nep-fmk New Economics Papers
on Financial Markets
Issue of 2020‒05‒04
nineteen papers chosen by

  1. Robo-Advising By Francesco D'Acunto; Alberto G. Rossi
  2. The new methods for equity fund selection and optimal portfolio construction By Yi Cao
  3. Measuring efficiency and risk preferences in dynamic portfolio choice By Jacopo Magnani; Jean Paul Rabanal; Olga A. Rud; Yabin Wang
  4. Mutual funds' performance: the role of distribution networks and bank affiliation By Giorgio Albareto; Andrea Cardillo; Andrea Hamaui; Giuseppe Marinelli
  5. Time-frequency forecast of the equity premium By Faria, Gonçalo; Verona, Fabio
  6. Persistence in the Market Risk Premium: Evidence across Countries By Guglielmo Maria Caporale; Luis A. Gil-Alana; Miguel Martin-Valmayor
  7. Investor-herding and risk-profiles: A State-Space Model-based Assessment By Harminder B. Nath; Robert D. Brooks
  8. Post-crisis international financial regulatory reforms: a primer By Claudio Borio; Marc Farag; Nikola Tarashev
  9. How ETFs Amplify the Global Financial Cycle in Emerging Markets By Nathan Converse; Eduardo Levy Yeyati; Tomás Williams
  10. Modeling Institutional Credit Risk with Financial News By Tam Tran-The
  11. Empirical Study of Market Impact Conditional on Order-Flow Imbalance By Anastasia Bugaenko
  12. Classical Option Pricing and Some Steps Further By Olkhov, Victor
  13. Optimal Strategies for ESG Portfolios By Fabio Alessandrini; Eric Jondeau
  14. Volatility Forecasting in European Government Bond Markets By Özbekler, Ali Gencay; Kontonikas, Alexandros; Triantafyllou, Athanasios
  15. Deep reinforcement learning for the optimal placement of cryptocurrency limit orders By Schnaubelt, Matthias
  16. Do Investors Care about Carbon Risk? By Patrick Bolton; Marcin Kacperczyk
  17. Covid-19 and corporate sector liquidity By Ryan Banerjee; Anamaria Illes; Enisse Kharroubi; José María Serena Garralda
  18. Are Corporate Payouts Abnormally High in the 2000s? By Kathleen Kahle; René M. Stulz
  19. Firms’ listings: what is new? Italy versus the main European stock exchanges By Paolo Finaldi Russo; Fabio Parlapiano; Daniele Pianeselli; Ilaria Supino

  1. By: Francesco D'Acunto; Alberto G. Rossi
    Abstract: In this chapter, we first discuss the limitations of traditional financial advice, which led to the emergence of robo-advising. We then describe the main features of robo-advising and propose a taxonomy of robo-advisors based on four defining dimensions---personalization, discretion, involvement, and human interaction. Building on these premises, we delve into the theoretical and empirical evidence on the design and effects of robo-advisors on two major sets of financial decisions, that is, investment choices (for both short- or long-term horizons) and the allocation of financial resources between spending and saving. We conclude by elaborating on five broadly open issues in robo-advising, which beget theoretical and empirical research by scholars in economics, finance, psychology, law, philosophy, as well as regulators and industry practitioners.
    Keywords: FinTech, behavioral economics, algorithmic advice, A1, financial regulation, financial literacy
    JEL: D14 G21
    Date: 2020
  2. By: Yi Cao
    Abstract: We relook at the classic equity fund selection and portfolio construction problems from a new perspective and propose an easy-to-implement framework to tackle the problem in practical investment. Rather than the conventional way by constructing a long only portfolio from a big universe of stocks or macro factors, we show how to produce a long-short portfolio from a smaller pool of stocks from mutual fund top holdings and generate impressive results. As these methods are based on statistical evidence, we need closely monitoring the model validity, and prepare repair strategies.
