|
on Financial Markets |
|
Issue of 2020‒06‒29
seven papers chosen by |
| By: | Seven, Unal; Yilmaz, Fatih |
| Abstract: | The actual macroeconomic impacts of the COVID-19 pandemic will be realized over time; however, its impact on financial markets was much faster and dramatic. Following the spread of the pandemic, most global equity markets experienced significant falls and started to rebound with the announcement of economic rescue packages. However, the equity markets’ responses to the packages have varied across countries. In this paper, we first look at what may explain the differences in the equity market falls across-countries. Secondly, we study the systematic relation between the size and the type of rescue packages, the severity of the outbreak and the recovery performance of equity markets. Using cross-country OLS regressions, we find that investors’ immediate reaction to equity markets in countries with higher pandemic related deaths is more negative. Moreover, our results show that not all types of rescue packages are effective in restoring investors’ valuation of equity markets. Among different types, fiscal stimulus support seems to be a stronger predictor of equity market recovery performance. We also find that the severity of the outbreak in each country affected the equity markets’ reactions. |
| Keywords: | COVID-19, Stock Markets, Stimulus Packages, Recovery |
| JEL: | G15 G18 |
| Date: | 2020–06–08 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:100987&r=all |
| By: | Franklin Allen; Xian Gu; Julapa Jagtiani |
| Abstract: | The intersection of finance and technology, known as fintech, has resulted in the dramatic growth of innovations and has changed the entire financial landscape. While fintech has a critical role to play in democratizing credit access to the unbanked and thin-file consumers around the globe, those consumers who are currently well served also turn to fintech for faster services and greater transparency. Fintech, particularly the blockchain, has the potential to be disruptive to financial systems and intermediation. Our aim in this paper is to provide a comprehensive fintech literature survey with relevant research studies and policy discussion around the various aspects of fintech. The topics include marketplace and peer-to-peer lending, credit scoring, alternative data, distributed ledger technologies, blockchain, smart contracts, cryptocurrencies and initial coin offerings, central bank digital currency, robo-advising, quantitative investment and trading strategies, cybersecurity, identity theft, cloud computing, use of big data and artificial intelligence and machine learning, identity and fraud detection, anti-money laundering, Know Your Customers, natural language processing, regtech, insuretech, sandboxes, and fintech regulations. |
| Keywords: | fintech; marketplace lending; P2P; alternative data; DLT; blockchain; robo advisor; regtech; insuretech; cryptocurrencies; ICOs; CBDC; cloud computing; AML; KYC; NLP; fintech regulations |
| JEL: | G21 G28 G18 L21 |
| Date: | 2020–05–28 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedpwp:88120&r=all |
| By: | Giulio Bottazzi; Francesco Cordoni; Giulia Livieri; Stefano Marmi |
| Abstract: | This paper presents two stocks recommendation systems based on a stochastic characterization of firm present value that extends the conventional discounted cash flow analysis. In the Single-Stock Quantile recommendation system, the market price of a company's stocks is compared with the estimated distribution of the company fair value to obtain an individual measure of mispricing, while in the Cross-Sectional Quantile system, a relative measure of mispricing is built using the fair value distribution of all firms at the same time. Both systems use mispricing information to build sell side and buy side portfolios. We provide a series of statistical exercises that show how these portfolios can consistently deliver significant excess returns, also when rebalancing costs are accounted for. |
| Keywords: | Stochastic Discounted Cash Flow; Asset Valuation; Valuation Uncertainty; Portfolio Strategy. |
| Date: | 2020–06–09 |
| URL: | http://d.repec.org/n?u=RePEc:ssa:lemwps:2020/17&r=all |
| By: | Biesinger, Markus; Bircan, Cagatay; Ljungqvist, Alexander P. |
| Abstract: | We open up the black box of value creation in private equity with the help of confidential information on value creation plans and their execution. Plans are tailored to each portfolio company's needs and circumstances, have become more hands-on, and vary with deal type, ownership, growth strategy, and geographic focus. Successful execution is subject to resource constraints, economies of specialization, and diminishing returns, and varies systematically across funds. Successful execution is a key driver of investor returns, especially in growth, buyout, and secondary deals. Company operations and profitability improve in ways consistent with successful execution, even beyond PE funds' exit. |
| Keywords: | financial returns; Growth investing; Machine Learning; private equity; secondaries; value creation; venture capital |
| JEL: | G11 G24 G30 G32 L26 |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:14676&r=all |
| By: | Chen, Andrew Y.; Zimmermann, Tom |
| Abstract: | We provide data and code that successfully reproduces nearly all crosssectional stock return predictors. Unlike most metastudies, we carefully examine the original papers to determine whether our predictability tests should produce t-stats above 1.96. For the 180 predictors that were clearly significant in the original papers, 98% of our reproductions find t-stats above 1.96. For the 30 predictors that had mixed evidence, our reproductions find t-stats of 2 on average. We include an additional 105 characteristics and 945 portfolios with alternative rebalancing frequencies to nest variables used in other metastudies. Our data covers all portfolios in Hou, Xue and Zhang (2017); 98% of the portfolios in McLean and Pontiff (2016); 90% of the characteristics from Green, Hand, and Zhang (2017); and 90% of the firm-level predictors in Harvey, Liu, and Zhu (2016) that use widelyavailable data. |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfrwps:2004&r=all |
| By: | Limbach, Peter; Rau, P. Raghavendra; Schürmann, Henrik |
| Abstract: | Across all industries in the U.S., we document a significant and unique decline in the level of generalized trust among finance professionals relative to the decline of trust in the general U.S. population. This decline occurs in different age cohorts and among different levels of seniority. It is related to a lack of confidence only in institutions that are relevant to the finance industry. The relative decline of trust is associated with changes in economic conditions, the professional environment in the finance industry, and with the decreasing level of socialization among finance professionals. |
| Keywords: | Finance industry,Generalized trust,Implicit incentives,Professional environment,Socialization |
| JEL: | G20 G21 G22 G24 L14 A14 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfrwps:2005&r=all |
| By: | Chaderina, Maria; Weiss, Patrick; Zechner, Josef |
| Abstract: | This paper shows that firms with longer debt maturities earn risk premia not explained by unconditional standard factor models. We develop a dynamic capital structure model and find that firms with long-term debt exhibit more countercyclical leverage, making them more highly levered in downturns, when the market price of risk is high. The induced covariance between risk exposure and the market price of risk generates a maturity premium which we estimate at 0.21% per month. Empirical results from a conditional CAPM as well as observed beta dynamics are consistent with the model. We also exploit exogenous variation of debt maturities at the onset of the financial crisis and find that firms with shorter debt maturities experienced a smaller increase in leverage during the crisis. Also, after an initial spike, the betas of short-maturity firms reverted to levels below those of long-maturity firms by the end of 2008. |
| Keywords: | CAPM; Cross-section of stock returns; Debt overhang; Maturity; value premium |
| JEL: | G12 G32 G33 |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:14570&r=all |