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on Financial Markets |
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Issue of 2020‒09‒21
eleven papers chosen by |
| By: | A. Fronzetti Colladon; S. Grassi; F. Ravazzolo; F. Violante |
| Abstract: | This paper uses a new textual data index for predicting stock market data. The index is applied to a large set of news to evaluate the importance of one or more general economic related keywords appearing in the text. The index assesses the importance of the economic related keywords, based on their frequency of use and semantic network position. We apply it to the Italian press and construct indices to predict Italian stock and bond market returns and volatilities in a recent sample period, including the COVID-19 crisis. The evidence shows that the index captures well the different phases of financial time series. Moreover, results indicate strong evidence of predictability for bond market data, both returns and volatilities, short and long maturities, and stock market volatility. |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2009.04975&r=all |
| By: | Christoph E. Boehm (University of Texas, Austin) |
| Abstract: | We provide evidence for a causal link between the US economy and the global financial cycle. Using a unique intraday dataset, we show that US macroeconomic news releases have large and significant effects on global risky asset prices. Stock price indexes of 27 countries, commodity prices, and the VIX all jump instantaneously upon news releases. The responses of stock indexes co-move across countries and are large -- often comparable in size to the response of the S&P 500. Further, these effects are persistent. US macroeconomic news explain up to 22% of the quarterly variation in foreign stock markets. The joint behavior of stock prices and long-term bond yields suggests that systematic monetary policy responses to news play a limited role for explaining the behavior of international stock markets. Instead, the evidence is consistent with a direct effect on investors' risk-taking capacity. Overall, our findings show that a byproduct of the United States' central position in the global financial system is that news about its business cycle have large effects on global financial conditions. |
| Keywords: | Global Financial Cycle; Macroeconomic announcements; International spillovers; Stock returns; VIX; Commodity prices; High-frequency event study |
| JEL: | E44 E52 F40 G12 G14 G15 |
| Date: | 2020–09–04 |
| URL: | http://d.repec.org/n?u=RePEc:mie:wpaper:677&r=all |
| By: | Jan J. J. Groen; Michael Nattinger; Adam I. Noble |
| Abstract: | We propose measures of financial market stress for forty-six countries and regions across the world. Our measures indicate that worldwide financial market stresses rose significantly in March following the widespread economic shutdowns in the wake of the COVID-19 pandemic. However, hardly anywhere in the world did these March peaks in financial stresses reach those seen during the trough of the 2007-09 Global Financial Crisis. Since March, financial market conditions normalized rapidly with financial market stresses around average levels. We also show that our financial stress measures have predictive power for the near-term economic outlook across most parts of the world, with the exception of China. A structural Bayesian VAR analysis indicates that historically, financial stress shocks, irrespective of the source of the shock, have significant impact on global economic activity, but in particular that emerging market economies are usually hit more severely than advanced economies. |
| Keywords: | financial markets; financial stress indices; emerging markets; advanced economies; SVAR |
| JEL: | C32 C51 E44 F30 F65 |
| Date: | 2020–09–04 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednsr:88692&r=all |
| By: | Abdulnasser Hatemi-J |
| Abstract: | This paper examines the dynamic interaction between falling and rising markets for both the real and the financial sectors of the largest economy in the world using asymmetric causality tests. These tests require that each underlying variable in the model be transformed into partial sums of the positive and negative components. The positive components represent the rising markets and the negative components embody the falling markets. The sample period covers some part of the COVID19 pandemic. Since the data is non normal and the volatility is time varying, the bootstrap simulations with leverage adjustments are used in order to create reliable critical values when causality tests are conducted. The results of the asymmetric causality tests disclose that the bear markets are causing the recessions as well as the bull markets are causing the economic expansions. The causal effect of bull markets on economic expansions is higher compared to the causal effect of bear markets on economic recessions. In addition, it is found that economic expansions cause bull markets but recessions do not cause bear markets. Thus, the policies that remedy the falling financial markets can also help the economy when it is in a recession. |
| Date: | 2020–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2009.01343&r=all |
| By: | Caio Vigo Pereira (Department of Economics, University of Kansas) |
| Abstract: | In this paper, we build efficient portfolios using different frameworks proposed in the literature with several datasets containing an increasing number of predictors as conditioning information. We carry an extensive empirical study to investigate several approaches to impose sparsity and dimensionality reduction, as well as possible latent factors driving the returns of the risky assets. In contrast to previous studies that made use of naive OLS and low-dimension information sets, we find that (i) accounting for large conditioning information sets, and (ii) the use of variable selection, shrinkage methods and factors models, such as the principal component regression and the partial least squares provide better out-of-sample results as measured by Sharpe ratios. |
| Keywords: | Dimensionality reduction. Shrinkage. Efficient Portfolios. Principal Components Regression (PCR). Partial Least Squares (PLS). Three-Pass Regression Filter (3PRF). Ridge Regression, LASSO. |
| JEL: | G11 G17 C32 C38 |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:kan:wpaper:202015&r=all |
| By: | Qiao Zhou; Ningning Liu |
| Abstract: | The prediction of a stock price has always been a challenging issue, as its volatility can be affected by many factors such as national policies, company financial reports, industry performance, and investor sentiment etc.. In this paper, we present a prediction model based on deep CNN and the candle charts, the continuous time stock information is processed. According to different information richness, prediction time interval and classification method, the original data is divided into multiple categories as the training set of CNN. In addition, the convolutional neural network is used to predict the stock market and analyze the difference in accuracy under different classification methods. The results show that the method has the best performance when the forecast time interval is 20 days. Moreover, the Moving Average Convergence Divergence and three kinds of moving average are added as input. This method can accurately predict the stock trend of the US NDAQ exchange for 92.2%. Meanwhile, this article distinguishes three conventional classification methods to provide guidance for future research. |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2009.03239&r=all |
