nep-fmk New Economics Papers
on Financial Markets
Issue of 2019‒10‒21
fifteen papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. The Banking View of Bond Risk Premia By Valentin Haddad; David A. Sraer
  2. Splitting credit risk into systemic, sectorial and idiosyncratic components By Álvaro Chamizo; Alfonso Novales
  3. The Value of Intermediation in the Stock Market By Di Maggio, Marco; Egan, Mark; Franzoni, Francesco
  4. A Supply and Demand Approach to Equity Pricing By Betermier, Sebastien; Calvet, Laurent; Jo, Evan
  5. Incorporating Fine-grained Events in Stock Movement Prediction By Deli Chen; Yanyan Zou; Keiko Harimoto; Ruihan Bao; Xuancheng Ren; Xu Sun
  6. Stock price formation: useful insights from a multi-agent reinforcement learning model By J. Lussange; S. Bourgeois-Gironde; S. Palminteri; B. Gutkin
  7. Pricing contingent claims with short selling bans By Guiyuan Ma; Song-Ping Zhu; Ivan Guo
  8. A Note to “Do ETFs Increase Volatility?”: An Improved Method to Predict Assignment of Stocks into Russell Indexes By Itzhak Ben-David; Francesco Franzoni; Rabih Moussawi
  9. Portfolio Cuts: A Graph-Theoretic Framework to Diversification By Bruno Scalzo Dees; Ljubisa Stankovic; Anthony G. Constantinides; Danilo P. Mandic
  10. Residual Switching Network for Portfolio Optimization By Jifei Wang; Lingjing Wang
  11. sPortfolio: Stratified Visual Analysis of Stock Portfolios By Xuanwu Yue; Jiaxin Bai; Qinhan Liu; Yiyang Tang; Abishek Puri; Ke Li; Huamin Qu
  12. The impact of economic policy uncertainty and commodity prices on CARB country stock market volatility By Syed Abul, Basher; Alfred A, Haug; Perry, Sadorsky
  13. Determinants of Capital Structure: An Empirical Analysis of Listed Companies in Thailand Stock Exchange SET 100 Index By Apichat Pongsupatt; Tharinee Pongsupatt
  14. Ownership, wealth, and risk taking: Evidence on private equity fund managers By Bienz, Carsten; Thorburn, Karin S; Walz, Uwe
  15. Systematic Asset Allocation using Flexible Views for South African Markets By Ann Sebastian; Tim Gebbie

  1. By: Valentin Haddad; David A. Sraer
    Abstract: Banks' balance-sheet exposure to fluctuations in interest rates strongly forecasts excess Treasury bond returns. This result is consistent with optimal risk management, a banking counterpart to the household Euler equation. In equilibrium, the bond risk premium compensates banks for bearing fluctuations in interest rates. When banks' exposure to interest rate risk increases, the price of this risk simultaneously rises. We present a collection of empirical observations supporting this view, but also discuss several challenges to this interpretation.
    JEL: G0 G12 G21
    Date: 2019–10
  2. By: Álvaro Chamizo (BBVA.); Alfonso Novales (Instituto Complutense de Análisis Económico (ICAE), and Department of Economic Analysis, Facultad de Ciencias Económicas y Empresariales, Universidad Complutense, 28223 Madrid, Spain.)
    Abstract: We provide a methodology to estimate a global credit risk factor from CDS spreads that can be very useful for risk management. The global risk factor (GRF) reproduces quite well the different epis- odes that have affected the credit market over the sample period. It is highly correlated with standard credit indices, but it contains much higher explanatory power for fluctuations in CDS spreads across sectors than the credit indices themselves. The additional information content over iTraxx seems to be related to some financial interest r ates. We first use the estimated GRF to analyze the extent to which the eleven sectors we consider are systemic. After that, we use it to split the credit risk of indi- vidual issuers into systemic, sectorial, and idiosyncratic components, and we perform some analyses to test that the estimated idiosyncratic components are actually firm-specific. The systemic and sec- torial components explain around 65% of credit risk in the European industrial and financial firms and 50% in the North American firms in those sectors, while 35% and 50% of risk, respectively, has an idiosyncratic nature. Thus, there is a significant margin for portfolio diversification. We also show that our decomposition allows us to identify those firms whose credit would be harder to hedge. We end up analyzing the relationship between the estimated components of risk and some synthetic risk factors, in order to learn about the different nature of the credit risk components.
    Keywords: Credit Risk; Systemic Risk; Sectorial Risk; Idiosyncratic Risk; Asset Allocation.
