nep-fmk New Economics Papers
on Financial Markets
Issue of 2020‒09‒14
sixteen papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Mutual Fund Performance and Flows During the COVID-19 Crisis By Lubos Pastor; M. Blair Vorsatz
  2. What COVID-19 revealed about the resilience of bond funds By Guillaume Ouellet Leblanc; Ryan Shotlander
  3. On the Efficiency of Foreign Exchange Markets in times of the COVID-19 Pandemic By Aslam, Faheem; Aziz, Saqib; Nguyen, Duc Khuong; Mughal, Khurram S.; Khan, Maaz
  4. Is the stock market pricing in a V‑shaped recovery? By James Kyeong
  5. The Effect of Managers on Systematic Risk By Antoinette Schoar; Kelvin Yeung; Luo Zuo
  6. The time function of stock price By Shengfeng Mei; Hong Gao
  7. Portfolio Optimization of 60 Stocks Using Classical and Quantum Algorithms By Jeffrey Cohen; Alex Khan; Clark Alexander
  8. Portfolio Optimization of 40 Stocks Using the DWave Quantum Annealer By Jeffrey Cohen; Alex Khan; Clark Alexander
  9. Learning low-frequency temporal patterns for quantitative trading By Joel da Costa; Tim Gebbie
  10. Image Processing Tools for Financial Time Series Classification By Bairui Du; Paolo Barucca
  11. How Does the Liquidity of New Treasury Securities Evolve? By Michael J. Fleming
  12. Fixed Income Market Structure: Treasuries vs. Agency MBS By James Collin Harkrader; Michael Puglia
  13. Value-at-risk — the comparison of state-of-the-art models on various assets By Karol Kielak; Robert Ślepaczuk
  14. Financial Advice and Household Financial Portfolios By Sarah Brown; Alessandro Bucciol; Alberto Montagnoli; Karl Taylor
  15. Share Price Prediction of Aerospace Relevant Companies with Recurrent Neural Networks based on PCA By Linyu Zheng; Hongmei He
  16. Stock Exchange Fungibility and Exchange Rate Volatility in Zimbabwe By Sakarombe, Upenyu; Marimbe-Makoni, Rudo

  1. By: Lubos Pastor; M. Blair Vorsatz
    Abstract: We present a comprehensive analysis of the performance and flows of U.S. actively-managed equity mutual funds during the COVID-19 crisis of 2020. We find that most active funds underperform passive benchmarks during the crisis, contradicting a popular hypothesis. Funds with high sustainability ratings perform well, as do funds with high star ratings. Fund outflows largely extend pre-crisis trends. Investors favor funds that apply exclusion criteria and funds with high sustainability ratings, especially environmental ones. Our finding that investors remain focused on sustainability during this major crisis suggests they view sustainability as a necessity rather than a luxury good.
    JEL: G01 G11 G12 G14 G23
    Date: 2020–07
  2. By: Guillaume Ouellet Leblanc; Ryan Shotlander
    Abstract: The liquidity management strategies of fund managers, supported by policy measures, have helped bond funds limit the increase in redemptions caused by COVID 19. This avoided further deterioration in liquidity in bond markets. Nevertheless, these funds were left with lower cash buffers, which could make them more vulnerable to additional large redemptions.
    Keywords: Financial markets; Financial stability
    JEL: G G1 G2 G20 G23
    Date: 2020–08
  3. By: Aslam, Faheem; Aziz, Saqib; Nguyen, Duc Khuong; Mughal, Khurram S.; Khan, Maaz
    Abstract: We employ multifractal detrended fluctuation analysis (MF-DFA) to provide the first look at the efficiency of forex markets during the initial period of ongoing COVID-19 pandemic, which has disrupted the financial markets globally. We use high frequency (5-min interval) data of six major currencies traded in the forex market for the period from 01 October 2019 to 31 March 2020. Prior to the application of MF-DFA, we examine the inner dynamics of multifractality using seasonal-trend decompositions using loess (STL) method. Overall, the results confirm the presence of multifractality in forex markets, which demonstrates, in particular: (i) a decline in the efficiency of forex markets during the period of COVID-19 outbreak, and (ii) the heterogeneity in the effects on the strength of multifractality of exchange rate returns under investigation. The largest effect is observed in the case of AUD as it shows the highest (lowest) efficiency before (during) COVID-19 assessed in terms of low (high) multifractality. During COVID-19 period, CAD and CHF exhibit the highest efficiency. Our findings may help policymakers in shaping a comprehensive response to improve the forex market efficiency during such a black swan event.
