nep-fmk New Economics Papers
on Financial Markets
Issue of 2022‒08‒15
eighteen papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Mutual fund trading and ESG stock resilience during the Covid-19 stock market crash By Rui Albuquerque; Yrjö Koskinen; Raffaele Santioni
  2. Are fund managers rewarded for taking cyclical risks? By Ryan, Ellen
  3. Crash Narratives By William N. Goetzmann; Dasol Kim; Robert J. Shiller
  4. Do ESG funds make stakeholder-friendly investments? By Raghunandan, Aneesh; Rajgopal, Shiva
  5. The Green Corporate Bond Issuance Premium By John Caramichael; Andreas Rapp
  6. Market-to-book Ratio in Stochastic Portfolio Theory By Donghan Kim
  7. Data and Welfare in Credit Markets By Mark Jansen; Fabian Nagel; Constantine Yannelis; Anthony Lee Zhang
  8. Common Fund Flows: Flow Hedging and Factor Pricing By Winston Wei Dou; Leonid Kogan; Wei Wu
  9. Bond Finance and the Leverage Ratio By Alfred V. Guender
  10. Deep Learning for Systemic Risk Measures By Yichen Feng; Ming Min; Jean-Pierre Fouque
  11. Accelerating Machine Learning Training Time for Limit Order Book Prediction By Mark Joseph Bennett
  12. The Virtue of Complexity in Return Prediction By Bryan T. Kelly; Semyon Malamud; Kangying Zhou
  13. CDS market structure and bond spreads By Bilan, Andrada; Gündüz, Yalın
  14. Intermediary Balance Sheets and the Treasury Yield Curve By Wenxin Du; Benjamin M. Hébert; Wenhao Li
  15. A comparative study of the MACD-base trading strategies: evidence from the US stock market By Pat Tong Chio
  16. Fed Implied Market Prices and Risk Premia By Charles W. Calomiris; Joanna Harris; Harry Mamaysky; Cristina Tessari
  17. BigTech cryptocurrencies - European regulatory solutions in sight By Kotovskaia, Anastasia; Meier, Nicola
  18. Efficiency of the Moscow Stock Exchange before 2022 By Andrey Shternshis; Piero Mazzarisi; Stefano Marmi

  1. By: Rui Albuquerque (Carroll School of Management, Boston College, ECGI, CEPR); Yrjö Koskinen (Haskayne School of Business, University of Calgary, ECGI); Raffaele Santioni (Bank of Italy)
    Abstract: Using proprietary monthly holdings data from Morningstar, we show that Environmental, Social, and Governance funds’ trading during the Covid-19 market crash was consistent with the choices of their clientele. Thus, ESG funds helped to stabilize the market for ESG stocks, but interestingly non-ESG funds did so too. First, all funds experiencing inflows helped to stabilize the market during the crash by increasing net purchases per dollar of inflows. This behaviour was more pronounced for ESG funds. Second, non-ESG funds experiencing outflows increased their net sales per dollar of outflow for non-ESG stocks, tilting their portfolios towards ESG stocks.
    Keywords: Environmental and social responsibility, clientele effects, fund flows, investor horizon, stock market crash
    JEL: G01 G12 G23 G32 M14
    Date: 2022–06
  2. By: Ryan, Ellen
    Abstract: The investment fund sector has expanded dramatically since the crisis of 2008-2009. As the sector grows, so do the implications of its risk-taking for the wider financial system and real economy. This paper provides empirical evidence for the existence of wide- spread risk-taking incentives in the investment fund sector, with a particular focus on incentives for synchronised, cyclical risk-taking which could have systemic effects. Incentives arise from the positive response of investors to returns achieved through cyclical risk-taking and non-linearities in the relationship between fund returns and fund flows, which may keep managers from fully internalising the effects of adverse outcomes on their portfolios. The fact that market discipline may not be sufficient to ensure prudential behaviour among managers, combined with the externalities of this risk-taking for the wider system, creates a clear case for macroprudential regulatory intervention. JEL Classification: G23, G11, G28
    Keywords: Financial stability, incentive, investment funds, risk-taking
    Date: 2022–07
  3. By: William N. Goetzmann; Dasol Kim; Robert J. Shiller
    Abstract: The financial press is a conduit for popular narratives that reflect collective memory about historical events. Some collective memories relate to major stock market crashes, and investors may rely on associated narratives, or “crash narratives,” to inform current beliefs and choices. Using recent advances in computational linguistics, we develop a higher-order measure of narrativity based on newspaper articles that appear following major crashes. We provide evidence that crash narratives propagate broadly once they appear in news articles, and significantly explain predictive variation in market volatility. We exploit investor heterogeneity using survey data to distinguish the effects of narrativity and fundamental conditions and find consistent evidence. Finally, we develop a measure of pure narrativity to examine when the financial press is more likely to employ narratives.
