nep-fmk New Economics Papers
on Financial Markets
Issue of 2020‒10‒12
eight papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Machine learning sentiment analysis, Covid-19 news and stock market reactions By Costola, Michele; Nofer, Michael; Hinz, Oliver; Pelizzon, Loriana
  2. Predictability and the cross-section of expected returns: A challenge for asset pricing models By Schlag, Christian; Semenischev, Michael; Thimme, Julian
  3. Investors' Uncertainty and Forecasting Stock Market Volatility By Ruipeng Liu; Rangan Gupta
  4. A Survey of Empirical Literature on Hedge Fund Performance By Fan Yang
  5. To securitize or to price credit risk? By Danny McGowan; Huyen Nguyen
  6. Climate Sin Stocks: Stock Price Reactions to Global Climate Strikes By Ramelli, Stefano; Ossola, Elisa; Rancan, Michela
  7. Financial integration in the EU28 equity markets: measures and drivers By Nardo, Michela; Ossola, Elisa; Papanagiotou, Evangalia
  8. Stock Markets and Exchange Rate Behaviour of the BRICS By Afees A. Salisu; Juncal Cunado; Kazeem Isah; Rangan Gupta

  1. By: Costola, Michele; Nofer, Michael; Hinz, Oliver; Pelizzon, Loriana
    Abstract: The possibility to investigate the impact of news on stock prices has observed a strong evolution thanks to the recent use of natural language processing (NLP) in finance and economics. In this paper, we investigate COVID-19 news, elaborated with the "Natural Language Toolkit" that uses machine learning models to extract the news' sentiment. We consider the period from January till June 2020 and analyze 203,886 online articles that deal with the pandemic and that were published on three platforms:, and Our findings show that there is a significant and positive relationship between sentiment score and market returns. This result indicates that an increase (decrease) in the sentiment score implies a rise in positive (negative) news and corresponds to positive (negative) market returns. We also find that the variance of the sentiments and the volume of the news sources for Reuters and MarketWatch, respectively, are negatively associated to market returns indicating that an increase of the uncertainty of the sentiment and an increase in the arrival of news have an adverse impact on the stock market.
    Keywords: COVID-19 news,Sentiment Analysis,Stock Markets
    JEL: G10 G14 G15
    Date: 2020
  2. By: Schlag, Christian; Semenischev, Michael; Thimme, Julian
    Abstract: Many modern macro finance models imply that excess returns on arbitrary assets are predictable via the price-dividend ratio and the variance risk premium of the aggregate stock market. We propose a simple empirical test for the ability of such a model to explain the cross-section of expected returns by sorting stocks based on the sensitivity of expected returns to these quantities. Models with only one uncertainty-related state variable, like the habit model or the long-run risks model, cannot pass this test. However, even extensions with more state variables mostly fail. We derive conditions under which models would be able to produce expected return patterns in line with the data and discuss various examples.
    Keywords: asset pricing,cross-section of stock returns,predictability
    JEL: G12 E44 D81
    Date: 2020
  3. By: Ruipeng Liu (Department of Finance, Deakin Business School, Deakin University, Melbourne, VIC 3125, Australia); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa)
    Abstract: This paper examines if incorporating investors' uncertainty, as captured by the conditional volatility of sentiment, can help forecasting volatility of stock markets. In this regard, using the Markov-switching multifractal (MSM) model, we find that investors' uncertainty can substantially increase the accuracy of the forecasts of stock market volatility according to the forecast encompassing test. We further provide evidence that the MSM outperforms the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model.
    Keywords: Investors' uncertainty, Stock market risk, MSM, Volatility forecasting
    Date: 2020–09
  4. By: Fan Yang (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Opletalova 26, 110 00, Prague, Czech Republic)
    Abstract: This paper reviews recent developments in empirical literature analyzing hedge fund performance. Popularity of hedge funds as an investment device has dramatically increased over the past decades. This prompted extensive academic research examining their performance. Systematic examination of hedge fund performance is plagued by the opaqueness of their operations, which complicates risk measurement, and by the lack of well-regulated systematic disclosure, which makes it difficult to obtain comprehensive bias-free data sets. Thus, various studies reach divergent conclusions about hedge funds’ ability to benefit from investment managers’ prowess in generating superior return. We survey this literature and classify it into several streams based on the underlying performance drivers. We compare and contrast conclusions of individual articles and conclude that even though there is little consensus on the magnitude and significance of hedge fund outperformance most published studies seem to suggest that hedge funds earn at least the excess return to cover the fees they charge. The relationship between the regulation and performance is complex but more stringent regulation seems to reduce managerial misreporting.