    Date: 2020–04
  3. By: Jacopo Magnani (EM Lyon); Jean Paul Rabanal (Monash University); Olga A. Rud (RMIT); Yabin Wang (Hong Kong Monetary Authority)
    Abstract: This paper uses non-parametric methods to study the efficiency (Dybvig, 1988) and risk-profile (Varian, 1988) of dynamic portfolio choices. We design an experiment which varies the number of states (complexity), and includes an equivalent static Arrow-Debreu problem. The results suggest that complexity reduces efficiency, as does lower cognitive ability. Efficiency is also lower in the static problem, and in the dynamic task it is mostly driven by a form of stop-loss strategy. Further, we find that a representative agent exhibits decreasing absolute risk aversion and constant relative risk aversion, despite significant individual heterogeneity.
    Date: 2020–04
  4. By: Giorgio Albareto (Bank of Italy); Andrea Cardillo (Bank of Italy); Andrea Hamaui (Harvard University); Giuseppe Marinelli (Bank of Italy)
    Abstract: The paper investigates how the characteristics of the distribution network and the affiliation to a banking group affect mutual funds performance exploiting a unique dataset with extremely detailed information on funds’ portfolios and bank-issuer relationships for the period 2006-2017. We find that bank-affiliated mutual funds underperform independent ones. The structure of the distribution channels is a key-factor affecting mutual funds' performance: when bank platforms become by far the prevalent channel for the distribution of funds’ shares, asset management companies are captured by banks. As for bank affiliation, results show a positive bias of bank-controlled mutual funds towards securities issued by their own banking group clients (of the lending and investment banking divisions) and by institutions belonging to their own banking group; this last bias is exacerbated for mutual funds belonging to undercapitalized banking groups. The structure of the distribution channels explains two thirds of bank-affiliated mutual funds underperformance, whereas investment biases explain one fourth of the observed differential in returns with independent mutual funds.
    Keywords: mutual funds, mutual funds performance, distribution networks, conflict of interest
    JEL: G23 G21 G11 G32
    Date: 2020–04
  5. By: Faria, Gonçalo; Verona, Fabio
    Abstract: Any time series can be decomposed into cyclical components fluctuating at different frequencies. Accordingly, in this paper we propose a method to forecast the stock market's equity premium which exploits the frequency relationship between the equity premium and several predictor variables. We evaluate a large set of models and find that, by selecting the relevant frequencies for equity premium forecasting, this method significantly improves in both statistical and economic sense upon standard time series forecasting methods. This improvement is robust regardless of the predictor used, the out-of-sample period considered, and the frequency of the data used.
    JEL: C58 G11 G17
    Date: 2020–04–27
  6. By: Guglielmo Maria Caporale; Luis A. Gil-Alana; Miguel Martin-Valmayor
    Abstract: This paper provides evidence on the degree of persistence of one of the key components of the CAPM, namely the market risk premium, as well as its volatility. The analysis applies fractional integration methods to data for the US, Germany and Japan, and for robustness purposes considers different time horizons (2, 5 and 10 years) and frequencies (monthly and weekly). The empirical findings in most cases imply that the market risk premium is a highly persistent variable which can be characterized as a random walk process, whilst its volatility is less persistent and exhibits stationary long-memory behaviour. There is also evidence that in the case of the US the degree of persistence has changed as a results of various events; this is confirmed by both endogenous break tests and the associated subsample estimates. Market participants should take this evidence into account when designing their investment strategies.
    Keywords: CAPM, risk premium, persistence, mean reversion, long memory
    JEL: C22 G11
    Date: 2020
  7. By: Harminder B. Nath; Robert D. Brooks
    Abstract: This paper, using the Australian stock market data, examines the investor-herding and riskprofiles link that has implications for asset pricing, portfolio diversification and foreign investments. As investors may herd towards a specific factor, sector or style to combat market conditions for optimizing investment returns, examining such herding can reveal investors' risk profiles. We employ State-Space models for extracting time series of herd dynamics and the proportion of signal explained by herding (PoSEH). Market volatility has a significant negative effect on PoSEH, with the most/least effect on high/low performance days of stock returns. Using quantile regression, we observe that herding and adverseherding can emerge during the worst and best performance days of stock returns, and that extreme volatility can bring herding to a near halt. The study reveals the presence of a regulated stock market environment and risk-aversion tendencies among investors.
    Keywords: Herd behaviour, risk aversion, state-space models, quantile regression.
