|
on Financial Markets |
|
Issue of 2021‒01‒25
ten papers chosen by |
| By: | Ruiqiang Song; Min Shu; Wei Zhu |
| Abstract: | Starting on February 20, 2020, the global stock markets began to suffer the worst decline since the Great Recession in 2008, and the COVID-19 has been widely blamed on the stock market crashes. In this study, we applied the log-periodic power law singularity (LPPLS) methodology based on multilevel time series to unravel the underlying mechanisms of the 2020 global stock market crash by analyzing the trajectories of 10 major stock market indexes from both developed and emergent stock markets, including the S&P 500, DJIA, NASDAQ, FTSE, DAX, NIKKEI, CSI 300, HSI, BSESN, and BOVESPA. In order to effectively distinguish between endogenous crash and exogenous crash, we proposed using the LPPLS confidence indicator as a classification proxy. The results show that the apparent LPPLS bubble patterns of the super-exponential increase, corrected by the accelerating logarithm-periodic oscillations, have indeed presented in the price trajectories of the seven indexes: S&P 500, DJIA, NASDAQ, DAX, CSI 300, BSESN, and BOVESPA, indicating that the large positive bubbles have formed endogenously prior to the 2020 stock market crash, and the subsequent crashes for the seven indexes are endogenous, stemming from the increasingly systemic instability of the stock markets, while the well-known external shocks such as the COVID-19 pandemic etc. only acted as sparks during the 2020 global stock market crash. In contrast, the obvious signatures of the LPPLS model have not been observed in the price trajectories of the three remaining indexes: FTSE, NIKKEI, and HSI, signifying that the crashes in these three indexes are exogenous, stemming from external shocks. The novel classification method of crash types proposed in this study can also be used to analyze regime changes of any price trajectories in global financial markets. |
| Date: | 2021–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2101.00327&r=all |
| By: | Tinic, Murat; Sensoy, Ahmet; Demir, Muge; Nguyen, Duc Khuong |
| Abstract: | We examine the relationship between broker network connectivity and stock returns in an order-driven market. Considering all stocks traded in Borsa Istanbul between January 2006 and November 2015, we estimate the monthly density, reciprocity and average weighted clustering coefficient as proxies for the broker network connectivity. Our firm-level cross-sectional regressions indicate a negative and significant predictive relationship between connectivity and one-month ahead stock returns. Our analyses also show that stocks in the lowest connectivity quintile earn 1.0% - 1.6% monthly return premiums. The connectivity premium is stronger in terms of both economic and statistical significance for small size stocks. |
| Keywords: | Stock market; trading networks; broker networks, network connectivity, pricing factors. |
| JEL: | G1 G12 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:104719&r=all |
| By: | Nina Boyarchenko; Richard K. Crump; Anna Kovner; Or Shachar |
| Abstract: | We measure dislocations in the market for corporate bonds in real time with the Corporate Bond Market Distress Index (CMDI), allowing for the aggregation of a broad set of measures of market functioning from primary and secondary bond markets into a single measure. The index quantifies dislocations from a preponderance-of-metrics perspective, ensuring that the measure of market distress is not driven by any one statistic. We document that the index correctly identifies periods of dislocations, is robust to alternative choices of the aggregation procedure, and provides differential predictive information for future real outcomes relative to common spread measures. |
| Keywords: | corporate bond market conditions; corporate bond spreads; corporate bond issuance; corporate bond liquidity |
| JEL: | C43 E37 G12 G19 |
| Date: | 2021–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednsr:89473&r=all |
| By: | Yvo Mudde; Anna Samarina; Robert Vermeulen |
| Abstract: | This paper investigates cross-border spillover effects from the Eurosystem's Public Sector Purchase Programme (PSPP) on euro area government bond yields. We distinguish between the direct effects of domestic bond purchases by national central banks and the indirect effects from bond purchases by national central banks in other euro area countries over the period March 2015 - December 2018. The results reveal substantial spillover effects across the euro area, providing evidence for strong arbitrage within the euro area. These spillover effects are particularly large for long-term bonds and for bonds issued by non-core countries. The larger impact of spillovers in these cases can be explained by investors rebalancing towards higher yielding government bonds. In addition, purchases under PSPP had their largest impact on bond yields in 2015. |
| Keywords: | Public Sector Purchase Programme; euro area; spillovers; government bonds |
| JEL: | E52 E58 G12 |
| Date: | 2021–01 |
| URL: | http://d.repec.org/n?u=RePEc:dnb:dnbwpp:706&r=all |
| By: | Alexis Marchal |
| Abstract: | I propose a new tool to characterize the resolution of uncertainty around FOMC press conferences. It relies on the construction of a measure capturing the level of discussion complexity between the Fed Chair and reporters during the Q&A sessions. I show that complex discussions are associated with higher equity returns and a drop in realized volatility. The method creates an attention score by quantifying how much the Chair needs to rely on reading internal documents to be able to answer a question. This is accomplished by building a novel dataset of video images of the press conferences and leveraging recent deep learning algorithms from computer vision. This alternative data provides new information on nonverbal communication that cannot be extracted from the widely analyzed FOMC transcripts. This paper can be seen as a proof of concept that certain videos contain valuable information for the study of financial markets. |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2012.06573&r=all |
