nep-fmk New Economics Papers
on Financial Markets
Issue of 2022‒06‒20
sixteen papers chosen by

  1. A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction By Yong Xie; Dakuo Wang; Pin-Yu Chen; Jinjun Xiong; Sijia Liu; Sanmi Koyejo
  2. Expectation-Driven Term Structure of Equity and Bond Yields By Ming Zeng; Guihai Zhao
  3. Randomized geometric tools for anomaly detection in stock markets By Cyril Bachelard; Apostolos Chalkis; Vissarion Fisikopoulos; Elias Tsigaridas
  4. The Market-Based Asset Price Probability By Olkhov, Victor
  5. Volatility Sensitive Bayesian Estimation of Portfolio VaR and CVaR By Taras Bodnar; Vilhelm Niklasson; Erik Thors\'en
  6. A Unified Bayesian Framework for Pricing Catastrophe Bond Derivatives By Dixon Domfeh; Arpita Chatterjee; Matthew Dixon
  7. A Volatility Estimator of Stock Market Indices Based on the Intrinsic Entropy Model By Claudiu Vinte; Marcel Ausloos; Titus Felix Furtuna
  8. Reducing liquidity mismatch in open-ended funds: a cost-benefit analysis By King, Benjamin; Semark, James
  9. Hedge and Safe Haven Properties of Gold, US Treasury, Bitcoin, and Dollar/CHF against the FAANA Companies and S&P 500 By Imran Yousaf; Vasilios Plakandaras; Elie Bouri; Rangan Gupta
  10. Price and liquidity discovery in European sovereign bonds and futures By Jappelli, Ruggero; Lucke, Konrad; Pelizzon, Loriana
  11. The rise of bond financing in Europe By Papoutsi, Melina; Darmouni, Olivier
  12. The Relevance of Banks to the European Stock Market By Andreas Kick; Horst Rottmann
  13. The Yield and Market Function Effects of the Reserve Bank of Australia's Bond Purchases By Richard Finlay; Dmitry Titkov; Michelle Xiang
  14. Deep learning based Chinese text sentiment mining and stock market correlation research By Chenrui Zhang
  15. A time-varying study of Chinese investor sentiment, stock market liquidity and volatility: Based on deep learning BERT model and TVP-VAR model By Chenrui Zhang; Xinyi Wu; Hailu Deng; Huiwei Zhang
  16. Stock Market and Economic Growth: Evidence from Africa By Manuel Ennes Ferreira; João Dias; Jelson Serafim

  1. By: Yong Xie; Dakuo Wang; Pin-Yu Chen; Jinjun Xiong; Sijia Liu; Sanmi Koyejo
    Abstract: More and more investors and machine learning models rely on social media (e.g., Twitter and Reddit) to gather real-time information and sentiment to predict stock price movements. Although text-based models are known to be vulnerable to adversarial attacks, whether stock prediction models have similar vulnerability is underexplored. In this paper, we experiment with a variety of adversarial attack configurations to fool three stock prediction victim models. We address the task of adversarial generation by solving combinatorial optimization problems with semantics and budget constraints. Our results show that the proposed attack method can achieve consistent success rates and cause significant monetary loss in trading simulation by simply concatenating a perturbed but semantically similar tweet.
    Date: 2022–05
  2. By: Ming Zeng; Guihai Zhao
    Abstract: Recent findings on the term structure of equity and bond yields pose serious challenges to existing models of equilibrium asset pricing. This paper presents a new equilibrium model of subjective expectations to explain the joint historical dynamics of equity and bond yields (and their yield spreads). The movements of equity and bond yields are driven mainly by subjective expectations of dividend and gross domestic product (GDP) growth. Yields on short-term dividend claims are more volatile because the expected short-term dividend growth mean-reverts to its less volatile long-run counterpart. The procyclical slope of equity yields is due to the countercyclical slope of dividend growth expectations. The correlation between equity returns/yields and nominal bond returns/yields switched from positive to negative after the late 1990s, owing mainly to a stronger correlation between expectations of real GDP growth and real dividend growth and only partially to procyclical inflation. Dividend strip returns are predictable, and the predictive power decreases with maturity as a result of predictable forecast errors and revisions. The model is also consistent with the data in generating persistent and volatile price-dividend ratios and excess return volatility.
