nep-fmk New Economics Papers
on Financial Markets
Issue of 2023‒06‒26
eight papers chosen by

  1. Asset prices, collateral and bank lending: the case of Covid-19 and real estate By Horan, Aoife; Jarmulska, Barbara; Ryan, Ellen
  2. More than Words: Twitter Chatter and Financial Market Sentiment By Andrea Ajello; Diego Silva; Travis Adams; Francisco Vazquez-Grande
  3. Reinforcement Learning and Portfolio Allocation: Challenging Traditional Allocation Methods By Lavko, Matus; Klein, Tony; Walther, Thomas
  4. A spectral approach to stock market performance By Ignacio Escanuela Romana; Clara Escanuela Nieves
  5. CBDC: Lesson from a Historical Experience By Grodecka-Messi, Anna; Zhang, Xin
  6. Liquidity buffers and open-end investment funds: containing outflows and reducing fire sales By Dekker, Lennart; Molestina Vivar, Luis; Wedow, Michael; Weistroffer, Christian
  7. Bank private information in CDS markets By Bilan, Andrada; Ongena, Steven; Pancaro, Cosimo
  8. Pricing climate transition risk: Evidence from European corporate CDS By Vozian, Katia; Costola, Michele

  1. By: Horan, Aoife; Jarmulska, Barbara; Ryan, Ellen
    Abstract: Our paper uses credit registry data for the euro area to examine how the banking system transmits asset price shocks to credit via revaluation of collateral and subsequent lending decisions. Specifically we examine banks’ treatment of real estate collateral during the Covid-19 crisis. First we find evidence of significant frictions in the trans-mission of asset price dynamics to collateral values. Despite this we find that lending relationships reliant on real estate collateral received one third less credit following the outbreak of the pandemic and that firms experiencing downward revaluations of their collateral were significantly less likely to be given new loans. Our findings confirm that the collateral channel does create an economically significant link between real estate values and credit but suggest that the banking system’s role in transmission may be more complex than traditional economic theory would imply. JEL Classification: G21, R3, C55
    Keywords: banking, collateral channel, financial accelerator, microdata, real estate
    Date: 2023–06
  2. By: Andrea Ajello; Diego Silva; Travis Adams; Francisco Vazquez-Grande
    Abstract: We build a new measure of credit and financial market sentiment using Natural Language Processing on Twitter data. We find that the Twitter Financial Sentiment Index (TFSI) correlates highly with corporate bond spreads and other price- and survey-based measures of financial conditions. We document that overnight Twitter financial sentiment helps predict next day stock market returns. Most notably, we show that the index contains information that helps forecast changes in the U.S. monetary policy stance: a deterioration in Twitter financial sentiment the day ahead of an FOMC statement release predicts the size of restrictive monetary policy shocks. Finally, we document that sentiment worsens in response to an unexpected tightening of monetary policy.
    Keywords: Financial Market Sentiment; Monetary policy; Natural Language Processing; Stock Returns; Twitter
    JEL: D53 C58 C55 E52
    Date: 2023–05–23
  3. By: Lavko, Matus; Klein, Tony; Walther, Thomas
    Abstract: We test the out-of-sample trading performance of model-free reinforcement learning (RL) agents and compare them with the performance of equally-weighted portfolios and traditional mean-variance (MV) optimization benchmarks. By dividing European and U.S. indices constituents into factor datasets, the RL-generated portfolios face different scenarios defined by these factor environments. The RL approach is empirically evaluated based on a selection of measures and probabilistic assessments. Training these models only on price data and features constructed from these prices, the performance of the RL approach yields better risk-adjusted returns as well as probabilistic Sharpe ratios compared to MV specifications. However, this performance varies across factor environments. RL models partially uncover the nonlinear structure of the stochastic discount factor. It is further demonstrated that RL models are successful at reducing left-tail risks in out-of-sample settings. These results indicate that these models are indeed useful in portfolio management applications.
    Keywords: Asset Allocation, Reinforcement Learning, Machine Learning, Portfolio Theory, Diversification
    JEL: G11 C44 C55 C58
    Date: 2023
  4. By: Ignacio Escanuela Romana; Clara Escanuela Nieves
