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on Financial Markets |
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Issue of 2020‒08‒24
nineteen papers chosen by |
| By: | Alqaralleh, Huthaifa; Canepa, Alessandra; Zanetti Chini, Emilio (University of Turin) |
| Abstract: | In this study, we examine the influence of the COVID-19 pandemic on stock market contagion. Empirical analysis is conducted on six major stock markets using a wavelet-copula GARCH approach to account for both the time and the frequency aspects of stock market correlation. We find strong evidence of contagion in the stock markets under consideration during the COVID-19 pandemic.. |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:uto:dipeco:202012&r=all |
| By: | Tirupam Goel; José María Serena Garralda |
| Abstract: | Borrowing by non-financial firms in global debt markets surged following the Covid-19 shock. Bond issuance boomed, while syndicated loan originations trailed. Led by easier access to bond markets, large firms significantly increased their borrowing. The rest of the firms faced bottlenecks due to their reliance on a strained syndicated loan market and hurdles in switching to bond markets. Large firms, which had lower cash buffers pre-crisis than smaller firms, used part of the fresh credit to raise their buffers in addition to meeting liquidity shortfalls. |
| Date: | 2020–08–14 |
| URL: | http://d.repec.org/n?u=RePEc:bis:bisblt:29&r=all |
| By: | David P. Glancy; Max Gross; Felicia Ionescu |
| Abstract: | Banks experienced significant balance sheet expansions in March 2020 due to unprecedented increases in commercial and industrial (C&I) loans and deposit funding. According to the Federal Reserve's H.8 data, "Assets and Liabilities of Commercial Banks in the U.S.", C&I loans increased by nearly $480 billion in March—the largest monthly increase in the history of this series, surpassing the nearly $90 billion increase in C&I loans in the six weeks following Lehman Brothers' collapse in 2008. |
| Date: | 2020–07–31 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfn:2020-07-31-1&r=all |
| By: | Hardik A. Marfatia (Northeastern Illinois University); Rangan Gupta (University of Pretoria); Stephen M. Miller (University of Nevada, Las Vegas) |
| Abstract: | This paper examines the effect of fiscal policy on financial markets over a long span of 125 years. Unlike existing studies that mainly focus on monetary policy shocks and model-based identification of fiscal policy shocks, we use a time-varying parameter model to study the effect of fiscal policy with much cleaner and direct identification of fiscal policy shocks. In addition, we extend our analysis by measuring the response volatility in these markets and separately study the effects of good and bad components of volatility. We find significant time-variation in the response of stock and bond market returns and volatility. The overall response of the stock market exceeds that of bond markets, with more pronounced effects in the pre-1950 period than in the last six decades. Fiscal consolidation generates long-term benefits that positively affect financial markets in the latter part of the 20th century, thus providing new insights into the dynamic role of fiscal policy. |
| Keywords: | Fiscal Policy; Time-Varying impact; Financial returns and risks |
| JEL: | E5 C32 G14 |
| Date: | 2020–08 |
| URL: | http://d.repec.org/n?u=RePEc:uct:uconnp:2020-12&r=all |
| By: | Dominique Pépin (University of Poitiers); Stephen M. Miller (University of Nevada, Las Vegas) |
| Abstract: | We investigate the time variations of the relative risk aversion parameter of a U.S. representative agent using 60 years of stock market data. We develop a methodology to identify the variables that explain the variations of risk aversion, based on an asset pricing model without valuation (or preference) risk. In this framework, the variables that predict the excess return of a market index (but not the second moments) also explain the variations of risk aversion. To wit, the variables include the price-dividend ratio and the short-term interest rate. A shock on the dividend-price ratio exerts a positive, highly persistent, though modest, effect on risk aversion, while a shock on the short-term interest rate exerts a highly negative, less persistent effect. The resulting measure of risk aversion follows a macroeconomically and financially countercyclical pattern. |
| Keywords: | Time-varying risk aversion, Price-dividend ratio, Short-term interest rate, Return predictors |
| JEL: | G10 G12 G17 |
| Date: | 2020–08 |
| URL: | http://d.repec.org/n?u=RePEc:uct:uconnp:2020-09&r=all |
| By: | Lysle Boller; Fiona Scott Morton |
| Abstract: | We test if an increase in common ownership changes future expected profits with an event study method. We collect instances of a stock entering the S&P 500 index and identify its product market competitors. We measure the change in institutional and common ownership (with product market rivals) and find that entering stocks experience a significant increase in both. We measure the stock returns of the entrant's product market rivals upon the entry news. We find that increases in common ownership (driven by the whole vector of ownership similarity) cause increases in stock returns, consistent with a hypothesis that common ownership raises profits. |