    Keywords: Asset pricing; Financial markets; Interest rates
    JEL: G00 G12 E43
    Date: 2022–05
  3. By: Cyril Bachelard; Apostolos Chalkis; Vissarion Fisikopoulos; Elias Tsigaridas
    Abstract: We propose novel randomized geometric tools to detect low-volatility anomalies in stock markets; a principal problem in financial economics. Our modeling of the (detection) problem results in sampling and estimating the (relative) volume of geodesically non-convex and non-connected spherical patches that arise by intersecting a non-standard simplex with a sphere. To sample, we introduce two novel Markov Chain Monte Carlo (MCMC) algorithms that exploit the geometry of the problem and employ state-of-the-art continuous geometric random walks (such as Billiard walk and Hit-and-Run) adapted on spherical patches. To our knowledge, this is the first geometric formulation and MCMC-based analysis of the volatility puzzle in stock markets. We have implemented our algorithms in C++ (along with an R interface) and we illustrate the power of our approach by performing extensive experiments on real data. Our analyses provide accurate detection and new insights into the distribution of portfolios' performance characteristics. Moreover, we use our tools to show that classical methods for low-volatility anomaly detection in finance form bad proxies that could lead to misleading or inaccurate results.
    Date: 2022–05
  4. By: Olkhov, Victor
    Abstract: This paper introduces the market-based asset price probability during time averaging interval Δ. We substitute the present problem of guessing the “correct” form of the asset price probability by description of the price probability as function of the market trade value and volume statistical moments during Δ. We define n-th price statistical moments as ratio of n-th statistical moments of the trade value to n-th statistical moments of the trade volume. That definition states no correlations between time-series of n-th power of the trade volume and price during Δ, but doesn’t result statistical independence between the trade volume and price. The set of price n-th statistical moments defines Taylor series of the price characteristic function. Approximations of the price characteristic function that reproduce only first m price statistical moments, generate approximations of the market-based price probability. That approach unifies probability description of market-based asset price, price indices, returns, inflation and their volatilities. Market-based price probability approach impacts the asset pricing models and uncovers hidden troubles and usage bounds of the widespread risk hedging tool – Value-at-Risk, lets you determine the price autocorrelations and revises the classical option pricing from one to two dimensional problem. Market-based approach doesn’t simplify the price probability puzzle but establishes direct economic ties between asset pricing, market randomness and economic theory. Description of the market-based price and returns volatility, Skewness and Kurtosis requires development of economic theories those model relations between second, third and forth order macroeconomic variables. Development of these theories will take a lot of efforts and years.
    Keywords: asset price; price probability; returns; inflation; market trades
    JEL: C01 C58 E31 E37 G12 G17
    Date: 2022–05–15
  5. By: Taras Bodnar; Vilhelm Niklasson; Erik Thors\'en
    Abstract: In this paper, a new way to integrate volatility information for estimating value at risk (VaR) and conditional value at risk (CVaR) of a portfolio is suggested. The new method is developed from the perspective of Bayesian statistics and it is based on the idea of volatility clustering. By specifying the hyperparameters in a conjugate prior based on two different rolling window sizes, it is possible to quickly adapt to changes in volatility and automatically specify the degree of certainty in the prior. This constitutes an advantage in comparison to existing Bayesian methods that are less sensitive to such changes in volatilities and also usually lack standardized ways of expressing the degree of belief. We illustrate our new approach using both simulated and empirical data. Compared to some other well known homoscedastic and heteroscedastic models, the new method provides a good alternative for risk estimation, especially during turbulent periods where it can quickly adapt to changing market conditions.