    Keywords: COVID-19 pandemic; forex market; MF-DFA; high frequency; efficiency
    JEL: C10 C32 G10 G15
    Date: 2020–05
  4. By: James Kyeong
    Abstract: Major stock indexes have bounced back from their March 23 trough to about 10 percent below their peaks. However, stocks that are more sensitive to the business cycle have not performed as well during this market rally. This suggests that stock markets are pricing in a slower, shallower economic recovery.
    Keywords: Asset pricing; Financial markets
    JEL: E E4 E44 G G1 G12 G14
    Date: 2020–07
  5. By: Antoinette Schoar; Kelvin Yeung; Luo Zuo
    Abstract: Tracking the movement of top managers across firms, we document the importance of manager-specific fixed effects in explaining heterogeneity in firm exposures to systematic risk. These differences in systematic risk are partially explained by managers’ corporate strategies, such as their preferences for internal growth and financial conservatism. Managers’ early-career experiences of starting their first job in a recession also contribute to differential loadings on systematic risk. These effects are more pronounced for smaller firms. Overall, our results suggest that managerial styles have important implications for asset prices.
    JEL: G12 G30
    Date: 2020–07
  6. By: Shengfeng Mei; Hong Gao
    Abstract: This paper tends to define the quantitative relationship between the stock price and time as a time function. Based on the empirical evidence that the log-return of a stock is the series of white noise, a mathematical model of the integral white noise is established to describe the phenomenon of stock price movement. A deductive approach is used to derive the auto-correlation function, displacement formula and power spectral density of the stock price movement, which reveals not only the characteristics and rules of the movement but also the predictability of the stock price. The deductive fundamental is provided for the price analysis, prediction and risk management of portfolio investment.
    Date: 2020–08
  7. By: Jeffrey Cohen; Alex Khan; Clark Alexander
    Abstract: We continue to investigate the use of quantum computers for building an optimal portfolio out of a universe of 60 U.S. listed, liquid equities. Starting from historical market data, we apply our unique problem formulation on the D-Wave Systems Inc. D-Wave 2000Q (TM) quantum annealing system (hereafter called D-Wave) to find the optimal risk vs return portfolio. We approach this first classically, then using the D-Wave, to select efficient buy and hold portfolios. Our results show that practitioners can use either classical or quantum annealing methods to select attractive portfolios. This builds upon our prior work on optimization of 40 stocks.
    Date: 2020–08
  8. By: Jeffrey Cohen; Alex Khan; Clark Alexander
    Abstract: We investigate the use of quantum computers for building a portfolio out of a universe of U.S. listed, liquid equities that contains an optimal set of stocks. Starting from historical market data, we look at various problem formulations on the D-Wave Systems Inc. D-Wave 2000Q(TM) System (hereafter called DWave) to find the optimal risk vs return portfolio; an optimized portfolio based on the Markowitz formulation and the Sharpe ratio, a simplified Chicago Quantum Ratio (CQR), then a new Chicago Quantum Net Score (CQNS). We approach this first classically, then by our new method on DWave. Our results show that practitioners can use a DWave to select attractive portfolios out of 40 U.S. liquid equities.