    JEL: C58 F34 G01 G32
    Date: 2019–09
  3. By: Di Maggio, Marco; Egan, Mark; Franzoni, Francesco
    Abstract: Brokers continue to play a critical role in intermediating institutional stock market transactions. More than half of all institutional investor order flow is still executed by high-touch (non-electronic) brokers. Despite the continued importance of brokers, we have limited information on what drives investors' choices among them. We develop and estimate an empirical model of broker choice that allows us to quantitatively examine each investor's' responsiveness to execution costs and access to research and order flow information. Studying over 300 million institutional trades, we find that investor demand is relatively inelastic with respect to commissions and that investors are willing to pay a premium for access to top research analysts and order-flow information. There is substantial heterogeneity across investors. Relative to other investors, hedge funds tend to be more price insensitive, place less value on sell-side research, and place more value on order-flow information. Furthermore, using trader-level data, we find that investors are more likely to trade with traders who are located physically closer and are less likely to trade with traders that have misbehaved in the past. Lastly, we use our empirical model to investigate the unbundling of equity research and execution services related to the MiFID II regulations. While under-reporting for the average firm is relatively small (4%), we find that the bundling of execution and research allows some institutional investors to under-report management fees by up to 15%.
    Keywords: Broker Networks; Equity Trading; Financial Intermediation; institutional investors; Research Analysts
    JEL: G14 G23 G24 L11
    Date: 2019–08
  4. By: Betermier, Sebastien; Calvet, Laurent; Jo, Evan
    Abstract: This paper presents a frictionless neoclassical model of financial markets in which firm sizes, stock returns, and the pricing kernel are all endogenously determined. The model parsimoniously specifies the supply and demand of financial capital allocated to each firm and provides general equilibrium sizes and returns in closed form. We show that the interaction of supply and demand can coherently explain a large number of asset pricing facts. The equilibrium security market line is flatter than the CAPM predicts and can be nonlinear or downward-sloping. The model also generates the size, profitability, investment growth, value, asymmetric volatility, betting-against-beta, and betting-against-correlation anomalies, while also fitting the cross-section of firm characteristics.
    Keywords: Anomalies; Asset Pricing; capital allocation; factor-based investing; General Equilibrium; production economy
    JEL: G11 G12
    Date: 2019–08
  5. By: Deli Chen; Yanyan Zou; Keiko Harimoto; Ruihan Bao; Xuancheng Ren; Xu Sun
    Abstract: Considering event structure information has proven helpful in text-based stock movement prediction. However, existing works mainly adopt the coarse-grained events, which loses the specific semantic information of diverse event types. In this work, we propose to incorporate the fine-grained events in stock movement prediction. Firstly, we propose a professional finance event dictionary built by domain experts and use it to extract fine-grained events automatically from finance news. Then we design a neural model to combine finance news with fine-grained event structure and stock trade data to predict the stock movement. Besides, in order to improve the generalizability of the proposed method, we design an advanced model that uses the extracted fine-grained events as the distant supervised label to train a multi-task framework of event extraction and stock prediction. The experimental results show that our method outperforms all the baselines and has good generalizability.
    Date: 2019–10
  6. By: J. Lussange; S. Bourgeois-Gironde; S. Palminteri; B. Gutkin
    Abstract: In the past, financial stock markets have been studied with previous generations of multi-agent systems (MAS) that relied on zero-intelligence agents, and often the necessity to implement so-called noise traders to sub-optimally emulate price formation processes. However recent advances in the fields of neuroscience and machine learning have overall brought the possibility for new tools to the bottom-up statistical inference of complex systems. Most importantly, such tools allows for studying new fields, such as agent learning, which in finance is central to information and stock price estimation. We present here the results of a new generation MAS stock market simulator, where each agent autonomously learns to do price forecasting and stock trading via model-free reinforcement learning, and where the collective behaviour of all agents decisions to trade feed a centralised double-auction limit order book, emulating price and volume microstructures. We study here what such agents learn in detail, and how heterogenous are the policies they develop over time. We also show how the agents learning rates, and their propensity to be chartist or fundamentalist impacts the overall market stability and agent individual performance. We conclude with a study on the impact of agent information via random trading.
    Date: 2019–10
  7. By: Guiyuan Ma; Song-Ping Zhu; Ivan Guo
    Abstract: Guo and Zhu (2017) recently proposed an equal-risk pricing approach to the valuation of contingent claims when short selling is completely banned and two elegant pricing formulae are derived in some special cases. In this paper, we establish a unified framework for this new pricing approach so that its range of application can be significantly expanded. The main contribution of our framework is that it not only recovers the analytical pricing formula derived by Guo and Zhu (2017) when the payoff is monotonic, but also numerically produces equal-risk prices for contingent claims with non-monotonic payoffs, a task which has not been accomplished before. Furthermore, we demonstrate how a short selling ban affects the valuation of contingent claims by comparing equal-risk prices with Black-Scholes prices.
