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on Financial Markets |
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Issue of 2020‒07‒27
thirteen papers chosen by |
| By: | Stefano Giglio; Matteo Maggiori; Johannes Stroebel; Stephen Utkus |
| Abstract: | We analyze how investor expectations about economic growth and stock returns changed during the February-March 2020 stock market crash induced by the COVID-19 pandemic, as well as during the subsequent partial stock market recovery. We surveyed retail investors who are clients of Vanguard at three points in time: (i) on February 11-12, around the all-time stock market high, (ii) on March 11-12, after the stock market had collapsed by over 20%, and (iii) on April 16-17, after the market had rallied 25% from its lowest point. Following the crash, the average investor turned more pessimistic about the short-run performance of both the stock market and the real economy. Investors also perceived higher probabilities of both further extreme stock market declines and large declines in short-run real economic activity. In contrast, investor expectations about long-run (10-year) economic and stock market outcomes remained largely unchanged, and, if anything, improved. Disagreement among investors about economic and stock market outcomes also increased substantially following the stock market crash, with the disagreement persisting through the partial market recovery. Those respondents who were the most optimistic in February saw the largest decline in expectations, and sold the most equity. Those respondents who were the most pessimistic in February largely left their portfolios unchanged during and after the crash. |
| Keywords: | surveys, expectations, sentiment, behavioural finance, trading, rare disasters |
| JEL: | G11 G12 R30 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_8334&r=all |
| By: | Foley, Sean (Macquarie University, Australia); Kwan, Amy (University of Sydney, Australia); Philip, Richard (University of Sydney, Australia); Ødegaard, Bernt Arne (University of Stavanger) |
| Abstract: | The Covid-19 epidemic has caused some of the largest - and fastest - market dislocations in modern history. Contemporaneous with the significant fall in equity market values is the evaporation of market liquidity. We document the evolution of transactions costs, depth and rewards to liquidity suppliers across a variety of countries affected by the virus. We show that transactions costs increase sharply in a coordinated fashion across global markets, with depth drying up almost overnight. The withdrawal of global liquidity suppliers is correlated with the increase of over 400% in margin requirements, driving a pro-cyclical downwards liquidity spiral. These affects are shown to be concentrated in securities most exposed to electronic market-makers. |
| Keywords: | Covid-19; Margin requirements; Stock market liquidity |
| JEL: | G01 G12 G14 G15 |
| Date: | 2020–07–08 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:stavef:2020_001&r=all |
| By: | Mahyar Kargar; Benjamin Lester; David Lindsay; Shuo Liu; Pierre-Olivier Weill; Diego Zúñiga |
| Abstract: | We study liquidity conditions in the corporate bond market since the onset of the COVID-19 pandemic. We find that in mid-March 2020, as selling pressure surged, dealers were wary of accumulating inventory on their balance sheets, perhaps out of concern for violating regulatory requirements. As a result, the cost to investors of trading immediately with a dealer surged. A portion of transactions migrated to a slower, less costly process wherein dealers arranged for trades directly between customers without using their own balance sheet space. Interventions by the Federal Reserve appear to have relaxed balance sheet constraints: soon after they were announced, dealers began absorbing inventory, bid-ask spreads declined, and market liquidity started to improve. Interestingly, liquidity conditions improved for bonds that were eligible for the Fed’s lending/purchase programs and for bonds that were ineligible. Hence, by allowing dealers to unload certain assets from their balance sheet, the Fed’s interventions may have helped dealers to better intermediate a wide variety of assets, including those not directly targeted. |
| JEL: | E5 E58 E65 G0 G01 G12 G21 G23 |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:27355&r=all |
| By: | Nils Jonathan Krakow (University of Zurich - Department of Banking and Finance); Timo Schäfer (Goethe University Frankfurt - Faculty of Economics and Business Administration) |
| Abstract: | Mutual funds are mandated by the Securities and Exchange Commission (SEC) to disclose information on their investment objectives and risks. In this paper, we study the informational value of U.S. mutual funds’ qualitative disclosures by analyzing the content of funds’ prospectuses. First, we find that funds disclosed risks increase with their exposed risk. They inform, in particular, extensively about their idiosyncratic risks and less about their systematic risks. Second, using methods from textual analysis, we document that around one-third of the variation in the content of funds’ risk disclosures is fund-specific, while a substantial part of a fund’s risk disclosures is determined at the fund group level. Our findings suggest that the relative informativeness in funds’ prospectuses has been, on average, decreasing over time. Third, we show that regular content-based updates of the disclosed risks provide relevant information in predicting future fund performance. Investors, however, do not react to this new information but rather to the content’s informativeness. Finally, using an event study framework relying on matching techniques, we document that investors also do not respond to the provision of additional simplified disclosure. |
| Keywords: | Mutual Funds, Risk Disclosure, Textual Analysis, Fund Performance, Fund Flows |
| JEL: | D8 G14 G23 |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2054&r=all |
| By: | Egger, Peter; Zhu, Jiaqing |
