nep-fmk New Economics Papers
on Financial Markets
Issue of 2020‒03‒23
ten papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. More on the Value of Financial Advisors By Claude Montmarquette; Alexandre Prud'Homme
  2. Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning By Ben Moews; Gbenga Ibikunle
  3. How Integrated Are Corporate Bond and Stock Markets? By Mirela Sandulescu
  4. Non-stationary neural network for stock return prediction By Steven Y. K. Wong; Jennifer Chan; Lamiae Azizi; Richard Y. D. Xu
  5. Let's Chat... When Communication Promotes Efficiency in Experimental Asset Markets By Brice Corgnet; Mark DeSantis; David Porter
  6. What is the information value of bank's stress tests? An investigation using banks' bond split ratings By Moustapha Daouda Dala; Isabelle Distinguin; Alain Sauviat
  7. Machine Learning Treasury Yields By Zura Kakushadze; Willie Yu
  8. Mortgage-related bank penalties and systemic risk among U.S. banks By Vaclav Broza; Evzen Kocenda
  9. Sector connectedness in the Chinese stock markets By Ying-Ying Shen; Zhi-Qiang Jiang; Jun-Chao Ma; Gang-Jin Wang; Wei-Xing Zhou
  10. Financial literacy and French behaviour on the stock market By Luc Arrondel

  1. By: Claude Montmarquette; Alexandre Prud'Homme
    Abstract: Over the years, inquiring about the role of financial advisors and their value has led to numerous studies that have somehow produced conflicting results. The industry generally has a more positive viewpoint than most academic papers do. Along with the increased visibility of advisors’ fees made mandatory by regulatory authorities, the recent focus on gamma factors rather than the usual alpha and beta benchmarks has, nonetheless, contributed to a more positive assessment of the profession of financial advisor. A report from the Investment Funds Institute of Canada highlights that, on average, investors who work with financial advisors have nearly three times the net worth and four times the investable assets of those who do not. This observation holds across all age groups and income levels. When asked, 61% of advised investors strongly agreed that their advisor had a positive impact on the value of their investments and their investment returns. An econometric analysis of the data gleaned from a major, original Canadian survey carried out in 2009–2010 showed that a financial advisor added significant value to a household's financial assets relative to a comparable household having no financial advisor. Two key elements underlie this positive effect: financial advisors raise households' savings rates and encourage households to behave in a more disciplined manner when the stock market drops significantly. That study has received extensive exposure in general and specialized media. It has been presented at numerous conferences, and an academic version has been published (see Montmarquette & Viennot-Briot, 2015). A second Canadian survey, conducted in 2013–14, confirmed the previous results. This second survey avoided the problem of causality in this type of study, that is, determining whether wealth attracts advisers or whether financial advisors affect the financial wealth of households. As in our previous study, the discipline imposed by a financial advisor on the financial behavior of households, and the increase in their savings rates are the dominant factors that help increase the value of their assets relative to comparable households without an advisor. Also, focusing on a subset of participants in both surveys, we found that the loss of a financial advisor between 2010 and 2014 was costly: households that retained their advisor saw the value of their assets increase by 16.4%, versus only 1.7% for the assets of households that abandoned their advisor during this period. Thus, the value of financial advice far exceeds the traditional alpha and beta measurements discussed in the literature. This study has also been widely distributed by the industry and has been published in the same scientific journal as the previous study: Montmarquette & Viennot-Briot (2019). In the two previous studies, we emphasized a potential limitation on the estimation of the extent of the financial advisor’s effect. Although we control for many factors, we have recognized that the positive effect of a financial advisor's services, notably on additional savings, may be overestimated due to the lack of measurable characteristics regarding a household’s desire to save and invest. A third survey, 2017–2018, conducted under similar conditions as the previous ones, afforded us another opportunity to validate the robustness of our initial results in a new financial and economic context. Furthermore, new questions helped us to gain a better understanding of the intrinsic willingness of survey respondents to invest with or without the help of a financial advisor. In short, we hoped to correct any potential bias described in the previous paragraph. Associated topics on the use of a financial advisor and its impact were also studied: The determinants of choosing a specific type of advisor and evaluating the impact differentially on wealth due to the different types of financial advice (advice in a bank branch vs. broker vs. individual advisor vs. automated advisor, etc.). The determinants of choosing a financial advisor and the impact on the value of assets by the level (broad category) of annual household income. Does a financial advisor's impact depend on the level of initial financial wealth? From a subset of respondents who replied to both the 2014 and 2018 surveys, how did changing the household situation concerning the involvement or not of an FA affect the value of the household’s financial assets (referred to as the survival principle in the 2014 survey). For example, was there a difference in asset values between households who retained their advisor relative to households who dropped their advisor over that period? Has increased fee transparency (CRM2 - Client-Consumer Relationship Model regulation) in recent years affected the use of a financial advisor? We refer readers to our previous studies for an exhaustive review of the literature on the impact of a financial advisor in general. Further references will be added as we proceed with the current study. The associated topics mentioned earlier should be regarded as breaking new ground in the literature on financial advice, as was the case of the survival principle in the second study. Following the introduction, Section 2 discusses the 2018 survey and presents the updated results (the determinants of having a financial advisor and the impact of a financial advisor on the value of assets). In Section 3, we replicate in part the analysis of Section 2 by type of financial advisor. We follow a similar pattern in Section 4 by examining the impact of a financial advisor by level of annual household income. In Section 5, we investigate the impact of the initial investment (financial wealth) at the time the household began working with an FA on the 2018 value of assets held by households. In Section 6, we revisit the survival principle by looking at household investment behavior and the consequences of respondents’ use or not of the services of a financial advisor between 2014 and 2018. In Section 7, we explore whether an increase in fee transparency (client-customer relationship model regulation, or CRM2) in recent years has affected the use of a financial advisor. Section 8 sets out our conclusions. In short, households in all income groups benefit from having a financial advisor. The impacts of FA involvement depend on the economic and financial contexts. Gamma factors continue to play their role.
