nep-fmk New Economics Papers
on Financial Markets
Issue of 2021‒07‒19
thirteen papers chosen by

  1. Hedge Fund Treasury Trading and Funding Fragility: Evidence from the COVID-19 Crisis By Mathias S. Kruttli; Phillip J. Monin; Lubomir Petrasek; Sumudu W. Watugala
  2. Financial Return Distributions: Past, Present, and COVID-19 By Marcin W\k{a}torek; Jaros{\l}aw Kwapie\'n; Stanis{\l}aw Dro\.zd\.z
  3. Open Source Cross-Sectional Asset Pricing By Andrew Y. Chen; Tom Zimmermann
  4. Two Price Regimes in Limit Order Books: Liquidity Cushion and Fragmented Distant Field By Sebastian M. Krause; Edgar Jungblut; Thomas Guhr
  5. Bitcoin's Crypto Flow Newtork By Yoshi Fujiwara; Rubaiyat Islam
  6. Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities By Rian Dolphin; Barry Smyth; Yang Xu; Ruihai Dong
  7. Equity Risk Factors for the Long and Short Run: Pricing and Performance at Different Frequencies By Terri van der Zwan; Erik Hennink; Patrick Tuijp
  8. Clustering and attention model based for Intelligent Trading By Mimansa Rana; Nanxiang Mao; Ming Ao; Xiaohui Wu; Poning Liang; Matloob Khushi
  9. Who Participates in Cleared Repo? By R. Jay Kahn; Luke Olson
  10. End-to-End Risk Budgeting Portfolio Optimization with Neural Networks By Ayse Sinem Uysal; Xiaoyue Li; John M. Mulvey
  11. Dynamic Spending Responses to Wealth Shocks: Evidence from Quasi-lotteries on the Stock Market By Asger Lau Andersen; Niels Johannesen; Adam Sheridan
  12. Financial Ratios Analysis of 7-Elaven: An Analysis of Five Years Financial Statement By ULLAH, NAZIM
  13. Basel III in Nigeria: making it work By Ozili, Peterson K

  1. By: Mathias S. Kruttli; Phillip J. Monin; Lubomir Petrasek; Sumudu W. Watugala
    Abstract: Hedge fund gross U.S. Treasury (UST) exposures doubled from 2018 to February 2020 to $2.4 trillion, primarily driven by relative value arbitrage trading and supported by corresponding increases in repo borrowing. In March 2020, amid unprecedented UST market turmoil, the average UST trading hedge fund had a return of -7% and reduced its UST exposure by close to 20%, despite relatively unchanged bilateral repo volumes and haircuts. Analyzing hedge fund-creditor borrowing data, we find the large, more regulated dealers provided disproportionately more funding during the crisis than other creditors. Overall, the step back in hedge fund UST activity was primarily driven by fund-specific liquidity management rather than dealer regulatory constraints. Hedge funds exited the turmoil with 20% higher cash holdings and smaller, more liquid portfolios, despite low contemporaneous outflows. This precautionary flight to cash was more pronounced among funds exposed to greater redemption risk through shorter share restrictions. Hedge funds predominantly trading the cash-futures basis faced greater margin pressure and reduced UST exposures and repo borrowing the most. After the market turmoil subsided following Fed intervention, hedge fund returns recovered quickly, but UST exposures did not revert to pre-shock levels over the subsequent months.
    Keywords: Hedge funds; Treasury markets; Relative value; Arbitrage; Liquidity; Redemption risk; Creditor constraints
    JEL: G11 G23 G24 G01
    Date: 2021–06–24
  2. By: Marcin W\k{a}torek; Jaros{\l}aw Kwapie\'n; Stanis{\l}aw Dro\.zd\.z
    Abstract: We analyze the price return distributions of currency exchange rates, cryptocurrencies, and contracts for differences (CFDs) representing stock indices, stock shares, and commodities. Based on recent data from the years 2017--2020, we model tails of the return distributions at different time scales by using power-law, stretched exponential, and $q$-Gaussian functions. We focus on the fitted function parameters and how they change over the years by comparing our results with those from earlier studies and find that, on the time horizons of up to a few minutes, the so-called "inverse-cubic power-law" still constitutes an appropriate global reference. However, we no longer observe the hypothesized universal constant acceleration of the market time flow that was manifested before in an ever faster convergence of empirical return distributions towards the normal distribution. Our results do not exclude such a scenario but, rather, suggest that some other short-term processes related to a current market situation alter market dynamics and may mask this scenario. Real market dynamics is associated with a continuous alternation of different regimes with different statistical properties. An example is the COVID-19 pandemic outburst, which had an enormous yet short-time impact on financial markets. We also point out that two factors -- speed of the market time flow and the asset cross-correlation magnitude -- while related (the larger the speed, the larger the cross-correlations on a given time scale), act in opposite directions with regard to the return distribution tails, which can affect the expected distribution convergence to the normal distribution.
