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on Financial Markets |
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Issue of 2020‒09‒28
seven papers chosen by |
| By: | Hammad, Siddiqi; Austin, Murphy |
| Abstract: | Using recent findings from brain sciences, we relax the implicit CAPM assumption of sufficient brain resources, and model human brain as solving two optimization problems instead of one, which are: 1) Optimal resource allocation in the brain. 2) Mean-variance optimization. A security market line with varying slopes (flat, upwards, and downwards) arises depending on the resource allocation decisions in the brain. Size, value, and momentum effects also emerge in this enriched framework. This suggests that the classical CAPM is not misspecified. Rather, what appears as misspecification may be the result of ignoring the optimal resource allocation problem in the brain. |
| Keywords: | CAPM, SML Slope, Resource Allocation, Size, Value, Momentum, High-Alpha-of-Low-Beta |
| JEL: | G10 G12 |
| Date: | 2020–08–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:102705&r=all |
| By: | Tengfei Zhang |
| Abstract: | This paper evidences the explanatory power of managersâ uncertainty for cross-sectional stock returns. I introduce a novel measure of the degree of managersâ uncertain beliefs about future states: manager uncertainty (MU), defined as the count of the word âuncertaintyâ over the sum of the count of the word âuncertaintyâ and the count of the word âriskâ in filings and conference calls. I find that managerâs level of uncertainty reveals valuation information about real options and thereby has significantly negative explanatory power for cross-sectional stock returns. Beyond existing market-based uncertainty measures, the manager uncertainty measure has incremental pricing power by capturing information frictions between managersâ reported uncertainty and investorsâ perception of uncertainty. Moreover, a short-long portfolio sorted by manager uncertainty has a significantly positive premium and cannot be spanned by existing factor models. An application on COVID-19 uncertainty shows consistent results. |
| JEL: | G11 G12 D81 |
| Date: | 2020–09–18 |
| URL: | http://d.repec.org/n?u=RePEc:jmp:jm2020:pzh934&r=all |
| By: | Fernando Moraes; Rodrigo De-Losso |
| Abstract: | The Factor Zoo phenomenon calls for answers as to which risk factors are in fact capable of providing independent information on the cross-section of expected excess returns, while considering that asset-pricing literature has produced hundreds of candidates. In this paper, we propose a new methodology to reduce risk factor predictor dimensions by selecting the key component (most central element) of their precision matrix. Our approach yields a significant shrinkage in the original set of risk factors, enables investigations on different regions of the risk factor covariance matrix, and requires only a swift algorithm for implementation. Our findings lead to sparse models that pose higher average in samples !" and lower root mean square out of sample error than those attained with classic models, in addition to specific alternative methods documented by Factor Zoo-related research papers. We base our methodology on the CRSP monthly stock return dataset in the time frame ranging from January 1981 to December 2016, in addition to the 51 risk factors suggested by Kozak, Nagel, and Santosh (2020). |
| Keywords: | Risk factors; factor zoo; graph lasso; network analysis |
| JEL: | G12 C55 D85 |
| Date: | 2020–09–15 |
| URL: | http://d.repec.org/n?u=RePEc:spa:wpaper:2020wpecon17&r=all |
| By: | Zhengyang Jiang; Hanno Lustig; Stijn Van Nieuwerburgh; Mindy Z. Xiaolan |
| Abstract: | Governments face a trade-off between insuring bondholders and taxpayers. If the government decides to fully insure bondholders by manufacturing risk-free debt, then it cannot insure taxpayers against permanent macro-economic shocks over long horizons. Instead, taxpayers will pay more in taxes in bad times. Conversely, if the government fully insures taxpayers against adverse macro shocks, then the debt becomes at least as risky as un-levered equity. Only when government debt earns convenience yields, may governments be able to insure both bondholders and taxpayers, and then only if the convenience yields are sufficiently counter-cyclical. |
| JEL: | F34 G12 H62 H63 |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:27786&r=all |
| By: | Bartłomiej Bollin (Quantitative Finance Research Group; Faculty of Economic Sciences, University of Warsaw); Robert Ślepaczuk (Quantitative Finance Research Group; Faculty of Economic Sciences, University of Warsaw) |
| Abstract: | The aim of this research is to explore the performance of different option pricing models in hedging the exotic options using the FX data. We analyze the narrow class of Lévy processes - the Variance Gamma process in hedging vanilla, Asian and lookback options. We pose a question of whether or not using additional level of complexity, by introducing more sophisticated models, improves the effectiveness of hedging options, assuming that hedging errors are measured as the differences between portfolio values according to the model and not real market data (which we don’t have). We compare this model with its special case and the Black-Scholes model. We use the data for EURUSD currency pair assuming that option prices change according to the model (as we don’t observe them directly). We use Monte Carlo methods in fitting the model’s parameters. Our results are not in line with the previous literature as there are no signs of the Variance Gamma process being better than the Black-Scholes and it seems that all three models perform equally well. |
| Keywords: | Monte Carlo, option pricing, Variance Gamma, BSM model, Lévy processes, FX market, hedging, Asian and lookback options |
| JEL: | C02 C4 C14 C15 C22 C45 C53 C58 C63 G12 G13 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:war:wpaper:2020-31&r=all |
| By: | Fernando Moraes; Rodrigo De-Losso |
| Abstract: | The Asset pricing literature has produced hundreds of risk factor candidates aimed at explaining the cross-section of expected excess returns, although risk factors which are in fact capable of providing independent information remains an open question. Appling a sparse model, Kozak, Nagel, and Santosh (2020) achieve satisfactory results on explaining cross-sectional returns only with PCs (principal components). In this paper, we propose a new methodology that seeks to reduce risk factor predictor dimensions by estimating the joint risk factor distribution with CPDAG (complete partial directed acyclic graph), in addition to selecting the CPDAG root as the only new risk factor candidate set. Our approach yields a significant shrinkage in the original set of risk factors, whereas our findings lead to sparse models that pose better results than those attained with the standard models and with alternative methods proposed by PCs factor zoo related research papers. |
| Keywords: | Risk factors; factor zoo; DAG; CPDAG |
| JEL: | G12 C55 D85 |
| Date: | 2020–09–15 |
| URL: | http://d.repec.org/n?u=RePEc:spa:wpaper:2020wpecon18&r=all |
| By: | Mykola Babiak; Jozef Barunik |
| Abstract: | We study optimal dynamic portfolio choice of a long-horizon investor who uses deep learning methods to predict equity returns when forming optimal portfolios. The results show statistically and economically significant out-of-sample portfolio benefits of deep learning as measured by high certainty equivalent returns and Sharpe ratios. Return predictability via deep learning generates substantially improved portfolio performance across different subsamples, particularly the recession periods. These gains are robust to including transaction costs, short-selling and borrowing constraints. |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2009.03394&r=all |