodology avoids look-ahead bias and addresses semantic shifts. War discourse positively predicts market returns, with an out-of-sample R2 of 1.35%, and negatively predicts returns on short-term government and investment-grade corporate bonds. The predictive power of war discourse increases in more recent time periods.
    JEL: G0 G00 G01 G02 G1 G10 G11 G13 G4 G41
    Date: 2023–05
  13. By: Datta, Susanta; Hatekar, Neeraj
    Abstract: The study examines volatility spillover across sectoral stock indices from one Emerging Market Economies, viz. India during COVID-19 pandemic. Our contributions are threefold: (a) incorporation of range volatility during the pandemic, (b) comparative assessment of volatility spillover at the sectoral level, and (c) identify evidence of volatility spillover across different sectoral indices. Using daily historical price data for 11 sectoral stock indices during the first wave of the pandemic; we find that Range GARCH (1, 1) performs better not only during the crisis but also during pandemic periods. The multivariate Range DCC model confirms evidence of volatility spillover across sectoral stock indices.
    Keywords: Forecasting, Volatility, Spillover, Return, Range, NIFTY, COVID 19
    JEL: C22 C58 G17
    Date: 2022–04–04

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