nep-fmk New Economics Papers
on Financial Markets
Issue of 2020‒07‒20
twelve papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Quant Bust 2020 By Zura Kakushadze
  2. Fear of the coronavirus and the stock markets By Lyócsa, Štefan; Baumöhl, Eduard; Výrost, Tomáš; Molnár, Peter
  3. Reconsidering Returns By Samuel M. Hartzmark; David H. Solomon
  4. Asset Prices and Capital Share Risks: Theory and Evidence By Joseph P. Byrne; Boulis M. Ibrahim; Xiaoyu Zong
  5. Multifactor Empirical Asset Pricing Under Higher-Order Moment Variations By Massimo Guidolin; Martin Lozano; Juan Arismendi Zambrano
  6. Investment Disputes and Abnormal Volatility of Stocks By Jozef Barunik; Zdenek Drabek; Matej Nevrla
  7. Optimal Asset Allocation For Outperforming A Stochastic Benchmark Target By Chendi Ni; Yuying Li; Peter Forsyth; Ray Carroll
  8. Which Investors Matter for Equity Valuations and Expected Returns? By Ralph S. J. Koijen; Robert J. Richmond; Motohiro Yogo
  9. Manifold Feature Index: A novel index based on high-dimensional data simplification By Chenkai Xu; Hongwei Lin; Xuansu Fang
  10. Can Private Equity Funds Act as Strategic Buyers? Evidence from Buy-and-Build Strategies By Dyaran Bansraj; Han Smit; Vadym Volosovych
  11. How does stock market reflect the change in economic demand? A study on the industry-specific volatility spillover networks of China's stock market during the outbreak of COVID-19 By Fu Qiao; Yan Yan
  12. A measure of South Africa’s sovereign risk premium By Luchelle Soobyah; Daan Steenkamp

  1. By: Zura Kakushadze
    Abstract: We explain in a nontechnical fashion why dollar-neutral quant trading strategies, such as equities Statistical Arbitrage, suffered substantial losses (drawdowns) during the COVID-19 market selloff. We discuss: (i) why these strategies work during "normal" times; (ii) the market regimes when they work best; and (iii) their limitations and the reasons for why they "break" during extreme market events. An accompanying appendix (with a link to freely accessible source code) includes backtests for various strategies, which put flesh on and illustrate the discussion in the main text.
    Date: 2020–06
  2. By: Lyócsa, Štefan; Baumöhl, Eduard; Výrost, Tomáš; Molnár, Peter
    Abstract: Since the outbreak of the COVID-19 pandemic, stock markets around the world have experienced unprecedented declines, which have resulted in extremely high stock market uncertainty, measured as price variation. In this paper, we show that during such periods, Google Trends data represent a timely and valuable data source for forecasting price variation. Fear of the coronavirus, as measured by Google searches is predictive of future stock market uncertainty for stock markets around the world. Google searches were also strongly correlated with the evolution of physical contagion (the number of new cases), and with implemented nonpharmaceutical interventions. The effect of pandemic-related policies on investors' attention and fear is thus very well captured by Google Trends data.
    Keywords: Coronavirus,Stock market,Uncertainty,Panic,Google Trends
    JEL: G01 G15
    Date: 2020
  3. By: Samuel M. Hartzmark; David H. Solomon
    Abstract: Investors' perception of performance is biased because the relevant measure, returns, is rarely displayed. Major indices ignore dividends thereby underreporting market performance. Newspapers are more pessimistic on ex-dividend days, consistent with mistaking the index for returns. Market betas should track returns, but track prices more than dividends, creating predictable returns. Mutual funds receive inflows for “beating the S&P 500,” price index based on net asset value (also not a return). Investors extrapolate market indices, not returns, when forming annual performance expectations. Displaying returns by default would ameliorate these issues, which arise despite high attention and agreement on the appropriate measure.
