nep-fmk New Economics Papers
on Financial Markets
Issue of 2019‒11‒25
ten papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. The Leverage Factor: Credit Cycles and Asset Returns By Josh Davis; Alan M. Taylor
  2. The Exclamation Mark of Cain: Risk Salience and Mutual Fund Flows By Yevgeny Mugerman; Nadav Steinberg; Zvi Wiener
  3. Unveil stock correlation via a new tensor-based decomposition method By Giuseppe Brandi; Ruggero Gramatica; Tiziana Di Matteo
  4. Reinforcement Learning for Market Making in a Multi-agent Dealer Market By Sumitra Ganesh; Nelson Vadori; Mengda Xu; Hua Zheng; Prashant Reddy; Manuela Veloso
  5. Quantitative earnings enhancement from share buybacks By Lawrence Middleton; James Dodd; Graham Baird
  6. A Simple and Precise Method for Pricing Convertible Bond with Credit Risk By Xiao, Tim
  7. Assessing systemic risk: An analysis of the German banking sector By Rotermund, Sophie-Dorothee
  8. German Bond Yields and Debt Supply: Is There a “Bund Premium”? By Anne-Charlotte Paret; Anke Weber
  9. Herding behaviour in P2P lending markets By Caglayan, Mustafa; Talavera, Oleksandr; Zhang, Wei
  10. Predicting Indian stock market using the psycho-linguistic features of financial news By B. Shravan Kumar; Vadlamani Ravi; Rishabh Miglani

  1. By: Josh Davis; Alan M. Taylor
    Abstract: Research finds strong links between credit booms and macroeconomic outcomes like financial crises and output growth. Are impacts also seen in financial asset prices? We document this robust and significant connection for the first time using a large sample of historical data for many countries. Credit boom periods tend to be followed by unusually low returns to equities, in absolute terms and relative to bonds. Return predictability due to this leverage factor is distinct from that of established factors like momentum and value and generates trading strategies with meaningful excess profits out-of-sample. These findings pose a challenge to conventional macro-finance theories.
    JEL: E17 E20 E21 E32 E44 G01 G11 G12 G17 G21 N10
    Date: 2019–11
  2. By: Yevgeny Mugerman (Bar-Ilan University); Nadav Steinberg (Bank of Israel); Zvi Wiener (The Hebrew University of Jerusalem)
    Abstract: We study a regulation that increased mutual funds' risk salience through name change. Using daily fund flow data and several identification strategies, we find that requiring certain mutual funds to affix an exclamation mark ("!") to their names caused a statistically and economically significant decline in their net flows, with a larger effect on fund inflows than outflows. The exclamation mark’s impact stems from retail investors, both those that seek financial advice and those that invest independently. Mutual funds “defamed” by the exclamation mark designation actually increased their exposure to the particular risk highlighted by the regulator.
    Keywords: Mutual Funds, Regulation, Investor Attention, Investor Protection
    JEL: G18 G28 G23
    Date: 2019–07
  3. By: Giuseppe Brandi; Ruggero Gramatica; Tiziana Di Matteo
    Abstract: Portfolio allocation and risk management make use of correlation matrices and heavily rely on the choice of a proper correlation matrix to be used. In this regard, one important question is related to the choice of the proper sample period to be used to estimate a stable correlation matrix. This paper addresses this question and proposes a new methodology to estimate the correlation matrix which doesn't depend on the chosen sample period. This new methodology is based on tensor factorization techniques. In particular, combining and normalizing factor components, we build a correlation matrix which shows emerging structural dependency properties not affected by the sample period. To retrieve the factor components, we propose a new tensor decomposition (which we name Slice-Diagonal Tensor (SDT) factorization) and compare it to the two most used tensor decompositions, the Tucker and the PARAFAC. We have that the new factorization is more parsimonious than the Tucker decomposition and more flexible than the PARAFAC. Moreover, this methodology applied to both simulated and empirical data shows results which are robust to two non-parametric tests, namely Kruskal-Wallis and Kolmogorov-Smirnov tests. Since the resulting correlation matrix features stability and emerging structural dependency properties, it can be used as alternative to other correlation matrices type of measures, including the Person correlation.
    Date: 2019–11
  4. By: Sumitra Ganesh; Nelson Vadori; Mengda Xu; Hua Zheng; Prashant Reddy; Manuela Veloso
    Abstract: Market makers play an important role in providing liquidity to markets by continuously quoting prices at which they are willing to buy and sell, and managing inventory risk. In this paper, we build a multi-agent simulation of a dealer market and demonstrate that it can be used to understand the behavior of a reinforcement learning (RL) based market maker agent. We use the simulator to train an RL-based market maker agent with different competitive scenarios, reward formulations and market price trends (drifts). We show that the reinforcement learning agent is able to learn about its competitor's pricing policy; it also learns to manage inventory by smartly selecting asymmetric prices on the buy and sell sides (skewing), and maintaining a positive (or negative) inventory depending on whether the market price drift is positive (or negative). Finally, we propose and test reward formulations for creating risk averse RL-based market maker agents.
