nep-fmk New Economics Papers
on Financial Markets
Issue of 2022‒03‒28
eleven papers chosen by

  1. Stock Market Response to Covid-19, Containment Measures and Stabilization Policies - The Case of Europe By Jens Klose; Peter Tillmann
  2. Machine Learning Models in Stock Market Prediction By Gurjeet Singh
  3. ESG and Systemic Risk By George-Marian Aevoae; Alin Marius Andries; Steven Ongena; Nicu Sprincean
  5. Investor sentiment, volatility and cross-market illiquidity dynamics: A threshold vector autoregression approach By Lin Qi
  6. On the Dynamics of Solid, Liquid and Digital Gold Futures By Toshiko Matsui; Ali Al-Ali; William J. Knottenbelt
  7. Investor Attention to the Fossil Fuel Divestment Movement and Stock Returns By Imane Ouadghiri; Mathieu Gomes; Jonathan Peillex; Guillaume Pijourlet
  8. Can LSTM outperform volatility-econometric models? By German Rodikov; Nino Antulov-Fantulin
  9. QE: Implications for Bank Risk-Taking, Profitability, and Systemic Risk By Supriya Kapoor; Adnan Velic
  10. Flash crashes on sovereign bond markets – EU evidence By Antoine Bouveret; Martin Haferkorn; Gaetano Marseglia; Onofrio Panzarino
  11. Analysing the spillover effects of the South African Reserve Banks bond purchase programme By Rhea Choudhary

  1. By: Jens Klose (THM Business School Giessen); Peter Tillmann (Justus-Liebig-University Giessen)
    Abstract: Policymakers imposed constraints on public life in order to contain the Covid-19 pandemic. At the same time, fiscal and monetary policy implemented a large range of of expansionary measures to limit the economic consequences of the pandemic and stimulate the recovery. In this paper, we assess the response of the equity market as a high-frequency indicator of economic activity to containment and stabilization policies for 29 European economies. We construct indicators of containment and stabilization policies and estimate a range of panel VAR models. The main results are threefold: First, we find that stock markets are highly responsive to containment and stabilization policies. We show that domestic fiscal policy as well as monetary policy support the recovery as reflected in the stock market. Second, expansionary fiscal policy conducted at the European level reduces rather raises stock prices. Third, we estimate the model over subsamples and show that the counter-intuitive stock market response to EU policies is driven by the responses in medium- and high-debt countries. These countries' stock markets are also particularly susceptible to monetary policy announcements.
    Keywords: COVID-19, stabilization policies, lockdown-measures, panel VAR
    JEL: E44 E52 E62
    Date: 2022
  2. By: Gurjeet Singh
    Abstract: The paper focuses on predicting the Nifty 50 Index by using 8 Supervised Machine Learning Models. The techniques used for empirical study are Adaptive Boost (AdaBoost), k-Nearest Neighbors (kNN), Linear Regression (LR), Artificial Neural Network (ANN), Random Forest (RF), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM) and Decision Trees (DT). Experiments are based on historical data of Nifty 50 Index of Indian Stock Market from 22nd April, 1996 to 16th April, 2021, which is time series data of around 25 years. During the period there were 6220 trading days excluding all the non trading days. The entire trading dataset was divided into 4 subsets of different size-25% of entire data, 50% of entire data, 75% of entire data and entire data. Each subset was further divided into 2 parts-training data and testing data. After applying 3 tests- Test on Training Data, Test on Testing Data and Cross Validation Test on each subset, the prediction performance of the used models were compared and after comparison, very interesting results were found. The evaluation results indicate that Adaptive Boost, k- Nearest Neighbors, Random Forest and Decision Trees under performed with increase in the size of data set. Linear Regression and Artificial Neural Network shown almost similar prediction results among all the models but Artificial Neural Network took more time in training and validating the model. Thereafter Support Vector Machine performed better among rest of the models but with increase in the size of data set, Stochastic Gradient Descent performed better than Support Vector Machine.