    Date: 2020–07
  9. By: Joel da Costa; Tim Gebbie
    Abstract: We consider the viability of a modularised mechanistic online machine learning framework to learn signals in low-frequency financial time series data. The framework is proved on daily sampled closing time-series data from JSE equity markets. The input patterns are vectors of pre-processed sequences of daily, weekly and monthly or quarterly sampled feature changes. The data processing is split into a batch processed step where features are learnt using a stacked autoencoder via unsupervised learning, and then both batch and online supervised learning are carried out using these learnt features, with the output being a point prediction of measured time-series feature fluctuations. Weight initializations are implemented with restricted Boltzmann machine pre-training, and variance based initializations. Historical simulations are then run using an online feedforward neural network initialised with the weights from the batch training and validation step. The validity of results are considered under a rigorous assessment of backtest overfitting using both combinatorially symmetrical cross validation and probabilistic and deflated Sharpe ratios. Results are used to develop a view on the phenomenology of financial markets and the value of complex historical data-analysis for trading under the unstable adaptive dynamics that characterise financial markets.
    Date: 2020–08
  10. By: Bairui Du; Paolo Barucca
    Abstract: Time series prediction is a challenge for many complex systems, yet in finance predictions are hindered by the very nature of how financial markets work. In efficient markets, the opportunities for stock price predictions leading to profitable trades are supposed to rapidly disappear. In the growing industry of high-frequency trading, the competition over extracting predictions on stock prices from the increasing amount of available information for performing profitable trades is becoming more and more severe. With the development of big data analysis and advanced deep learning methodologies, traders hope to fruitfully analyse market information, e.g. price time series, through machine learning. Spot prices of stocks provide a simple snapshot representation of a financial market. Stock prices fluctuate over time, affected by numerous factors, and the prediction of their changes is at the core of both long-term and short-term financial investing. The collective patterns of price movements are generally referred to as market states. As a paramount example, when stock prices follow an upward trend, it is called a bull market, and when stock prices follow a downward trend is called a bear market
    Date: 2020–08
  11. By: Michael J. Fleming
    Abstract: In a recent Liberty Street Economics post, we showed that the newly reintroduced 20-year bond trades less than other on-the-run Treasury securities and has similar liquidity to that of the more interest‑rate‑sensitive 30-year bond. Is it common for newly introduced securities to trade less and with higher transaction costs, and how does security trading behavior change over time? In this post, we look back at how liquidity evolved for earlier reintroductions of Treasury securities so as to gain insight into how liquidity might evolve for the new 20-year bond.
    Keywords: Treasury; liquidity; reintroduction
    JEL: G1
    Date: 2020–08–26
  12. By: James Collin Harkrader; Michael Puglia
    Abstract: This FEDS Note analyzes the structure of the agency mortgage-backed securities (MBS) market through the lens of the TRACE Treasury data initiative, which is a significant component of a broader inter-agency effort to enhance understanding and transparency of the Treasury securities market. As in several previous FEDS Notes describing the Treasury cash market structure, this note uses transactions reported to the Financial Industry Regulatory Authority (FINRA)'s Trade Reporting and Compliance Engine (TRACE) to examine aggregate trading volumes in the agency MBS market across venues, security types and participants. We show how agency MBS provide a useful counterfactual to cash Treasuries when analyzing the evolution of Treasury cash market structure and its implications for liquidity. We provide evidence that the participation of Principal Trading Firms (PTFs) in Treasury markets has caused the overall volume of intermediation to rise there, particularly in the interdealer broker (IDB) venue. We also find that, relative to Treasury markets, intermediation in the agency MBS market is concentrated among fewer firms, and in particular the primary dealers, suggesting that PTF participation in Treasury markets has diversified intermediation in the IDB venue across a larger number of firms.
  13. By: Karol Kielak (Quantitative Finance Research Group; Faculty of Economic Sciences, University of Warsaw); Robert Ślepaczuk (Quantitative Finance Research Group; Faculty of Economic Sciences, University of Warsaw)
    Abstract: This paper compares different approaches to Value-at-Risk measurement based on parametric and non-parametric approaches. Three portfolios are taken into consideration — the first one containing only stocks from the London Stock Exchange, the second one based on different assets of various origins and the third one consisting of cryptocurrencies. Data used cover the period of more than 20y. In the empirical part of the study, parametric methods based on mean-variance framework are compared with GARCH(1,1) and EGARCH(1,1) models. Different assumptions concerning returns’ distribution are taken into consideration. Adjustment for the fat tails effect is made by using Student t distribution in the analysis. One-day-ahead 95%VaR estimation is then calculated. Thereafter, models are validated using Kupiec and Christoffersen tests and Monte Carlo Simulation for reliable verification of the hypotheses. The overall goal of this paper is to establish if analyzed models accurately estimate Value-at-Risk measure, especially if we take into account assets with various returns distribution characteristics.