    Date: 2022–05
  6. By: Dixon Domfeh; Arpita Chatterjee; Matthew Dixon
    Abstract: Catastrophe (CAT) bond markets are incomplete and hence carry uncertainty in instrument pricing. As such various pricing approaches have been proposed, but none treat the uncertainty in catastrophe occurrences and interest rates in a sufficiently flexible and statistically reliable way within a unifying asset pricing framework. Consequently, little is known empirically about the expected risk-premia of CAT bonds. The primary contribution of this paper is to present a unified Bayesian CAT bond pricing framework based on uncertainty quantification of catastrophes and interest rates. Our framework allows for complex beliefs about catastrophe risks to capture the distinct and common patterns in catastrophe occurrences, and when combined with stochastic interest rates, yields a unified asset pricing approach with informative expected risk premia. Specifically, using a modified collective risk model -- Dirichlet Prior-Hierarchical Bayesian Collective Risk Model (DP-HBCRM) framework -- we model catastrophe risk via a model-based clustering approach. Interest rate risk is modeled as a CIR process under the Bayesian approach. As a consequence of casting CAT pricing models into our framework, we evaluate the price and expected risk premia of various CAT bond contracts corresponding to clustering of catastrophe risk profiles. Numerical experiments show how these clusters reveal how CAT bond prices and expected risk premia relate to claim frequency and loss severity.
    Date: 2022–05
  7. By: Claudiu Vinte; Marcel Ausloos; Titus Felix Furtuna
    Abstract: Grasping the historical volatility of stock market indices and accurately estimating are two of the major focuses of those involved in the financial securities industry and derivative instruments pricing. This paper presents the results of employing the intrinsic entropy model as a substitute for estimating the volatility of stock market indices. Diverging from the widely used volatility models that take into account only the elements related to the traded prices, namely the open, high, low, and close prices of a trading day (OHLC), the intrinsic entropy model takes into account the traded volumes during the considered time frame as well. We adjust the intraday intrinsic entropy model that we introduced earlier for exchange-traded securities in order to connect daily OHLC prices with the ratio of the corresponding daily volume to the overall volume traded in the considered period. The intrinsic entropy model conceptualizes this ratio as entropic probability or market credence assigned to the corresponding price level. The intrinsic entropy is computed using historical daily data for traded market indices (S&P 500, Dow 30, NYSE Composite, NASDAQ Composite, Nikkei 225, and Hang Seng Index). We compare the results produced by the intrinsic entropy model with the volatility estimates obtained for the same data sets using widely employed industry volatility estimators. The intrinsic entropy model proves to consistently deliver reliable estimates for various time frames while showing peculiarly high values for the coefficient of variation, with the estimates falling in a significantly lower interval range compared with those provided by the other advanced volatility estimators.
    Date: 2022–05
  8. By: King, Benjamin (Bank of England); Semark, James (Bank of England)
    Abstract: Macroprudential authorities increasingly find themselves needing to assess, and act on, risks from outside the traditional banking system. How should they think about the costs and benefits of these actions? In this paper we present an approach to cost-benefit analysis for one topical issue related to non-banks – liquidity mismatch in open-ended funds (OEFs). In particular, we analyse the benefits and costs of more extensive use of swing pricing by UK corporate bond OEFs. Using several models, we quantify the impact of liquidity mismatch and swing pricing on corporate bond spreads and expected GDP growth. We estimate that greater use of swing pricing could reduce amplification of investment grade corporate bond spreads by around 8%, and improve the distribution of GDP growth. We discuss qualitatively the impact of swing pricing on fund liquidity buffers, and the possible costs of swing pricing. We conclude that there are likely to be financial stability benefits from more extensive use of swing pricing by UK corporate bond OEFs.
