nep-fmk New Economics Papers
on Financial Markets
Issue of 2019‒08‒19
fifteen papers chosen by

  1. A Quantile-based Asset Pricing Model By Ando, Tomohiro; Bai, Jushan; Nishimura, Mitohide; Yu, Jun
  2. Co-skewness across Return Horizons By Thomas Conlon; John Cotter; Chenglu Jin
  3. The Value of Intermediation in the Stock Market By Marco Di Maggio; Mark L. Egan; Francesco Franzoni
  4. Hedging Non-Tradable Risks with Transaction Costs and Price Impact By Alvaro Cartea; Ryan Donnelly; Sebastian Jaimungal
  5. Machine Learning for Forecasting Excess Stock Returns – The Five-Year-View By Ioannis Kyriakou; Parastoo Mousavi; Jens Perch Nielsen; Michael Scholz
  6. Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for Midterm Stock Prediction By Xinyi Li; Yinchuan Li; Xiao-Yang Liu; Christina Dan Wang
  7. Machine learning explainability in finance: an application to default risk analysis By Bracke, Philippe; Datta, Anupam; Jung, Carsten; Sen, Shayak
  8. Fast Trading and the Virtue of Entropy: Evidence from the Foreign Exchange Market By Corsetti, G.; Lafarguette, R.; Mehl, A.
  9. Bundling, Belief Dispersion, and Mispricing in Financial Markets By Milo Bianchi; Philippe Jehiel
  10. US Risk Premia under Emerging Markets Constraints By Elias Cavalcante-Filho, Fernando Chague, Rodrigo De Losso, Bruno Giovannetti
  11. Resilience of trading networks: evidence from the sterling corporate bond market By Mallaburn, David; Roberts-Sklar, Matt; Silvestri, Laura
  12. Relative Value of Government of Canada Bonds By Jean-Sébastien Fontaine; Jabir Sandhu; Adrian Walton
  13. The Determinants of Securities Trading Activity: Evidence from four European Equity Markets By Camilleri, Silvio John; Galea, Francelle
  14. The Standard Portfolio Choice Problem in Germany By Breunig, Christoph; Huck, Steffen; Schmidt, Tobias; Weizsäcker, Georg
  15. Reforms’ Effects on Chinese stock markets world integration - An Empirical analysis with t-DCCGARCH model By Yang Mestre-Zhou

  1. By: Ando, Tomohiro (Melbourne University); Bai, Jushan (Columbia University); Nishimura, Mitohide (Nikko Asset Management Co. Ltd); Yu, Jun (School of Economics, Singapore Management University)
    Abstract: It is well-known that the standard estimators of the risk premium in asset pricing models are biased when some price factors are omitted. To address this problem, we propose a novel quantile-based asset pricing model and a new estimation method. Our new asset pricing model allows for the risk premium to be quantile-dependent and our estimation method is applicable to models with unobserved factors. It avoids biased estimation results and always ensures a positive risk premium. The method is applied to the U.S., Japan, and U.K. stock markets. The empirical analysis demonstrates the clear benefits of our approach.
    Keywords: Five-factor model; Quantile-based asset pricing model; Risk premium
    JEL: G12 G15
    Date: 2019–07–13
  2. By: Thomas Conlon (Smurfit Graduate Business School, University College Dublin); John Cotter (Smurfit Graduate Business School, University College Dublin); Chenglu Jin (School of Finance, Zhejiang University of Finance and Economics)
    Abstract: In this paper, the impact of investment horizon on asset co-skewness is examined both empirically and theoretically. We detail a strong horizon-based estimation bias for co-skewness. An asset that has positive co-skewness in one horizon may have negative co-skewness in another. This phenomenon is particularly evident for small-capitalization stocks. We propose a theoretical model to estimate long-horizon co-skewness using the shortest horizon data, which emphasizes the role of adjustment delays in pricing market-wide information among securities. Moreover, in the absence of intertemporal correlation, we show that co-skewness remains horizon-dependent. Our findings are robust to alternative specifications and have strong implications for asset pricing or portfolio allocation with co-skewness.
    Keywords: Co-skewness; The Horizon Effect; Intertemporal Correlation; Asset Pricing
    JEL: G10 G12 G14
    Date: 2019–07–28
  3. By: Marco Di Maggio; Mark L. Egan; Francesco Franzoni
    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%.
