nep-fmk New Economics Papers
on Financial Markets
Issue of 2022‒07‒18
nine papers chosen by

  1. Hedge Fund Performance: A Quantitative Survey By Yang, Fan; Havranek, Tomas; Irsova, Zuzana; Novak, Jiri
  2. Distributionally Robust End-to-End Portfolio Construction By Giorgio Costa; Garud N. Iyengar
  3. Steering a Ship in Illiquid Waters: Active Management of Passive Funds By Naz Koont; Yiming Ma; Lubos Pastor; Yao Zeng
  4. Stock Trading Optimization through Model-based Reinforcement Learning with Resistance Support Relative Strength By Huifang Huang; Ting Gao; Yi Gui; Jin Guo; Peng Zhang
  5. Portfolio Transformer for Attention-Based Asset Allocation By Damian Kisiel; Denise Gorse
  6. The End of the Crypto-Diversification Myth By Luciano Somoza; Antoine Didisheim
  7. Policy Uncertainty and Stock Market Volatility Revisited: The Predictive Role of Signal Quality By Afees A. Salisu; Riza Demirer; Rangan Gupta
  8. Stock Market Bubbles and the Forecastability of Gold Returns (and Volatility) By David Gabauer; Rangan Gupta; Sayar Karmakar; Joshua Nielsen
  9. The Rest of the World’s Dollar-Weighted Return on U.S. Treasurys By Zhengyang Jiang; Arvind Krishnamurthy; Hanno Lustig