| JEL: | G14 G34 L4 L41 |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:27515&r=all |
| By: | Sumit Agarwal; Xudong An; Lawrence R. Cordell; Raluca Roman |
| Abstract: | Using Federal Reserve (Fed) confidential stress test data, we exploit the gap between the Fed and bank capital projections as an exogenous shock to banks and analyze how this shock is transmitted to consumer credit markets. First, we document that banks in the 90th percentile of the capital gap reduce their new supply of risky credit by 13 percent compared with those in the 10th percentile and cut their overall credit card risk exposure on an annual basis. Next, we show that these banks find alternative ways to remain competitive and attract customers by lowering interest rates and offering more rewards and promotions to select groups of borrowers. Finally, we show that consumers at banks with a gap increase their credit card spending and debt payoff and at the same time experience fewer delinquencies. We also show that our results are generalizable to other lending products such as mortgages and home equity. Overall, our results demonstrate a positive feedback loop among credit supply, credit usage, and credit performance due to the stress tests. |
| Keywords: | bank stress tests; credit supply; cost of credit; credit usage; credit performance; credit cards |
| JEL: | G21 G28 Z1 |
| Date: | 2020–07–31 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedpwp:88463&r=all |
| By: | Longbing Cao |
| Abstract: | Smart FinTech has emerged as a new area that synthesizes and transforms AI and finance, and broadly data science, machine learning, economics, etc. Smart FinTech also transforms and drives new economic and financial businesses, services and systems, and plays an increasingly important role in economy, technology and society transformation. This article presents a highly summarized research overview of smart FinTech, including FinTech businesses and challenges, various FinTech-associated data and repositories, FinTech-driven business decision and optimization, areas in smart FinTech, and research methods and techniques for smart FinTech. |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2007.12681&r=all |
| By: | A. R. Provenzano; D. Trifir\`o; A. Datteo; L. Giada; N. Jean; A. Riciputi; G. Le Pera; M. Spadaccino; L. Massaron; C. Nordio |
| Abstract: | In this work we build a stack of machine learning models aimed at composing a state-of-the-art credit rating and default prediction system, obtaining excellent out-of-sample performances. Our approach is an excursion through the most recent ML / AI concepts, starting from natural language processes (NLP) applied to economic sectors' (textual) descriptions using embedding and autoencoders (AE), going through the classification of defaultable firms on the base of a wide range of economic features using gradient boosting machines (GBM) and calibrating their probabilities paying due attention to the treatment of unbalanced samples. Finally we assign credit ratings through genetic algorithms (differential evolution, DE). Model interpretability is achieved by implementing recent techniques such as SHAP and LIME, which explain predictions locally in features' space. |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2008.01687&r=all |
| By: | Anindya Goswami; Sharan Rajani; Atharva Tanksale |
| Abstract: | We propose three different data driven approaches for pricing European style call options using supervised machine-learning algorithms. The proposed approaches are tested on two stock market indices, NIFTY50 and BANKNIFTY from the Indian equity market. Although neither historical nor implied volatility is used as an input, the results show that the trained models have been able to capture the option pricing mechanism better than or similar to the Black Scholes formula for all the experiments. Our choice of scale free I/O allows us to train models using combined data of multiple different assets from a financial market. This not only allows the models to achieve far better generalization and predictive capability, but also solves the problem of paucity of data, the primary limitation of using machine learning techniques. We also illustrate the performance of the trained models in the period leading up to the 2020 Stock Market Crash, Jan 2019 to April 2020. |
| Date: | 2020–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2008.00462&r=all |
| By: | Mahdi Ghaemi Asl (Kharazmi University); Giorgio Canarella (University of Nevada, Las Vegas); Stephen M. Miller (University of Nevada, Las Vegas) |