    JEL: G14 G2 G23 G24 G28 L14
    Date: 2019–08
  4. By: Alvaro Cartea; Ryan Donnelly; Sebastian Jaimungal
    Abstract: A risk-averse agent hedges her exposure to a non-tradable risk factor $U$ using a correlated traded asset $S$ and accounts for the impact of her trades on both factors. The effect of the agent's trades on $U$ is referred to as cross-impact. By solving the agent's stochastic control problem, we obtain a closed-form expression for the optimal strategy when the agent holds a linear position in $U$. When the exposure to the non-tradable risk factor $\psi(U_T)$ is non-linear, we provide an approximation to the optimal strategy in closed-form, and prove that the value function is correctly approximated by this strategy when cross-impact and risk-aversion are small. We further prove that when $\psi(U_T)$ is non-linear, the approximate optimal strategy can be written in terms of the optimal strategy for a linear exposure with the size of the position changing dynamically according to the exposure's "Delta" under a particular probability measure.
    Date: 2019–07
  5. By: Ioannis Kyriakou (Cass Business School, City, University of London, UK); Parastoo Mousavi (Cass Business School, City, University of London, UK); Jens Perch Nielsen (Cass Business School, City, University of London, UK); Michael Scholz (University of Graz, Austria)
    Abstract: In this paper, we apply machine learning to forecast stock returns in excess of different benchmarks, including the short-term interest rate, long-term interest rate, earnings-by-price ratio, and the inflation. In particular, we adopt and implement a fully nonparametric smoother with the covariates and the smoothing parameter chosen by cross-validation. We find that for both one-year and five-year returns, the term spread is, overall, the most powerful predictive variable for excess stock returns. Differently combined covariates can then achieve higher predictability for different forecast horizons. Nevertheless, the set of earnings-by-price and term spread predictors under the inflation benchmark strikes the right balance between the one-year and five-year horizon.
    Keywords: Benchmark; Cross-validation; Prediction; Stock returns; Long-term forecasts; Overlapping returns; Autocorrelation
    JEL: C14 C53 C58 G17 G22
    Date: 2019–08
  6. By: Xinyi Li; Yinchuan Li; Xiao-Yang Liu; Christina Dan Wang
    Abstract: Midterm stock price prediction is crucial for value investments in the stock market. However, most deep learning models are essentially short-term and applying them to midterm predictions encounters large cumulative errors because they cannot avoid anomalies. In this paper, we propose a novel deep neural network Mid-LSTM for midterm stock prediction, which incorporates the market trend as hidden states. First, based on the autoregressive moving average model (ARMA), a midterm ARMA is formulated by taking into consideration both hidden states and the capital asset pricing model. Then, a midterm LSTM-based deep neural network is designed, which consists of three components: LSTM, hidden Markov model and linear regression networks. The proposed Mid-LSTM can avoid anomalies to reduce large prediction errors, and has good explanatory effects on the factors affecting stock prices. Extensive experiments on S&P 500 stocks show that (i) the proposed Mid-LSTM achieves 2-4% improvement in prediction accuracy, and (ii) in portfolio allocation investment, we achieve up to 120.16% annual return and 2.99 average Sharpe ratio.
    Date: 2019–08
  7. By: Bracke, Philippe (UK Financial Conduct Authority); Datta, Anupam (Carnegie Mellon University); Jung, Carsten (Bank of England); Sen, Shayak (Carnegie Mellon University)
    Abstract: We propose a framework for addressing the ‘black box’ problem present in some Machine Learning (ML) applications. We implement our approach by using the Quantitative Input Influence (QII) method of Datta et al (2016) in a real‑world example: a ML model to predict mortgage defaults. This method investigates the inputs and outputs of the model, but not its inner workings. It measures feature influences by intervening on inputs and estimating their Shapley values, representing the features’ average marginal contributions over all possible feature combinations. This method estimates key drivers of mortgage defaults such as the loan‑to‑value ratio and current interest rate, which are in line with the findings of the economics and finance literature. However, given the non‑linearity of ML model, explanations vary significantly for different groups of loans. We use clustering methods to arrive at groups of explanations for different areas of the input space. Finally, we conduct simulations on data that the model has not been trained or tested on. Our main contribution is to develop a systematic analytical framework that could be used for approaching explainability questions in real world financial applications. We conclude though that notable model uncertainties do remain which stakeholders ought to be aware of.