    JEL: C31 C32 G12 G14
    Date: 2020
  8. By: Claudio Borio; Marc Farag; Nikola Tarashev
    Abstract: This paper reviews post-crisis financial regulatory reforms, examines how they fit together and identifies open issues. Specifically, it takes stock of the salient new features of bank and CCP international standards within a unified analytical framework. The key notion in this framework is "shock-absorbing capacity", which is higher when (i) there is less exposure to the losses that a shock generates and (ii) there are more resources to absorb such losses. How do the reforms strengthen this capacity, individually and as a package? Which areas merit further attention? We argue that, given the political economy pressures and technical obstacles that the reforms have faced, as well as the inherent uncertainty about the reforms' effects, it is important to maintain a conservative regulatory approach. A higher cost of balance sheet space is a healthy side effect of the backstops underpinning such an approach.
    Keywords: bank regulation, CCPs, asset managers, macroprudential
    JEL: G21 G23 G28
    Date: 2020–04
  9. By: Nathan Converse; Eduardo Levy Yeyati; Tomás Williams
    Abstract: This paper examines how the growth of exchange-traded funds (ETFs) has affected the sensitivity of international capital flows to global financial conditions. Using data on individual emerging market funds worldwide, we employ a novel identification strategy that controls for unobservable time-varying economic conditions at the investment destination. We find that the sensitivity of flows to global financial conditions for equity (bond) ETFs is 2.5 (2.25) times higher than for equity (bond) mutual funds. We then show that our findings have macroeconomic implications. In countries where ETFs hold a larger share of the equity market, total cross-border equity flows and returns are significantly more sensitive to global financial conditions. Our results imply that the increasing role of ETFs as a channel for international capital flows has amplified the global financial cycle in emerging markets.
    Keywords: Exchange-traded funds; Mutual funds; Global financial cycle; Global risk; Push and pull factors; Capital flows; Emerging markets
    JEL: F32 G11 G15 G23
    Date: 2019–01–17
  10. By: Tam Tran-The
    Abstract: Credit risk management, the practice of mitigating losses by understanding the adequacy of a borrower's capital and loan loss reserves, has long been imperative to any financial institution's long-term sustainability and growth. MassMutual is no exception. The company is keen on effectively monitoring downgrade risk, or the risk associated with the event when credit rating of a company deteriorates. Current work in downgrade risk modeling depends on multiple variations of quantitative measures provided by third-party rating agencies and risk management consultancy companies. As these structured numerical data become increasingly commoditized among institutional investors, there has been a wide push into using alternative sources of data, such as financial news, earnings call transcripts, or social media content, to possibly gain a competitive edge in the industry. The volume of qualitative information or unstructured text data has exploded in the past decades and is now available for due diligence to supplement quantitative measures of credit risk. This paper proposes a predictive downgrade model using solely news data represented by neural network embeddings. The model standalone achieves an Area Under the Receiver Operating Characteristic Curve (AUC) of more than 80 percent. The output probability from this news model, as an additional feature, improves the performance of our benchmark model using only quantitative measures by more than 5 percent in terms of both AUC and recall rate. A qualitative evaluation also indicates that news articles related to our predicted downgrade events are specially relevant and high-quality in our business context.
    Date: 2020–04
  11. By: Anastasia Bugaenko
    Abstract: In this research we have empirically investigated the key drivers affecting liquidity in equity markets. We illustrated how theoretical models, such as Kyle's model, of agents' interplay in the financial markets, are aligned with the phenomena observed in publicly available trades and quotes data. Specifically, we confirmed that for small signed order-flows, the price impact grows linearly with increase in the order-flow imbalance. We have, further, implemented a machine learning algorithm to forecast market impact given a signed order-flow. Our findings suggest that machine learning models can be used in estimation of financial variables; and predictive accuracy of such learning algorithms can surpass the performance of traditional statistical approaches. Understanding the determinants of price impact is crucial for several reasons. From a theoretical stance, modelling the impact provides a statistical measure of liquidity. Practitioners adopt impact models as a pre-trade tool to estimate expected transaction costs and optimize the execution of their strategies. This further serves as a post-trade valuation benchmark as suboptimal execution can significantly deteriorate a portfolio performance. More broadly, the price impact reflects the balance of liquidity across markets. This is of central importance to regulators as it provides an all-encompassing explanation of the correlation between market design and systemic risk, enabling regulators to design more stable and efficient markets.