| Abstract: | This paper investigates returns and volatility transmission between SPGCE (index of clean energy stocks), SPGO (index of oil and gas stocks), two non-renewable energy commodities (natural gas and crude oil), and three products of crude oil distillation (heating oil, gasoline, and propane). We estimate a VAR(1) asymmetric BEKK-MGARCH(1,1) using daily U.S. data from March 1, 2010, to February 25, 2020. The empirical findings reveal a vast heterogeneity in spillover patterns of returns, volatilities, and shocks. We employ the empirical results to derive optimal portfolio weights, hedge ratios, and effectiveness measures for SPGCE and SPGO diversified portfolios. We find dynamic diversification advantages of energy commodities, especially heating oil, for energy-related stock markets. We also find that SPGCE and SPGO stocks possess the highest average optimal weight and hedging effectiveness for each other, implying that the positive performance of SPGCE stocks considerably compensates for the negative performance of SPGO stocks. For investors and regulators, the advancement and implementation of clean energy programs and policies, while reducing environmental debt and enhancing “green” growth and sustainable development, provide instruments and strategies to hedge the equity risks inherent in the oil and gas industry. |
| Keywords: | Clean energy stocks, Oil and gas stocks, Asymmetric BEKK, Dynamic Optimal Portfolios. |
| JEL: | Q43 G11 C33 |
| Date: | 2020–08 |
| URL: | http://d.repec.org/n?u=RePEc:uct:uconnp:2020-07&r=all |
| By: | James Collin Harkrader; Michael Puglia |
| Abstract: | This FEDS Note aims to share insights on Treasury cash transactions reported in the Financial Industry Regulatory Authority (FINRA)'s Trade Reporting and Compliance Engine (TRACE). Following earlier joint FEDS Notes and Liberty Street Economics blog posts that examined aggregate trading volume in the Treasury cash market across venues and security types, this post sheds light on the trading activity of Principal Trade Firms (PTFs) and other market participants that are not registered broker-dealer members of FINRA. |
| Date: | 2020–08–04 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfn:2020-08-04&r=all |
| By: | Vinicius Ratton Brandi |
| Abstract: | The Efficient Market Hypothesis is one of the most popular subjects in the empirical finance literature. Previous studies in the stock markets, which are mostly based on fixed time price variations, do not provide conclusive findings, in which evidence of short-term predictability varies according to different samples and methodologies. In this work, we propose a novel approach and use drawdowns and drawups as trig-gers in order to investigate the existence of short-term abnormal returns in the stock markets. As these measures are not computed within a fixed time horizon, they are flexible enough to capture time-dependent subordinated processes that could be driving market under or overreaction. According to our results the Efficient Market Hypothesis is supported by the majority of estimates. Results also provide stronger support for underreation hypothesis than for overreaction, with the highest preva-lence of return continuations than reversals. Evidence for the Uncertain Information Hypothesis is present in some markets, mainly after events of lower magnitude. |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:bcb:wpaper:529&r=all |
| By: | Tim D. Maurer; Thomas Nitschka |
| Abstract: | We decompose unexpected movements in the stock market returns of 40 countries into different news components to assess why expansionary US monetary policy surprises are good news for stock markets. Our results suggest that prior to the zero lower bound (ZLB) period, federal funds rate surprises affect foreign stock markets mainly because such surprises are associated with news about future real interest rates. The effects of forward guidance surprises are negligible. At the ZLB, large-scale asset purchases (LSAP) reflect more than commitment to forward guidance. LSAP surprises constitute cash-flow news, while unanticipated forward guidance primarily reflects real interest rate news. |
| Keywords: | International spillovers, news, monetary policy, stock returns, vector autoregression |
| JEL: | E44 E52 F36 G15 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:snb:snbwpa:2020-10&r=all |
| By: | Jiawei Du |
| Abstract: | We studied the volatility and cross-sectional return dispersion effect of S&P Health Care Sector under the covid-19 epidemic. We innovatively used the Google index to proxy the impact of the epidemic and modeled the volatility. We also studied the influencing factors of the log-return of S&P Energy Sector and S&P Health Care Sector. We found that volatility is significantly affected by both the epidemic and cross-sectional return dispersion, and the coefficients in front of them are all positive, which means that the herding behaviour did not exist and as the cross-sectional return dispersion increases and the epidemic becomes more severe, the volatility of stock returns is also increasing. We also found that the epidemic has a significant negative impact on the return of the energy sector, and finally we provided our suggestions to investors. |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2007.11546&r=all |