| Abstract: | At the beginning of 2018, President Trump started taking protective tariff measures against products from China in a sequence of events which started a "trade war" between the United States (U.S.) and China. As the value of trade flows affected on both sides rose to a significant amount, this episode will become an interesting research object in the future. A thorough analysis of many outcomes of interest is at this point in time -- and even will be in the next few years -- impossible due to a lack of data which will only become available at a later point. However, as is customary with historical preferential liberalizations in trade agreements and potentially the opposite of it through Brexit, it is possible to gauge consequences of this "trade war" or "trade dispute" when focusing on the stocks of listed companies around related tariff-change announcements or implementations by the U.S. and China in the relevant time span. This paper proposes such an analysis and finds, very much consistent with the rumors from business, that the associated protectionist tariffs appear to have done to a large extent the opposite of what was intended: they hurt domestic firms in targeted and also other, untargeted sectors of an acting country, and they affect third countries and territories which are not even party to the "trade war" or "dispute". |
| Keywords: | event study; Stock market; U.S.-China "trade war" |
| JEL: | F15 F23 G14 |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:14164&r=all |
| By: | Martin, Ian; Nagel, Stefan |
| Abstract: | Modern investors face a high-dimensional prediction problem: thousands of observable variables are potentially relevant for forecasting. We reassess the conventional wisdom on market efficiency in light of this fact. In our model economy, which resembles a typical machine learning setting, N assets have cash flows that are a linear function of J firm characteristics, but with uncertain coefficients. Risk-neutral Bayesian investors impose shrinkage (ridge regression) or sparsity (Lasso) when they estimate the J coefficients of the model and use them to price assets. When J is comparable in size to N, returns appear cross-sectionally predictable using firm characteristics to an econometrician who analyzes data from the economy ex post. A factor zoo emerges even without p-hacking and data-mining. Standard in-sample tests of market efficiency reject the no-predictability null with high probability, despite the fact that investors optimally use the information available to them in real time. In contrast, out-of-sample tests retain their economic meaning. |
| Keywords: | Big Data; Machine Learning; Market Efficiency |
| JEL: | C11 C12 C58 G10 G12 G14 |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:14235&r=all |
| By: | Samuel Mugel; Carlos Kuchkovsky; Escolastico Sanchez; Samuel Fernandez-Lorenzo; Jorge Luis-Hita; Enrique Lizaso; Roman Orus |
| Abstract: | In this paper we tackle the problem of dynamic portfolio optimization, i.e., determining the optimal trading trajectory for an investment portfolio of assets over a period of time, taking into account transaction costs and other possible constraints. This problem, well-known to be NP-Hard, is central to quantitative finance. After a detailed introduction to the problem, we implement a number of quantum and quantum-inspired algorithms on different hardware platforms to solve its discrete formulation using real data from daily prices over 8 years of 52 assets, and do a detailed comparison of the obtained Sharpe ratios, profits and computing times. In particular, we implement classical solvers (Gekko, exhaustive), D-Wave Hybrid quantum annealing, two different approaches based on Variational Quantum Eigensolvers on IBM-Q (one of them brand-new and tailored to the problem), and for the first time in this context also a quantum-inspired optimizer based on Tensor Networks. In order to fit the data into each specific hardware platform, we also consider doing a preprocessing based on clustering of assets. From our comparison, we conclude that D-Wave Hybrid and Tensor Networks are able to handle the largest systems, where we do calculations up to 1272 fully-connected qubits for demonstrative purposes. Finally, we also discuss how to mathematically implement other possible real-life constraints, as well as several ideas to further improve the performance of the studied methods. |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2007.00017&r=all |
| By: | Jonathan Readshaw; Stefano Giani |
| Abstract: | This work presents a Convolutional Neural Network (CNN) for the prediction of next-day stock fluctuations using company-specific news headlines. Experiments to evaluate model performance using various configurations of word-embeddings and convolutional filter widths are reported. The total number of convolutional filters used is far fewer than is common, reducing the dimensionality of the task without loss of accuracy. Furthermore, multiple hidden layers with decreasing dimensionality are employed. A classification accuracy of 61.7\% is achieved using pre-learned embeddings, that are fine-tuned during training to represent the specific context of this task. Multiple filter widths are also implemented to detect different length phrases that are key for classification. Trading simulations are conducted using the presented classification results. Initial investments are more than tripled over a 838 day testing period using the optimal classification configuration and a simple trading strategy. Two novel methods are presented to reduce the risk of the trading simulations. Adjustment of the sigmoid class threshold and re-labelling headlines using multiple classes form the basis of these methods. A combination of these approaches is found to more than double the Average Trade Profit (ATP) achieved during baseline simulations. |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2006.12426&r=all |
| By: | Kim, Hyonok; Yasuda, Yukihiro |