    Date: 2020–04
  12. By: Olkhov, Victor
    Abstract: This paper modifies single assumption in the base of classical option pricing model and derives further extensions for the Black-Scholes-Merton equation. We regard the price as the ratio of the cost and the volume of market transaction and apply classical assumptions on stochastic Brownian motion not to the price but to the cost and the volume. This simple replacement leads to 2-dimensional BSM-like equation with two constant volatilities. We argue that decisions on the cost and the volume of market transactions are made under agents expectations. Random perturbations of expectations impact the market transactions and through them induce stochastic behavior of the underlying price. We derive BSM-like equation driven by Brownian motion of agents expectations. Agents expectations can be based on option trading data. We show how such expectations can lead to nonlinear BSM-like equations. Further we show that the Heston stochastic volatility option pricing model can be applied to our approximations and as example derive 3-dimensional BSM-like equation that describes option pricing with stochastic cost volatility and constant volume volatility. Diversity of BSM-like equations with 2 – 5 or more dimensions emphasizes complexity of option pricing problem. Such variety states the problem of reasonable balance between the accuracy of asset and option price description and the complexity of the equations under consideration. We hope that some of BSM-like equations derived in this paper may be useful for further development of assets and option market modeling.
    Keywords: Option Pricing; Black-Scholes-Merton Equations; Stochastic Volatility; Market Transactions; Expectations; Nonlinear equations
    JEL: G1 G12 G17
    Date: 2020–04–27
  13. By: Fabio Alessandrini (University of Lausanne; Banque Cantonale Vaudoise); Eric Jondeau (University of Lausanne - Faculty of Business and Economics (HEC Lausanne); Swiss Finance Institute)
    Abstract: In a previous paper (Alessandrini and Jondeau, 2020), we demonstrate that in the last decade, investing according to screening based on environmental, social, and governance (ESG) criteria would have allowed investors to considerably improve the ESG quality of their portfolio without deteriorating its financial performance. However, a drawback of such a screening process is that it possibly generates undesirable regional, sectoral, and risk factor exposures. In this paper, we propose an investment strategy that maximizes the ESG quality of the portfolio while maintaining regional, sectoral, and risk factor exposures within stated limits. We provide evidence that such a portfolio would have produced a risk-adjusted performance at least as high as the standard MSCI benchmark for a wide range of ESG criteria and regions over the 2007-2018 investment period.
    Date: 2020–04
  14. By: Özbekler, Ali Gencay; Kontonikas, Alexandros; Triantafyllou, Athanasios
    Abstract: In this paper we examine the predictive power of the Heterogeneous Autoregressive (HAR) model on Treasury bond return volatility of major European government bond markets. The HAR-type volatility forecasting models show that short term and medium term volatility is a robust and statistically significant predictor of the term structure of intradayvolatility of bonds with maturities ranging from 1-year up to 30-years. When decomposing volatility into its continuous and discontinuous (jump) component, we find that the jump tail risk component is a significant predictor of bond market volatility. We lastly show that approximately half of the monetary policy announcement dates coincide with the presence of jumps in bond returns, and the pre-announcement drift is present in the bond market. Hence, the monetary policy announcements are important determinant of European bond market volatility.
    Keywords: Treasury Bonds, Jumps, Realized Volatility, Macroeconomic Announcements, Volatility Forecasting
    Date: 2020–04–24
  15. By: Schnaubelt, Matthias
    Abstract: This paper presents the first large-scale application of deep reinforcement learning to optimize the placement of limit orders at cryptocurrency exchanges. For training and out-of-sample evaluation, we use a virtual limit order exchange to reward agents according to the realized shortfall over a series of time steps. Based on the literature, we generate features that inform the agent about the current market state. Leveraging 18 months of high-frequency data with 300 million historic trades and more than 3.5 million order book states from major exchanges and currency pairs, we empirically compare state-of-the-art deep reinforcement learning algorithms to several benchmarks. We find proximal policy optimization to reliably learn superior order placement strategies when compared to deep double Q-networks and other benchmarks. Further analyses shed light into the black box of the learned execution strategy. Important features are current liquidity costs and queue imbalances, where the latter can be interpreted as predictors of short-term mid-price returns. To preferably execute volume in limit orders to avoid additional market order exchange fees, order placement tends to be more aggressive in expectation of unfavorable price movements.
