nep-fmk New Economics Papers
on Financial Markets
Issue of 2019‒07‒15
eight papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. In Search of Systematic Risk and the Idiosyncratic Volatility Puzzle in the Corporate Bond Market By Jennie Bai; Turan G. Bali; Quan Wen
  2. Active Short Selling by Hedge Funds By Appel, Ian; Bulka, Jordan; Fos, Vyacheslav
  3. Investor Sentiment as a Predictor of Market Returns By Kim Kaivanto; Peng Zhang
  4. Size matters for OTC market makers: viscosity approach and dimensionality reduction technique By Philippe Bergault; Olivier Gu\'eant
  5. BERT-based Financial Sentiment Index and LSTM-based Stock Return Predictability By Joshua Zoen Git Hiew; Xin Huang; Hao Mou; Duan Li; Qi Wu; Yabo Xu
  6. Improved Forecasting of Cryptocurrency Price using Social Signals By Maria Glenski; Tim Weninger; Svitlana Volkova
  7. Is market liquidity less resilient after the financial crisis? Evidence for us treasuries By Carmen Broto; Matías Lamas
  8. Liquidity in the German stock market By Johann, Thomas; Scharnowski, Stefan; Theissen, Erik; Westheide, Christian; Zimmermann, Lukas

  1. By: Jennie Bai; Turan G. Bali; Quan Wen
    Abstract: We propose a comprehensive measure of systematic risk for corporate bonds as a nonlinear function of robust risk factors and find a significantly positive link between systematic risk and the time-series and cross-section of future bond returns. We also find a positive but insignificant relation between idiosyncratic risk and future bond returns, suggesting that institutional investors dominating the bond market hold well-diversified portfolios with a negligible exposure to bond-specific risk. The composite measure of systematic risk also predicts the distribution of future market returns, and the systematic risk factor earns a positive price of risk, consistent with Merton's (1973) ICAPM.
    JEL: C13 G10 G11 G12
    Date: 2019–06
  2. By: Appel, Ian; Bulka, Jordan; Fos, Vyacheslav
    Abstract: Short selling campaigns by hedge funds have become increasingly common in the last decade. Using a hand-collected sample of 252 campaigns, we document abnormal returns for targets of approximately -7% around the announcement date. Firm stakeholders, including the media, plaintiffs' attorneys, and other short sellers, play an important role in campaigns. Changes in aggregate short interest do not drive the effects on firm value and stakeholder behavior. Campaigns are primarily undertaken by activist hedge funds. Evidence suggests disclosure costs and information are important channels through which activism technology affects short selling.
    Date: 2019–06
  3. By: Kim Kaivanto; Peng Zhang
    Abstract: Investor sentiment's effect on asset prices has been studied extensively to date, without delivering consistent results across samples and datasets. We investigate the asset-pricing impacts of eight widely cited investor-sentiment indicators (one direct, six indirect, one composite), within a unified long-horizon regression framework, predicting real NYSE-index returns over horizon lengths of 1, 3, 12, 24, 36, and 48 months. Results reveal that three of the non-composite indicators have consistent predictive power: the Michigan Index of Consumer Sentiment (MICS), IPO volume (NIPO), and the dividend premium (PDND). This finding has implications for the widely cited Baker-Wurgler first principal component (SFPC) composite indicator, which extracts information from the full set of six indirect indicators. As the diffusion-index literature shows, this type of wide-net approach is likely to impound idiosyncratic noise into the composite summary indicator, exacerbating forecasting errors. Therefore we create a new `targeted' composite indicator from the first principal component of the three indicators that perform well in long-horizon regressions, i.e. MICS, NIPO, and PDND. The resulting targeted composite indicator outperforms SFPC in a market-returns prediction horse race. Whereas SFPC primarily predicts Equally Weighted Returns (EWR) rather than Value Weighted Returns (VWR), our new sentiment indicator performs better than SFPC in predicting both VWR and EWR. This improved performance is due in part to a reduction in overfitting, and in part to incorporation of the direct sentiment indicator MICS.
