nep-fmk New Economics Papers
on Financial Markets
Issue of 2022‒10‒17
eleven papers chosen by

  1. Predicting Performances of Mutual Funds using Deep Learning and Ensemble Techniques By Nghia Chu; Binh Dao; Nga Pham; Huy Nguyen; Hien Tran
  2. Liquidity derivatives By Bagnara, Matteo; Jappelli, Ruggero
  3. Portfolio Optimization with Cumulative Prospect Theory Utility via Convex Optimization By Eric Luxenberg; Philipp Schiele; Stephen Boyd
  4. Model-based gym environments for limit order book trading By Joseph Jerome; Leandro Sanchez-Betancourt; Rahul Savani; Martin Herdegen
  5. The coming long-run slowdown in corporate profit growth and stock returns By Michael Smolyansky
  6. Pricing of green bonds: drivers and dynamics of the greenium By Pietsch, Allegra; Salakhova, Dilyara
  7. Dollar Reserves and U.S. Yields: Identifying the Price Impact of Official Flows By Rashad Ahmed; Alessandro Rebucci
  8. Stock market development as a leading indicator of future economic growth in the BRICS countries By Klara Zalesakova
  9. Is the EU money market fund regulation fit for purpose? Lessons from the COVID-19 turmoil By Capotă, Laura-Dona; Grill, Michael; Molestina Vivar, Luis; Schmitz, Niklas; Weistroffer, Christian
  10. Precision measurement of the return distribution property of the Chinese stock market index By Peng Liu; Yanyan Zheng
  11. Fintech in sub-Saharan Africa By Njuguna Ndung'u