    Keywords: Finance,Optimal Execution,Limit Order Markets,Machine learning,Deep Reinforcement Learning
    Date: 2020
  16. By: Patrick Bolton; Marcin Kacperczyk
    Abstract: This paper explores whether carbon emissions affect the cross-section of U.S. stock returns. We find that stocks of firms with higher total CO2 emissions (and changes in emissions) earn higher returns, after controlling for size, book-to-market, momentum, and other factors that predict returns. We cannot explain this carbon premium through differences in unexpected profitability or other known risk factors. We also find that institutional investors implement exclusionary screening based on direct emission intensity in a few salient industries. Overall, our results are consistent with an interpretation that investors are already demanding compensation for their exposure to carbon emission risk.
    JEL: G12 H23 Q54
    Date: 2020–04
  17. By: Ryan Banerjee; Anamaria Illes; Enisse Kharroubi; José María Serena Garralda
    Abstract: The Covid-19 shock is placing enormous strains on corporates cash buffers. Corporate financial statements from 2019 suggest that, 50% of firms do not have sufficient cash to cover total debt servicing costs over the coming year. Credit lines could provide firms with additional liquidity. On average undrawn credit stood around 120% of debt servicing costs at end 2019. However, access is uneven and banks may be reluctant to renew or extend them in the current environment. Sticky operating expenses result in many firms running operating losses, placing an additional burden on cash buffers. Estimates indicate that following a 10% drop in revenues, operating expenses only fall by 6% on average. Simulations suggest that if revenues fall by 25% in 2020, then closing the entire funding gap with debt would raise firm leverage by around 10 percentage points.
    Date: 2020–04–28
  18. By: Kathleen Kahle; René M. Stulz
    Abstract: Adjusting for inflation, the annual amount paid out through dividends and share repurchases by public non-financial firms is three times larger in the 2000s than from 1971 to 1999. We find that an increase in aggregate corporate income explains 38% of the increase in the average of aggregate annual payouts from 1971-1999 to the 2000s, while an increase in the aggregate payout rate explains 62%. At the firm level, changes in firm characteristics explain 71% of the increase in average payout rate for the population and 49% of the increase in the average payout rate of firms with payouts. Though there is a negative relation between payouts and investment, most of the increase in payouts is unrelated to the decrease in investment. Models estimated over 1971-1999 underpredict the payout rate of firms with payouts in the 2000s. These models perform better when we forecast non-debt-financed payouts for a sample of larger firms, but not for the sample as a whole. Payouts are more responsive to firm characteristics in the 2000s than before, which is consistent with management having stronger payout incentives.
    JEL: G35
    Date: 2020–04
  19. By: Paolo Finaldi Russo (Bank of Italy); Fabio Parlapiano (Bank of Italy); Daniele Pianeselli (Bank of Italy); Ilaria Supino (Bank of Italy)
    Abstract: Over the last decade and a half non-financial corporations’ (NFCs) listings have displayed a heterogeneous pattern across European countries. The number of listed NFCs has increased in Italy and Spain, while it has declined in Germany, France and the United Kingdom. In Italy, the increase in the number of listed firms has been driven by SMEs’ listings, leaving the stock market small by international standards. We break down the size gap of the Italian equity market (with respect to its European peers) into the share of listed companies and their relative size. We show that the lower share of listed NFCs in Italy accounts for the gap with France and the UK, while the smaller size of Italian public firms has a crucial bearing on the differences with Germany and Spain. Counterfactual exercises provide evidence that there is limited room to bridge these gaps, as the structure of the Italian economy leans towards small enterprises. Policy measures aimed at fostering SMEs’ propensity to go public may be more effective in promoting the further development of the Italian stock exchange.
    Keywords: Capital markets, stock market, IPOs, SMEs listings
    JEL: G1 G3
    Date: 2020–04

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