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on Financial Markets |
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Issue of 2021‒06‒28
eighteen papers chosen by |
| By: | Bo Becker; Efraim Benmelech |
| Abstract: | Corporate bond markets proved remarkably resilient against a sharp contraction caused by the 2020 Covid-19 pandemic. We document three important findings: (1) bond issuance increased immediately when the contraction hit, whereas, in contrast, syndicated loan issuance was low; (2) Federal Reserve interventions increased bond issuance, while loan issuance also increased, but to a lesser degree; and (3) bond issuance was concentrated in the investment-grade segment for large and profitable issuers. We compare these results to previous crises and recessions and document similar patterns. We conclude that the U.S. bond market is an important and resilient source of funding for corporations. |
| JEL: | E43 E44 E51 G01 G21 G23 |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:28868&r= |
| By: | Ahmet Goncu |
| Abstract: | In this study, empirical moments and the cointegration for all the liquid commodity futures traded in the Chinese futures markets are analyzed for the periods before and after Covid-19, which is important for trading strategies such as pairs trading. The results show that the positive change in the average returns of the products such as soybean, corn, corn starch, and iron ore futures are significantly stronger than other products in the post Covid-19 era, whereas the volatility increased most for silver, petroleum asphalt and egg futures after the pandemic started. The number of cointegrated pairs are reduced after the pandemic indicating the differentiation in returns due to the structural changes caused in the demand and supply conditions across commodities. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.09250&r= |
| By: | Kamaladdin Fataliyev; Aneesh Chivukula; Mukesh Prasad; Wei Liu |
| Abstract: | Stock market movements are influenced by public and private information shared through news articles, company reports, and social media discussions. Analyzing these vast sources of data can give market participants an edge to make profit. However, the majority of the studies in the literature are based on traditional approaches that come short in analyzing unstructured, vast textual data. In this study, we provide a review on the immense amount of existing literature of text-based stock market analysis. We present input data types and cover main textual data sources and variations. Feature representation techniques are then presented. Then, we cover the analysis techniques and create a taxonomy of the main stock market forecast models. Importantly, we discuss representative work in each category of the taxonomy, analyzing their respective contributions. Finally, this paper shows the findings on unaddressed open problems and gives suggestions for future work. The aim of this study is to survey the main stock market analysis models, text representation techniques for financial market prediction, shortcomings of existing techniques, and propose promising directions for future research. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.12985&r= |
| By: | Jaydip Sen; Sidra Mehtab |
| Abstract: | Building predictive models for robust and accurate prediction of stock prices and stock price movement is a challenging research problem to solve. The well-known efficient market hypothesis believes in the impossibility of accurate prediction of future stock prices in an efficient stock market as the stock prices are assumed to be purely stochastic. However, numerous works proposed by researchers have demonstrated that it is possible to predict future stock prices with a high level of precision using sophisticated algorithms, model architectures, and the selection of appropriate variables in the models. This chapter proposes a collection of predictive regression models built on deep learning architecture for robust and precise prediction of the future prices of a stock listed in the diversified sectors in the National Stock Exchange (NSE) of India. The Metastock tool is used to download the historical stock prices over a period of two years (2013- 2014) at 5 minutes intervals. While the records for the first year are used to train the models, the testing is carried out using the remaining records. The design approaches of all the models and their performance results are presented in detail. The models are also compared based on their execution time and accuracy of prediction. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.09664&r= |
| By: | Michele Costola; Matteo Iacopini; Carlo R. M. A. Santagiustina |
| Abstract: | The meme stock phenomenon is yet to be explored. In this note, we provide evidence that these stocks display common stylized facts on the dynamics of price, trading volume, and social media activity. Using a regime-switching cointegration model, we identify the meme stock "mementum" which exhibits a different characterization with respect to other stocks with high volumes of activity (persistent and not) on social media. Understanding these properties helps the investors and market authorities in their decision. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.03691&r= |
