nep-fmk New Economics Papers
on Financial Markets
Issue of 2020‒01‒13
thirteen papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. What are the risk factors relevant to investors? Evidence from the Brazilian Funds Market By Rodrigo De-Losso; Elias Cavalcante Filho, José Carlos de Souza Santos
  2. Defining an intrinsic "stickiness" parameter of stock price returns By Naji Massad; Jørgen Vitting Andersen
  3. Creations and Redemptions in Fixed-Income Exchange-Traded Funds: A Shift from Bonds to Cash By Rohan Arora; Sébastien Betermier; Guillaume Ouellet Leblanc; Adriano Palumbo; Ryan Shotlander
  4. Portfolio liquidation under transient price impact -- theoretical solution and implementation with 100 NASDAQ stocks By Ying Chen; Ulrich Horst; Hoang Hai Tran
  5. Portfolio Optimization under Correlation Constraint By Aditya Maheshwari; Traian Pirvu
  6. A new method for similarity and anomaly detection in cryptocurrency markets By Nick James; Max Menzies; Jennifer Chan
  7. Option Pricing in an Investment Risk-Return Setting By Abootaleb Shirvani; Frank J. Fabozzi; Stoyan V. Stoyanov
  8. Sanction or Financial Crisis? An Artificial Neural Network-Based Approach to model the impact of oil price volatility on Stock and industry indices By Somayeh Kokabisaghi; Mohammadesmaeil Ezazi; Reza Tehrani; Nourmohammad Yaghoubi
  9. A Gated Recurrent Unit Approach to Bitcoin Price Prediction By Aniruddha Dutta; Saket Kumar; Meheli Basu
  10. Hedger of Last Resort: Evidence from Brazilian FX Interventions, Local Credit and Global Financial Cycles By Rodrigo Barbone Gonzalez; Dmitry Khametshin; RJose-Luis Peydro; Andrea Polo
  11. How do machine learning and non-traditional data affect credit scoring? New evidence from a Chinese fintech firm By Leonardo Gambacorta; Yiping Huang; Han Qiu; Jingyi Wang
  12. The loan cost advantage of public firms and financial market conditions: evidence from the European syndicated loan market By Raffaele Gallo
  13. Factors Affecting Stock Price: The Case of Thailand Stock Exchange SET 100 Index By Tharinee Pongsupatt; Apichat Pongsupatt

  1. By: Rodrigo De-Losso; Elias Cavalcante Filho, José Carlos de Souza Santos
    Abstract: This article investigates what determines the flow of funds to investment funds. Brazilian Investors are more aware of market risk (beta) when evaluating funds, while tending to attribute the return of factors such as size, value, momentum, illiquidity and industry risk to alpha. Using measures of variation in the sophistication of investors, it is also noted that more sophisticated investors tend to value funds based on more complex criteria. The result is in line with that observed for the US. Additionally, one observes that less sophisticated investors prove to be more sensitive to all past return metrics; however, by decomposing the bottom alphas into persistent component and random component, it is evident that this sensitivity is concentrated on the random alpha component.
    Keywords: Mutual funds; performance measures; factor models
    JEL: G12 G13 G14
    Date: 2019–12–19
  2. By: Naji Massad (Centre d'Economie de la Sorbonne;; Jørgen Vitting Andersen (Centre d'Economie de la Sorbonne;
    Abstract: We introduce a non-linear pricing model of individual stock returns that defines a "stickiness" parameter of the returns. The pricing model resembles the capital asset pricing model (CAPM) used in finance but has a non-linear component inspired from models of earth quake tectonic plate movements. The link to tectonic plate movements happens, since price movements of a given stock index is seen adding "stress" to its components of individual stock returns, in order to follow the index. How closely individual stocks follow the index's price movements, can then be used to define their "stickiness"
    Keywords: non-linear CAPM; "stickiness" of stock returns
    JEL: G1
    Date: 2019–10
  3. By: Rohan Arora; Sébastien Betermier; Guillaume Ouellet Leblanc; Adriano Palumbo; Ryan Shotlander
    Abstract: The creation and redemption activity of fixed-income exchange-traded funds listed in the United States has shifted. Funds of established issuers have traditionally exchanged their shares for baskets of bonds. In contrast, young funds managed by new issuers tend to create and redeem their shares almost exclusively in cash. Cash transactions imply that new funds are taking on exposure to liquidity risk. This has implications for financial stability.