    JEL: E03 G00 G02 G11 G23
    Date: 2022–07
  4. By: Raghunandan, Aneesh; Rajgopal, Shiva
    Abstract: Investment funds that claim to focus on socially responsible stocks have proliferated in recent times. In this paper, we verify whether ESG mutual funds actually invest in firms that have stakeholder-friendly track records. Using a comprehensive sample of self-labelled ESG mutual funds (as identified by Morningstar) in the United States from 2010 to 2018, we find that these funds hold portfolio firms with worse track records for compliance with labor and environmental laws, relative to portfolio firms held by non-ESG funds managed by the same financial institutions in the same years. Relative to other funds offered by the same asset managers in the same years, ESG funds hold stocks that are more likely to voluntarily disclose carbon emissions performance but also stocks with higher carbon emissions per unit of revenue. Despite these findings, ESG funds hold portfolio firms with higher average ESG scores. We show that ESG scores are correlated with the quantity of voluntary ESG-related disclosures but not with firms’ compliance records or actual levels of carbon emissions. Finally, ESG funds appear to underperform financially relative to other funds within the same asset manager and year, and to charge higher fees. Our findings suggest that socially responsible funds do not appear to follow through on proclamations of concerns for stakeholders.
    Keywords: social responsibility; ESG; SEC; environmental and labor laws; mutual fund; violation tracker; Springer deal
    JEL: M14 G23 G34 M41
    Date: 2022–06–27
  5. By: John Caramichael; Andreas Rapp
    Abstract: We study a global panel of green and conventional bonds to assess the borrowing cost advantage at issuance for green bond issuers. We find that, on average, green bonds have a yield spread that is 8 basis points lower relative to conventional bonds. This borrowing cost advantage, or greenium, emerges as of 2019 and coincides with the growth of the sustainable asset management industry following EU regulation. Within this context, we find that the greenium is linked to two proxies of demand pressure, bond oversubscription and bond index inclusion. Moreover, while green bond governance appears to matter for the greenium, the credibility of the underlying projects does not have a significant impact. Instead, the greenium is unevenly distributed to large, investment-grade issuers, primarily within the banking sector and developed economies. These findings have implications for the role of green bonds in incentivizing meaningful green investments throughout the global economy.
    Keywords: Green bonds; Corporate bonds; Green finance; Sustainable finance; Climate finance; Green bond premium; Bond issuance
    JEL: C33 G15 G18 G23 G28 Q54 Q56
    Date: 2022–06–02
  6. By: Donghan Kim
    Abstract: We study market-to-book ratios of stocks in the context of Stochastic Portfolio Theory. Functionally generated portfolios that depend on auxiliary economic variables other than relative capitalizations ("sizes") are developed in two ways, together with their relative returns with respect to the market. This enables us to identify the value factor (i.e., market-to-book ratio) in returns of such generated portfolios when the auxiliary variables are stocks' book values. Examples of portfolios, as well as their empirical results, are given, with the evidence that, in addition to size, the value factor does affect the performance of the portfolio.
    Date: 2022–06
  7. By: Mark Jansen; Fabian Nagel; Constantine Yannelis; Anthony Lee Zhang
    Abstract: We show how to measure the welfare effects arising from increased data availability. When lenders have more data on prospective borrower costs, they can charge prices that are more aligned with these costs. This increases total social welfare, and transfers surplus from borrowers to lenders. We show that the magnitudes of the welfare changes can be estimated using only quantity data and variation in prices. We apply the methodology on bankruptcy flag removals, and find that removing prior bankruptcy information increases the surplus of previously bankrupt consumers substantially, at the cost of decreasing total social welfare modestly, suggesting that flag removals have low efficiency costs for redistributing surplus to previously bankrupt borrowers.
    JEL: D6 G20 G21 G28 G5 H81
    Date: 2022–07
  8. By: Winston Wei Dou; Leonid Kogan; Wei Wu
    Abstract: Active equity funds care about fund size, affected by fund flows that obey a strong factor structure with the common component responding to macroeconomic shocks. Funds hedge against common flows by tilting their portfolios toward low-flow-beta stocks, while household/retail and index investors overweight high-flow-beta stocks in equilibrium. Consequently, common flows earn a risk premium, leading to a multi-factor asset-pricing model resembling the ICAPM, even with myopic agents and unsophisticated fund clients. Exploiting quasi-experiments induced by the local-natural-disaster occurrences and the unexpected trade-war announcements, we find that an increased outflow risk faced by funds leads to more aggressive flow-hedging portfolio tilts.