| By: | Jaehyung Choi; Hyangju Kim; Young Shin Kim |
| Abstract: | We introduce diversified risk parity embedded with various reward-risk measures and more general allocation rules for portfolio construction. We empirically test advanced reward-risk parity strategies and compare their performance with an equally-weighted risk portfolio in various asset universes. All reward-risk parity strategies we tested exhibit consistent outperformance evidenced by higher average returns, Sharpe ratios, and Calmar ratios. The alternative allocations also reflect less downside risks in Value-at-Risk, conditional Value-at-Risk, and maximum drawdown. In addition to the enhanced performance and reward-risk profile, transaction costs can be reduced by lowering turnover rates. The Carhart four-factor analysis also indicates that the diversified reward-risk parity allocations gain superior performance. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.09055&r= |
| By: | Anders D. Sleire; B{\aa}rd St{\o}ve; H{\aa}kon Otneim; Geir Drage Berentsen; Dag Tj{\o}stheim; Sverre Hauso Haugen |
| Abstract: | It is well known that there are asymmetric dependence structures between financial returns. In this paper we use a new nonparametric measure of local dependence, the local Gaussian correlation, to improve portfolio allocation. We extend the classical mean-variance framework, and show that the portfolio optimization is straightforward using our new approach, only relying on a tuning parameter (the bandwidth). The new method is shown to outperform the equally weighted (1/N) portfolio and the classical Markowitz portfolio for monthly asset returns data. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.12425&r= |
| By: | Samuel Palmer; Serkan Sahin; Rodrigo Hernandez; Samuel Mugel; Roman Orus |
| Abstract: | In this paper we show how to implement in a simple way some complex real-life constraints on the portfolio optimization problem, so that it becomes amenable to quantum optimization algorithms. Specifically, first we explain how to obtain the best investment portfolio with a given target risk. This is important in order to produce portfolios with different risk profiles, as typically offered by financial institutions. Second, we show how to implement individual investment bands, i.e., minimum and maximum possible investments for each asset. This is also important in order to impose diversification and avoid corner solutions. Quite remarkably, we show how to build the constrained cost function as a quadratic binary optimization (QUBO) problem, this being the natural input of quantum annealers. The validity of our implementation is proven by finding the efficient frontier, using D-Wave Hybrid and its Advantage quantum processor, on static portfolios taking assets from the S&P500. We use three different subsets of this index. First, the S&P100 which consists of 100 of the largest companies of the S&P500; second, the 200 best-performing companies of the S&P500; and third, the full S&P500 itself. Our results show how practical daily constraints found in quantitative finance can be implemented in a simple way in current NISQ quantum processors, with real data, and under realistic market conditions. In combination with clustering algorithms, our methods would allow to replicate the behaviour of more complex indexes, such as Nasdaq Composite or others, in turn being particularly useful to build and replicate Exchange Traded Funds (ETF). |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.06735&r= |
| By: | Liao Zhu; Haoxuan Wu; Martin T. Wells |
| Abstract: | The paper proposes a new asset pricing model -- the News Embedding UMAP Selection (NEUS) model, to explain and predict the stock returns based on the financial news. Using a combination of various machine learning algorithms, we first derive a company embedding vector for each basis asset from the financial news. Then we obtain a collection of the basis assets based on their company embedding. After that for each stock, we select the basis assets to explain and predict the stock return with high-dimensional statistical methods. The new model is shown to have a significantly better fitting and prediction power than the Fama-French 5-factor model. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.07103&r= |
| By: | Luca Merlo; Lea Petrella; Valentina Raponi |