    Keywords: Cost-benefit analysis; mutual funds; swing pricing; corporate bonds
    JEL: D61 G12 G23 G28
    Date: 2022–04–22
  9. By: Imran Yousaf (School of Management, Air University, Islamabad, Pakistan); Vasilios Plakandaras (Department of Economics, Democritus University of Thrace, Komotini, 69100, Greece); Elie Bouri (School of Business, Lebanese American University, Lebanon); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)
    Abstract: The sudden market crash of 20 February 2020 arising from the COVID-19 pandemic has accelerated the digitalization phenomenon and revived the interest for risk mitigation during stress periods. In this paper, we examine the hedging, diversifying, and safe haven properties of gold, U.S. treasury bonds, Bitcoin, and Dollar/CHF for the FAANA (Facebook, Apple, Amazon, Netflix, and Alphabet) stocks and the S&P 500 index. FAANA exhibited positive returns with remarkable resilience throughout the pandemic period, suggesting a change in their investing character from risk to riskless assets. In our approach we examine both an extended sample period and an alternate focused evaluation of heightened uncertainty periods during the recent pandemic period. Furthermore, we estimate the optimal weights, hedge ratios, and hedging effectiveness for the pairs of stock and alternative assets (gold, US treasury, Bitcoin, and Dollar/CHF) during the full sample period and the COVID-19 pandemic period. Our empirical findings suggest that FAANA, once thought as risky high growth tech stocks, have matured and become a safe blanket during the latest turbulent period. onal convergence, related to the evolution of gaps in real GDP per capita, long-term interest rates and population growth across countries.
    Keywords: : Safe haven assets, Hedging, Diversification, FAANA stocks, COVID-19 outbreak
    JEL: C32 G15
    Date: 2022–05
  10. By: Jappelli, Ruggero; Lucke, Konrad; Pelizzon, Loriana
    Abstract: This work uses financial markets connected by arbitrage relations to investigate the dynamics of price and liquidity discovery, which refer to the cross-instrument forecasting power for prices and liquidity, respectively. Specifically, we seek to understand the linkage between the cheapest to deliver bond and closest futures pairs by using high-frequency data on European governments obligations and derivatives. We split the 2019-2021 sample into three subperiods to appreciate changes in the liquidity discovery induced by the COVID-19 pandemic. Within a cointegration model, we find that price discovery occurs on the futures market, and document strong empirical support for liquidity spillovers both from the futures to the cash market as well as from the cash to the futures market.
    Keywords: Fixed Income,Limits to Arbitrage,Market Liquidity
    JEL: G12 G13 G15
    Date: 2022
  11. By: Papoutsi, Melina; Darmouni, Olivier
    Abstract: Using large panel data of public and private firms, this paper dissects the growth of bond financing in the Euro Area through the lens of the cross-section of issuers. In recent years, the composition of bond issuers has shifted, with the entry of many smaller and riskier issuers. New issuers invest and grow, instead of simply repaying bank loans. Moreover, holdings of ‘buy-and-hold’ bond investors are large in aggregate but small for weaker issuers. Nevertheless, the bond investors’ sell-off after March 2020 was largely directed at bonds of larger, safer issuers. This micro-evidence can shed light on the implications of corporate bonds market development for smaller firms and financial stability. JEL Classification: G21, G32, E44
    Keywords: bond investors, Corporate bond market, debt structure, disintermediation, ECB, financial fragility, monetary policy, quantitative easing
    Date: 2022–05
  12. By: Andreas Kick; Horst Rottmann
    Abstract: Banks have always played an ambivalent role in financial markets. On the one hand, they provide essential services for the market; on the other hand, problems in the banking sector can send shock waves through the entire economy. Given this prominent role, it is not surprising that Pereira and Rua (2018) found that the health of the banking sector exerts an influence on stock returns in the US. Understanding the relationship between banks and their impact on the asset prices of non-financials is essential to evaluate the risk emanating from an unhealthy banking sector and should be considered in new regulatory requirements. The aim of this study is to determine if the health of European banks is of such importance for the European stock market so that spillover effects are visible. Our results show that none of our banking-health variables have explanatory power on the cross-section of European stock returns. These findings contrast those for the US. The reasons may be manifold, from an unimportant liquidity provisioning channel over reduced room for actions due to regulatory requirements up to a moral hazard situation in Europe, where investors strongly rely on the governmental bailouts of distressed banks.