    JEL: G11 G12 G23
    Date: 2022–07
  9. By: Alfred V. Guender (University of Canterbury)
    Abstract: A binding pledgeable income constraint limits movements in the leverage ratio but permits some flexibility in the choice of bond versus loan finance in response to changes in key parameters. Due to the existence of distress costs of bond finance in the low payoff state, the share of bond finance remains low compared to more expensive loan finance under both constrained and unconstrained profit maximization.
    Keywords: Bonds, Loans, Leverage ratio, Distress cost, Pledgeable income constraint
    JEL: E44 G21 G32
    Date: 2022–06–01
  10. By: Yichen Feng; Ming Min; Jean-Pierre Fouque
    Abstract: The aim of this paper is to study a new methodological framework for systemic risk measures by applying deep learning method as a tool to compute the optimal strategy of capital allocations. Under this new framework, systemic risk measures can be interpreted as the minimal amount of cash that secures the aggregated system by allocating capital to the single institutions before aggregating the individual risks. This problem has no explicit solution except in very limited situations. Deep learning is increasingly receiving attention in financial modelings and risk management and we propose our deep learning based algorithms to solve both the primal and dual problems of the risk measures, and thus to learn the fair risk allocations. In particular, our method for the dual problem involves the training philosophy inspired by the well-known Generative Adversarial Networks (GAN) approach and a newly designed direct estimation of Radon-Nikodym derivative. We close the paper with substantial numerical studies of the subject and provide interpretations of the risk allocations associated to the systemic risk measures. In the particular case of exponential preferences, numerical experiments demonstrate excellent performance of the proposed algorithm, when compared with the optimal explicit solution as a benchmark.
    Date: 2022–07
  11. By: Mark Joseph Bennett
    Abstract: Financial firms are interested in simulation to discover whether a given algorithm involving financial machine learning will operate profitably. While many versions of this type of algorithm have been published recently by researchers, the focus herein is on a particular machine learning training project due to the explainable nature and the availability of high frequency market data. For this task, hardware acceleration is expected to speed up the time required for the financial machine learning researcher to obtain the results. As the majority of the time can be spent in classifier training, there is interest in faster training steps. A published Limit Order Book algorithm for predicting stock market direction is our subject, and the machine learning training process can be time-intensive especially when considering the iterative nature of model development. To remedy this, we deploy Graphical Processing Units (GPUs) produced by NVIDIA available in the data center where the computer architecture is geared to parallel high-speed arithmetic operations. In the studied configuration, this leads to significantly faster training time allowing more efficient and extensive model development.
    Date: 2022–06
  12. By: Bryan T. Kelly; Semyon Malamud; Kangying Zhou
    Abstract: The extant literature predicts market returns with “simple” models that use only a few parameters. Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to “complex” models in which the number of parameters exceeds the number of observations. We empirically document the virtue of complexity in US equity market return prediction. Our findings establish the rationale for modeling expected returns through machine learning.
    JEL: C1 C45 G1
    Date: 2022–07
  13. By: Bilan, Andrada; Gündüz, Yalın
    Abstract: We study the response of bond spreads to a liquidity supply shock in the credit default swap (CDS) market. Our identification strategy exploits the exogenous exit of a large dealer from the single-name CDS market as well as granular data on CDS transactions and bond portfolio holdings of German investors. Following the shock, CDS market liquidity declines and bond spreads increase, especially for the reference firms intermediated by the dealer. Individual portfolio data indicate hedging motives as a mechanism: as CDS insurance on their bond holdings becomes costlier, investors offload the bonds. Our results therefore show that frictions in derivative markets affect the underlying securities, which can raise firms' cost of capital.
    Keywords: credit default swaps,dealer markets,bonds markets,creditrisk,Depository Trust and Clearing Corporation (DTCC)
    JEL: G11 G18 G20 G28
    Date: 2022
  14. By: Wenxin Du; Benjamin M. Hébert; Wenhao Li
    Abstract: We document regime change in the U.S. Treasury market post-Global Financial Crisis (GFC): dealers switched from a net short to a net long position in the Treasury market. We first derive bounds on Treasury yields that account for dealer balance sheet costs, which we call the net short and net long curves. We show that actual Treasury yields moved from the net short curve pre-GFC to the net long curve post-GFC, consistent with the shift in the dealers' net position. We then use a stylized model to demonstrate that increased bond supply and tightening leverage constraints can explain this change in regime. This regime change in turn helps explain negative swap spreads and the co-movement between swap spreads, dealer positions, yield curve slope, and covered-interest-parity violations, and implies changing effects for a wide range of monetary policy and regulatory policy interventions.