    Date: 2019–11
  5. By: Lawrence Middleton; James Dodd; Graham Baird
    Abstract: This paper aims to explore the mechanical effect of a company's share repurchase on earnings per share (EPS). In particular, while a share repurchase scheme will reduce the overall number of shares, suggesting that the EPS may increase, clearly the expenditure will reduce the net earnings of a company, introducing a trade-off between these competing effects. We first of all review accretive share repurchases, then characterise the increase in EPS as a function of price paid by the company. Subsequently, we analyse and quantify the estimated difference in earnings growth between a company's natural growth in the absence of buyback scheme to that with its earnings altered as a result of the buybacks. We conclude with an examination of the effect of share repurchases in two cases studies in the US stock-market.
    Date: 2019–11
  6. By: Xiao, Tim
    Abstract: This paper presents a new model for valuing hybrid defaultable financial instruments, such as, convertible bonds. In contrast to previous studies, the model relies on the probability distribution of a default jump rather than the default jump itself, as the default jump is usually inaccessible. As such, the model can back out the market prices of convertible bonds. A prevailing belief in the market is that convertible arbitrage is mainly due to convertible underpricing. Empirically, however, we do not find evidence supporting the underpricing hypothesis. Instead, we find that convertibles have relatively large positive gammas. As a typical convertible arbitrage strategy employs delta-neutral hedging, a large positive gamma can make the portfolio highly profitable, especially for a large movement in the underlying stock price.
    Date: 2019–09–16
  7. By: Rotermund, Sophie-Dorothee
    Abstract: This paper examines the impact of bank heterogeneity on the assessment of systemic risk in the context of the German banking sector. Precisely, it is questioned whether currently employed systemic risk indicators are able to account for banks' heterogeneity and to signal systemic risk reliably regardless of different bank types' individual characteristics. For the assessment, currently employed systemic risk indicators are applied to bank-type-specific data for six different bank types from 1990 until 2018 and benchmarked against crises that occurred during the assessment period. The findings suggest that these indicators are indeed able to account for the German banking sector's heterogeneity, providing insight into different bank types' behavior. Moreover, the indicators allow for the identification of individual bank types' role in the accumulation of systemic risk. Yet, they are only partially able to signal crises correctly and behave more like thermometers than barometers of risk. Structural features of the German banking sector amplify the risk of individual institutions and thus their contribution to systemic risk at large. The analysis further identifies three distinct episodes over the assessment period, finding evidence of intra-sectoral behavioral shifts across time. The distinctiveness of banks' behavior in these three episodes suggests that heterogeneity within the German banking system not only prevails between bank types but also across time. In sum, the research developed here, while fragmentary, illustrates the complexity of systemic risk developments in the German banking sector, which in turn proposes that these developments derive from multiple factors that vary over time. Further research into the causes and consequences of this heterogeneity is warranted.
    Keywords: Banks,Banking,Bank heterogeneity,Germany,Systemic risk,Systemic riskindicators
    JEL: G00 G21
    Date: 2019
  8. By: Anne-Charlotte Paret; Anke Weber
    Abstract: Are Bunds special? This paper estimates the “Bund premium” as the difference in convenience yields between other sovereign safe assets and German government bonds adjusted for sovereign credit risk, liquidity and swap market frictions. A higher premium suggests less substitutability of sovereign bonds. We document a rise in the “Bund premium” in the post-crisis period. We show that there is a negative relationship of the premium with the relative supply of German sovereign bonds, which is more pronounced for higher maturities and when risk aversion proxied by bond market volatility is high. Going forward, we expect German government debt supply to remain scarce, with important implications for the ECB’s monetary policy strategy.
    Date: 2019–11–01
  9. By: Caglayan, Mustafa; Talavera, Oleksandr; Zhang, Wei
    Abstract: We explore individual lender behaviour on, a leading Chinese peer-to-peer (P2P) crowdlending platform. Using a sample of roughly 5 million investor-loan-hour observations and applying a high-dimension fixed effect estimator, we establish evidence of herding behaviour: the investors in our sample tend to prefer assets that had attracted strong interest in previous periods. The herding behaviour relates to both the experience of the investor and the length of time of each investment session. The results show that herding happens mostly in the first or final hour of long sessions. Herding behaviour is further confirmed by estimates at the listing-hour data.
    JEL: G21
    Date: 2019–11–14
  10. By: B. Shravan Kumar; Vadlamani Ravi; Rishabh Miglani
    Abstract: Financial forecasting using news articles is an emerging field. In this paper, we proposed hybrid intelligent models for stock market prediction using the psycholinguistic variables (LIWC and TAALES) extracted from news articles as predictor variables. For prediction purpose, we employed various intelligent techniques such as Multilayer Perceptron (MLP), Group Method of Data Handling (GMDH), General Regression Neural Network (GRNN), Random Forest (RF), Quantile Regression Random Forest (QRRF), Classification and regression tree (CART) and Support Vector Regression (SVR). We experimented on the data of 12 companies stocks, which are listed in the Bombay Stock Exchange (BSE). We employed chi-squared and maximum relevance and minimum redundancy (MRMR) feature selection techniques on the psycho-linguistic features obtained from the new articles etc. After extensive experimentation, using the Diebold-Mariano test, we conclude that GMDH and GRNN are statistically the best techniques in that order with respect to the MAPE and NRMSE values.
    Date: 2019–11

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