    Keywords: asset pricing, banking, spillover, errors-in-variables, individual stocks, distance-to-default
    JEL: G12 G21
    Date: 2022
  13. By: Richard Finlay (Reserve Bank of Australia); Dmitry Titkov (Reserve Bank of Australia); Michelle Xiang (Reserve Bank of Australia)
    Abstract: We examine the effect on government bond yields of three Reserve Bank of Australia policy measures implemented following the onset of the COVID-19 pandemic. We also assess the impact of the three measures on government bond market functioning. The three measures were: purchases to support government bond market function over early 2020; the yield target on 3-year Australian government bonds; and the bond purchase program to lower longer-term yields from late 2020 until early 2022. For purchases to support market function, we find that the announcement lowered short-dated Australian Government Securities (AGS) yields, but did not lower longer-dated AGS yields. We also find that such purchases led to lower yields as and when they were implemented, and that they supported market function by lowering bid-offer spreads. For the yield target, we find a substantial announcement effect and moderate implementation effects on yields. Conversely, the yield target appears to have detrimentally affected some aspects of government bond market function. For the bond purchase program, we find an announcement effect of around 30 basis points for longer-term AGS yields, while any implementation effects were small and temporary.
    Keywords: market function; yield target; quantitative easing; event study
    JEL: E52 E58 G12
    Date: 2202–05
  14. By: Chenrui Zhang
    Abstract: We explore how to crawl financial forum data such as stock bars and combine them with deep learning models for sentiment analysis. In this paper, we will use the BERT model to train against the financial corpus and predict the SZSE Component Index, and find that applying the BERT model to the financial corpus through the maximum information coefficient comparison study. The obtained sentiment features will be able to reflect the fluctuations in the stock market and help to improve the prediction accuracy effectively. Meanwhile, this paper combines deep learning with financial text, in further exploring the mechanism of investor sentiment on stock market through deep learning method, which will be beneficial for national regulators and policy departments to develop more reasonable policy guidelines for maintaining the stability of stock market.
    Date: 2022–05
  15. By: Chenrui Zhang; Xinyi Wu; Hailu Deng; Huiwei Zhang
    Abstract: Based on the commentary data of the Shenzhen Stock Index bar on the EastMoney website from January 1, 2018 to December 31, 2019. This paper extracts the embedded investor sentiment by using a deep learning BERT model and investigates the time-varying linkage between investment sentiment, stock market liquidity and volatility using a TVP-VAR model. The results show that the impact of investor sentiment on stock market liquidity and volatility is stronger. Although the inverse effect is relatively small, it is more pronounced with the state of the stock market. In all cases, the response is more pronounced in the short term than in the medium to long term, and the impact is asymmetric, with shocks stronger when the market is in a downward spiral.
    Date: 2022–05
  16. By: Manuel Ennes Ferreira; João Dias; Jelson Serafim
    Abstract: We assessed the impact of stock market development on growth in Africa. It uses annual data from a panel of 9 countries in Africa over the period 1992–2017. Panel Vector Autoregressive econometrics technique is used in data analysis. Our main findings are that stock market development has a positive effect on economic growth. Investment, human capital, and openness also positively influence economic growth in Africa. The inflation and government expenditure affect economic growth negatively. The paper also finds that using the impulse response function, economic growth reacts to the stock market for 8 years and goes back to the initial level.
    Keywords: Stock market, Economic growth, Panel vector autoregressive
    JEL: G00 O16 C23
    Date: 2022–05

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