    JEL: F3 G12 G15 G2
    Date: 2022–07
  15. By: Pat Tong Chio
    Abstract: In recent years, more and more investors use technical analysis methods in their own trading. Evaluating the effectiveness of technical analysis has become more feasible due to increasing computing capability and blooming public data, which indie investors can perform stock analysis and backtest their own trading strategy conveniently. The Moving Average Convergence Divergence (MACD) indicator is one of the popular technical indicators that are widely used in different strategies. In order to verify the MACD effectiveness, in this thesis, I use the MACD indicator with traditional parameters (12, 26, 9) to build various trading strategies. Then, I apply these strategies to stocks listed on three indices in the US stock market (i.e., Dow-Jones, Nasdaq, and S&P 500) and evaluate its performance in terms of win rate, profitability, Sharpe ratio, number of trades and maximum drawdown. The backtesting is programmed using Python, covering the period between 01/01/2015 and 28-08-2021. The result shows that the win-rate of the strategy with only the MACD indicator is less than 50%. However, the win-rate is improved for the trading strategies that combine the MACD indicator with other momentum indicators like the Money Flow Index (MFI) and the Relative Strength Index (RSI). Based on this result, I redesign the MACD mathematical formula by taking the trading volume and daily price volatility into consideration to derive a new indicator called VPVMA. The results show that the win-rate and risk-adjust performance of this new trading strategy have been improved significantly. In general, the findings suggest that while all the MACD trading strategies mentioned above can generate positive returns, the performance is not good without using other momentum indicators. Hence, the VPVMA indicator performs better.
    Date: 2022–06
  16. By: Charles W. Calomiris; Joanna Harris; Harry Mamaysky; Cristina Tessari
    Abstract: We introduce FDIF, a measure of Fed communication surprise based on the text of FOMC statements. FDIF measures the difference between text-implied and actual values of key market variables. Positive FDIF of countercyclical variables (e.g., credit spreads) is associated with negative macroeconomic forecast revisions; the opposite holds for procyclical variables. Industries that hedge bad FDIF news earn low returns on FOMC announcement days, but high returns on non-FOMC days. The opposite holds for FDIF-exposed industries, and the return differences are large. Controlling for FDIF exposure, rate-based policy surprise measures are not priced in the cross-section of industry returns.
    JEL: E32 E44 E52 G1 G12
    Date: 2022–07
  17. By: Kotovskaia, Anastasia; Meier, Nicola
    Abstract: Large technology firms ("BigTechs") increasingly extend their influence in finance, primarily taking over market shares in payment services. A further expansion of their businesses into the territory of cryptocurrencies could entail new and unprecedented risks for the future, namely for financial stability, competition in the private sector and monetary policy. When creating a regulatory toolbox to address these risks, financial regulatory, antitrust, and platform-specific solutions should be closely intertwined in order to fully absorb all the potential threats and to take account of the complex risks these platform companies bear. This policy letter evaluates the solutions lately proposed by the European Commission, with specific focus on the upcoming regulation of Markets in crypto-assets (MiCA), but also the Digital Markets Act (DMA) and Digital services act (DSA), against the background of cryptocurrencies issued by BigTechs and sheds light on financial regulatory, competition and monetary law issues coming along with the possible designs of these cryptocurrencies.
    Keywords: Cryptocurrencies,Big Techs,MiCA,DMA,DSA,European Commision
    Date: 2022
  18. By: Andrey Shternshis; Piero Mazzarisi; Stefano Marmi
    Abstract: This paper investigates the degree of efficiency for the Moscow Stock Exchange. A market is called efficient if prices of its assets fully reflect all available information. We show that the degree of market efficiency is significantly low for most of the months from 2012 to 2021. We calculate the degree of market efficiency by (i) filtering out regularities in financial data and (ii) computing the Shannon entropy of the filtered return time series. We have developed a simple method for estimating volatility and price staleness in empirical data, in order to filter out such regularity patterns from return time series. The resulting financial time series of stocks' returns are then clustered into different groups according to some entropy measures. In particular, we use the Kullback-Leibler distance and a novel entropy metric capturing the co-movements between pairs of stocks. By using Monte Carlo simulations, we are then able to identify the time periods of market inefficiency for a group of 18 stocks. The inefficiency of the Moscow Stock Exchange that we have detected is a signal of the possibility of devising profitable strategies, net of transaction costs. The deviation from the efficient behavior for a stock strongly depends on the industrial sector it belongs.
    Date: 2022–07

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