    Abstract: We pose the estimation and predictability of stock market performance. Three cases are taken: US, Japan, Germany, the monthly index of the value of realized investment in stocks, prices plus the value of dividend payments (OECD data). Once deflated and trend removed, harmonic analysis is applied. The series are taken with and without the periods with evidence of exogenous shocks. The series are erratic and the random walk hypothesis is reasonably falsified. The estimation reveals relevant hidden periodicities, which approximate stock value movements. From July 2008 onwards, it is successfully analyzed whether the subsequent fall in share value would have been predictable. Again, the data are irregular and scattered, but the sum of the first five harmonics in relevance anticipates the fall in stock market values that followed.
    Date: 2023–05
  5. By: Grodecka-Messi, Anna (Monetary Policy Department, Central Bank of Sweden); Zhang, Xin (Research Department, Central Bank of Sweden)
    Abstract: Central banks have been considering the introduction of central bank digital currencies (CBDCs). The theoretical literature indicates that this may influence private banks’ lending activity and their profitability with implications for financial stability. To provide empirical evi dence on this debate, we study the effects of the arrival of a new central-bank issued currency on commercial banks in a historical setup. We use the opening of the Bank of Canada in 1935 as a natural experiment to provide evidence that banks mostly affected by the currency competition experienced lower profitability but did not decrease their lending compared to unaffected peers.
    Keywords: Money and Banking; Central Bank Digital Currencies; Central Banks; Bank Profitability; Bank Lending; Bank of Canada; Banknote Monopoly
    JEL: E42 E50 G21 G28 N22
    Date: 2023–06–01
  6. By: Dekker, Lennart; Molestina Vivar, Luis; Wedow, Michael; Weistroffer, Christian
    Abstract: Using a sample of open-end corporate bond funds domiciled in the euro area, we exploit the COVID-19 market turmoil in March 2020 to examine two channels through which liquidity buffers can reduce procyclicality in the investment fund sector. First, we find that liquidity buffers reduced outflows during March 2020 only to a limited extent. Second, we find that funds entering the crisis with higher liquidity buffers were less likely to involve in cash hoarding and more likely to use cash buffers to meet outflows. Our results suggest that higher liquidity buffers can reduce procyclicality primarily through supporting the liquidity management strategies employed by fund managers. JEL Classification: G01, G11, G23
    Keywords: corporate bond funds, COVID-19 pandemic, investor redemptions, liquidity management
    Date: 2023–06
  7. By: Bilan, Andrada; Ongena, Steven; Pancaro, Cosimo
    Abstract: Can banks trade credit default swaps (CDSs) referenced on their current corporate clients at competitive prices, or are banks penalized for potentially holding private information? To answer this question we merge CDS trades reported under the European Market Infrastructure Regulation (EMIR) with syndicated loans from DealScan, and compare the prices on similar CDSs that the same dealer offers to banks and to other investors. We find that banks lending to a corporation purchase CDSs on this corporation at lower prices, and that, after trading with banks, dealers can earn higher margins on these CDSs when trading with other investors. Our findings suggest that banks hold valuable private information which is shared in their trades with dealers. Dealers then disseminate this information to financial markets. JEL Classification: G14, G21, G23
    Keywords: banks, credit derivatives, EMIR, price discovery, syndicated loans
    Date: 2023–05
  8. By: Vozian, Katia; Costola, Michele
    Abstract: The European low-carbon transition began in the last few decades and is accelerating to achieve net-zero emissions by 2050. This paper examines how climate-related transition indicators of a large European corporate firm relate to its CDS-implied credit risk across various time horizons. Findings show that firms with higher GHG emissions have higher CDS spreads at all tenors, including the 30-year horizon, particularly after the 2015 Paris Agreement, and in prominent industries such as Electricity, Gas, and Mining. Results suggest that the European CDS market is currently pricing, to some extent, albeit small, the exposure to transition risk for a firm across different time horizons. However, it fails to account for a company's efforts to manage transition risks and its exposure to the EU Emissions Trading Scheme. CDS market participants seem to find challenging to risk-differentiate ETS-participating firms from other firms.
    Keywords: climate change, transition risk, credit risk, credit default swap, emissionstrading system (ETS), financial markets
    JEL: G1 E58 G32 Q51 D53
    Date: 2023

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