    Keywords: Financial Advice, Conseil financier
    Date: 2020–03–10
    URL: http://d.repec.org/n?u=RePEc:cir:cirpro:2020rp-04&r=all
  2. By: Ben Moews; Gbenga Ibikunle
    Abstract: Standard methods and theories in finance can be ill-equipped to capture highly non-linear interactions in financial prediction problems based on large-scale datasets, with deep learning offering a way to gain insights into correlations in markets as complex systems. In this paper, we apply deep learning to econometrically constructed gradients to learn and exploit lagged correlations among S&P 500 stocks to compare model behaviour in stable and volatile market environments, and under the exclusion of target stock information for predictions. In order to measure the effect of time horizons, we predict intraday and daily stock price movements in varying interval lengths and gauge the complexity of the problem at hand with a modification of our model architecture. Our findings show that accuracies, while remaining significant and demonstrating the exploitability of lagged correlations in stock markets, decrease with shorter prediction horizons. We discuss implications for modern finance theory and our work's applicability as an investigative tool for portfolio managers. Lastly, we show that our model's performance is consistent in volatile markets by exposing it to the environment of the recent financial crisis of 2007/2008.
    Date: 2020–02
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2002.10385&r=all
  3. By: Mirela Sandulescu (University of Lugano; Swiss Finance Institute)
    Abstract: In this paper, I study the degree of market integration between US corporate bonds and stocks of the corresponding issuing firms, accounting for their characteristics. I find that short-selling constraints are essential restrictions to optimal Sharpe ratio portfolios that yield admissible portfolio positions and implied pricing errors within quoted bid-ask spreads. My empirical evidence suggests that markets are more integrated for larger firms, with more liquid corporate bonds and stocks. Similarly, firms that are more leveraged, have a higher asset growth and profitability feature a greater extent of integration between their debt and equity securities.
    Keywords: stochastic discount factor, corporate bonds, stocks, market integration, firm characteristics
    JEL: G11 G12 G14
    Date: 2020–03
    URL: http://d.repec.org/n?u=RePEc:chf:rpseri:rp2009&r=all
  4. By: Steven Y. K. Wong (University of Technology Sydney); Jennifer Chan (University of Sydney); Lamiae Azizi (University of Sydney); Richard Y. D. Xu (University of Technology Sydney)
    Abstract: We consider the problem of neural network training in a time-varying context. Machine learning algorithms have excelled in problems that do not change over time. However, problems encountered in financial markets are often non-stationary. We propose the online early stopping algorithm and show that a neural network trained using this algorithm can track a function changing with unknown dynamics. We applied the proposed algorithm to the stock return prediction problem studied in Gu et al. (2019) and achieved mean rank correlation of 4.69%, almost twice as high as the expanding window approach. We also show that prominent factors, such as the size effect and momentum, exhibit time varying stock return predictiveness.
    Date: 2020–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2003.02515&r=all
  5. By: Brice Corgnet (EMLYON Business School); Mark DeSantis (Argyros School of Business and Economics & Economic Science Institute, Chapman University); David Porter (Argyros School of Business and Economics & Economic Science Institute, Chapman University)
    Abstract: The growing prevalence of stock market chat rooms and social media suggests communication between traders may affect market outcomes. Using data from a series of laboratory experiments, we study the causal effect of trader communication on the price efficiency of markets. We show that communication allows markets to convey private information more effectively. This effect is most pronounced when the communication platform publicizes a reputation score that might identify a person as not being truthful. This illustrates the need for market designers to consider social interactions when designing market institutions to leverage the social motives that foster information aggregation.