    Keywords: Financial markets; Financial stability
    JEL: G1 G20 G23
    Date: 2019–12
  4. By: Ying Chen; Ulrich Horst; Hoang Hai Tran
    Abstract: We derive an explicit solution for deterministic market impact parameters in the Graewe and Horst (2017) portfolio liquidation model. The model allows to combine various forms of market impact, namely instantaneous, permanent and temporary. We show that the solutions to the two benchmark models of Almgren and Chris (2001) and of Obizhaeva and Wang (2013) are obtained as special cases. We relate the different forms of market impact to the microstructure of limit order book markets and show how the impact parameters can be estimated from public market data. We investigate the numerical performance of the derived optimal trading strategy based on high frequency limit order books of 100 NASDAQ stocks that represent a range of market impact profiles. It shows the strategy achieves significant cost savings compared to the benchmark models of Almgren and Chris (2001) and of Obizhaeva and Wang (2013).
    Date: 2019–12
  5. By: Aditya Maheshwari; Traian Pirvu
    Abstract: We consider the problem of portfolio optimization with a correlation constraint. The framework is the multiperiod stochastic financial market setting with one tradable stock, stochastic income and a non-tradable index. The correlation constraint is imposed on the portfolio and the non-tradable index at some benchmark time horizon. The goal is to maximize portofolio's expected exponential utility subject to the correlation constraint. Two types of optimal portfolio strategies are considered: the subgame perfect and the precommitment ones. We find analytical expressions for the constrained subgame perfect (CSGP) and the constrained precommitment (CPC) portfolio strategies. Both these portfolio strategies yield significantly lower risk when compared to the unconstrained setting, at the cost of a small utility loss. The performance of the CSGP and CPC portfolio strategies is similar.
    Date: 2019–12
  6. By: Nick James; Max Menzies; Jennifer Chan
    Abstract: We propose a new approach using the MJ$_1$ semi-metric, from the more general MJ$_p$ class of semi-metrics \cite{James2019}, to detect similarity and anomalies in collections of cryptocurrencies. Since change points are signals of potential risk, we apply this metric to measure distance between change point sets, with respect to returns and variance. Such change point sets can be identified using algorithms such as the Mann-Whitney test, while the distance matrix is analysed using three approaches to detect similarity and identify clusters of similar cryptocurrencies. This aims to avoid constructing portfolios with highly similar behaviours, reducing total portfolio risk.
    Date: 2019–12
  7. By: Abootaleb Shirvani; Frank J. Fabozzi; Stoyan V. Stoyanov
    Abstract: In this paper, we combine modern portfolio theory and option pricing theory so that a trader who takes a position in a European option contract and the underlying assets can construct an optimal portfolio such that at the moment of the contract's maturity the contract is perfectly hedged. We derive both the optimal holdings in the underlying assets for the trader's optimal mean-variance portfolio and the amount of unhedged risk prior to maturity. Solutions assuming the cases where the price dynamics in the underlying assets follow discrete binomial price dynamics, continuous diffusions, stochastic volatility, volatility-of-volatility, and Merton-jump diffusion are derived.
    Date: 2020–01
  8. By: Somayeh Kokabisaghi; Mohammadesmaeil Ezazi; Reza Tehrani; Nourmohammad Yaghoubi
    Abstract: Financial market in oil-dependent countries has been always influenced by any changes in international energy market, In particular, oil price.It is therefore of considerable interest to investigate the impact of oil price on financial markets. The aim of this paper is to model the impact of oil price volatility on stock and industry indices by considering gas and gold price,exchange rate and trading volume as explanatory variables. We also propose Feed-forward networks as an accurate method to model non-linearity. we use data from 2009 to 2018 that is split in two periods during international energy sanction and post-sanction. The results show that Feed-forward networks perform well in predicting variables and oil price volatility has a significant impact on stock and industry market indices. The result is more robust in the post-sanction period and global financial crisis in 2014. Herein, it is important for financial market analysts and policy makers to note which factors and when influence the financial market, especially in an oil-dependent country such as Iran with uncertainty in the international politics. This research analyses the results in two different periods, which is important in the terms of oil price shock and international energy sanction. Also, using neural networks in methodology gives more accurate and reliable results. Keywords: Feed-forward networks,Industry index,International energy sanction,Oil price volatility
    Date: 2019–12
  9. By: Aniruddha Dutta; Saket Kumar; Meheli Basu
    Abstract: In today's era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent neural network (RNN) and long short-term memory (LSTM) have been shown to perform better than traditional time series models in cryptocurrency price prediction. However, very few studies have applied sequence models with robust feature engineering to predict future pricing. in this study, we investigate a framework with a set of advanced machine learning methods with a fixed set of exogenous and endogenous factors to predict daily Bitcoin prices. We study and compare different approaches using the root mean squared error (RMSE). Experimental results show that gated recurring unit (GRU) model with recurrent dropout performs better better than popular existing models. We also show that simple trading strategies, when implemented with our proposed GRU model and with proper learning, can lead to financial gain.