    Date: 2019–10
  8. By: Itzhak Ben-David; Francesco Franzoni; Rabih Moussawi
    Abstract: A growing literature uses the Russell 1000/2000 reconstitution event as an identification strategy to investigate corporate finance and asset pricing questions. To implement this identification strategy, researchers need to approximate the ranking variable used to assign stocks to indexes. We develop a procedure that predicts assignment to the Russell 1000/2000 with significant improvements relative to previous approaches. We apply this methodology to extend the tests in Ben-David, Franzoni, and Moussawi (2018).
    JEL: G12 G14 G15
    Date: 2019–10
  9. By: Bruno Scalzo Dees; Ljubisa Stankovic; Anthony G. Constantinides; Danilo P. Mandic
    Abstract: Investment returns naturally reside on irregular domains, however, standard multivariate portfolio optimization methods are agnostic to data structure. To this end, we investigate ways for domain knowledge to be conveniently incorporated into the analysis, by means of graphs. Next, to relax the assumption of the completeness of graph topology and to equip the graph model with practically relevant physical intuition, we introduce the portfolio cut paradigm. Such a graph-theoretic portfolio partitioning technique is shown to allow the investor to devise robust and tractable asset allocation schemes, by virtue of a rigorous graph framework for considering smaller, computationally feasible, and economically meaningful clusters of assets, based on graph cuts. In turn, this makes it possible to fully utilize the asset returns covariance matrix for constructing the portfolio, even without the requirement for its inversion. The advantages of the proposed framework over traditional methods are demonstrated through numerical simulations based on real-world price data.
    Date: 2019–10
  10. By: Jifei Wang; Lingjing Wang
    Abstract: This paper studies deep learning methodologies for portfolio optimization in the US equities market. We present a novel residual switching network that can automatically sense changes in market regimes and switch between momentum and reversal predictors accordingly. The residual switching network architecture combines two separate residual networks (ResNets), namely a switching module that learns stock market conditions, and the main module that learns momentum and reversal predictors. We demonstrate that over-fitting noisy financial data can be controlled with stacked residual blocks and further incorporating the attention mechanism can enhance powerful predictive properties. Over the period 2008 to H12017, the residual switching network (Switching-ResNet) strategy verified superior out-of-sample performance with an average annual Sharpe ratio of 2.22, compared with an average annual Sharpe ratio of 0.81 for the ANN-based strategy and 0.69 for the linear model.
    Date: 2019–10
  11. By: Xuanwu Yue; Jiaxin Bai; Qinhan Liu; Yiyang Tang; Abishek Puri; Ke Li; Huamin Qu
    Abstract: Quantitative Investment, built on the solid foundation of robust financial theories, is at the center stage in investment industry today. The essence of quantitative investment is the multi-factor model, which explains the relationship between the risk and return of equities. However, the multi-factor model generates enormous quantities of factor data, through which even experienced portfolio managers find it difficult to navigate. This has led to portfolio analysis and factor research being limited by a lack of intuitive visual analytics tools. Previous portfolio visualization systems have mainly focused on the relationship between the portfolio return and stock holdings, which is insufficient for making actionable insights or understanding market trends. In this paper, we present sPortfolio, which, to the best of our knowledge, is the first visualization that attempts to explore the factor investment area. In particular, sPortfolio provides a holistic overview of the factor data and aims to facilitate the analysis at three different levels: a Risk-Factor level, for a general market situation analysis; a Multiple-Portfolio level, for understanding the portfolio strategies; and a Single-Portfolio level, for investigating detailed operations. The system's effectiveness and usability are demonstrated through three case studies. The system has passed its pilot study and is soon to be deployed in industry.
    Date: 2019–10
  12. By: Syed Abul, Basher; Alfred A, Haug; Perry, Sadorsky
    Abstract: This paper investigates the impact of economic policy uncertainty shocks and shocks to commodity prices on the realized stock market volatility of the CARB (Canada, Australia, Russia, and Brazil) countries. The CARB countries are important countries to study because they are major commodity exporters. The analysis is conducted using sign restricted impulse response functions (IRFs) and structural vector-autoregressive IRFs. There are some common results across the CARB countries. A positive shock to commodity prices lowers realized stock market volatility while a shock to economic policy uncertainty has a significant positive impact on realized stock market volatility. The magnitudes of the initial impact of these two shocks are similar. Shocks to global economic activity and short-term interest rates lower realized stock market volatility. The impacts of these shocks are more pronounced in models that use sign restrictions. These results have implications for investors and policy makers.