    Keywords: Information Aggregation; Market Efficiency; Communication; Experimental Asset Markets; Social Market Design
    JEL: C92 G02 G14
    Date: 2020
    URL: http://d.repec.org/n?u=RePEc:chu:wpaper:20-12&r=all
  6. By: Moustapha Daouda Dala (Epoka University, Department of Banking and Finance, Rruga Tirane-Rinas Km 12, 1032, Vore, Tirana, Albania); Isabelle Distinguin (LAPE - Laboratoire d'Analyse et de Prospective Economique - IR SHS UNILIM - Institut Sciences de l'Homme et de la Société - UNILIM - Université de Limoges); Alain Sauviat (LAPE - Laboratoire d'Analyse et de Prospective Economique - IR SHS UNILIM - Institut Sciences de l'Homme et de la Société - UNILIM - Université de Limoges)
    Abstract: We study the informative value of stress tests by investigating the impact of the disclosure of their results on banks' bonds split ratings taken as a measure of bank opacity. We consider bonds jointly rated by Moody's and Standard & Poor's and issued by banks that participated to the European and US banks' stress tests. Our results suggest that the disclosure of stress results has mixed effect on split ratings. Our findings also suggest a frequent divergence of interpretation of the stress test results between the two rating agencies meaning that information would not be as relevant as hoped by regulators. Market players certainly could not extract an unambiguous signal from all the results disclosed by the stress tests.
    Keywords: stress tests,credit rating,split rating,banks' opacity
    Date: 2020–02–12
    URL: http://d.repec.org/n?u=RePEc:hal:wpaper:hal-02475512&r=all
  7. By: Zura Kakushadze; Willie Yu
    Abstract: We give explicit algorithms and source code for extracting factors underlying Treasury yields using (unsupervised) machine learning (ML) techniques, such as nonnegative matrix factorization (NMF) and (statistically deterministic) clustering. NMF is a popular ML algorithm (used in computer vision, bioinformatics/computational biology, document classification, etc.), but is often misconstrued and misused. We discuss how to properly apply NMF to Treasury yields. We analyze the factors based on NMF and clustering and their interpretation. We discuss their implications for forecasting Treasury yields in the context of out-of-sample ML stability issues.
    Date: 2020–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2003.05095&r=all
  8. By: Vaclav Broza (Institute of Economic Studies, Charles University); Evzen Kocenda
    Abstract: We analyze link between mortgage-related regulatory penalties levied on banks and the level of systemic risk in the U.S. banking industry. We employ a frequency decomposition of volatility spillovers to draw conclusions about system-wide risk transmission with short-, medium-, and long-term dynamics. We find that after the possibility of a penalty is first announced to the public, long-term systemic risk among banks tends to increase. Short- and medium-term risk marginally declines. In contrast, a settlement with regulatory authorities leads to a decrease in the long-term systemic risk. Our analysis is robust with respect to several criteria.
    Keywords: bank, financial stability, global financial crisis, mortgage, penalty, systemic risk
    JEL: C14 C58 G14 G21 G28 K41
    Date: 2020–03
    URL: http://d.repec.org/n?u=RePEc:kyo:wpaper:1024&r=all
  9. By: Ying-Ying Shen (ECUST); Zhi-Qiang Jiang (ECUST); Jun-Chao Ma (ECUST); Gang-Jin Wang (HNU); Wei-Xing Zhou (ECUST)
    Abstract: Uncovering the risk transmitting path within economic sectors in China is crucial for understanding the stability of the Chinese economic system, especially under the current situation of the China-US trade conflicts. In this paper, we try to uncover the risk spreading channels by means of volatility spillovers within the Chinese sectors using stock market data. By applying the generalized variance decomposition framework based on the VAR model and the rolling window approach, a set of connectedness matrices is obtained to reveal the overall and dynamic spillovers within sectors. It is found that 17 sectors (mechanical equipment, electrical equipment, utilities, and so on) are risk transmitters and 11 sectors (national defence, bank, non-bank finance, and so on) are risk takers during the whole period. During the periods with the extreme risk events (the global financial crisis, the Chinese interbank liquidity crisis, the Chinese stock market plunge, and the China-US trade war), we observe that the connectedness measures significantly increase and the financial sectors play a buffer role in stabilizing the economic system. The robust tests suggest that our results are not sensitive to the changes of model parameters. Our results not only uncover the spillover effects within the Chinese sectors, but also highlight the deep understanding of the risk contagion patterns in the Chinese stock markets.
    Date: 2020–02
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2002.09097&r=all
  10. By: Luc Arrondel (PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Panthéon-Sorbonne - ENS Paris - École normale supérieure - Paris - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, PSE - Paris School of Economics)
    Abstract: This article looks back over the different dimensions of financial literacy: theoretical, methodological and empirical. First, the theoretical foundations of the notion of financial literacy are presented with reference to recent contributions by psychological or behavioural economics: "household finance" refers to the concept of financial literacy based on the empirical dead-ends of standard saver theory. This raises the question as to how to measure and evaluate it. Is the "standard" methodology, based on a few straightforward questions (interest calculations, notion of inflation and risk diversification), adequate or do other definitions need to be developed? As is often said, are the French really "useless at finance" ? Is their financial behaviour, in terms of their portfolio choices, affected by it ? And last but not least, how effective are economic education programmes and is a public financial literacy policy required?
    Keywords: Household finance,financial literacy,stock ownership
    Date: 2020–03
    URL: http://d.repec.org/n?u=RePEc:hal:psewpa:halshs-02505320&r=all

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