    Date: 2019–12
  10. By: Rodrigo Barbone Gonzalez; Dmitry Khametshin; RJose-Luis Peydro; Andrea Polo
    Abstract: Bail-in regulation is a centrepiece of the post-crisis overhaul of bank resolution. It requires major banks to maintain a sufficient amount of "bail-in debt" that can absorb losses during resolution. If resolution regimes are credible, investors in bail-in debt should have a strong incentive to monitor banks and price bail-in risk. We study the pricing of senior bail-in bonds to evaluate whether this is the case. We identify the bail-in risk premium by matching these bonds with comparable senior bonds that are issued by the same banking group but are not subject to bail-in risk. The premium is higher for riskier issuers, consistent with the notion that bond investors exert market discipline on banks. Yet the premium varies pro-cyclically: a decline in marketwide credit risk lowers the bail-in risk premium for all banks, with the compression much stronger for riskier issuers. Banks, in turn, time their bail-in bond issuance to take advantage of periods of low premia.
    Keywords: foreign exchange, monetary policy, central bank, bank credit, hedging
    JEL: E5 F3 G01 G21 G28
    Date: 2019–12
  11. By: Leonardo Gambacorta; Yiping Huang; Han Qiu; Jingyi Wang
    Abstract: This paper compares the predictive power of credit scoring models based on machine learning techniques with that of traditional loss and default models. Using proprietary transaction-level data from a leading fintech company in China for the period between May and September 2017, we test the performance of different models to predict losses and defaults both in normal times and when the economy is subject to a shock. In particular, we analyse the case of an (exogenous) change in regulation policy on shadow banking in China that caused lending to decline and credit conditions to deteriorate. We find that the model based on machine learning and non-traditional data is better able to predict losses and defaults than traditional models in the presence of a negative shock to the aggregate credit supply. One possible reason for this is that machine learning can better mine the non-linear relationship between variables in a period of stress. Finally, the comparative advantage of the model that uses the fintech credit scoring technique based on machine learning and big data tends to decline for borrowers with a longer credit history.
    Keywords: fintech, credit scoring, non-traditional information, machine learning, credit risk
    JEL: G17 G18 G23 G32
    Date: 2019–12
  12. By: Raffaele Gallo (Bank of Italy, Directorate General for Economics, Statistics and Research.)
    Abstract: This paper analyses the relationship between financial market conditions and the loan cost advantage of being a public firm, verifying whether the borrowing costs for public companies are more sensitive to the financial market climate than those of private firms. The analysis examines the spread of syndicated loans granted to European non-financial firms between 2004 and 2016. The results indicate that a rise in financial instability, proxied by the VSTOXX index, leads to an increase in loan spreads greater for public borrowers than for private ones. The decline in the loan cost benefit of public firms during high volatility periods is due to a weakening in their bargaining power (bargaining power channel) and in the information benefits of being listed on a market (transparency channel). Moreover, a well-developed stock market in the borrower’s home country significantly mitigates the increase in public firms’ borrowing costs observed following a worsening of financial market conditions.
    Keywords: financial instability, syndicated loan, public firm, loan spread, financial markets
    JEL: G10 G20 G21 G32
    Date: 2019–12
  13. By: Tharinee Pongsupatt (Kasetsart University); Apichat Pongsupatt (Kasetsart University)
    Abstract: A number of researches have been examined the volatility of stock price in capital market for quite some time. Many studies have been undertaken to explore determinants influencing fluctuation in stock prices in different markets and dissimilar conclusions are found. The purpose of this study attempts to determine the factors that cause stock prices to increase or decrease. Eight explanatory variables including dividend yield, growth, leverage, return on equity, bookvalue per share, earnings per share, price-earning (P/E) ratio, and net profit after tax have been selected, while one controllable variable is set as firm-size. Completed financial data of577 samples from companies listed in Thailand Stock Exchange (TSE) SET 100 Index, excluding financing and banking sector, during the period of 2009-2018 are analyzed. Multiple regression model with statistic testing at the significant level 0.05 has been implemented. The results indicate strongly positive significant association between return on equity, earnings per share, price earnings and net profit after tax on firm?s stock price. Whereas dividend yield is the only factor that has negatively relationship with stock price. This model is supported with high R2 of 0.88. The findings in this study can assist investors or managers to comprehend the effect of specific determinants to company?s stock price in Thai capital market.
    Keywords: Stock prices, factors, dividend yield, earnings per share, ROE, capital markets
    JEL: L25 M19 M41
    Date: 2019–10

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