nep-fmk New Economics Papers
on Financial Markets
Issue of 2017‒09‒24
three papers chosen by

  1. A Counterfactual Valuation of the Stock Index as a Predictor of Crashes By Tom Roberts
  2. Global Macro Risks in Currency Excess Returns By Kimberly A. Berg; Nelson Mark
  3. Asset returns, news topics, and media effects By Vegard Høghaug Larsen; Leif Anders Thorsrud

  1. By: Tom Roberts
    Abstract: Stock market fundamentals would not seem to meaningfully predict returns over a shorter-term horizon—instead, I shift focus to severe downside risk (i.e., crashes). I use the cointegrating relationship between the log S&P Composite Index and log earnings over 1871 to 2015, combined with smoothed earnings, to first construct a counterfactual valuation benchmark. The price-versus-benchmark residual shows an improved, and economically meaningful, logit estimation of the likelihood of a crash over alternatives such as the dividend yield and price momentum. Rolling out-of-sample estimates highlight the challenges in this task. Nevertheless, the overall results support the common popular belief that a higher stock market valuation in relation to fundamentals entails a higher risk of a crash.
    Keywords: Asset Pricing, Financial stability
    JEL: C50 C58 G0 G01 G12 G17 G19
    Date: 2017
  2. By: Kimberly A. Berg; Nelson Mark
    Abstract: We study the cross-sectional variation of carry-trade-generated currency excess returns in terms of their exposure to global macroeconomic fundamental risk. The risk factor is the cross-country high-minus-low conditional skewness of the unemployment rate gap. It gives a measure of global macroeconomic uncertainty and is robustly priced in currency excess returns. A widening of the high-minus-low skewness of the unemployment rate gap signifies increasing divergence, disparity, and inequality of economic performance across countries.
    JEL: F3 F4 G1
    Date: 2017–09
  3. By: Vegard Høghaug Larsen; Leif Anders Thorsrud
    Abstract: We decompose the textual data in a daily Norwegian business newspaper into news topics and investigate their predictive and causal role for asset prices. Our three main findings are: (1) a one unit innovation in the news topics predict roughly a 1 percentage point increase in close-to-open returns and significant continuation patterns peaking at 4 percentage points after 15 business days, with little sign of reversal; (2) simple zero-cost news-based investment strategies yield significant annualized risk-adjusted returns of up to 20 percent; and (3) during a media shortage, due to an exogenous strike, returns for firms particularly exposed to our news measure experience a substantial fall. Our estimates suggest that between 20 to 40 percent of the news topics’ predictive power is due to the causal media effect. Together these findings lend strong support for a rational attention view where the media alleviate information frictions and disseminate fundamental information to a large population of investors.
    Keywords: Stock returns, News, Machine learning, Latent Dirichlet Allocation (LDA)
    Date: 2017–09

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