nep-fmk New Economics Papers
on Financial Markets
Issue of 2023‒07‒17
fifteen papers chosen by

  1. Does Shareholder Activism Create Value? A Meta-Analysis By Bajzik, Josef; Havranek, Tomas; Irsova, Zuzana; Novak, Jiri
  2. ChatGPT Informed Graph Neural Network for Stock Movement Prediction By Zihan Chen; Lei Nico Zheng; Cheng Lu; Jialu Yuan; Di Zhu
  3. War Discourse and the Cross Section of Expected Stock Returns By David Hirshleifer; Dat Mai; Kuntara Pukthuanthong
  4. Passive Demand and Active Supply: Evidence from Maturity-Mandated Corporate Bond Funds By Lorenzo Bretscher; Lukas Schmid; Tiange Ye
  5. Green Tilts By Lubos Pastor; Robert F. Stambaugh; Lucian A. Taylor
  6. Winners and losers from recent asset price changes By Edmund Crawley; William L. Gamber
  7. Maximally Machine-Learnable Portfolios By Philippe Goulet Coulombe; Maximilian Goebel
  8. A Simple Model of a Central Bank Digital Currency By Mishra, Bineet; Prasad, Eswar
  9. Are Cryptos Different? Evidence from Retail Trading By Shimon Kogan; Igor Makarov; Marina Niessner; Antoinette Schoar
  10. Bank profitability and central bank digital currency By Bellia, Mario; Calès, Ludovic
  11. Causality between Sentiment and Cryptocurrency Prices By Lubdhak Mondal; Udeshya Raj; Abinandhan S; Began Gowsik S; Sarwesh P; Abhijeet Chandra
  12. Crypto-asset markets: structure, stress episodes in 2022 and policy considerations By Giorgio Abate; Nicola Branzoli; Raffaele Gallo
  13. DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting By Lifan Zhao; Shuming Kong; Yanyan Shen
  14. Examining the dependence structure between carry trade and equity market returns in BRICS countries By Makhanya, Kabelo Collen; Bonga-Bonga, Lumengo; Manguzvane, Mathias Mandla
  15. Sovereign bond and CDS market contagion: A story from the Eurozone crisis By Georgios Bampinas; Theodore Panagiotidis; Panagiotis N. Politsidis

  1. By: Bajzik, Josef; Havranek, Tomas; Irsova, Zuzana; Novak, Jiri
    Abstract: We conduct a meta-analysis of 1, 973 estimates of stock price responses to shareholder activism reported in 67 primary studies. We document publication bias in the literature. Corrected activism effects range from 0% to 1.5%. Effects are stronger when shareholder rights are better protected and when stock markets are smaller. Markets respond more positively to activism by individual investors, confrontational activism, and activism aimed at company sale. Estimates based on longer periods, simpler risk-adjustment approaches, more recent and longer datasets, as well as those published in more reputable journals tend to be larger.
    Date: 2023–06–19
  2. By: Zihan Chen; Lei Nico Zheng; Cheng Lu; Jialu Yuan; Di Zhu
    Abstract: ChatGPT has demonstrated remarkable capabilities across various natural language processing (NLP) tasks. However, its potential for inferring dynamic network structures from temporal textual data, specifically financial news, remains an unexplored frontier. In this research, we introduce a novel framework that leverages ChatGPT's graph inference capabilities to enhance Graph Neural Networks (GNN). Our framework adeptly extracts evolving network structures from textual data, and incorporates these networks into graph neural networks for subsequent predictive tasks. The experimental results from stock movement forecasting indicate our model has consistently outperformed the state-of-the-art Deep Learning-based benchmarks. Furthermore, the portfolios constructed based on our model's outputs demonstrate higher annualized cumulative returns, alongside reduced volatility and maximum drawdown. This superior performance highlights the potential of ChatGPT for text-based network inferences and underscores its promising implications for the financial sector.