    Keywords: Machine learning; explainability; mortgage defaults
    JEL: G21
    Date: 2019–08–09
  8. By: Corsetti, G.; Lafarguette, R.; Mehl, A.
    Abstract: Focusing on the foreign exchange reaction to macroeconomic announcements, we show that fast trading is positively and significantly correlated with the entropy of the distribution of quoted prices in reaction to news: a larger share of fast trading increases the degree of diversity of quotes in the order book, for given liquidity, order book depth and size of order flows. Exploiting the WM Reuters’ reform of the fixing methodology in February 2015 as a natural experiment, we provide evidence that fast trading raises entropy, rather than reacting to it. While more entropy in quoted prices means noisier information and arguably complicates price discovery from an individual trader’s perspective, we show that, in the aggregate, more entropy actually brings traded prices closer to the random walk hypothesis, and improves indicators of market efficiency and quality of trade execution. We estimate that a 10 percent increase in entropy reduces the negative impact of macro news by over 60% for effective spreads, against over 40% for realized spreads and price impacts. Our findings suggest that the main mechanism by which fast trading may have desirable effects on market performance specifically hinges on enhanced heterogeneity in trading patterns, best captured by entropy.
    Keywords: High-Frequency Quoting, Asset Pricing, Macroeconomic News, Market Efficiency, Random Walk, Quality of Trade Execution
    JEL: F31 G14 G15
    Date: 2019–08–05
  9. By: Milo Bianchi (TSE - Toulouse School of Economics - UT1 - Université Toulouse 1 Capitole - CNRS - Centre National de la Recherche Scientifique - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales); Philippe Jehiel (PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Panthéon-Sorbonne - ENS Paris - École normale supérieure - Paris - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics, UCL - University College of London [London])
    Abstract: Bundling assets of heterogeneous quality results in dispersed valuations when these are based on investor-specific samples from the pool. A monop olistic bank has the incentive to create heterogeneous bundles only when investors have enough money as in that case prices are driven by more opti- mistic valuations. When the number of banks is sufficiently large, oligopolistic banks choose extremely heterogeneous bundles even when investors have little money and even if this turns out to be collectively detrimental to the banks, which we refer to as a Bundler.s Dilemma.
    Keywords: complexnancial products,bounded rationality,disagreement,market efficiency
    Date: 2019–07
  10. By: Elias Cavalcante-Filho, Fernando Chague, Rodrigo De Losso, Bruno Giovannetti
    Abstract: USA market is the benchmark for empirical finance and considered the closest example of how an efficient market should behave. On the other hand, divergent results from the observed in the USA are often associated with unreliable and due deviations from efficient hypothesis. However, how would the US market results behave had the data the same constraints as an emerging market economy? To answer that question, we analyze the risk premia market estimation under the typical constraints from emerging equity markets: the small number of assets and the short time-series sample available for estimation. We use parameters of time-series length, number of assets and accounting variables distribution from the Brazilian equity market. Surprisingly, we conclude that the US market risk premia convey the same data features as the Brazilian risk premia if under the same time constraints. Then, we evaluate two potential causes of problems in risk premia estimations with small T: i) small sample bias on betas, and ii) divergence between ex-post and ex-ante risk premia. Through Monte Carlo simulations, we conclude that for the T around 5 years the beta estimates are no longer a problem. However, it is necessary to analyze a time-series sample exceeding 40 years to obtain robust ex-ante risk premia.
    Keywords: Equity Risk premia; Asset pricing; Multi-factor model
    JEL: G12 G17
    Date: 2019–07–30
  11. By: Mallaburn, David (Bank of England); Roberts-Sklar, Matt (Bank of England); Silvestri, Laura (Bank of England)
    Abstract: We study the network structure and resilience of the sterling investment-grade and high-yield corporate bond markets. Using proprietary, transaction-level data, first we analyse the key properties of the trading networks in these markets. We find that the trading networks exhibit a core-periphery structure where a large number of non-dealers trade with a small number of dealers. Consistent with dealer behaviour in the primary market, we find that trading activity is particularly concentrated for newly issued bonds, where the top three dealers account for 45% of trading volume. Second, we test the resilience of these markets to the failure or paralysis of a key dealer, or to bond rating downgrades. We find that whilst the network structure has been broadly stable and the market broadly resilient around bond downgrades over our 2012–2017 sample period, the reliance on a small number of participants makes the trading network somewhat fragile to the withdrawal of a few key dealers from the market.