  1. By: Nghia Chu; Binh Dao; Nga Pham; Huy Nguyen; Hien Tran
    Abstract: Predicting fund performance is beneficial to both investors and fund managers, and yet is a challenging task. In this paper, we have tested whether deep learning models can predict fund performance more accurately than traditional statistical techniques. Fund performance is typically evaluated by the Sharpe ratio, which represents the risk-adjusted performance to ensure meaningful comparability across funds. We calculated the annualised Sharpe ratios based on the monthly returns time series data for more than 600 open-end mutual funds investing in listed large-cap equities in the United States. We find that long short-term memory (LSTM) and gated recurrent units (GRUs) deep learning methods, both trained with modern Bayesian optimization, provide higher accuracy in forecasting funds' Sharpe ratios than traditional statistical ones. An ensemble method, which combines forecasts from LSTM and GRUs, achieves the best performance of all models. There is evidence to say that deep learning and ensembling offer promising solutions in addressing the challenge of fund performance forecasting.
    Date: 2022–09
  2. By: Bagnara, Matteo; Jappelli, Ruggero
    Abstract: It is well established that investors price market liquidity risk. Yet, there exists no financial claim contingent on liquidity. We propose a contract to hedge uncertainty over future transaction costs, detailing potential buyers and sellers. Introducing liquidity derivatives in Brunnermeier and Pedersen (2009) improves financial stability by mitigating liquidity spirals. We simulate liquidity option prices for a panel of NYSE stocks spanning 2000 to 2020 by fitting a stochastic process to their bidask spreads. These contracts reduce the exposure to liquidity factors. Their prices provide a novel illiquidity measure reflecting cross-sectional commonalities. Finally, stock returns significantly spread along simulated prices.
    Keywords: Asset Pricing,Market Liquidity,Liquidity Risk
    JEL: G12 G13 G17
    Date: 2022
  3. By: Eric Luxenberg; Philipp Schiele; Stephen Boyd
    Abstract: We consider the problem of choosing a portfolio that maximizes the cumulative prospect theory (CPT) utility on an empirical distribution of asset returns. We show that while CPT utility is not a concave function of the portfolio weights, it can be expressed as a difference of two functions. The first term is the composition of a convex function with concave arguments and the second term a composition of a convex function with convex arguments. This structure allows us to derive a global lower bound, or minorant, on the CPT utility, which we can use in a minorization-maximization (MM) algorithm for maximizing CPT utility. We further show that the problem is amenable to a simple convex-concave (CC) procedure which iteratively maximizes a local approximation. Both of these methods can handle small and medium size problems, and complex (but convex) portfolio constraints. We also describe a simpler method that scales to larger problems, but handles only simple portfolio constraints.
    Date: 2022–09
  4. By: Joseph Jerome; Leandro Sanchez-Betancourt; Rahul Savani; Martin Herdegen
    Abstract: Within the mathematical finance literature there is a rich catalogue of mathematical models for studying algorithmic trading problems -- such as market-making and optimal execution -- in limit order books. This paper introduces \mbtgym, a Python module that provides a suite of gym environments for training reinforcement learning (RL) agents to solve such model-based trading problems. The module is set up in an extensible way to allow the combination of different aspects of different models. It supports highly efficient implementations of vectorized environments to allow faster training of RL agents. In this paper, we motivate the challenge of using RL to solve such model-based limit order book problems in mathematical finance, we explain the design of our gym environment, and then demonstrate its use in solving standard and non-standard problems from the literature. Finally, we lay out a roadmap for further development of our module, which we provide as an open source repository on GitHub so that it can serve as a focal point for RL research in model-based algorithmic trading.
    Date: 2022–09
  5. By: Michael Smolyansky
    Abstract: Over the past two decades, the corporate profits of stock market listed firms have been substantially boosted by declining interest rate expenses and lower corporate tax rates. This note's key finding is that the reduction in interest and tax expenses is responsible for a full one-third of all profit growth for S&P 500 nonfinancial firms over the prior two-decade period.
    Date: 2022–09–06
  6. By: Pietsch, Allegra; Salakhova, Dilyara
    Abstract: The green bond market has increased rapidly in recent years amid growing concerns about climate change and wider environmental issues. However, whether green bonds provide cheaper funding to issuers by trading at a premium, so-called greenium, is still an open discussion. This paper provides evidence that a key factor explaining the greenium is the credibility of a green bond itself or that of its issuer. We define credible green bonds as those which have been under external review. Credible issuers are either firms in green sectors or banks signed up to UNEP FI. Another important factor is investors’ demand as the greenium becomes more statistically and economically significant over time. This is potentially driven by increased climate concerns as the green bond market follows a similar trend to that observed in ESG/green equity and investment fund sectors. To run our analysis, we construct a database of daily pricing data on closely matched green and non-green bonds of the same issuer in the euro area from 2016 to 2021. We then use Securities Holdings Statistics by Sector (SHSS) to analyse investors’ demand for green bonds. JEL Classification: G12, G14, Q50, A56
    Keywords: climate change, corporate sustainability, impact investing, sustainable finance
    Date: 2022–09
  7. By: Rashad Ahmed; Alessandro Rebucci
    Abstract: This paper shows that the price impact of foreign official (FO) purchases or sales of U.S. Treasuries (USTs) is about twice as large as previously reported in the literature once critical sources of endogeneity are addressed. We also show that prevailing estimates of this price impact suffer from omitted variable bias when foreign government bond yields and Federal Reserve policies are not controlled for. By exploiting changes in the volatility of FO flows and U.S. yields after the 2008 Global Financial Crisis, we identify a FO flow shock via heteroskedasticity in a structural VAR. We estimate that a $100B flow shock moves the 5-year, 10-year, and 30-year yields by more than 100 basis points on impact, compared to the 19-44 basis points range that we estimate by assuming FO flows are price inelastic and without controlling for foreign yields and Fed actions. Our findings suggest that FO sales of USTs played a critical role during the March 2020 episode of Treasury market turmoil and that even a small reduction in the Dollar's share of China's reserves could have a significant impact on U.S. long-term interest rates.
    JEL: E4 F2 F30 G1
    Date: 2022–09
  8. By: Klara Zalesakova (Department of Statistics and Operation Analysis, Faculty of Business and Economics, Mendel University in Brno, Zemedelska 1, 613 00 Brno, Czech Republic)
    Abstract: This paper deals with the verification of the assumption of forecasting ability of stock indices in the BRICS countries. The literature review focuses on the definition of the financial and stock markets, measuring the economic performance and the interdependence of stock markets and economic growth. The analytical part is based on time series of GDP and stock indices of the BRICS countries, which are processed using correlation analysis, VAR models and Granger causality test, which is used to determine the existence and possible direction and strength of the causal relationship between the variables. The results show that the role of stock indices as a leading economic indicator is overestimated. However, GDP and stock indices interact, the strength and direction of causal relationships is affected by number of factors.
    Keywords: BRICS, stock market, stock index, economic growth, GDP, correlation analysis, VAR model, causality, Granger test causality
    JEL: C32 E44 F43 H54
    Date: 2022–09
  9. By: Capotă, Laura-Dona; Grill, Michael; Molestina Vivar, Luis; Schmitz, Niklas; Weistroffer, Christian
    Abstract: The market turmoil in March 2020 highlighted key vulnerabilities in the EU money market fund (MMF) sector. This paper assesses the effectiveness of the EU's regulatory framework from a financial stability perspective, based on a panel analysis of EU MMFs at a daily frequency. First, we find that investment in private debt assets exposes MMFs to liquidity risk. Second, we find that low volatility net asset value (LVNAV) funds, which invest in non-public debt assets while offering a stable NAV, face higher redemptions than other fund types. The risk of breaching the regulatory NAV limit may have incentivised outflows among some LVNAV investors in March 2020. Third, MMFs with lower levels of liquidity buffers use their buffers less than other funds, suggesting low levels of buffer usability in stress periods. Our findings suggest fragility in the EU MMF sector and call for a strengthened regulatory framework of private debt MMFs. JEL Classification: G11, G15, G23, G28
    Keywords: COVID-19, financial fragility, money market funds, regulation
    Date: 2022–10
  10. By: Peng Liu; Yanyan Zheng
    Abstract: Systematical and precise analysis on the 1-min datasets over the 17-year period 2005-2021 for both the Shanghai Stock Exchange and the Shenzhen Stock Exchange composite index is conducted in this paper. Here we precisely measure the property of return distributions of composite indices over time scale $\Delta t$ ranging from 1 min up to almost 4,000 min, to reveal the difference between the Chinese stock market and the mature stock market in developed countries. The return distributions of composite indices for both exchanges show similar behavior. Main findings in this paper are as follows. (1) The central part of return distribution is well described by symmetrical L$\acute{e}$vy $\alpha$-stable process with stability parameter comparable with the value of about 1.4 extracted in the U.S. stock market. (2) Distinctively, the stability parameter shows a potential change when $\Delta t$ increases, and thus a crossover region located at 15 $
    Date: 2022–09
  11. By: Njuguna Ndung'u
    Abstract: This paper traces the development of fintech in sub-Saharan Africa, its evolution over time, and the unfolding benefits attained at each stage of its adoption and market evolution. From the onset, fintechs have revolutionized retail electronic payment systems—a revolution that has evolved into a technological platform to manage micro-savers' accounts, virtual savings and credit systems, public financial management, and cross-border remittances, and has led to the adoption of new monetary policy frameworks.
    Keywords: Fintech, Financial inclusion, Saving, Technology, Sub-Saharan Africa
    Date: 2022

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