|
on Financial Markets |
|
Issue of 2020‒07‒13
ten papers chosen by |
| By: | Delis, Manthos; Savva, Christos; Theodossiou, Panayiotis |
| Abstract: | We study an equilibrium risk and return model to explore the effects of the coronavirus crisis and associated skewness. We derive the moment and equilibrium equations, specifying skew-ness price of risk as an additive component of the effect of variance on mean expected return. We estimate our model using the flexible skewed generalized error distribution, for which we derive the distribution of returns and the likelihood function. Using S&P 500 Index returns from January 1990 to mid-May 2020, our results show that the coronavirus crisis generated the most negative reaction in the skewness price of risk, more negative even than the subprime crisis. |
| Keywords: | Asset pricing; Risk and return; Skewness; Coronavirus crisis; Subprime crisis |
| JEL: | C32 C51 G01 G11 G12 |
| Date: | 2020–06–04 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:100877&r=all |
| By: | Zhiguo He; Stefan Nagel; Zhaogang Song |
| Abstract: | In sharp contrast to most previous crisis episodes, the Treasury market experienced severe stress and illiquidity during the COVID-19 crisis, raising concerns that the safe-haven status of U.S. Treasuries may be eroding. We document large shifts in Treasury ownership during this period and the accumulation of Treasury and reverse repo positions on dealer balance sheets. To understand the pricing consequences, we build a model in which balance sheet constraints of dealers and demand/supply shocks from habitat agents determine the term structure of Treasury yields. A novel element of our model is the inclusion of levered investors' repo financing as part of dealers' intermediation activities. Both direct holdings of Treasuries and reverse repo positions of dealers are subject to a regulatory balance sheet constraint. According to the model, Treasury inconvenience yields, measured as the spread between Treasuries and overnight-index swap (OIS) rates, as well as spreads between dealers' reverse repo and repo rates, should be increasing in dealers' balance sheet costs. Consistent with model predictions, we find that both spreads are large and positive during the COVID-19 crisis. We further show that the same model, adapted to the institutional setting in 2007-2009, also helps explain the opposite signs of repo spreads and Treasury convenience yields during the financial crisis. |
| JEL: | E4 E5 G01 G21 G23 |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:27416&r=all |
| By: | Ramelli, Stefano; Wagner, Alexander F |
| Abstract: | The market reactions to the 2019 novel Coronavirus disease (COVID-19) shed light on the importance of international trade and financial policies for firm value. Initially, investors priced negative consequences for internationally-oriented US firms, especially those with China exposure. As the virus spread to Europe and the US, markets moved feverishly. Corporate debt and cash holdings emerged as important value drivers, relevant even after the Fed intervened in the bond market. The content and tone of conference calls mirror this development over time. Overall, the results illustrate how the health crisis morphed into an economic crisis amplified through financial channels. |
| Keywords: | Coronavirus; Corporate Debt; COVID-19; event study; global value chains; leverage; Pandemic; SARS-CoV-2; Supply Chains |
| JEL: | F15 F23 F36 G01 G02 G14 G15 |
| Date: | 2020–03 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:14511&r=all |
| By: | Favero, Carlo A.; Melone, Alessandro |
| Abstract: | Standard factor-portfolio models focus on returns and leave prices undetermined. This approach ignores information contained in the time-series of asset prices, relevant for long-term investors and for detecting potential mis-pricing. To address this issue, we provide a new (co-)integrated methodology to factor modeling based on both prices and returns. Given a long-run relationship between the value of buy-and-hold portfolios in test assets and factors, we argue that a term---naturally labeled as Equilibrium Correction Term (ECT)---should be included when regressing returns on factors. We also propose to validate factor models by the existence of such a term. Empirically, we show that the ECT predicts equity returns, both in-sample and out-of-sample. |
| Keywords: | Dynamic Factor-Portfolio Models; Equilibrium Correction Term; mispricing; return predictability |
| JEL: | C38 G11 G17 |
| Date: | 2020–03 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:14417&r=all |
| By: | Bartram, Söhnke M; Branke, Jürgen; Motahari, Mehrshad |
| Abstract: | Artificial intelligence (AI) has a growing presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and returns forecasts and under more complex constraints. Trading algorithms utilize AI to devise novel trading signals and execute trades with lower transaction costs, and AI improves risk modelling and forecasting by generating insights from new sources of data. Finally, robo-advisors owe a large part of their success to AI techniques. At the same time, the use of AI can create new risks and challenges, for instance as a result of model opacity, complexity, and reliance on data integrity. |
| Keywords: | Algorithmic trading; decision trees; deep learning; evolutionary algorithms; Lasso; Machine Learning; neural networks; NLP; random forests; SVM |
| JEL: | G11 G17 |
| Date: | 2020–03 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:14525&r=all |
| By: | Akash Doshi; Alexander Issa; Puneet Sachdeva; Sina Rafati; Somnath Rakshit |