    Date: 2023–05
  3. By: David Hirshleifer; Dat Mai; Kuntara Pukthuanthong
    Abstract: A war-related factor model derived from textual analysis of media news reports explains the cross section of expected asset returns. Using a semi-supervised topic model to extract discourse topics from 7, 000, 000 New York Times stories spanning 160 years, the war factor predicts the cross section of returns across test assets derived from both traditional and machine learning construction techniques, and spanning 138 anomalies. Our findings are consistent with assets that are good hedges for war risk receiving lower risk premia, or with assets that are more positively sensitive to war prospects being more overvalued. The return premium on the war factor is incremental to standard effects.
    JEL: G0 G02 G1 G10 G11 G4 G41
    Date: 2023–06
  4. By: Lorenzo Bretscher (University of Lausanne; Swiss Finance Institute; and Centre for Economic Policy Research (CEPR)); Lukas Schmid (University of Southern California); Tiange Ye (University of Southern California)
    Abstract: We identify a novel and common exogenous demand shock caused by passive funds in the corporate bond market. Specifically, passive fund demand for corporate bonds displays discontinuity around the maturity cutoffs separating long-term, intermediate-term, and short-term bonds. Passive funds' demand increases significantly upon a bond's crossing of 10-, 5-, and 3-year time-to-maturity cutoffs. We develop a novel identification strategy to study the impact of passive fund demand in the corporate bond market. First, we find that these non-fundamental demand shifts lead to a significant and lasting decrease in yield spreads, as well as persistent liquidity improvements. Second, we find that passive fund demand shocks spill over to the primary market, causing lower issuing yield spreads and actively higher net debt issuance, thereby impacting firms' financing and investment activities.
    Keywords: demand shifts, passive funds, corporate bonds, demand elasticity, ETFs, mutual funds
    Date: 2023–03
  5. By: Lubos Pastor; Robert F. Stambaugh; Lucian A. Taylor
    Abstract: We estimate financial institutions' portfolio tilts that relate to stocks' environmental, social, and governance (ESG) characteristics. We find ESG-related tilts totaling 6% of the investment industry's assets under management in 2021. ESG tilts are significant at both the extensive margin (which stocks are held) and the intensive margin (weights on stocks held). The latter tilts are larger. Institutions divest from brown stocks more by reducing positions than by eliminating them. The industry tilts increasingly toward green stocks, due to only the largest institutions. Other institutions and households tilt increasingly toward brown stocks. UNPRI signatories tilt greener; banks tilt browner.
    JEL: G11 G23
    Date: 2023–06
  6. By: Edmund Crawley; William L. Gamber
    Abstract: Asset prices and interest rates have changed dramatically and unexpectedly over the last two years as the Federal Reserve has raised its policy rate to combat higher inflation. In this note, we clarify the redistributive effects of these asset price changes in terms of welfare, which contrast sharply with those of wealth. Figure 1 depicts changes in the paths of six macroeconomic aggregates in the February 2023 CBO projection relative to their paths in the July 2021 projection.
    Date: 2023–05–12
  7. By: Philippe Goulet Coulombe; Maximilian Goebel
    Abstract: When it comes to stock returns, any form of predictability can bolster risk-adjusted profitability. We develop a collaborative machine learning algorithm that optimizes portfolio weights so that the resulting synthetic security is maximally predictable. Precisely, we introduce MACE, a multivariate extension of Alternating Conditional Expectations that achieves the aforementioned goal by wielding a Random Forest on one side of the equation, and a constrained Ridge Regression on the other. There are two key improvements with respect to Lo and MacKinlay's original maximally predictable portfolio approach. First, it accommodates for any (nonlinear) forecasting algorithm and predictor set. Second, it handles large portfolios. We conduct exercises at the daily and monthly frequency and report significant increases in predictability and profitability using very little conditioning information. Interestingly, predictability is found in bad as well as good times, and MACE successfully navigates the debacle of 2022.