    Keywords: investor sentiment, market return, predictability, long-horizon regression, bootstrap diffusion index, composite index, overfitting
    JEL: G12 G17
    Date: 2019
  4. By: Philippe Bergault; Olivier Gu\'eant
    Abstract: In most OTC markets, a small number of market makers provide liquidity to clients from the buy side. More precisely, they set prices at which they agree to buy and sell the assets they cover. Market makers face therefore an interesting optimization problem: they need to choose bid and ask prices for making money out of their bid-ask spread while mitigating the risk associated with holding inventory in a volatile market. Many market making models have been proposed in the academic literature, most of them dealing with single-asset market making whereas market makers are usually in charge of a long list of assets. The rare models tackling multi-asset market making suffer however from the curse of dimensionality when it comes to the numerical approximation of the optimal quotes. The goal of this paper is to propose a dimensionality reduction technique to address multi-asset market making with grid methods. Moreover, we generalize existing market making models by the addition of an important feature for OTC markets: the variability of transaction sizes and the possibility for the market maker to answer different prices to requests with different sizes.
    Date: 2019–07
  5. By: Joshua Zoen Git Hiew; Xin Huang; Hao Mou; Duan Li; Qi Wu; Yabo Xu
    Abstract: Traditional sentiment construction in finance relies heavily on the dictionary-based approach, with a few exceptions using simple machine learning techniques such as Naive Bayes classifier. While the current literature has not yet invoked the rapid advancement in the natural language processing, we construct in this research a textual-based sentiment index using a novel model BERT recently developed by Google, especially for three actively trading individual stocks in Hong Kong market with hot discussion on On the one hand, we demonstrate a significant enhancement of applying BERT in sentiment analysis when compared with existing models. On the other hand, by combining with the other two existing methods commonly used on building the sentiment index in the financial literature, i.e., option-implied and market-implied approaches, we propose a more general and comprehensive framework for financial sentiment analysis, and further provide convincing outcomes for the predictability of individual stock return for the above three stocks using LSTM (with a feature of a nonlinear mapping), in contrast to the dominating econometric methods in sentiment influence analysis that are all of a nature of linear regression.
    Date: 2019–06
  6. By: Maria Glenski; Tim Weninger; Svitlana Volkova
    Abstract: Social media signals have been successfully used to develop large-scale predictive and anticipatory analytics. For example, forecasting stock market prices and influenza outbreaks. Recently, social data has been explored to forecast price fluctuations of cryptocurrencies, which are a novel disruptive technology with significant political and economic implications. In this paper we leverage and contrast the predictive power of social signals, specifically user behavior and communication patterns, from multiple social platforms GitHub and Reddit to forecast prices for three cyptocurrencies with high developer and community interest - Bitcoin, Ethereum, and Monero. We evaluate the performance of neural network models that rely on long short-term memory units (LSTMs) trained on historical price data and social data against price only LSTMs and baseline autoregressive integrated moving average (ARIMA) models, commonly used to predict stock prices. Our results not only demonstrate that social signals reduce error when forecasting daily coin price, but also show that the language used in comments within the official communities on Reddit (r/Bitcoin, r/Ethereum, and r/Monero) are the best predictors overall. We observe that models are more accurate in forecasting price one day ahead for Bitcoin (4% root mean squared percent error) compared to Ethereum (7%) and Monero (8%).
    Date: 2019–07
  7. By: Carmen Broto (Banco de España); Matías Lamas (Banco de España)
    Abstract: We analyse the market liquidity level and resilience of US 10-year Treasury bonds. Having checked that five indicators show inconclusive results on the liquidity level, we fit a bivariate CC-GARCH model to evaluate its resilience, that is, how liquidity reacts to financial shocks. According to our results, spillovers from liquidity volatility to returns volatility and viceversa are more intense after the crisis. Further, the volatility persistence of both returns and liquidity becomes lower after the crisis. These results are consistent with the existence of more frequent short-lived episodes of high volatility and more unstable liquidity that is more prone to evaporation.
    Keywords: market liquidity, volatility, US Treasuries; CC-GARCH model
    JEL: G24 C33
    Date: 2019–06
  8. By: Johann, Thomas; Scharnowski, Stefan; Theissen, Erik; Westheide, Christian; Zimmermann, Lukas
    Abstract: This paper presents the most extensive analysis of liquidity in the German equity market so far. We examine the evolution of liquidity over time, the determinants of liquidity, and commonality across liquidity measures and countries. We make use of a new publicly available dataset, the Market Microstructure Database Xetra (MMDB-Xetra). We find that liquidity has generally increased over time, and that in times of crisis liquidity is lower and the volatility of liquidity is significantly higher. Commonality in liquidity is highest during the financial crisis. We also find significant commonality between liquidity in the US and the German equity markets.
    Keywords: Market Microstructure,Liquidity,Volatility,Germany,Xetra
    JEL: G10 G14 G15
    Date: 2019

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