  1. By: Yang, Fan; Havranek, Tomas; Irsova, Zuzana; Novak, Jiri
    Abstract: We provide the first quantitative survey of the empirical literature on hedge fund performance. We examine the impact of potential biases on the reported results. Empirical analysis in prior studies has been plagued by fragmentation of underlying data and by limited consensus on how hedge fund performance should be measured. Using a sample of 1,019 intercept terms from regressions of hedge fund returns on risk factors (the “alphas”) collected from 74 studies published between 2001 and 2021 we show that inferences about hedge fund returns are not significantly contaminated by publication selection bias. Most of our monthly alpha estimates adjusted for the (small) bias fall within a relatively narrow range of 30 to 40 basis points. Considering several partitions of our sample, we document a modest publication bias only for estimates based on instrumental variables (IV), for which relatively large standard errors are common and that tend to be less precise. In contrast, studies that explicitly control for the potential biases in the underlying data (e.g. the backfilling bias and the survivorship bias) report lower alphas. Our results demonstrate that despite the prevalence of the publication selection bias in numerous other research settings, publication may not be selective when there is no strong a priori theoretical prediction about the sign of estimated coefficients, which may induce greater readiness to publish statistically insignificant results.
    Keywords: Hedge funds,meta-analysis,publication bias
    JEL: J23 J24 J31
    Date: 2022
  2. By: Giorgio Costa; Garud N. Iyengar
    Abstract: We propose an end-to-end distributionally robust system for portfolio construction that integrates the asset return prediction model with a distributionally robust portfolio optimization model. We also show how to learn the risk-tolerance parameter and the degree of robustness directly from data. End-to-end systems have an advantage in that information can be communicated between the prediction and decision layers during training, allowing the parameters to be trained for the final task rather than solely for predictive performance. However, existing end-to-end systems are not able to quantify and correct for the impact of model risk on the decision layer. Our proposed distributionally robust end-to-end portfolio selection system explicitly accounts for the impact of model risk. The decision layer chooses portfolios by solving a minimax problem where the distribution of the asset returns is assumed to belong to an ambiguity set centered around a nominal distribution. Using convex duality, we recast the minimax problem in a form that allows for efficient training of the end-to-end system.
    Date: 2022–06
  3. By: Naz Koont; Yiming Ma; Lubos Pastor; Yao Zeng
    Abstract: Exchange-traded funds (ETFs) are typically viewed as passive index trackers. In contrast, we show that corporate bond ETFs actively manage their portfolios, trading off index tracking against liquidity transformation. In our model, ETFs optimally choose creation and redemption baskets that include cash and only a subset of index assets, especially if those assets are illiquid. Our evidence supports the model's predictions. We find that ETFs dynamically adjust their baskets to correct portfolio imbalances while facilitating ETF arbitrage. Basket inclusion improves bond liquidity, except in periods of large imbalance between ETF creations and redemptions, such as the COVID-19 crisis of 2020.
    JEL: G12 G23
    Date: 2022–05
  4. By: Huifang Huang; Ting Gao; Yi Gui; Jin Guo; Peng Zhang
    Abstract: Reinforcement learning (RL) is gaining attention by more and more researchers in quantitative finance as the agent-environment interaction framework is aligned with decision making process in many business problems. Most of the current financial applications using RL algorithms are based on model-free method, which still faces stability and adaptivity challenges. As lots of cutting-edge model-based reinforcement learning (MBRL) algorithms mature in applications such as video games or robotics, we design a new approach that leverages resistance and support (RS) level as regularization terms for action in MBRL, to improve the algorithm's efficiency and stability. From the experiment results, we can see RS level, as a market timing technique, enhances the performance of pure MBRL models in terms of various measurements and obtains better profit gain with less riskiness. Besides, our proposed method even resists big drop (less maximum drawdown) during COVID-19 pandemic period when the financial market got unpredictable crisis. Explanations on why control of resistance and support level can boost MBRL is also investigated through numerical experiments, such as loss of actor-critic network and prediction error of the transition dynamical model. It shows that RS indicators indeed help the MBRL algorithms to converge faster at early stage and obtain smaller critic loss as training episodes increase.
    Date: 2022–05
  5. By: Damian Kisiel; Denise Gorse
    Abstract: Traditional approaches to financial asset allocation start with returns forecasting followed by an optimization stage that decides the optimal asset weights. Any errors made during the forecasting step reduce the accuracy of the asset weightings, and hence the profitability of the overall portfolio. The Portfolio Transformer (PT) network, introduced here, circumvents the need to predict asset returns and instead directly optimizes the Sharpe ratio, a risk-adjusted performance metric widely used in practice. The PT is a novel end-to-end portfolio optimization framework, inspired by the numerous successes of attention mechanisms in natural language processing. With its full encoder-decoder architecture, specialized time encoding layers, and gating components, the PT has a high capacity to learn long-term dependencies among portfolio assets and hence can adapt more quickly to changing market conditions such as the COVID-19 pandemic. To demonstrate its robustness, the PT is compared against other algorithms, including the current LSTM-based state of the art, on three different datasets, with results showing that it offers the best risk-adjusted performance.
    Date: 2022–06
  6. By: Luciano Somoza (University of Lausanne, HEC; Swiss Finance Institute); Antoine Didisheim (Swiss Finance Institute, UNIL)
    Abstract: We propose a mechanism explaining the recent high positive correlation between cryptocurrencies and the stock market. With a unique dataset of investor-level holdings from a bank offering trading accounts and cryptocurrency wallets, we show that retail investors’ net trading volumes of stocks and cryptocurrencies are positively correlated. Theoretically, this micro-level pattern translates into a cross-asset class correlation as long as the two markets are not fully integrated. We provide suggestive evidence showing that this micro-level pattern emerged in March 2020 and that stocks preferred by crypto-traders exhibit a stronger correlation with Bitcoin, especially when the cross asset retail volume is high.
    Keywords: cryptocurrencies, Bitcoin, retail investors, correlation
    JEL: G11 G12 G29
    Date: 2022–06
  7. By: Afees A. Salisu (Centre for Econometrics & Applied Research, Ibadan, Nigeria; Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)
    Abstract: This paper provides novel mixed-frequency insight to the growing literature on the (monthly) economic policy uncertainty-(daily) stock market volatility nexus by examining the out-of-sample predictive ability of the quality of political signals over stock market volatility at various forecast horizons, and whether or not accounting for the signal quality in forecasting models can help achieve economic gains for investors. Both in- and out-of-sample tests, based on a GARCH-MIDAS framework, show that the quality of the policy signal indeed matters when it comes to the predictive role played by policy uncertainty over subsequent stock market volatility. While high EPU is found to predict high volatility, particularly when the signal quality is high, the positive relationship between EPU and volatility breaks down when the signal quality is low. The improved out-of-sample volatility forecasts obtained from the models that account for the quality of policy signals also helps typical mean-variance investors achieve improved economic outcomes captured by higher certainty equivalent returns and Sharpe ratios. Although our results indicate clear distinctions between the U.S. and U.K. stock markets in terms of how policy signals are processed by market participants, they highlight the role of the quality of policy signals as a driver of volatility forecasts with significant economic implications.
    Keywords: Economic policy uncertainty, Signal quality, Market Volatility, Forecasting
    JEL: C32 C53 D8 E32 G15
    Date: 2022–06
  8. By: David Gabauer (Data Analysis Systems, Software Competence Center Hagenberg, Austria); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Sayar Karmakar (Department of Statistics, University of Florida, 230 Newell Drive, Gainesville, FL, 32601, USA); Joshua Nielsen (Boulder Investment Technologies, LLC, 1942 Broadway Suite 314C, Boulder, CO, 80302, USA)
    Abstract: Firstly, we use the Multi-Scale LPPLS Confidence Indicator approach to detect both positive and negative bubbles at short-, medium- and long-term horizons for the stock markets of the G7 and the BRICS countries. We were able to detect major crashes and rallies in the 12 stock markets over the period of the 1st week of January, 1973 to the 2nd week of September, 2020. We also observed similar timing of strong (positive and negative) LPPLS indicator values across both G7 and BRICS countries, suggesting interconnectedness of the extreme movements in these stock markets. Secondly, we utilize these indicators to forecast gold returns and its volatility, using a method involving block means of residuals obtained from the popular LASSO routine, given that the number of covariates ranged between 42 to 72, and gold returns demonstrated a heavy upper tail. We found that, our bubbles indicators, particularly when both positive and negative bubbles are considered simultaneously, can accurately forecast gold returns at short- to medium-term, and also time-varying estimates of gold returns volatility to a lesser extent. Our results have important implications for the portfolio decisions of investors who seek a safe haven during boom-bust cycles of major global stock markets.
    Keywords: Gold, Stock Markets, Bubbles, Forecasting, Returns, Volatility
    JEL: C22 C53 G15 Q02
    Date: 2022–06
  9. By: Zhengyang Jiang; Arvind Krishnamurthy; Hanno Lustig
    Abstract: Since 1980, foreign investors have timed their purchases and sales of U.S. Treasurys to yield particularly low returns. Their annual dollar-weighted returns, measured by IRRs, are around 3% lower than a buy-and-hold strategy over the same horizon. In comparison, the IRRs achieved by domestic investors are at least 1% higher, while the IRRs achieved by the Federal Reserve are similarly low. Our results are consistent with theories where foreign investors are price-inelastic buyers of safe dollar assets, which provide them with convenience services.
    JEL: F32 G12
    Date: 2022–05

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