    Date: 2023–06
  8. By: Mishra, Bineet (Cornell University); Prasad, Eswar (Cornell University)
    Abstract: We develop a general equilibrium model that highlights the trade-offs between physical and digital forms of retail central bank money. The key differences between cash and central bank digital currency (CBDC) include transaction efficiency, possibilities for tax evasion, and, potentially, nominal rates of return. We establish conditions under which cash and CBDC can co-exist and show how government policies can in uence relative holdings of cash, CBDC, and other assets. We illustrate how a CBDC can facilitate negative nominal interest rates and helicopter drops, and also how a CBDC can be structured to prevent capital flight from other assets.
    Keywords: central bank digital currency, cash, medium of exchange, store of value, transaction efficiency
    JEL: E4 E5 E61
    Date: 2023–05
  9. By: Shimon Kogan; Igor Makarov; Marina Niessner; Antoinette Schoar
    Abstract: Trading in cryptocurrencies has grown rapidly over the last decade, primarily dominated by retail investors. Using a dataset of 200, 000 retail traders from eToro, we show that they have a different model of the underlying price dynamics in cryptocurrencies relative to other assets. Retail traders in our sample are contrarian in stocks and gold, yet the same traders follow a momentum-like strategy in cryptocurrencies. Individual characteristics do not explain the differences in how people trade cryptocurrencies versus stocks, suggesting that our results are orthogonal to differences in investor composition or clientele effects. Furthermore, our findings are not explained by inattention, differences in fees, or preference for lotterylike stocks. We conjecture that retail investors hold a model of cryptocurrency prices, where price changes imply a change in the likelihood of future widespread adoption, which in turn pushes asset prices further in the same direction.
    JEL: G12 G14 G41
    Date: 2023–06
  10. By: Bellia, Mario (European Commission); Calès, Ludovic
    Abstract: This paper analyzes the potential effect of a European Central Bank Digital Currency (CBDC) on banks’ profitability. We use a large sample of EU banks that span the period from 2007 to 2021 to assess the sensitivity of banks’ profits to the deposits. Using quantile regression, we estimate the conditional profit distribution of a representative bank. We then introduce a shock on the amount of deposits that would be replaced by the CBDC. Our results show that, for a large take-up of CBDC, there might be substantial challenges for the profitability of banks, especially for small banks, that mostly rely on deposits as a source of funding.
    Keywords: Central Bank Digital Currency, CBDC, ECB, bank deposits
    JEL: G18 G28 G32
    Date: 2023–05
  11. By: Lubdhak Mondal; Udeshya Raj; Abinandhan S; Began Gowsik S; Sarwesh P; Abhijeet Chandra
    Abstract: This study investigates the relationship between narratives conveyed through microblogging platforms, namely Twitter, and the value of crypto assets. Our study provides a unique technique to build narratives about cryptocurrency by combining topic modelling of short texts with sentiment analysis. First, we used an unsupervised machine learning algorithm to discover the latent topics within the massive and noisy textual data from Twitter, and then we revealed 4-5 cryptocurrency-related narratives, including financial investment, technological advancement related to crypto, financial and political regulations, crypto assets, and media coverage. In a number of situations, we noticed a strong link between our narratives and crypto prices. Our work connects the most recent innovation in economics, Narrative Economics, to a new area of study that combines topic modelling and sentiment analysis to relate consumer behaviour to narratives.
    Date: 2023–06
  12. By: Giorgio Abate (Bank of Italy); Nicola Branzoli (Bank of Italy); Raffaele Gallo (Bank of Italy)
    Abstract: This paper provides a conceptual framework to analyse risks and vulnerabilities in crypto-asset markets and describes the episodes of stress observed in these markets in 2022. The analysis is used to provide preliminary policy considerations that are relevant for financial stability. We highlight the importance of setting a clear perimeter for financial regulation and of developing global rules to address financial stability risks in the areas covered by financial regulation following the principle "same risk, same regulatory outcome". Authorities could discourage and monitor exposures of supervised or overseen operators to areas not directly covered by financial regulation while trying to incentive the adoption of safe and sound risk management practices by entities involved or operating in these areas. Finally, we provide concrete proposals to implement these considerations.