| Abstract: | We provide the first large sample comparison of earnings management by Japanese listed and unlisted firms. Based on the theoretical predictions by Stein (1989), we empirically examine whether managers’ myopic behaviors exist through inflating current earnings at the expense of long-term earnings. We find that listed firms are more likely to engage in earnings management. We also find that firm managers are more likely to manage earnings as the information content of current earnings about future earnings (stock price) increases. More importantly, we note that this manipulation is pronounced only for listed firms. This is the first study that empirically shows the market pressure for raising stock price induces earnings manipulation. |
| Keywords: | Earnings Management, Myopic, Short-termism, Stock market pressure, Unlisted firms, Private firms |
| JEL: | D80 G21 G31 G32 |
| Date: | 2020–07–21 |
| URL: | http://d.repec.org/n?u=RePEc:hit:hcfrwp:g-1-24&r=all |
| By: | Massimo Guidolin; Alexei Orlov |
| Abstract: | We test whether the unconventional monetary policy (UMP) announcements by the Federal Reserve and the European Central Bank represent a risk factor for the hedge fund industry as a whole and for ten commonly used strategies in particular. Using modified event studies and Markov switching models, we find that UMP announcements represent a risk factor for Convertible Arbitrage, Dedicated Short Bias, Emerging Markets, Equity Market Neutral, Fixed Income Arbitrage strategies as well as the Multi-Strategy type. We further test whether UMP announcements have an indirect effect on hedge funds’ performance through breaks in the parameters of the conventional risk factors. Using Chow and Bai-Perron tests, we find that for the industry as a whole and for all strategies, most of the UMP announcements correspond to break dates for the traditional factor loadings. |
| Keywords: | Hedge fund strategies, unconventional monetary policy, risk factors, modified event studies, Markov switching models, breakpoint tests |
| JEL: | G12 G11 G17 C32 C53 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:baf:cbafwp:cbafwp20146&r=all |
| By: | Mark J. Flannery; Jan Hanousek; Anastasiya Shamshur; Jiri Tresl |
| Abstract: | Using a large sample of European acquisitions, we find that acquired firms substantially close the gap between their actual and optimal leverage ratios. The bulk of this adjustment occurs quite rapidly – within a year of the acquisition. The typical over-levered firm adjusts its debtto-assets ratio from 34.4% in the year before acquisition to 20% in the year after. (The adjustment is smaller, but still quite rapid, for targets that had been under-leveraged.) These adjustments occur primarily through debt issuances or retirements. We also investigate whether target firms’ pre-merger leverage contributes to the probability of them being acquired. We find that firms further away from their optimal leverage are more likely to be acquired: for an average firm, an increase in the absolute leverage deviation from 1% to 10% of total assets increases the probability of being acquired by 4.1% to 5.6% (The larger effect applies to overleveraged firms.) Overall, our results provide support for the trade-off theory of capital structure and suggest that financial synergies have a significant role in the typical European acquisition decision. |
| Keywords: | M&A; target capital structure; leverage deficit; |
| JEL: | G30 G32 G34 |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:cer:papers:wp661&r=all |
| By: | Jules H. van Binsbergen |
| Abstract: | Interest rates across maturities have dropped to all-time low levels around the world. These unexpected shocks to discount rates have an important effect on the valuation of long duration assets. To quantify this effect, I construct a number of counterfactual fixed income portfolios that match the duration of the dividend strips of the aggregate stock market. I show that such fixed income portfolios have performed as well, if not better, than the U.S. stock market in the past five decades, while exhibiting similar (or higher) levels of volatility. Therefore, investors have received little to no compensation for taking long duration nominal dividend risk in the past half century. Further, if anything, stocks seem to have too little volatility (not excess volatility) compared to these fixed income counterfactuals. I discuss several explanations for these findings, including a secular decline in economic growth rates, dividends' potential to hedge against inflation, as well as the diversification of dividend risk across maturities. These results also have important implications for research on the cross-section of stock returns and capital structure. |
| JEL: | E2 E21 E4 G1 G11 G12 G15 G31 G32 O4 |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:27367&r=all |
| By: | Czech, Robert (Bank of England); Huang, Shiyang (University of Hong Kong); Lou, Dong (London School of Economics and CEPR); Wang, Tianyu (Tsinghua University) |
| Abstract: | Using comprehensive regulatory data, we examine trading by different investor types in government bond markets. Our sample covers virtually all secondary market trading in gilts and contains detailed information on each transaction, including the identities of both counterparties. We find that hedge funds’ daily trading positively forecasts gilt returns in the following one to five days, which is then fully reversed in the following month. A part of this short-term return predictability is due to hedge funds’ ability to anticipate future demand of other investors. Mutual fund trading also positively predicts gilt returns, but over a longer horizon of one to two months. This return pattern does not revert in the following year and is partly due to mutual funds’ ability to forecast changes in short-term interest rates. |
| Keywords: | Government bonds; informed trading; return predictability; asset managers |
| JEL: | G11 G12 G14 G23 |
| Date: | 2020–06–15 |
| URL: | http://d.repec.org/n?u=RePEc:boe:boeewp:0871&r=all |