| Abstract: | Forecasting stock prices can be interpreted as a time series prediction problem, for which Long Short Term Memory (LSTM) neural networks are often used due to their architecture specifically built to solve such problems. In this paper, we consider the design of a trading strategy that performs portfolio optimization using the LSTM stock price prediction for four different companies. We then customize the loss function used to train the LSTM to increase the profit earned. Moreover, we propose a data driven approach for optimal selection of window length and multi-step prediction length, and consider the addition of analyst calls as technical indicators to a multi-stack Bidirectional LSTM strengthened by the addition of Attention units. We find the LSTM model with the customized loss function to have an improved performance in the training bot over a regressive baseline such as ARIMA, while the addition of analyst call does improve the performance for certain datasets. |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2006.04992&r=all |
| By: | Pedro Bordalo; Nicola Gennaioli; Rafael La Porta; Andrei Shleifer |
| Abstract: | We revisit several leading puzzles about the aggregate stock market by incorporating into a standard dividend discount model survey expectations of earnings of S&P 500 firms. Using survey expectations, while keeping discount rates constant, explains a significant part of “excess” stock price volatility, price-earnings ratio variation, and return predictability. The evidence is consistent with a mechanism in which good news about fundamentals leads to excessively optimistic forecasts of earnings, especially at long horizons, which inflate stock prices and lead to subsequent low returns. Relaxing rational expectations of fundamentals in a standard asset pricing model accounts for stock market anomalies in a parsimonious way. |
| JEL: | G02 G12 |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:27283&r=all |
| By: | Elie Bouri (Holy Spirit University of Kaslik (USEK), USEK Business School, Jounieh, Lebanon); David Gabauer (Software Competence Center Hagenberg, Data Analysis Systems, Softwarepark 21, 4232 Hagenberg, Austria); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Aviral Kumar Tiwari (Rajagiri Business School, Rajagiri Valley Campus, Kochi, India) |
| Abstract: | In this paper, we first obtain a time-varying measure of volatility connectedness involving fifteen major cryptocurrencies based on a dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model, and then analyze the role of investor sentiment in explaining the movement of the connectedness metric within a quantile-on-quantile framework. Our findings show that lower quantiles of investor happiness, built on Twitter feed data as a proxy for investor sentiment, is positively associated with the entire conditional distribution of connectedness, but the opposite is observed at higher values of investor happiness. In addition, when we look at the effect of sentiment on the common market volatility, we are able to deduce that as investors become exceedingly unhappy, overall market volatility increases and this is associated with high market connectedness. The heightened volatility possibly due to higher trading, seems to suggest that cryptocurrencies are used for hedging when investor sentiment is weak, with evidence in favor of this behavior being relatively stronger than the possible speculative motive associated with happy investors, as low total connectedness is coupled with high common volatility. Our results tend to suggest that, relatively more diversification opportunities are available when investors are happy rather than when sentiment is weak. |
| Keywords: | Cryptocurrency Market, DCC-GARCH, Volatility Connectedness, Investor Happiness, Quantile-on-Quantile Regression |
| JEL: | C22 C32 G10 |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202059&r=all |
| By: | Yan Liu; Jing Cynthia Wu |
| Abstract: | The constant-maturity zero-coupon Treasury yield curve is one of the most studied datasets. We construct a new dataset using a non-parametric kernel-smoothing method with a novel adaptive bandwidth specifically designed to fit the Treasury yield curve. Our curve is globally smooth while still capturing important local variation. Economically, we show that applying our data leads to different conclusions from using the leading alternative data of Gurkaynak et al. (2007) (GSW) when we repeat two popular studies of Cochrane and Piazzesi (2005) and Giglio and Kelly (2018). Statistically, we show our dataset preserves information in the raw data and has much smaller pricing errors than GSW. Our new yield curve is maintained and updated online, complemented by bandwidths that summarize information content in the raw data: https://sites.google.com/view/jingcynthi awu/yield-data. |
| JEL: | E43 |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:27266&r=all |
| By: | Mishra, Abinash; Srivastava, Pranjal; Chakrabarti, Anindya S. |
| Abstract: | Complex mutual dependencies of asset returns are recognized to contribute to systemic risk. A growing literature emphasizes that identification of vulnerable firms is a fundamental requirement for mitigating systemic risk in a given asset market. However, in reality, firms are generally active in multiple asset markets with potentially different degrees of vulnerabilities in different markets. Therefore, to assess combined risks of the firms, we need to know how systemic risk measures of firms are related across markets? In this paper, we answer this question by studying US firms that are active in both stock as well as corporate bond markets. The main results are twofold. One, firms that exhibit higher systemic risk in the stock market also tend to exhibit higher systemic risk in the bond market. Two, systemic risk within an asset category is related to firm size, indicating that `too-big-to-fail’ firms also tend to be `too-central-to fail'. Our results are robust with respect to choose of asset classes, maturity horizons, model selection, time length of the data as well as controlling for all major market level factors. These results have prominent policy implications for identification of vulnerabilities and targeted interventions in financial networks. |
| Date: | 2020–06–20 |
| URL: | http://d.repec.org/n?u=RePEc:iim:iimawp:14628&r=all |