| Abstract: | In this paper we propose a multivariate quantile regression framework to forecast Value at Risk (VaR) and Expected Shortfall (ES) of multiple financial assets simultaneously, extending Taylor (2019). We generalize the Multivariate Asymmetric Laplace (MAL) joint quantile regression of Petrella and Raponi (2019) to a time-varying setting, which allows us to specify a dynamic process for the evolution of both VaR and ES of each asset. The proposed methodology accounts for the dependence structure among asset returns. By exploiting the properties of the MAL distribution, we then propose a new portfolio optimization method that minimizes the portfolio risk and controls for well-known characteristics of financial data. We evaluate the advantages of the proposed approach on both simulated and real data, using weekly returns on three major stock market indices. We show that our method outperforms other existing models and provides more accurate risk measure forecasts compared to univariate ones. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.06518&r= |
| By: | Nektarios Aslanidis; Aurelio F. Bariviera; \'Oscar G. L\'opez |
| Abstract: | This paper shows that Bitcoin is not correlated to a general uncertainty index as measured by the Google Trends data of Castelnuovo and Tran (2017). Instead, Bitcoin is linked to a Google Trends attention measure specific for the cryptocurrency market. First, we find a bidirectional relationship between Google Trends attention and Bitcoin returns up to six days. Second, information flows from Bitcoin volatility to Google Trends attention seem to be larger than information flows in the other direction. These relations hold across different sub-periods and different compositions of the proposed Google Trends Cryptocurrency index. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.07104&r= |
| By: | Liping Yang |
| Abstract: | In recent years, Bitcoin price prediction has attracted the interest of researchers and investors. However, the accuracy of previous studies is not well enough. Machine learning and deep learning methods have been proved to have strong prediction ability in this area. This paper proposed a method combined with Ensemble Empirical Mode Decomposition (EEMD) and a deep learning method called long short-term memory (LSTM) to research the problem of next-day Bitcoin price forecast. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.12961&r= |
| By: | Roberto Mota Navarro; Paulino Monroy Castillero; Francois Leyvraz |
| Abstract: | Several studies have shown that large changes in the returns of an asset are associated with the sized of the gaps present in the order book In general, these associations have been studied without explicitly considering the dynamics of either gaps or returns. Here we present a study of these relationships. Our results suggest that the causal relationship between gaps and returns is limited to instantaneous causation. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.02187&r= |
| By: | Beatrice Bertelli; Gianna Boero; Costanza Torricelli |
| Abstract: | Fostered by an empirical literature providing disparate evidence on the green premium, we propose a two-factor model to explain returns on green bonds not only as a function of market risk but also of the bond greenness. The second factor can be interpreted as a greenness premium, which can be either positive or negative depending on the product of the price given by the market to greenness and the sensitivity of the specific green bond to the latter. Based on the model proposed and its Fama-Mac Beth estimation on a sample of Euro-denominated bonds over the period 08.10-2014-31.12.2019, we are able to conclude that the market does price greenness, but the price is very small: including Government green bonds is 0.7 bps, and focusing on corporate green bonds only is – 1.3 bps. In all cases the dynamics of the price for greenness has a positive drift as the market reaches a more mature phase, landing to a positive average value (2 bps), which implies greenness being viewed as a small penalty. However, differences emerge when we look at the issuer sector level and at single bonds, thus our model is able to explain the disparate empirical evidence provided by the literature on the greenium. On the whole, results hint to a market where the difference in pricing between conventional and green bonds is, ceteris paribus, shrinking, which is consistent with greenness becoming a new normal. These results are of interest for many economic agents, including market participants and financial intermediaries, whereby the latter are also called by the regulator to manage their portfolio in consideration of climate risk |
| Keywords: | green bonds, green premium, sustainable finance, factor models, asset pricing |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:mod:wcefin:0083&r= |
| By: | Hengxu Lin; Dong Zhou; Weiqing Liu; Jiang Bian |