    Date: 2022–02
  3. By: George-Marian Aevoae (Alexandru Ioan Cuza University - Faculty of Economics and Business Administration); Alin Marius Andries (Alexandru Ioan Cuza University of Iasi; Romanian Academy - Institute for Economic Forecasting); Steven Ongena (University of Zurich - Department of Banking and Finance; Swiss Finance Institute; KU Leuven; NTNU Business School; Centre for Economic Policy Research (CEPR)); Nicu Sprincean (Alexandru Ioan Cuza University of Iasi)
    Abstract: How do changes in Environmental, Social and Governance (ESG) scores influence banks’ systemic risk contribution? We document a beneficial impact of the ESG Combined Score and Governance pillar on banks’ contribution to system-wide distress analysing a panel of 367 publicly listed banks from 47 countries over the period 2007-2020. Stakeholder theory and theory relating social performance to expected returns in which enhanced investments in corporate social responsibility mitigate bank specific risks explain our findings. However, only better corporate governance represents a tool in reducing bank interconnectedness and maintaining financial stability. A similar relationship for banks’ exposure to systemic risk is also found. Our findings stress the importance of integrating banks’ ESG disclosure into regulatory authorities’ supervisory mechanisms as qualitative information.
    Keywords: Systemic Risk; Financial Stability, Corporate Social Responsibility (CSR), Environmental, Social and Governance (ESG) Scores
    JEL: G01 G21 M14
    Date: 2022–03
  4. By: Haim Shalit (BGU)
    Date: 2022
  5. By: Lin Qi
    Abstract: This paper discusses the role that stock market volatility plays in the linkages between the U.S. stock and Treasury bond markets through liquidity under different regimes of investor sentiment in a threshold vector autoregression model. The baseline analysis shows that the interaction between volatility and illiquidity dynamics coincides with the flight-to-safety phenomenon. Moreover, the empirical evidence in the high investor sentiment regime points to the potential existence of flight-from-maturity where market participants tend to shorten their lending maturities for precautionary purposes. This result is robust under either an exogenously or an endogenously chosen investor sentiment threshold value. Further analysis verifies this relationship in the period after the Global Financial Crisis (GFC) and finds evidence of flight-from-maturity in the medium-term and the short-term bond markets. Finally, this paper finds that an adverse stock market volatility shock increases the probability of moving from a high sentiment to a low sentiment regime. This probability gets higher in the post-GFC era.
    Keywords: Liquidity, Flight-from-maturity, Flight-to-safety
    JEL: G10 G12 G40 C32
    Date: 2022–03
  6. By: Toshiko Matsui; Ali Al-Ali; William J. Knottenbelt
    Abstract: This paper examines the determinants of the volatility of futures prices and basis for three commodities: gold, oil and bitcoin -- often dubbed solid, liquid and digital gold -- by using contract-by-contract analysis which has been previously applied to crude oil futures volatility investigations. By extracting the spot and futures daily prices as well as the maturity, trading volume and open interest data for the three assets from 18th December 2017 to 30th November 2021, we find a positive and significant role for trading volume and a possible negative influence of open interest, when significant, in shaping the volatility in all three assets, supporting earlier findings in the context of oil futures. Additionally, we find maturity has a relatively positive significance for bitcoin and oil futures price volatility. Furthermore, our analysis demonstrates that maturity affects the basis of bitcoin and gold positively -- confirming the general theory that the basis converges to zero as maturity nears for bitcoin and gold -- while oil is affected in both directions.
