nep-fmk New Economics Papers
on Financial Markets
Issue of 2020‒01‒27
four papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Is Monetary Policy Gender Neutral? Evidence from the Stock Market By Caterina Forti Grazzini; Chi Hyun Kim
  2. DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News By Xinyi Li; Yinchuan Li; Hongyang Yang; Liuqing Yang; Xiao-Yang Liu
  3. Price Discovery and Market Segmentation in China's Credit Market By Zhe Geng; Jun Pan
  4. Shock and Volatility Spillovers between Crude Oil Price and Stock Returns: Evidence for Thailand By Theplib, Krit; Sethapramote, Yuthana; Jiranyakul, Komain

  1. By: Caterina Forti Grazzini; Chi Hyun Kim
    Abstract: We use US household survey data from 2001-2017 to investigate whether monetary policy has heterogeneous effects on women's and men's financial portfolio decisions by analyzing their equity investment. On the one hand, monetary policy significantly affects the entry decisions of women, but not of men: after a contractionary shock, the probability of women entering the stock market decreases. On the other hand, monetary policy is gender-neutral for stock market participants: there are no significant differences in exit or in portfolio rebalancing decisions between women and men. Our results suggest that monetary policy does not have a heterogeneous effect on portfolio decisions across genders once women participate in the stock market.
    Keywords: Monetary policy, gender, stock market participation, portfolio choices
    JEL: E58 J16 G11
    Date: 2020
    URL: http://d.repec.org/n?u=RePEc:diw:diwwpp:dp1841&r=all
  2. By: Xinyi Li; Yinchuan Li; Hongyang Yang; Liuqing Yang; Xiao-Yang Liu
    Abstract: Stock price prediction is important for value investments in the stock market. In particular, short-term prediction that exploits financial news articles is promising in recent years. In this paper, we propose a novel deep neural network DP-LSTM for stock price prediction, which incorporates the news articles as hidden information and integrates difference news sources through the differential privacy mechanism. First, based on the autoregressive moving average model (ARMA), a sentiment-ARMA is formulated by taking into consideration the information of financial news articles in the model. Then, an LSTM-based deep neural network is designed, which consists of three components: LSTM, VADER model and differential privacy (DP) mechanism. The proposed DP-LSTM scheme can reduce prediction errors and increase the robustness. Extensive experiments on S&P 500 stocks show that (i) the proposed DP-LSTM achieves 0.32% improvement in mean MPA of prediction result, and (ii) for the prediction of the market index S&P 500, we achieve up to 65.79% improvement in MSE.
    Date: 2019–12
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1912.10806&r=all
  3. By: Zhe Geng; Jun Pan
    Abstract: We study the extent of price discovery in the onshore Chinese corporate bond market, focusing in particular on the information content of credit spreads in China. Using Merton's model of default, we construct credit measures of publicly listed firms, using information from their financial statements and stock valuation. We find that, only after the first default in 2014, do credit spreads in China become informative. Compared with the findings in the US credit market, the magnitude of price discovery in the Chinese market is rather limited. We also find that the presence of outside government support for state-owned enterprises (SOEs) in China results in a market segmentation between SOE and non-SOE issuers that is harmful to price efficiency and market stability. Since 2018, the non-SOE issuers have suffered from explosive credit spreads, unprecedented defaults, and shrinking new issuance, while the SOE issuers have remained largely intact. Meanwhile, our default measures show that the non-SOE issuers are in fact stronger in credit quality than their SOE counterparts of the same rating category. Examining the impact of this segmentation on price discovery, we find that non-SOE credit spreads become significantly more informative since 2018, as concerns over credit become front and center for the non-SOE issuers. By contrast, as investors seek safety in SOE bonds, there is no improvement in the information content of SOE credit spreads.
    JEL: G0 G1 G10 G12 G15
    Date: 2019–12
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:26575&r=all
  4. By: Theplib, Krit; Sethapramote, Yuthana; Jiranyakul, Komain
    Abstract: This paper employs a bivariate BEKK-GARCH(1,1) model to examine shock and volatility spillovers between crude oil and stock markets by taking into account the impact of the 2008 global financial crisis. Daily data from crude oil market and the Thai stock market during February 6, 2004 and September 14, 2015 are used in the analyses. The whole sample is divided into the pre- and post- crisis periods. The results show that there are no spillover effects between oil price and stock returns in the pre-crisis period. In the post-crisis period, there are unilateral spillover effects from oil price to some equity sector returns. In the market level, there are unilateral spillovers of shock and volatility from oil price to stock market return. The findings in this paper are crucial for financial market participations to understand shock and volatility transmissions from oil to stock markets such that portfolio management should take into account the presence of oil price risk.
    Keywords: Stock returns, oil price shock, volatility spillover, bivariate GARCH
    JEL: G1 G12 Q43
    Date: 2020–01
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:98094&r=all

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