    Keywords: Corporate bond market; financial networks
    JEL: G10 G20
    Date: 2019–08–02
  12. By: Jean-Sébastien Fontaine; Jabir Sandhu; Adrian Walton
    Abstract: Government of Canada bonds in circulation that promise very similar payoffs can have different prices. We study the reason for these differences. Bonds that trade more often and earn high rental income in the repurchase agreement (repo) market tend to have higher prices. Bonds with longer tenors and times to maturity tend to have lower prices. This contrast between cheap and expensive bonds is important because trading volume and rental income can change rapidly, unlike tenor and time to maturity, which are stable.
    Keywords: Financial markets
    JEL: G10 G11 G12 G23 G32
    Date: 2019–08
  13. By: Camilleri, Silvio John; Galea, Francelle
    Abstract: Purpose: The main objective of this study is to obtain new empirical evidence about the connections between equity trading activity and five possible liquidity determinants: market capitalisation, dividend yield, earnings yield, company growth, and the distinction between recently-listed firms as opposed to more established ones. Design / Methodology / Approach: We use a sample of 172 stocks from four European markets and estimate models using the entire sample data and different sub-samples to check the relative importance of the above determinants. We also conduct a factor analysis to re-classify the variables into a more succinct framework. Findings: The evidence suggests that market capitalisation is the most important trading activity determinant, and the number of years listed ranks thereafter. Research limitations / implications: The positive relation between trading activity and market capitalisation is in line with prior literature, while the findings relating to the other determinants offer further empirical evidence which is a worthy addition in view of the contradictory results in prior research. Practical implications: This study is of relevance to practitioners who would like to understand the cross-sectional variation in stock liquidity at a more detailed level. Originality / value: The originality of the paper rests on two important grounds: (a) we focus on trading turnover rather than on other liquidity proxies, since the former is accepted as an important determinant of the liquidity generation process, and (b) we adopt a rigorous approach towards checking the robustness of the results by considering various sub-sample configurations.
    Keywords: Dividend yield, European equity markets, Factor analysis, Liquidity, Liquidity determinants, Market capitalisation, Newly established firms, Securities markets, Trading activity.
    JEL: G10 G12 G15
    Date: 2019
  14. By: Breunig, Christoph (HU Berlin); Huck, Steffen (WZB Berlin and UCL); Schmidt, Tobias (QuantCo); Weizsäcker, Georg (HU Berlin and DIW Berlin)
    Abstract: We study an investment experiment with a representative sample of German households. Respondents invest in a safe asset and a risky asset whose return is tied to the German stock market. Experimental investments correlate with beliefs about stock market returns and exhibit desirable external validity at least in one respect: they predict real-life stock market participation. But many households are unresponsive to an exogenous increase in the risky asset\'s return. The data analysis and a series of additional laboratory experiments suggest that task complexity decreases the responsiveness to incentives. Modifying the safe asset\'s return has a larger effect on behaviour than modifying the risky asset\'s return.
    Keywords: stock market expectations; stock market participation; portfolio choice; financial literacy; complexity;
    JEL: D01 D14 D84 G11
    Date: 2019–07–30
  15. By: Yang Mestre-Zhou (MRE, Université de Montpellier)
    Abstract: In recent years the Chinese government has instituted a series of reforms to restructure and open the Chinese financial system. This paper studies the dynamic correlations and sensitivities between Chinese mainland stock market and five major stock markets with the multivariate t-DCC-GARCH model. We also consider a Normal-DCC model and results show that t-DCC improves slightly the results. The analysis of reforms’ effects on dynamic correlations and sensitivities prove that the Chinese mainland market is more closely tied to Asian stock markets over time, followed by the United States, and with relatively lower correlations with Europe and the United Kingdom. We highlight that the implementation of reforms changes theirs correlations and sensitivities over time. Since the reforms, the correlation between China and international stock markets has been reinforced.
    Keywords: DCC-GARCH, bivariate t distribution, Chinese Stock Market, Dynamic Correlation, Timevarying sensitivity, Chinese reforms
    JEL: C32 C58 G15
    Date: 2019–08

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