    JEL: G02 G11 G12 G14 N2 N21 N22
    Date: 2020–06
  4. By: Joseph P. Byrne; Boulis M. Ibrahim; Xiaoyu Zong
    Abstract: An asset pricing model using long-run capital share growth risk has recently been found to successfully explain U.S. stock returns. Our paper adopts a recursive preference utility framework to derive an heterogeneous asset pricing model with capital share risks.While modeling capital share risks, we account for the elevated consumption volatility of high income stockholders. Capital risks have strong volatility effects in our recursive asset pricing model. Empirical evidence is presented in which capital share growth is also a source of risk for stock return volatility. We uncover contrasting unconditional and conditional asset pricing evidence for capital share risks.
    Date: 2020–06
  5. By: Massimo Guidolin (CAIR, Manchester Business School, and IGIER, Bocconi University. Address: MBS Crawford House, Manchester, United Kingdom.); Martin Lozano (University of Monterrey - UDEM, Monterrey, Mexico.); Juan Arismendi Zambrano (Department of Economics, Finance and Accounting, Maynooth University, Ireland & ICMA Centre, Henley Business School, University of Reading, Whiteknights, Reading, United Kingdom.)
    Abstract: Even though an asset pricing model can be expressed in a classical Beta or in the relatively new stochastic discount factor (SDF) representation, their key empirical features - efficiency and robustness - may differ when estimated by the generalized method of moments. Using US and UK data we find that the SDF approach is more likely to be less efficient but more robust than Beta method. We derive the analytical asymptotic variance and show that the main drivers of this trade-off are the higher-order moments of the factors, in which skewness and covariance between returns and factors play an important role.
    Keywords: Empirical Asset Pricing, Factor Models, Financial Econometrics, Generalized Method of Moments, Stochastic Discount Factor, Beta pricing, Efficiency.
    JEL: C51 C52 G12
    Date: 2020
  6. By: Jozef Barunik; Zdenek Drabek; Matej Nevrla
    Abstract: Dramatic growth of investment disputes between foreign investors and host states rises serious questions about the impact of those disputes on investors. This paper is the first to explain increased uncertainty of investors about the outcome of arbitration, which may or may not lead to compensation for damages claimed by the investor. We find robust evidence that investment disputes lead to abnormal share fluctuations of companies involved in disputes with host countries. Importantly, while a positive outcome for an investor decreases uncertainty back to original levels, we document strong increase in the volatility of companies with negative outcome for the investor. We find that several variables including size of the award, political instability, location of arbitration, country of origin of investor or public policy considerations in host country explain large portion of the investor's uncertainty.
    Date: 2020–06
  7. By: Chendi Ni; Yuying Li; Peter Forsyth; Ray Carroll
    Abstract: We propose a data-driven Neural Network (NN) optimization framework to determine the optimal multi-period dynamic asset allocation strategy for outperforming a general stochastic target. We formulate the problem as an optimal stochastic control with an asymmetric, distribution shaping, objective function. The proposed framework is illustrated with the asset allocation problem in the accumulation phase of a defined contribution pension plan, with the goal of achieving a higher terminal wealth than a stochastic benchmark. We demonstrate that the data-driven approach is capable of learning an adaptive asset allocation strategy directly from historical market returns, without assuming any parametric model of the financial market dynamics. Following the optimal adaptive strategy, investors can make allocation decisions simply depending on the current state of the portfolio. The optimal adaptive strategy outperforms the benchmark constant proportion strategy, achieving a higher terminal wealth with a 90% probability, a 46% higher median terminal wealth, and a significantly more right-skewed terminal wealth distribution. We further demonstrate the robustness of the optimal adaptive strategy by testing the performance of the strategy on bootstrap resampled market data, which has different distributions compared to the training data.
    Date: 2020–06
  8. By: Ralph S. J. Koijen; Robert J. Richmond; Motohiro Yogo
    Abstract: Much work in finance is devoted to identifying characteristics of firms, such as measures of fundamentals and beliefs, that explain differences in asset prices and expected returns. We develop a framework to quantitatively trace the connection between valuations, expected returns, and characteristics back to institutional investors and households. We use it to analyze (i) what information is important to investors in forming their demand beyond prices and (ii) what is the relative importance of different investors—differentiated by type, size, and active share—in the price formation process. We first show that a small set of characteristics explains the majority of variation in a panel of firm-level valuation ratios across countries. We then estimate an asset demand system using investor-level holdings data, allowing for flexible substitution patterns within and across countries. We find that hedge funds and small, active investment advisors are most influential per dollar of assets under management, while long-term investors, such as pension funds and insurance companies are least influential. In terms of pricing characteristics, small, active investment advisors are most important for the pricing of payout policy, cash flows, and the fraction of sales sold abroad. Large, passive investment advisors are most influential in pricing the Lerner index, a measure of markups, and hedge funds for the CAPM beta.