| By: | Zhiqiang Ma; Grace Bang; Chong Wang; Xiaomo Liu |
| Abstract: | Earnings calls are hosted by management of public companies to discuss the company's financial performance with analysts and investors. Information disclosed during an earnings call is an essential source of data for analysts and investors to make investment decisions. Thus, we leverage earnings call transcripts to predict future stock price dynamics. We propose to model the language in transcripts using a deep learning framework, where an attention mechanism is applied to encode the text data into vectors for the discriminative network classifier to predict stock price movements. Our empirical experiments show that the proposed model is superior to the traditional machine learning baselines and earnings call information can boost the stock price prediction performance. |
| Date: | 2020–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2009.01317&r=all |
| By: | Giovanni Calice; Carlo Sala; Daniele Tantari |
| Abstract: | We study the role of contingent convertible bonds (CoCos) in a network of interconnected banks. We first confirm the phase transitions documented by Acemoglu et al. (2015) in absence of CoCos, thus revealing that the structure of the interbank network is of fundamental importance for the effectiveness of CoCos as a financial stability enhancing mechanism. Furthermore, we show that in the presence of a moderate financial shock lightly interconnected financial networks are more robust than highly interconnected networks, and can possibly be the optimal choice for both CoCos issuers and buyers. Finally our results show that, under some network structures, the presence of CoCos can increase (and not reduce) financial fragility, because of the occurring of unneeded triggers and consequential suboptimal conversions that damage CoCos investors. |
| Date: | 2020–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2009.00062&r=all |
| By: | Olkhov, Victor |
| Abstract: | This paper presents probability distributions for price and returns random processes for averaging time interval Δ. These probabilities determine properties of price and returns volatility. We define statistical moments for price and returns random processes as functions of the costs and the volumes of market trades aggregated during interval Δ. These sets of statistical moments determine characteristic functionals for price and returns probability distributions. Volatilities are described by first two statistical moments. Second statistical moments are described by functions of second degree of the cost and the volumes of market trades aggregated during interval Δ. We present price and returns volatilities as functions of number of trades and second degree costs and volumes of market trades aggregated during interval Δ. These expressions support numerous results on correlations between returns volatility, number of trades and the volume of market transactions. Forecasting the price and returns volatilities depend on modeling the second degree of the costs and the volumes of market trades aggregated during interval Δ. Second degree market trades impact second degree of macro variables and expectations. Description of the second degree market trades, macro variables and expectations doubles the complexity of the current macroeconomic and financial theory. |
| Keywords: | price and returns volatility, price-volume relations, macro theory |
| JEL: | C02 C10 E00 E44 G00 G10 G11 G12 G17 |
| Date: | 2020–08–15 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:102434&r=all |
| By: | Andrej Gill; Matthias Heinz; Heiner Schumacher; Matthias Sutter |
| Abstract: | The financial industry has been struggling with widespread misconduct and public mistrust. Here we argue that the lack of trust into the financial industry may stem from the selection of subjects with little, if any, trustworthiness into the financial industry. We identify the social preferences of business and economics students, and follow up on their first job placements. We find that during college, students who want to start their career in the financial industry are substantially less trustworthy. Most importantly, actual job placements several years later confirm this association. The job market in the financial industry does not screen out less trustworthy subjects. If anything the opposite seems to be the case: Even among students who are highly motivated to work in finance after graduation, those who actually start their career in finance are significantly less trustworthy than those who work elsewhere. |
| Keywords: | trustworthiness, financial industry, selection, social preferences, experiment |
| JEL: | C91 G20 M51 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_8501&r=all |
| By: | Zhengxin Joseph Ye; Bjorn W. Schuller |
| Abstract: | While Post-Earnings-Announcement Drift (PEAD) is one of the most studied stock market anomalies, the current literature is often limited in explaining this phenomenon by a small number of factors using simpler regression methods. In this paper, we use a machine learning based approach instead, and aim to capture the PEAD dynamics using data from a large group of stocks and a wide range of both fundamental and technical factors. Our model is built around the Extreme Gradient Boosting (XGBoost) and uses a long list of engineered input features based on quarterly financial announcement data from 1,106 companies in the Russell 1000 index between 1997 and 2018. We perform numerous experiments on PEAD predictions and analysis and have the following contributions to the literature. First, we show how Post-Earnings-Announcement Drift can be analysed using machine learning methods and demonstrate such methods' prowess in producing credible forecasting on the drift direction. It is the first time PEAD dynamics are studied using XGBoost. We show that the drift direction is in fact driven by different factors for stocks from different industrial sectors and in different quarters and XGBoost is effective in understanding the changing drivers. Second, we show that an XGBoost well optimised by a Genetic Algorithm can help allocate out-of-sample stocks to form portfolios with higher positive returns to long and portfolios with lower negative returns to short, a finding that could be adopted in the process of developing market neutral strategies. Third, we show how theoretical event-driven stock strategies have to grapple with ever changing market prices in reality, reducing their effectiveness. We present a tactic to remedy the difficulty of buying into a moving market when dealing with PEAD signals. |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2009.03094&r=all |