    Keywords: Economic policy uncertainty; commodity prices; stock market volatility, sign restricted VAR.
    JEL: E60 G15 G18
    Date: 2019–10
  13. By: Apichat Pongsupatt (Faculty of Business Administration, Kasetsart University); Tharinee Pongsupatt (Faculty of Business Administration, Kasetsart University)
    Abstract: The main purpose of this study is to investigate some financial indicators that affect the debt ratio in Thailand?s capital market. Two competing theories that explicate the capital structure are old-fashioned pecking order and static trade-off model. From existing literature reviews, we select seven traditional factors: profitability, asset structure, size, liquidity, non-debt tax shields, dividend policy and growth as explanatory variables. While long-term debt and total debt are used as proxies for dependent variables. This study uses secondary data collected from annual financial statements of companies in SET 100 index exclude financial business sector. All firms rank highest market capitalization and top trading liquidity in Thailand Stock Exchange for a period of 10 years during 2009-2018. After examine the data, only 760 samples are qualified under criteria. Two panel multiple regression models are implemented for statistic testing at the significant level 0.05.The results for model 1 (Long term debt) show positive and statistical significant effect of asset structure, size, liquidity and growth. While other three factors comprising profitability, non-debt tax shield and dividend policy indicate negative statistical relationships. The results for model 2 (Total debt) show positive and statistical significant effect of asset structure and growth. Whereas, two factors including profitability and liquidity display negative statistical correlation. The results of the two models are consistent with the Pecking Order theory for profitability and growth. High growth firms have higher need for funds then expect to borrow more. While asset structure is consistent with trade-off theory which hold that there should be a positive relationship between fixed assets and debt since fixed assets can serve as collateral. The explanatory variables which have the highest impact on capital structure choices for long term debt and total debt are non-debt tax shield and profitability respectively. Other independent variables such as product uniqueness, risk and macroeconomic indicators are subject to future research.
    Keywords: Capital Structure; Thailand SET 100 Index; Pecking Order; Static trade-off; Leverage
    JEL: G30 L25 P10
    Date: 2019–10
  14. By: Bienz, Carsten; Thorburn, Karin S; Walz, Uwe
    Abstract: We examine the incentive effects of private equity (PE) professionals' ownership in the funds they manage. In a simple model, we show that managers select less risky firms and use more debt financing the higher their ownership. We test these predictions for a sample of PE funds in Norway, where the professionals' private wealth is public. Consistent with the model, firm risk decreases and leverage increases with the manager's ownership in the fund, but largely only when scaled with her wealth. Moreover, the higher the ownership, the smaller is each individual investment, increasing fund diversification. Our results suggest that wealth is of first order importance when designing incentive contracts requiring PE fund managers to coinvest.
    Keywords: buyouts; general partner; incentives; ownership; private equity; Risk Taking; Wealth
    JEL: D86 G12 G31 G32 G34
    Date: 2019–08
  15. By: Ann Sebastian; Tim Gebbie
    Abstract: We implement a systematic asset allocation model using the Historical Simulation with Flexible Probabilities (HS-FP) framework developed by Meucci. The HS-FP framework is a flexible non-parametric estimation approach that considers future asset class behavior to be conditional on time and market environments, and derives a forward looking distribution that is consistent with this view while remaining close as possible to the prior distribution. The framework derives the forward looking distribution by applying unequal time and state conditioned probabilities to historical observations of asset class returns. This is achieved using relative entropy to find estimates with the least distortion to the prior distribution. Here, we use the HS-FP framework on South African financial market data for asset allocation purposes; by estimating expected returns, correlations and volatilities that are better represented through the measured market cycle. We demonstrated a range of state variables that can be useful towards understanding market environments. Concretely, we compare the out-of-sample performance for a specific configuration of the HS-FP model relative to classic Mean Variance Optimization(MVO) and Equally Weighted (EW) benchmark models. The framework displays low probability of backtest overfitting and the out-of-sample net returns and Sharpe ratio point estimates of the HS-FP model outperforms the benchmark models. However, the results are inconsistent when training windows are varied, the Sharpe ratio is seen to be inflated, and the method does not demonstrate statistically significant out-performance on a gross and net basis.
    Date: 2019–10

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