|
on Financial Markets |
|
Issue of 2021‒05‒17
ten papers chosen by |
| By: | Hai Anh La (National Centre for Social and Economic Modelling, University of Canberra, Australia); Riyana Miranti (National Centre for Social and Economic Modelling, University of Canberra, Australia) |
| Abstract: | This study investigates the impact of various government interventions on the spread of COVID-19 as well as stock markets in South-East and East Asia. It finds that stricter interventions – including gathering restrictions, public event cancellations, and mask requirements – helped mitigate the severity of the pandemic significantly in the region. Total border closures had a moderate effect on flattening COVID-19 spread, especially during the onset of the pandemic. Other policies, such as school closures or stay-at-home orders, worked effectively later in the pandemic. The study also shows evidence of herding behaviours in regional stock markets during the pandemic. School closures, gathering restrictions, stay-at-home orders, domestic travelling bans, robust testing policies, and government income support programmes tended to reduce herding behaviour. More stock market integration is found during the onset of the pandemic, compared to the periods before and later in the pandemic, implying the short-term impact of a sudden shock from COVID-19. |
| Keywords: | COVID-19, stock markets, minimum spanning trees, government interventions |
| JEL: | G15 G18 C13 H12 |
| Date: | 2021–04–29 |
| URL: | http://d.repec.org/n?u=RePEc:era:wpaper:dp-2021-07&r= |
| By: | Baron, Matthew; Laeven, Luc; Pénasse, Julien; Usenko, Yevhenii |
| Abstract: | We investigate asset returns around banking crises in 44 advanced and emerging economies from 1960 to 2018. In contrast to the view that buying assets during banking crises is a profitable long-run strategy, we find returns of equity and other asset classes generally underperform after banking crises. While prices are depressed during crises and partially recover after acute stress ends, consistent with theories of fire sales and intermediary-based asset pricing, we argue that investors do not fully anticipate the consequences of debt overhang, which result in lower long-run dividends. Our results on bank stock underperformance suggest that government-funded bank recapitalizations can often lead to substantial taxpayer losses. JEL Classification: G11, G14, G15, G41 |
| Keywords: | financial crises, fire sales, investments, returns |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20212548&r= |
| By: | Sinan Gokkaya; Xi Liu; René M. Stulz |
| Abstract: | We open the black box of the M&A decision process by constructing a comprehensive sample of US firms with specialized M&A staff. We investigate whether specialized M&A staff improves acquisition performance or facilitates managerial empire building instead. We find that firms with specialized M&A staff make better acquisitions when acquisition performance is measured by stock price reactions to announcements, long-run stock returns, operating performance, divestitures, and analyst earnings forecasts. This effect does not hold when the CEO is powerful, overconfident, or entrenched. Acquisitions by firms without specialized staff do not create value, on average. We provide evidence on mechanisms through which specialized M&A staff improves acquisition performance. For identification, we use the staggered recognition of inevitable disclosure doctrine as a source of exogenous variation in the employment of specialized M&A staff. |
| JEL: | G14 G24 G30 G34 |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:28778&r= |
| By: | Ghysels, Eric; Liu, Hanwei; Raymond, Steve |
| Abstract: | The U.S. equity markets are largely driven by actions of institutional investors. Using quarterly 13-F holdings, we construct the Herfindahl-Hirschman Index of institutional investor concentration as a measure of granularity. We study how granularity affects: the cross-section of returns, conditional variances and downside risk. Next, we study the impact of granularity in a demand-driven asset pricing model introduced by Koijen and Yogo (2019). We derive a decomposition of expected returns in terms of equally weighted asset demands and granularity residuals. Using this decomposition, we revisit the empirical stylized facts pertaining to granularity and asset pricing. |
| Date: | 2021–01 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:15654&r= |
| By: | Emily Johnston-Ross; Song Ma; Manju Puri |
| Abstract: | This paper investigates the role of private equity (PE) in failed bank resolutions after the 2008 financial crisis, using proprietary FDIC failed bank acquisition data. PE investors made substantial investments in underperforming and riskier failed banks, particularly in geographies where local banks were also distressed, filling the gap created by a weak, undercapitalized banking sector. Using a quasi-random empirical design based on detailed bidding information, we show PE-acquired banks performed better ex post, with positive real effects for the local economy. Overall, PE investors had a positive role in stabilizing the financial system through their involvement in failed bank resolution. |
| JEL: | E65 G18 G21 |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:28751&r= |
| By: | Sihang Chen; Weiqi Luo; Chao Yu |