    JEL: G1
    Date: 2020–06
  9. By: Chenkai Xu; Hongwei Lin; Xuansu Fang
    Abstract: In this paper, we propose a novel stock index model, namely the manifold feature(MF) index, to reflect the overall price activity of the entire stock market. Based on the theory of manifold learning, the researched stock dataset is assumed to be a low-dimensional manifold embedded in a higher-dimensional Euclidean space. After data preprocessing, its manifold structure and discrete Laplace-Beltrami operator(LBO) matrix are constructed. We propose a high-dimensional data feature detection method to detect feature points on the eigenvectors of LBO, and the stocks corresponding to these feature points are considered as the constituent stocks of the MF index. Finally, the MF index is generated by a weighted formula using the price and market capitalization of these constituents. The stock market studied in this research is the Shanghai Stock Exchange(SSE). We propose four metrics to compare the MF index series and the SSE index series (SSE 50, SSE 100, SSE 150, SSE 180 and SSE 380). From the perspective of data approximation, the results demonstrate that our indexes are closer to the stock market than the SSE index series. From the perspective of risk premium, MF indexes have higher stability and lower risk.
    Date: 2020–06
  10. By: Dyaran Bansraj (Erasmus University Rotterdam); Han Smit (Erasmus University Rotterdam); Vadym Volosovych (Erasmus University Rotterdam)
    Abstract: By holding assets longer and increasingly focusing on growth strategies private equity firms enter the territory of strategic buyers. In one such strategy, a private equity firm buys a company and then builds on that “platform†through add-on acquisitions. We ask whether such serial (buy-and-build) acquisition strategies deliver operating synergies, as expected from strategic buyers, or rather are a form of “window-dressing.†We collect a sample of buy-and-build strategies from seven major European markets and find that the profitability of these strategies improves more than that of the comparable strategies, constructed by us from stand-alone companies. We analyze a number of operating outcomes across various strategy sub-types and confirm that these operational improvements are consistent with the synergy interpretation.
    Keywords: Private Equity, Leveraged Buyouts, Buy-and-Build, Operating Performance, Synergies
    JEL: L2 G24 G34
    Date: 2020–07–09
  11. By: Fu Qiao; Yan Yan
    Abstract: Using the carefully selected industry classification standard, we divide 102 industry securities indices in China's stock market into four demand-oriented sector groups and identify demand-oriented industry-specific volatility spillover networks. The "deman-oriented" is a new idea of reconstructing the structure of the networks considering the relationship between industry sectors and the economic demand their outputs meeting. Networks with the new structure help us improve the understanding of the economic demand change, especially when the macroeconomic is dramatically influenced by exogenous shocks like the outbreak of COVID-19. At the beginning of the outbreak of COVID-19, in China's stock market, spillover effects from industry indices of sectors meeting the investment demand to those meeting the consumption demands rose significantly. However, these spillover effects fell after the outbreak containment in China appeared to be effective. Besides, some services sectors including utility, transportation and information services have played increasingly important roles in the networks of industry-specific volatility spillovers as of the COVID-19 out broke. By implication, firstly, being led by Chinese government, the COVID-19 is successfully contained and the work resumption is organized with a high efficiency in China. The risk of the investment demand therefore was controlled and eliminated relatively fast. Secondly, the intensive using of non-pharmaceutical interventions (NPIs) led to supply restriction in services in China. It will still be a potential threat for the Chinese economic recovery in the next stage.
    Date: 2020–07
  12. By: Luchelle Soobyah; Daan Steenkamp
    Date: 2020–06–17

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