    Keywords: hedge fund, literature, review, survey
    JEL: G12 G28
    Date: 2020–09
  5. By: Danny McGowan (University of Birmingham); Huyen Nguyen (Halle Institute for Economic Research (IWH), and Friedrich Schiller University Jena)
    Abstract: We evaluate if lenders price or securitize mortgages to mitigate credit risk. Exploiting exogenous variation in regional credit risk created by differences in foreclosure law along US state borders, we find that financial institutions respond to the law in heterogeneous ways. In the agency market where Government Sponsored Enterprises (GSEs) provide implicit loan guarantees, lenders transfer credit risk using securitization and do not price credit risk into mortgage contracts. In the non-agency market, where there is no such guarantee, lenders increase interest rates as they are unable to shift credit risk to loan purchasers. The results inform the debate about the design of loan guarantees, the common interest rate policy, and show that underpricing regional credit risk leads to an increase in the GSEs’ debt holdings by $79.5 billion per annum, exposing taxpayers to preventable losses in the housingmarket.
    Keywords: loan pricing, securitization, credit risk, GSEs
    JEL: G21 G28 K11
    Date: 2020–09–11
  6. By: Ramelli, Stefano (University of Zurich); Ossola, Elisa (European Commission); Rancan, Michela (Universita Politecnica delle Marche)
    Abstract: The First Global Climate Strike on March 15, 2019 has represented a historical turn in climate activism. We investigate the cross-section of European stock price reactions to this event. Looking at a large sample of European firms, we find that the unanticipated success of this event caused a substantial stock price reaction on high-carbon intensity companies. These findings are likely driven by an update of investors' beliefs about the level of environmental social norms in the economy and the anticipation of future developments of climate regulation.
    Keywords: climate risks, stock returns, event study, environmental preferences, sustainable finance, investor attention
    JEL: Q01 G14 G23
    Date: 2020–06
  7. By: Nardo, Michela (European Commission); Ossola, Elisa (European Commission); Papanagiotou, Evangalia (European Commission)
    Abstract: We examine time-invariant and time-varying market integration across European stock markets. Market integration has been increasing especially during the crisis period. Among others, market capitalization, technological developments and overall political uncertainty drive financial integration and systematic volatility, while macroeconomic variables do not impact idiosyncratic volatility. High market integration is associated with decreasing diversification benefit. During crisis periods investors select portfolios that are not explained only by firm characteristics.
    Keywords: financial integration, equity markets, common factor approach, diversification benefits, drivers of integration
    JEL: F3 C23
    Date: 2020–09
  8. By: Afees A. Salisu (Centre for Econometric & Allied Research, University of Ibadan, Ibadan, Nigeria); Juncal Cunado (Economics Department, University of Navarra, Spain); Kazeem Isah (Centre for Econometric & Allied Research, University of Ibadan, Ibadan, Nigeria); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)
    Abstract: Relying on the Uncovered Equity Parity, we examine whether stock returns contain useful information that can be exploited to improve the forecast accuracy of exchange rate movements of the BRICS using a long range of data sample. Thus, we formulate a predictive model that links exchange rate movements to stock return differential between the domestic market and the foreign (US) market. We also test for any probable asymmetric relationship between the two variables while also accounting for the role of observed common (global) factor such as oil price. We find a positive relationship between stock return differential and exchange rate return for three of the BRICS countries namely Brazil, India and South Africa, thus validating the UEP hypothesis while a contrasting evidence is observed for China as well as Russia (after accounting for “asymmetry†effect†). Our in-sample and out-of-sample forecasts validate the significance of the predictive content of stock returns for exchange rate movements of the BRICS while accounting for the role of observed common (global) factor and asymmetry may further improve the forecast accuracy. Our results have implications for portfolio diversification and foreign exchange management.
    Keywords: Stock market, Exchange rate, Uncovered Equity Parity, Forecast evaluation
    JEL: F31 G11 G15
    Date: 2020–09

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