    Date: 2021–07
  3. By: Andrew Y. Chen; Tom Zimmermann
    Abstract: We provide data and code that successfully reproduces nearly all crosssectional stock return predictors. Our 319 characteristics draw from previous meta-studies, but we differ by comparing our t-stats to the original papers' results. For the 161 characteristics that were clearly significant in the original papers, 98% of our long-short portfolios find t-stats above 1.96. For the 44 characteristics that had mixed evidence, our reproductions find t-stats of 2 on average. A regression of reproduced t-stats on original longshort t-stats finds a slope of 0.90 and an R2 of 83%. Mean returns aremonotonic in predictive signals at the characteristic level. The remaining 114 characteristics were insignificant in the original papers or are modifications of the originals created byHou, Xue, and Zhang (2020). These remaining characteristics are almost always significant if the original characteristic was also significant.
    Keywords: Replication; Stock market anomalies
    JEL: G10
    Date: 2021–06–23
  4. By: Sebastian M. Krause; Edgar Jungblut; Thomas Guhr
    Abstract: The distribution of liquidity within the limit order book is essential for the impact of market orders on the stock price and the emergence of price shocks. Hence it is of great interest to improve the understanding of the time-dependent dynamics of the limit order book. In our analysis we find a broad distribution of limit order lifetimes. Around the quotes we find a densely filled regime with mostly short living limit orders, far away from the quotes we find a sparse filling with mostly long living limit orders. We determine the characteristics of those two regimes and point out the main differences. Based on our research we propose a model for simulating the regime around the quotes.
    Date: 2021–06
  5. By: Yoshi Fujiwara; Rubaiyat Islam
    Abstract: How crypto flows among Bitcoin users is an important question for understanding the structure and dynamics of the cryptoasset at a global scale. We compiled all the blockchain data of Bitcoin from its genesis to the year 2020, identified users from anonymous addresses of wallets, and constructed monthly snapshots of networks by focusing on regular users as big players. We apply the methods of bow-tie structure and Hodge decomposition in order to locate the users in the upstream, downstream, and core of the entire crypto flow. Additionally, we reveal principal components hidden in the flow by using non-negative matrix factorization, which we interpret as a probabilistic model. We show that the model is equivalent to a probabilistic latent semantic analysis in natural language processing, enabling us to estimate the number of such hidden components. Moreover, we find that the bow-tie structure and the principal components are quite stable among those big players. This study can be a solid basis on which one can further investigate the temporal change of crypto flow, entry and exit of big players, and so forth.
    Date: 2021–06
  6. By: Rian Dolphin; Barry Smyth; Yang Xu; Ruihai Dong
    Abstract: Forecasting stock returns is a challenging problem due to the highly stochastic nature of the market and the vast array of factors and events that can influence trading volume and prices. Nevertheless it has proven to be an attractive target for machine learning research because of the potential for even modest levels of prediction accuracy to deliver significant benefits. In this paper, we describe a case-based reasoning approach to predicting stock market returns using only historical pricing data. We argue that one of the impediments for case-based stock prediction has been the lack of a suitable similarity metric when it comes to identifying similar pricing histories as the basis for a future prediction -- traditional Euclidean and correlation based approaches are not effective for a variety of reasons -- and in this regard, a key contribution of this work is the development of a novel similarity metric for comparing historical pricing data. We demonstrate the benefits of this metric and the case-based approach in a real-world application in comparison to a variety of conventional benchmarks.
    Date: 2021–07
  7. By: Terri van der Zwan (Erasmus University Rotterdam); Erik Hennink (Ortec Finance); Patrick Tuijp (Ortec Finance)
    Abstract: We find that the outperformance for Fama-French factors compared to macroeconomic factors in terms of fitting the cross-section of expected returns disappears when accounting for horizon effects. In addition, we obtain novel empirical relations between macroeconomic factors and Fama-French factors at longer horizons. To obtain our results, we introduce a general linear multifactor asset pricing methodology that integrates systematic risk measured at different frequencies into a single pricing equation. Our setup allows for a setting where investors with different investment horizons may experience different levels of systematic risk, which could arise from delayed stock price reaction to systematic factor news.
    Keywords: Cross-Section of Stock Returns, Factors, Frequency Decomposition, Horizon Effects, Investment Horizon
    JEL: G12 C58 G11
    Date: 2021–07–04
  8. By: Mimansa Rana; Nanxiang Mao; Ming Ao; Xiaohui Wu; Poning Liang; Matloob Khushi
    Abstract: The foreign exchange market has taken an important role in the global financial market. While foreign exchange trading brings high-yield opportunities to investors, it also brings certain risks. Since the establishment of the foreign exchange market in the 20th century, foreign exchange rate forecasting has become a hot issue studied by scholars from all over the world. Due to the complexity and number of factors affecting the foreign exchange market, technical analysis cannot respond to administrative intervention or unexpected events. Our team chose several pairs of foreign currency historical data and derived technical indicators from 2005 to 2021 as the dataset and established different machine learning models for event-driven price prediction for oversold scenario.