| Abstract: | Successful quantitative investment usually relies on precise predictions of the future movement of the stock price. Recently, machine learning based solutions have shown their capacity to give more accurate stock prediction and become indispensable components in modern quantitative investment systems. However, the i.i.d. assumption behind existing methods is inconsistent with the existence of diverse trading patterns in the stock market, which inevitably limits their ability to achieve better stock prediction performance. In this paper, we propose a novel architecture, Temporal Routing Adaptor (TRA), to empower existing stock prediction models with the ability to model multiple stock trading patterns. Essentially, TRA is a lightweight module that consists of a set of independent predictors for learning multiple patterns as well as a router to dispatch samples to different predictors. Nevertheless, the lack of explicit pattern identifiers makes it quite challenging to train an effective TRA-based model. To tackle this challenge, we further design a learning algorithm based on Optimal Transport (OT) to obtain the optimal sample to predictor assignment and effectively optimize the router with such assignment through an auxiliary loss term. Experiments on the real-world stock ranking task show that compared to the state-of-the-art baselines, e.g., Attention LSTM and Transformer, the proposed method can improve information coefficient (IC) from 0.053 to 0.059 and 0.051 to 0.056 respectively. Our dataset and code used in this work are publicly available: https://github.com/microsoft/qlib. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.12950&r= |
| By: | Alexander Barzykin; Philippe Bergault; Olivier Gu\'eant |
| Abstract: | In OTC markets, one of the main tasks of dealers / market makers consists in providing prices at which they agree to buy and sell the assets and securities they have in their scope. With ever increasing trading volume, this quoting task has to be done algorithmically. Over the last ten years, many market making models have been designed that can be the basis of quoting algorithms in OTC markets. However, in the academic literature, most market making models adapted to OTC markets are general and only a few focus on specific market characteristics. In particular, to the best of our knowledge, in all OTC market making models, the market maker only sets quotes and/or waits for clients. However, on many markets such as foreign exchange cash markets, market makers have access to liquidity pools where they can unwind part of their inventory. In this paper, we propose a model taking this possibility into account, therefore allowing market makers to trade ``actively'' in the market. The model displays an important feature well known to practitioners that in a certain inventory range the market maker does not actually want to capitalize on this active trading opportunity but should rather ``internalize'' the flow by appropriately adjusting the quotes. The larger the market making franchise, the wider is the inventory range suitable for internalization. The model is illustrated numerically with realistic parameters for USDCNH spot market. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.06974&r= |
| By: | Katsafados, Apostolos G.; Leledakis, George N.; Pyrgiotakis, Emmanouil G.; Androutsopoulos, Ion; Fergadiotis, Manos |
| Abstract: | This paper investigates the role of textual information in a U.S. bank merger prediction task. Our intuition behind this approach is that text could reduce bank opacity and allow us to understand better the strategic options of banking firms. We retrieve textual information from bank annual reports using a sample of 9,207 U.S. bank-year observations during the period 1994-2016. To predict bidders and targets, we use textual information along with financial variables as inputs to several machine learning models. Our key findings suggest that: (1) when textual information is used as a single type of input, the predictive accuracy of our models is similar, or even better, compared to the models using only financial variables as inputs, and (2) when we jointly use textual information and financial variables as inputs, the predictive accuracy of our models is substantially improved compared to models using a single type of input. Therefore, our findings highlight the importance of textual information in a bank merger prediction task. |
| Keywords: | Bank merger prediction; Textual analysis; Natural language processing; Machine learning |
| JEL: | C38 C45 G1 G2 G21 G3 G34 |
| Date: | 2021–06–12 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:108272&r= |
| By: | Sriya Anbil; Alyssa G. Anderson; Zeynep Senyuz |
| Abstract: | We show that the segmented structure of the U.S. Treasury repo market, in which some participants have limited access across the segments, leads to rate dispersion, even in this essentially riskless market. Using confidential data on repo trading, we demonstrate how the rate dispersion between the centrally cleared and over-the-counter (OTC) segments of the Treasury repo market was exacerbated during the stress episode of September 2019. Our results highlight that, while segmentation can increase fragility in the repo market, the presence of strong trading relationships in the OTC segment helps mitigate it by reducing rate dispersion. |
| Keywords: | Repo market; OTC market; CCP; Segmentation; Financial stability |
| JEL: | E52 G10 E43 G23 |
| Date: | 2021–04–30 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2021-28&r= |