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on Financial Markets |
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Issue of 2020‒02‒24
six papers chosen by |
| By: | James J. Choi; Kevin Zhao |
| Abstract: | A seminal study of persistence in mutual fund performance is Carhart (1997), who found that U.S. equity mutual funds’ past-year returns positively predict their raw excess return and one-factor alpha over the next year. Based on these results, an investor may believe that she can earn higher returns by buying mutual funds with high past-year returns. We are able to replicate Carhart’s results in his 1963-1993 sample period, but we find that significant performance persistence does not exist in the 1994-2018 period. Even during the 1963-1993 period, performance persistence weakened in later years. The disappearance of significant performance persistence is due to lower returns to favorable styles, as well as less favorable style tilts and increased style-adjusted underperformance by past winning funds. |
| JEL: | G11 G12 G23 |
| Date: | 2020–01 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:26707&r=all |
| By: | Valentin Haddad; Serhiy Kozak; Shrihari Santosh |
| Abstract: | The optimal factor timing portfolio is equivalent to the stochastic discount factor. We propose and implement a method to characterize both empirically. Our approach imposes restrictions on the dynamics of expected returns which lead to an economically plausible SDF. Market-neutral equity factors are strongly and robustly predictable. Exploiting this predictability leads to substantial improvement in portfolio performance relative to static factor investing. The variance of the corresponding SDF is larger, more variable over time, and exhibits different cyclical behavior than estimates ignoring this fact. These results pose new challenges for theories that aim to match the cross-section of stock returns. |
| JEL: | G0 G11 G12 |
| Date: | 2020–01 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:26708&r=all |
| By: | Sang Il Lee |
| Abstract: | In recent years, hyperparameter optimization (HPO) has become an increasingly important issue in the field of machine learning for the development of more accurate forecasting models. In this study, we explore the potential of HPO in modeling stock returns using a deep neural network (DNN). The potential of this approach was evaluated using technical indicators and fundamentals examined based on the effect the regularization of dropouts and batch normalization for all input data. We found that the model using technical indicators and dropout regularization significantly outperforms three other models, showing a positive predictability of 0.53% in-sample and 1.11% out-of-sample, thereby indicating the possibility of beating the historical average. We also demonstrate the stability of the model in terms of the changes in its feature importance over time. |
| Date: | 2020–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2001.10278&r=all |
| By: | Bonsoo Koo; Davide La Vecchia; Oliver Linton |
| Abstract: | We develop estimation methodology for an additive nonparametric panel model that is suitable for capturing the pricing of coupon-paying government bonds followed over many time periods. We use our model to estimate the discount function and yield curve of nominally riskless government bonds. The novelty of our approach is the combination of two different techniques: cross-sectional nonparametric methods and kernel estimation for time varying dynamics in the time series context. The resulting estimator is used for predicting individual bond prices given the full schedule of their future payments. In addition, it is able to capture the yield curve shapes and dynamics commonly observed in the fixed income markets. We establish the consistency, the rate of convergence, and the asymptotic normality of the proposed estimator. A Monte Carlo exercise illustrates the good performance of the method under different scenarios. We apply our methodology to the daily CRSP bond market dataset, and compare ours with the popular Diebold and Li (2006) method. |
| Keywords: | nonparametric inference, panel data, time varying, yield curve dynamics |
| JEL: | C13 C14 C22 G12 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2020-4&r=all |
| By: | Zimmerman, Peter (Bank of England) |
| Abstract: | I present a model of cryptocurrency price formation that endogenizes both the financial market for coins and the fee-based market for blockchain space. A cryptocurrency has two distinctive features: a price determined by the extent of its usage as money, and a blockchain structure that restricts settlement capacity. Limited settlement space creates competition between users of the currency, so speculative activity can crowd out monetary usage. This crowding-out undermines the ability of a cryptocurrency to act as a medium of payment, lowering its value. Higher speculative demand can reduce prices, contrary to standard economic models. Crowding-out also raises the riskiness of investing in cryptocurrency, explaining high observed price volatility. |
| Keywords: | Blockchain; cryptocurrency; global games; price volatility |
| JEL: | D04 E42 G13 |
| Date: | 2020–02–14 |
| URL: | http://d.repec.org/n?u=RePEc:boe:boeewp:0855&r=all |
| By: | Fisera,Boris; Horvath,Roman; Melecky,Martin |
| Abstract: | This paper examines the effect of Basel III implementation on the access to finance of small and medium-size enterprises in 32 emerging markets and developing economies. Analyzing rich, repeated cross-sectional data and a panel of matched firm-bank data in a difference-in-differences setting with sample selection adjustment, the authors find a short-term, moderately negative effect of Basel III on small and medium-size enterprises'access to financing. The results suggest that firms with access to bank credit prior to Basel III implementation could have been affected less than firms that were initially on the fringes of financial inclusion?firms with only a bank account. The paper fails to find any additional heterogeneous effects across firm size or age, bank capitalization or liquidity, or across countries that transitioned to Basel III from Basel II versus Basel 2.5. Overall, the initial conditions of the banking system as well as of complementary business and financial regulation can co-determine the size of short-term costs from the newly implemented global financial regulation in emerging markets and developing economies. |
| Date: | 2019–12–02 |
| URL: | http://d.repec.org/n?u=RePEc:wbk:wbrwps:9069&r=all |