| By: | Ricardo Correa; Wenxin Du; Gordon Y. Liao |
| Abstract: | We characterize how U.S. global systemically important banks (GSIBs) supply short-term dollar liquidity in repo and foreign exchange swap markets in the post-Global Financial Crisis regulatory environment and serve as the "lenders-of-second-to-last-resort". Using daily supervisory bank balance sheet information, we find that U.S. GSIBs modestly increase their dollar liquidity provision in response to dollar funding shortages, particularly at period-ends, when the U.S. Treasury General Account balance increases, and during the balance sheet taper of the Federal Reserve. The increase in the dollar liquidity provision is mainly financed by reducing excess reserve balances at the Federal Reserve. Intra-firm transfers between depository institutions and broker-dealer subsidiaries within the same bank holding company are crucial to this type of "reserve-draining" intermediation. Finally, we discuss factors that contributed to the repo spike in September 2019 and the subseque nt response of U.S. GSIBs to recent policy interventions by the Federal Reserve. |
| Keywords: | Liquidity; Global banks; Repos; Reserves; Covered interest rate parity |
| JEL: | G20 F30 E40 |
| Date: | 2020–07–08 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgif:1289&r=all |
| By: | Alex Aronovich; Andrew C. Meldrum |
| Abstract: | Long-term U.S. interest rates have fallen substantially over the last two decades. The 5-to-10-year nominal forward interest rate implied by the prices of U.S. Treasury securities is now about 7 percentage points lower than it was at the start of the 1990s. |
| Date: | 2020–08–03 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfn:2020-08-03&r=all |
| By: | Marcin Chlebus (Faculty of Economic Sciences, University of Warsaw); Michał Dyczko (Faculty of Mathematics and Computer Science, Warsaw University of Technology); Michał Woźniak (Faculty of Economic Sciences, University of Warsaw) |
| Abstract: | The main aim of this paper was to predict daily stock returns of Nvidia Corporation company quoted on Nasdaq Stock Market. The most important problems in this research are: statistical specificity of return ratios i.e. time series might occur to be a white noise and the fact of necessity of applying many atypical machine learning methods to handle time factor influence. The period of study covered 07/2012 - 12/2018. Models used in this paper were: SVR, KNN, XGBoost, LightGBM, LSTM, ARIMA, ARIMAX. Features which, were used in models comes from such classes like: technical analysis, fundamental analysis, Google Trends entries, markets related to Nvidia. It was empirically proved that there is a possibility to construct prediction model of Nvidia daily return ratios which can outperform simple naive model. The best performance was obtained by SVR based on stationary attributes. Generally, it was shown that models based on stationary variables perform better than models based on stationary and non-stationary variables. Ensemble approach designed especially for time series failed to make an improvement in forecast precision. It seems that usage of machine learning models for the problem of time series with various explanatory variable classes brings good results. |
| Keywords: | nvidia, stock returns, machine learning, technical analysis, fundamental analysis, google trends, stationarity, ensembling |
| JEL: | C32 C38 C44 C51 C52 C61 C65 G11 G15 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:war:wpaper:2020-22&r=all |
| By: | Alexandre de Carvalho; Thiago Trafane Oliveira Santos |
| Abstract: | The ex-post or historical equity risk premium in Brazil is low compared to other countries. In this paper we seek to evaluate whether this is a result of a compressed ex-ante equity risk premium, using two different approaches. First, we investigate the effects of government-controlled shareholders, which could lower the risk premium if the government is also interested in nonpecuniary benefits. To verify this, we estimate the Brazilian equity risk premium from 2002 to 2017 using cross-section regressions based on the CAPM and the Gordon model, but supposing stocks are priced differently by government and private investors. An important feature of this approach is that we control for the possible impact of the government as firm’s manager on the perceived risk of the firm. Our results suggest the government does not compress the equity risk premium, although the government as a manager seems to influence the firms’ risk. Second, we decompose the Brazilian equity risk premium using a global CAPM estimated with quarterly data from 47 countries and find it is consistent with international risk premia. Therefore, the findings from the two approaches indicate the low ex-post risk premium in Brazil seems to be a consequence of a relatively short time series rather than a Brazilian idiosyncrasy. |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:bcb:wpaper:527&r=all |