nep-fmk New Economics Papers
on Financial Markets
Issue of 2017‒02‒26
five papers chosen by

  1. Bubbles for Fama By Robin Greenwood; Andrei Shleifer; Yang You
  2. CDS spreads as an independent measure of credit risk By Kiesel, F.; Spohnholtz, J.
  3. Political Cycles and Stock Returns By Lubos Pastor; Pietro Veronesi
  4. Company Stock Reactions to the 2016 Election Shock: Trump, Taxes and Trade By Alexander Wagner; Richard J. Zeckhauser; Alexandre Ziegler
  5. Stock market efficiency in South Eastern Europe: testing return predictability and presence of calendar effects By Filipovski, Vladimir; Tevdovski, Dragan

  1. By: Robin Greenwood; Andrei Shleifer; Yang You
    Abstract: We evaluate Eugene Fama?s claim that stock prices do not exhibit price bubbles. Based on US industry returns 1926-2014 and international sector returns 1985-2014, we present four findings: (1) Fama is correct in that a sharp price increase of an industry portfolio does not, on average, predict unusually low returns going forward; (2) such sharp price increases predict a substantially heightened probability of a crash; (3) attributes of the price run-up, including volatility, turnover, issuance, and the price path of the run-up can all help forecast an eventual crash and future returns; and (4) some of these characteristics can help investors earn superior returns by timing the bubble. Results hold similarly in US and international samples.
    Date: 2017–02
  2. By: Kiesel, F.; Spohnholtz, J.
    Abstract: Purpose The creditworthiness of corporates is most visible in credit ratings. This paper presents an alternative credit rating measure independently of credit rating agencies. The credit rating score is based on the CDS market trading. Design/methodology/approach A credit rating score is developed which is a linear function of logarithmized credit default swap (CDS) spreads. This new credit rating score is the first one completely independent of rating agency. The estimated ratings are compared with ratings provided by Fitch Ratings for 310 European and US non-financial corporates. Findings The empirical analysis shows that logarithmized CDS spreads and issuer credit ratings by agencies have a linear relationship. The new credit rating score provides market participants with an alternative risk assessment, which is solely based on market factors, and does not rely on credit rating analysts. The results indicate that our credit rating score is able to anticipate agency ratings in advance. Moreover, the analysis demonstrates that the trading volume has only limited influence in the anticipation of rating changes. Originality/value This study shows a new approach to measure the creditworthiness of firms by analyzing CDS spreads. This is highly relevant for regulation, firm monitoring, and investors.
    Date: 2017–02–15
  3. By: Lubos Pastor; Pietro Veronesi
    Abstract: We develop a model of political cycles driven by time-varying risk aversion. Heterogeneous agents make two choices: whether to work in the public or private sector and which of two political parties to vote for. The model implies that when risk aversion is high, agents are more likely to elect the party promising more fiscal redistribution. The model predicts higher average stock market returns under Democratic than Republican presidencies, explaining the well-known “presidential puzzle.” Under sufficient complementarity between the public and private sectors, the model also predicts faster economic growth under Democratic presidencies, which is observed in the data.
    JEL: D72 G12 G18 P16
    Date: 2017–02
  4. By: Alexander Wagner; Richard J. Zeckhauser; Alexandre Ziegler
    Abstract: The election of Donald J. Trump as the 45th President of the United States of America on 11/8/2016 came as a surprise. Markets responded swiftly and decisively. This note investigates both the initial stock market reaction to the election, and the longer-term reaction through the end of 2016. We find that the individual stock price reactions to the election – that is, the market’s vote – reflect investor expectations on economic growth, taxes, and trade policy. Heavy industry and banking were relative winners, whereas healthcare, medical equipment, pharmaceuticals, textiles, and apparel were among the relative losers. High-beta stocks and companies with a hitherto high tax burden benefited from the election. Although internationally-oriented companies may profit under some plans of the new administration, several other arguments suggest a more favorable climate for domestically-oriented companies. Investors have found the domestic-favoring arguments to be stronger. While investors incorporated the expected consequences of the election for US growth and tax policy into prices relatively quickly, it took them more time to digest the consequences of shifts in trade policy on firms’ prospects.
    JEL: G12 G14 H25 O24
    Date: 2017–02
  5. By: Filipovski, Vladimir; Tevdovski, Dragan
    Abstract: This paper examines the calendar effects in ten South Eastern European (SEE) stock markets daily returns during the period 2007 - 2014. We focus on three calendar effects: the day of the week effect, the half month effect and the turn of the month effect. Specifically, we analyze existence of each calendar effect separately in the mean and in the volatility of the index returns. We apply standard regression models with dummy variables for the effects in the mean returns, while we apply GARCH(1,1) models with dummy variables for the effects in the volatility of returns. The results present evidence that the day of the week effects in both mean and volatility are present in nine out of ten SEE stock markets. Contrary, the half month effect in mean returns is present only in one SEE stock market, while half month effect in volatility is present in five out of ten SEE stock markets. The turn of the month effect in mean returns is present in six out of ten SEE stock markets. The turn of the month effect in volatility is present in all SEE stock markets.
    Keywords: Calendar anomalies, Daily returns, Generalized autoregressive models, South Eastern Europe.
    JEL: C32 G14
    Date: 2017–02

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