| By: | Paweł Sakowski (Quantitative Finance Research Group, Department of Quantitative Finance, Faculty of Economic Sciences, University of Warsaw); Anna Turovtseva |
| Abstract: | The aim of the paper is to reveal if the classical approach for asset allocation can be reflected on an innovative market of cryptocurrencies. Markowitz rebalanced portfolio technique is employed for this purpose. The filtering of coins for optimization is done on the whole scope of cryptocurrencies available for the time horizon of the study and only 52 coins get into the portfolio at least once. There are four primary strategies produced within a set of assumed optimization parameters together with four benchmarks for each. The benchmarks are Bitcoin buy-and-hold, S&P500 buy-and-hold, equally weighted portfolio and portfolio weighted by market capitalization. While looking at the performance measures, it is concluded that Markowitz strategies outperform their benchmarks for every set of parameters. The results of the sensitivity analysis suggest that there is a big potential in finding profitable strategy of investment on cryptocurrencies. The change of parameters: look-back period, rebalancing window, transaction cost as well as optimization objective impact the strategies performance significantly. Eventually, there appear a strategy in the sensitivity analysis, which performs better than the primary ones due to the prolonged parameters of look-back period and rebalancing window. |
| Keywords: | Portfolio analysis, Markowitz framework, cryptocurrencies, investment strategies, asset allocation |
| JEL: | C20 C22 C61 C80 G14 G17 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:war:wpaper:2020-41&r=all |
| By: | Kentaro Imajo; Kentaro Minami; Katsuya Ito; Kei Nakagawa |
| Abstract: | Recent developments in deep learning techniques have motivated intensive research in machine learning-aided stock trading strategies. However, since the financial market has a highly non-stationary nature hindering the application of typical data-hungry machine learning methods, leveraging financial inductive biases is important to ensure better sample efficiency and robustness. In this study, we propose a novel method of constructing a portfolio based on predicting the distribution of a financial quantity called residual factors, which is known to be generally useful for hedging the risk exposure to common market factors. The key technical ingredients are twofold. First, we introduce a computationally efficient extraction method for the residual information, which can be easily combined with various prediction algorithms. Second, we propose a novel neural network architecture that allows us to incorporate widely acknowledged financial inductive biases such as amplitude invariance and time-scale invariance. We demonstrate the efficacy of our method on U.S. and Japanese stock market data. Through ablation experiments, we also verify that each individual technique contributes to improving the performance of trading strategies. We anticipate our techniques may have wide applications in various financial problems. |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2012.07245&r=all |
| By: | Xavier Warin |
| Abstract: | We propose deep neural network algorithms to calculate efficient frontier in some Mean-Variance and Mean-CVaR portfolio optimization problems. We show that we are able to deal with such problems when both the dimension of the state and the dimension of the control are high. Adding some additional constraints, we compare different formulations and show that a new projected feedforward network is able to deal with some global constraints on the weights of the portfolio while outperforming classical penalization methods. All developed formulations are compared in between. Depending on the problem and its dimension, some formulations may be preferred. |
| Date: | 2021–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2101.02044&r=all |
| By: | Mykola Babiak; Jozef Barunik |
| Abstract: | We study dynamic portfolio choice of a long-horizon investor who uses deep learning methods to predict equity returns when forming optimal portfolios. Our results show statistically and economically significant benefits from using deep learning to form optimal portfolios through certainty equivalent returns and Sharpe ratios. Return predictability via deep learning also generates substantially improved portfolio performance across different subsamples, particularly during recessionary periods. These gains are robust to including transaction costs, short-selling and borrowing constraints. |
| Keywords: | return predictability; portfolio allocation; machine learning; neural networks; empirical asset pricing; |
| JEL: | C45 C53 E37 G11 G17 |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:cer:papers:wp677&r=all |
| By: | Le Trung Hieu |
| Abstract: | Stock portfolio optimization is the process of constant re-distribution of money to a pool of various stocks. In this paper, we will formulate the problem such that we can apply Reinforcement Learning for the task properly. To maintain a realistic assumption about the market, we will incorporate transaction cost and risk factor into the state as well. On top of that, we will apply various state-of-the-art Deep Reinforcement Learning algorithms for comparison. Since the action space is continuous, the realistic formulation were tested under a family of state-of-the-art continuous policy gradients algorithms: Deep Deterministic Policy Gradient (DDPG), Generalized Deterministic Policy Gradient (GDPG) and Proximal Policy Optimization (PPO), where the former two perform much better than the last one. Next, we will present the end-to-end solution for the task with Minimum Variance Portfolio Theory for stock subset selection, and Wavelet Transform for extracting multi-frequency data pattern. Observations and hypothesis were discussed about the results, as well as possible future research directions.1 |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2012.06325&r=all |