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on Financial Markets |
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Issue of 2018‒01‒29
four papers chosen by |
| By: | Mert Demirer (MIT); Umut Gokcen (Koc University); Kamil Yilmaz (Koc University) |
| Abstract: | We apply the Diebold and Yilmaz (2014) methodology to daily stock prices of the largest 40 U.S. financial institutions to construct a volatility connectedness index. We then estimate the contemporaneous return sensitivity of every non-financial U.S. company to this index. We find that there is a large statistically significant difference between the returns of firms with positive and negative exposures to financial connectedness. The four-factor alpha of a strategy that goes long in the bottom decile and short in the top decile of stocks sorted on their connectedness betas is roughly 15% per annum. Bivariate portfolio tests reveal that abnormal returns are robust to market beta, size, book-to-market ratio, momentum, debt, illiquidity, and idiosyncratic volatility. Abnormal returns are asymmetric; they are primarily driven by firms whose returns covary negatively with the index. These firms tend to be young and small, with poor past performance and low credit quality. |
| Keywords: | Cross-section of returns, Anomalies, Financial connectedness, Vector autoregressions. |
| JEL: | G12 G21 C32 |
| Date: | 2018–01 |
| URL: | http://d.repec.org/n?u=RePEc:koc:wpaper:1803&r=fmk |
| By: | Becker, Janis; Leschinski, Christian |
| Abstract: | The level of daily stock returns is generally regarded as unpredictable. Instead of the level, we focus on the signs of these returns and generate forecasts using various statistical classification techniques, such as logistic regression, generalized additive models, or neural networks. The analysis is carried out using a data set consisting of all stocks that were part of the Dow Jones Industrial Average in 1996. After selecting the relevant explanatory variables in the subsample from 1996 to 2003, forecast evaluations are conducted in an out-of-sample environment for the period from 2004 to 2017. Since the model selection and the forecasting period are strictly separated, the procedure mimics the situation a forecaster would face in real time. It is found that the sign of daily returns is predictable to an extent that is statistically significant. Moreover, trading strategies based on these forecasts generate positive alpha, even after accounting for transaction costs. This underlines the economic significance of the predictability and implies that there are periods during which markets are not fully efficient. |
| Keywords: | Asset Pricing; Market Efficiency; Directional Predictability; Statistical Classification |
| JEL: | G12 G14 G17 C38 |
| Date: | 2018–01 |
| URL: | http://d.repec.org/n?u=RePEc:han:dpaper:dp-624&r=fmk |
| By: | Ali Hortaçsu; Jakub Kastl; Allen Zhang |
| Abstract: | We analyze bidding data from uniform price auctions of U.S. Treasury bills and notes between July 2009-October 2013. Primary dealers consistently bid higher yields compared to direct and indirect bidders. We estimate a structural model of bidding that takes into account informational asymmetries introduced by the bidding system employed by the U.S. Treasury. While primary dealers’ estimated willingness-to-pay is higher than direct and indirect bidders’, their ability to bid-shade is even higher, leading to higher yield/lower price bids. Total bidder surplus averaged to about 3 basis points across the sample period along with efficiency losses around 2 basis points. |
| JEL: | G12 L1 |
| Date: | 2017–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:24024&r=fmk |
| By: | Manh Ha Nguyen (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - UN - Université de Nantes); Olivier Darné (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - UN - Université de Nantes) |
| Abstract: | This paper analyzes volatility models and their risk forecasting abilities with the presence of jumps for the Vietnam Stock Exchange (VSE). We apply GARCH-type models, which capture short and long memory and the leverage effect, estimated from both raw and filtered returns. The data sample covers two VSE indexes, the VN index and HNX index, provided by the Ho Chi Minh City Stock Exchange (HOSE) and Hanoi Stock Exchange (HNX), respectively, during the period 2007 - 2015. The empirical results reveal that the FIAPARCH model is the most suitable model for the VN index and HNX index. |
| Keywords: | Vietnam Stock exchange,volatility,GARCH models,Value-at-Risk. |
| Date: | 2018–01–09 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:halshs-01679456&r=fmk |