    Keywords: risk management, Value-at-Risk, GARCH models, returns distribution, Monte Carlo Simulation, asset class, cryptocurrencies
    JEL: C4 C14 C45 C53 C58 G13
    Date: 2020
  14. By: Sarah Brown (University of Sheffield); Alessandro Bucciol (Department of Economics (University of Verona)); Alberto Montagnoli (University of Sheffiled); Karl Taylor (University of Sheffield)
    Abstract: We investigate the role of financial advice in shaping the composition of UK household portfolios. Our findings suggest that advice is associated with a reallocation of wealth away from real estate and towards bonds and stocks. Among the various reasons why households seek financial advice, "advice for investments" consistently has the largest effect, especially on the portfolio shares held in stocks (positively) and in real estate (negatively). With respect to the type of financial advisor, having a consultation with a stockbroker has a particularly large effect on the portfolio share in stocks. In addition, even free financial advice has a positive effect on the portfolio shares in bonds and stocks compared to not receiving advice. Finally, we explore the relationship between portfolio shares and risk, whilst accounting for the effects of financial advice. We find a positive association between the portfolio shares in bonds and stocks and the portfolio risk.
    Keywords: Financial Advice, Financial Risk, Household Financial Portfolios
    JEL: D81 G11 D14
    Date: 2020–09
  15. By: Linyu Zheng; Hongmei He
    Abstract: The capital market plays a vital role in marketing operations for aerospace industry. However, due to the uncertainty and complexity of the stock market and many cyclical factors, the stock prices of listed aerospace companies fluctuate significantly. This makes the share price prediction challengeable. To improve the prediction of share price for aerospace industry sector and well understand the impact of various indicators on stock prices, we provided a hybrid prediction model by the combination of Principal Component Analysis (PCA) and Recurrent Neural Networks. We investigated two types of aerospace industries (manufacturer and operator). The experimental results show that PCA could improve both accuracy and efficiency of prediction. Various factors could influence the performance of prediction models, such as finance data, extracted features, optimisation algorithms, and parameters of the prediction model. The selection of features may depend on the stability of historical data: technical features could be the first option when the share price is stable, whereas fundamental features could be better when the share price has high fluctuation. The delays of RNN also depend on the stability of historical data for different types of companies. It would be more accurate through using short-term historical data for aerospace manufacturers, whereas using long-term historical data for aerospace operating airlines. The developed model could be an intelligent agent in an automatic stock prediction system, with which, the financial industry could make a prompt decision for their economic strategies and business activities in terms of predicted future share price, thus improving the return on investment. Currently, COVID-19 severely influences aerospace industries. The developed approach can be used to predict the share price of aerospace industries at post COVID-19 time.
    Date: 2020–08
  16. By: Sakarombe, Upenyu; Marimbe-Makoni, Rudo
    Abstract: Investors, policymakers, and Economists have debated whether high volatility in the parallel exchange rate in Zimbabwe was driven by stock exchange fungibility or not. This study investigated the interaction between the stock exchange fungibility market and the parallel exchange rate market. The study utilised the Granger Causality, Cointegration Test, and the Engle-Granger Error Correction Model to determine the short-run, long-run relationships and speed of adjustment between the variables. Stock exchange fungibility was found to granger-cause exchange rate volatility implying a Portfolio Balance Approach Model. The bearish market activities would chase away investors, so they would sell their shares, convert their monies into foreign currency to turn to the alternative bullish market where shares are fungible. This would lead to the depreciation of the local currency. The results also showed evidence of cointegration with a perfect long-run speed of adjustment towards the equilibrium.
    Keywords: stock market, fungibility, exchange rate volatility
    JEL: F21 F3 F31 F37 F38 F4 F42 G1 G11
    Date: 2020

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