    Keywords: cryptoassets, financial stability, stablecoin, decentralized finance, leverage, investor run
    JEL: G0 G1 G18
    Date: 2023–06
  13. By: Lifan Zhao; Shuming Kong; Yanyan Shen
    Abstract: Stock trend forecasting is a fundamental task of quantitative investment where precise predictions of price trends are indispensable. As an online service, stock data continuously arrive over time. It is practical and efficient to incrementally update the forecast model with the latest data which may reveal some new patterns recurring in the future stock market. However, incremental learning for stock trend forecasting still remains under-explored due to the challenge of distribution shifts (a.k.a. concept drifts). With the stock market dynamically evolving, the distribution of future data can slightly or significantly differ from incremental data, hindering the effectiveness of incremental updates. To address this challenge, we propose DoubleAdapt, an end-to-end framework with two adapters, which can effectively adapt the data and the model to mitigate the effects of distribution shifts. Our key insight is to automatically learn how to adapt stock data into a locally stationary distribution in favor of profitable updates. Complemented by data adaptation, we can confidently adapt the model parameters under mitigated distribution shifts. We cast each incremental learning task as a meta-learning task and automatically optimize the adapters for desirable data adaptation and parameter initialization. Experiments on real-world stock datasets demonstrate that DoubleAdapt achieves state-of-the-art predictive performance and shows considerable efficiency.
    Date: 2023–06
  14. By: Makhanya, Kabelo Collen; Bonga-Bonga, Lumengo; Manguzvane, Mathias Mandla
    Abstract: This paper contributes to the literature on carry trade by investigating the dynamic correlation and the dependence structure between the US-dollar carry trade and equity markets in the BRICS economies during sample observations that include regular and crisis periods. Furthermore, the nonlinear Granger causality test based on the feed-forward neural networks (FFNN) model is used to assess how global volatility predicts the dynamic correlation between the US-dollar carry trade and equity markets in BRICS. The paper finds that the dynamic correlations between carry trade and equity markets in BRICS are more pronounced during most global crises. Moreover, the results of the SJC model showed that the lower tail dependence between the two series is higher during the various crises. Furthermore, the results of the empirical analysis show that global volatility predicts the dynamic correlations between carry trade and equity markets in BRICS only during crises. Asset managers and investors can benefit from this paper's findings regarding portfolio diversification, risk management, asset allocation, and hedging when dealing with equity assets and carry trades.
    Keywords: Carry trade, BRICS, dynamic conditional correlation, copula.
    JEL: C1 F3 G15
    Date: 2023
  15. By: Georgios Bampinas (Department of Economics and Regional Development, Panteion University of Social and Political Sciences, Greece); Theodore Panagiotidis (Department of Economics, University of Macedonia, Greece); Panagiotis N. Politsidis (Finance Department, Audencia Business School, France)
    Abstract: We examine the asymmetric and nonlinear nature of the cross- and intra-market linkages of eleven EMU sovereign bond and CDS markets during 2006-2018. By adopting the excess correlation concept of Bekaert et al. (2005) and the local Gaussian correlation approach of Tjøstheim and Hufthammer (2013), we find that contagion phenomena occurred during two major phases. The first, extends from late 2009 to mid 2011 and concerns the outright contagion transmission from EMU South bond markets towards all European CDS markets. The second, is during the revived fears of a Greek exit in November 2011 and is characterized by contagion from (i) CDS spreads in the EMU South towards bond yields in the same bloc and Belgium, and (ii) from Italian and Spanish CDS spreads towards all European CDS spreads. Consistent with their “too big to bail out” status, Italy and Spain emerge as pivotal for the evolution of sovereign credit risk across the Eurozone. Our examination of the relevant mechanisms, highlights the importance of credit risk over liquidity risk, and the containment effect of the naked CDS ban.
    Keywords: sovereign bond market, sovereign CDS market, nonlinear dependence, contagion, local Gaussian correlation
    JEL: G01 G14 G15 C1 C58
    Date: 2023–06

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