    Date: 2021–07
  9. By: R. Jay Kahn (Office of Financial Research); Luke Olson (Office of Financial Research)
    Abstract: The U.S. repo market, which is split among four markets, links a wide range of banks and nonbanks who lend and borrow short-term against securities pledged as collateral. This brief uses the OFR's collection of repo market data to highlight some basic facts about the two cleared repo markets. The broadness of cleared repo market participants underscores two increasingly important trends in U.S. financial markets. First, the rising importance of market-based finance among hedge funds and money market funds. Second, the global scope of U.S. financial markets, as a significant portion of net repo borrowing in cleared markets is by foreign banks. The diversity of institution types also means reference rates based on repo transactions represent a broad range of financial market participants.
    Keywords: Repurchase agreement, cleared markets, financial markets, reference rate
    Date: 2020–07–08
  10. By: Ayse Sinem Uysal; Xiaoyue Li; John M. Mulvey
    Abstract: Portfolio optimization has been a central problem in finance, often approached with two steps: calibrating the parameters and then solving an optimization problem. Yet, the two-step procedure sometimes encounter the "error maximization" problem where inaccuracy in parameter estimation translates to unwise allocation decisions. In this paper, we combine the prediction and optimization tasks in a single feed-forward neural network and implement an end-to-end approach, where we learn the portfolio allocation directly from the input features. Two end-to-end portfolio constructions are included: a model-free network and a model-based network. The model-free approach is seen as a black-box, whereas in the model-based approach, we learn the optimal risk contribution on the assets and solve the allocation with an implicit optimization layer embedded in the neural network. The model-based end-to-end framework provides robust performance in the out-of-sample (2017-2021) tests when maximizing Sharpe ratio is used as the training objective function, achieving a Sharpe ratio of 1.16 when nominal risk parity yields 0.79 and equal-weight fix-mix yields 0.83. Noticing that risk-based portfolios can be sensitive to the underlying asset universe, we develop an asset selection mechanism embedded in the neural network with stochastic gates, in order to prevent the portfolio being hurt by the low-volatility assets with low returns. The gated end-to-end with filter outperforms the nominal risk-parity benchmarks with naive filtering mechanism, boosting the Sharpe ratio of the out-of-sample period (2017-2021) to 1.24 in the market data.
    Date: 2021–07
  11. By: Asger Lau Andersen (University of Copenhagen and CEBI); Niels Johannesen (University of Copenhagen and CEBI); Adam Sheridan (University of Copenhagen and CEBI)
    Abstract: How much and over what horizon do households adjust their consumption in response to stock market wealth shocks? We address these questions using granular data on spending and stock portfolios from a large bank and exploiting lottery-like variation in gains across investors with similar portfolio characteristics. Consistent with the permanent income hypothesis, spending responses to stock market gains are immediate and persistent. The responses cumulate to a marginal propensity to consume of around 4% over a one-year horizon. The estimates differ substantially by household liquidity, but not by financial attention, as measured by the frequency of account logins.
    Keywords: wealth shocks, household consumption, marginal propensity to consume, stock markets, permanent income hypothesis
    JEL: D12 G51 E21
    Date: 2021–07–06
  12. By: ULLAH, NAZIM
    Abstract: The purpose of the study is to analyse the financial ratios of the 7-Eleven Malaysia Sdn Bhd. A number of financial ratios are estimate and analyse. For example, profitability ratios, liquidity ratios, solvency ratios, working capital management, and stock market performance. Data is collected from the Annual Report of the 7-Eleven. The study concludes that the liquidity ratios of 7 eleven were not efficient at all. The gearing ratio trend indicates that 7 eleven suffered a huge risk of going bankrupt in 2016 and 2017, it just managed to do fine in 2018. Moreover, there was an extremely low return on investment recorded for all the five years. Hence, keeping all the findings in consideration, it can be said that even though 7 eleven is doing good in terms of profitability, it is still not a good idea to invest in the company.
    Keywords: 7-Eleven, profitability ratios, liquidity ratios, solvency ratios, working capital management, stock market performance
    JEL: G23
    Date: 2021
  13. By: Ozili, Peterson K
    Abstract: Basel III is a framework to preserve the stability of the international banking system. Nigeria adopts Basel capital framework for capital regulation in the banking sector. This article is a policy discussion on how to make Basel III work in Nigeria. The significance of Basel III is discussed, and some ideas to consider when implementing Basel III to make it work in Nigeria, are provided. Under Basel III, the Nigerian banking system should expect better capital quality, higher levels of capital, the imposition of minimum liquidity requirement for banks, reduced systemic risk, and a transitional arrangement for transitioning across Basel I and II. This article also emphasizes that (i) there should be enough time for the transition to Basel III in Nigeria, (ii) a combination of micro- and macro- prudential regulations is needed; and (iii) the need to repair the balance sheets of banks, in preparation for Basel III. The study recommends that the Nigerian regulator should enforce strict market discipline and ensure effective supervision under the Basel framework. There should be international cooperation between the domestic bank regulator and bank regulators in other countries. The regulator should have a contingency plan to reassure the public of the safety of their deposits, and there should be emergency liquidity solutions to support the financial system in bad times.
    Keywords: Basel III, Bank Business Models, Bank Performance, Financial Stability, Capital Regulation, Bank Regulation, Nigeria
    JEL: G01 G20 G21 G22 G23 G24 G28 G29
    Date: 2021

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