    Date: 2022–02
  7. By: Imane Ouadghiri; Mathieu Gomes (CleRMa - Clermont Recherche Management - ESC Clermont-Ferrand - École Supérieure de Commerce (ESC) - Clermont-Ferrand - UCA [2017-2020] - Université Clermont Auvergne [2017-2020]); Jonathan Peillex; Guillaume Pijourlet
    Abstract: This study investigates whether investor attention to the fossil fuel divestment (FFD) movement is related to the stock returns of firms involved in extracting fossil fuels. We consider three complementary indicators of investor attention to the FFD movement: (1) the US weekly Google Search Volume Index on the topic "fossil fuel divestment," (2) the US weekly media coverage of fossil fuel divestment, and (3) the number of weekly visits to the "fossil fuel divestment" page on Wikipedia. Based on a sample of weekly returns on 1,850 US firms over the period 2012-2020, our econometric estimations report a positive relationship between investor attention to FFD and excess stock returns for US fossil fuel-related firms. Therefore, contrary to what the FFD campaigners might expect, the stigmatization of the fossil fuel industry does not drive down the stock returns on fossil fuel-related firms.
    Keywords: fossil fuel-related firms,investor attention,stock returns,fossil fuel divestment
    Date: 2022–01–29
  8. By: German Rodikov; Nino Antulov-Fantulin
    Abstract: Volatility prediction for financial assets is one of the essential questions for understanding financial risks and quadratic price variation. However, although many novel deep learning models were recently proposed, they still have a "hard time" surpassing strong econometric volatility models. Why is this the case? The volatility prediction task is of non-trivial complexity due to noise, market microstructure, heteroscedasticity, exogenous and asymmetric effect of news, and the presence of different time scales, among others. In this paper, we analyze the class of long short-term memory (LSTM) recurrent neural networks for the task of volatility prediction and compare it with strong volatility-econometric models.
    Date: 2022–02
  9. By: Supriya Kapoor (Technological University Dublin); Adnan Velic (Technological University Dublin)
    Abstract: In the aftermath of the sub-prime mortgage bubble, the Federal Reserve implemented large scale asset purchase (LSAP) programmes that aimed to increase bank liquidity and lending. The excess liquidity created by quantitative easing (QE) in turn may have stimulated bank risk-taking in search of higher profits. Using comprehensive data on balance sheets, risk measures, and daily market returns in the U.S., we investigate the link between QE, bank risk-taking, profitability, and systemic risk. We find that, particularly during the third round of QE, banks that were more exposed to the unconventional monetary policy increased their risk-taking behavior and profitability. However, these banks also reduced their contribution to systemic risk indicating that the implementation of QE had an overall stabilizing effect on the banking sector. These results highlight the different distributional effects of QE.
    Keywords: large-scale asset purchases, quantitative easing, bank risk-taking, systemic risk, expected shortfall
    JEL: E52 E58 G21
    Date: 2022–02
  10. By: Antoine Bouveret (European Securities and Markets Authority); Martin Haferkorn (European Securities and Markets Authority); Gaetano Marseglia (Bank of Italy); Onofrio Panzarino (Bank of Italy)
    Abstract: The development of electronic and automated trading in sovereign bond markets has been accompanied by a more frequent occurrence of flash crashes, i.e. episodes of sudden and abrupt price changes that are to a large extent reversed shortly afterwards. We focus our analysis on two flash events in the German and Italian bond markets and show how liquidity vanished ahead of the crashes, resulting in trades having a large price impact on prices. We document that, during the flash event of 29 May 2018, activity on Italian bonds futures and cash markets diverged: trading activity in futures surged, while it plummeted on the cash market. In addition, we show that the effects of flash events on the liquidity in the affected markets can last up to several weeks. Our findings call for increased monitoring of electronic trading markets, taking into account the pace of financial innovation, and for pursuing more integrated approaches in the presence of highly interlinked markets.
    Keywords: Market liquidity, flash crash, sovereign bonds.
    JEL: G01 G10 G12 G18
    Date: 2022–03
  11. By: Rhea Choudhary
    Abstract: AnalysingthespillovereffectsoftheSouthAf ricanReserveBanksbondpurchaseprogramme
    Date: 2022–03–17

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