| Abstract: | In recent years, quantitative investment methods combined with artificial intelligence have attracted more and more attention from investors and researchers. Existing related methods based on the supervised learning are not very suitable for learning problems with long-term goals and delayed rewards in real futures trading. In this paper, therefore, we model the price prediction problem as a Markov decision process (MDP), and optimize it by reinforcement learning with expert trajectory. In the proposed method, we employ more than 100 short-term alpha factors instead of price, volume and several technical factors in used existing methods to describe the states of MDP. Furthermore, unlike DQN (deep Q-learning) and BC (behavior cloning) in related methods, we introduce expert experience in training stage, and consider both the expert-environment interaction and the agent-environment interaction to design the temporal difference error so that the agents are more adaptable for inevitable noise in financial data. Experimental results evaluated on share price index futures in China, including IF (CSI 300) and IC (CSI 500), show that the advantages of the proposed method compared with three typical technical analysis and two deep leaning based methods. |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2105.03844&r= |
| By: | Nathan Converse (Federal Reserve Board); Eduardo Levy Yeyati (Universidad Torcuato Di Tella/The Brookings Institution); Tomas Williams (George Washington University) |
| Abstract: | Since the early 2000s exchange-traded funds (ETFs) have grown to become an important in- vestment vehicle worldwide. In this paper, we study how their growth affects the sensitivity of international capital flows to the global financial cycle. We combine comprehensive fund- level data on investor flows with a novel identification strategy that controls for unobservable time-varying economic conditions at the investment destination. For dedicated emerging market funds, we find that the sensitivity of investor flows to global financial conditions for equity (bond) ETFs is 2.5 (2.25) times higher than for equity (bond) mutual funds. In turn, we show that in countries where ETFs hold a larger share of financial assets, total cross-border equity flows and prices are significantly more sensitive to global financial conditions. We conclude that the growing role of ETFs as a channel for international capital flows amplifies the global financial cycle in emerging markets. |
| Keywords: | exchange-traded funds mutual funds global financial cycle global risk push and pull factors capital flows emerging markets |
| JEL: | F32 G11 G15 G23 |
| Date: | 2021–04 |
| URL: | http://d.repec.org/n?u=RePEc:aoz:wpaper:57&r= |
| By: | Paul Lavery; Jose-Maria Serena; Marina-Eliza Spaliara; Serafeim Tsoukas |
| Abstract: | We analyse the impact of private equity buyouts on firm exports, on a panel of UK non-financial firms over 2004-2017. Using difference-in-differences estimations, we show that private equity ownership increases the probability of exporting, the value of exports, and the export to sales ratio. We further show that the positive impact of private equity ownership on exports holds only after private-to-private buyouts, or acquisitions of small or young target firms. Our findings suggest that private equity investors mitigate the credit constraints faced by their portfolio companies, hence boosting their exports. |
| Keywords: | Private equity buyouts; exporting; credit constraints; transactions |
| JEL: | G34 G32 |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:gla:glaewp:2021_06&r= |
| By: | Abdi, Farshid; Kormanyos, Emily; Pelizzon, Loriana; Getmansky, Mila; Simon, Zorka |
| Abstract: | We focus on the role of social media as a high-frequency, unfiltered mass information transmission channel and how its use for government communication affects the aggregate stock markets. To measure this effect, we concentrate on one of the most prominent Twitter users, the 45th President of the United States, Donald J. Trump. We analyze around 1,400 of his tweets related to the US economy and classify them by topic and textual sentiment using machine learning algorithms. We investigate whether the tweets contain relevant information for financial markets, i.e. whether they affect market returns, volatility, and trading volumes. Using high-frequency data, we find that Trump's tweets are most often a reaction to pre-existing market trends and therefore do not provide material new information that would influence prices or trading. We show that past market information can help predict Trump's decision to tweet about the economy. |
| Keywords: | Market efficiency,Social media,Twitter,High-frequency event study,Machine learning,ETFs |
| JEL: | G10 G14 C58 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:safewp:314&r= |
| By: | Thilini Mahanama; Abootaleb Shirvani; Svetlozar Rachev |
| Abstract: | Crime can have a volatile impact on investments. Despite the potential importance of crime rates in investments, there are no indices dedicated to evaluating the financial impact of crime in the United States. As such, this paper presents an index-based insurance portfolio for crime in the United States by utilizing the financial losses reported by the Federal Bureau of Investigation for property crimes and cybercrimes. Our research intends to help investors envision risk exposure in our portfolio, gauge investment risk based on their desired risk level, and hedge strategies for potential losses due to economic crashes. Underlying the index, we hedge the investments by issuing marketable European call and put options and providing risk budgets (diversifying risk to each type of crime). We find that real estate, ransomware, and government impersonation are the main risk contributors. We then evaluate the performance of our index to determine its resilience to economic crisis. The unemployment rate potentially demonstrates a high systemic risk on the portfolio compared to the economic factors used in this study. In conclusion, we provide a basis for the securitization of insurance risk from certain crimes that could forewarn investors to transfer their risk to capital market investors. |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2105.03514&r= |