nep-fmk New Economics Papers
on Financial Markets
Issue of 2020‒02‒03
sixteen papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Zooming In on Equity Factor Crowding By Valerio Volpati; Michael Benzaquen; Zoltan Eisler; Iacopo Mastromatteo; Bence Toth; Jean-Philippe Bouchaud
  2. The fundamentals of safe assets By Habib, Maurizio Michael; Stracca, Livio; Venditti, Fabrizio
  3. Comparison of various risk measures for an optimal portfolio By Alev Meral
  4. Diverse Risk Preferences and Heterogeneous Expectations in an Asset Pricing Model By Thomas Gomez; Giulia Piccillo
  5. Market Efficiency in the Age of Big Data By Ian Martin; Stefan Nagel
  6. Design of High-Frequency Trading Algorithm Based on Machine Learning By Boyue Fang; Yutong Feng
  7. A principal component-guided sparse regression approach for the determination of bitcoin returns By Thanasis Stengos; Theodore Panagiotidis; Orestis Vravosinos
  8. Path-dependent volatility models By Antoine Jacquier; Chloe Lacombe
  9. A new approach for trading based on Long Short Term Memory technique By Zineb Lanbouri; Saaid Achchab
  10. Corporate Governance, Noise Trading and Liquidity of Stocks By Jianhao Su
  11. The U.S. Public Debt Valuation Puzzle By Zhengyang Jiang; Hanno Lustig; Stijn Van Nieuwerburgh; Mindy Z. Xiaolan
  12. Oil price uncertainty as a predictor of stock market volatility By Vlastakis, Nikolaos; Triantafyllou, Athanasios; Kellard, Neil
  13. Pricing of the Geometric Asian Options Under a Multifactor Stochastic Volatility Model By Gifty Malhotra; R. Srivastava; H. C. Taneja
  14. Accuracy of European Stock Target Prices By Joana Almeida; Raquel M. Gaspar
  15. What is behind extreme negative returns co-movement in the South Eastern European stock markets? By Tevdovski, Dragan; Stojkoski, Viktor
  16. The impact of return shocks on mutual funds’ flows: an empirical study of French bond mutual funds By Raphaëlle BELLANDO; Laura-Dona CAPOTA; Sébastien GALANTI

  1. By: Valerio Volpati; Michael Benzaquen; Zoltan Eisler; Iacopo Mastromatteo; Bence Toth; Jean-Philippe Bouchaud
    Abstract: Crowding is most likely an important factor in the deterioration of strategy performance, the increase of trading costs and the development of systemic risk. We study the imprints of \emph{crowding} on both anonymous market data and a large database of metaorders from institutional investors in the U.S. equity market. We propose direct metrics of crowding that capture the presence of investors contemporaneously trading the same stock in the same direction by looking at fluctuations of the imbalances of trades executed on the market. We identify significant signs of crowding in well known equity signals, such as Fama-French factors and especially Momentum. We show that the rebalancing of a Momentum portfolio can explain between 1-2\% of order flow, and that this percentage has been significantly increasing in recent years.
    Date: 2020–01
  2. By: Habib, Maurizio Michael; Stracca, Livio; Venditti, Fabrizio
    Abstract: We study what makes government bonds a safe asset. Building on a sample of monthly changes in government bond yields in 40 advanced and emerging countries, we analyse the sensitivity of yields to country specific fundamentals interacted with changes in global risk (VIX). We find that inertia (whether the bond behaved as a safe asset in the past) and good institutions foster a safe asset status, while the size of the debt market is also significant, reflecting the special role of the US. Within advanced and emerging markets, drivers are heterogeneous, with external sustainability in particular being relevant for the latter countries after the global financial crisis. Finally, the safe asset status does not appear to depend on whether the change in global risk is driven by financial shocks rather than by US monetary policy. JEL Classification: E42, E52, F31, F36, F41
    Keywords: fundamentals, global risk, monetary policy, safe assets
    Date: 2020–01
  3. By: Alev Meral
    Abstract: In this paper, we search for optimal portfolio strategies in the presence of various risk measure that are common in financial applications. Particularly, we deal with the static optimization problem with respect to Value at Risk, Expected Loss and Expected Utility Loss measures. To do so, under the Black- Scholes model for the financial market, Martingale method is applied to give closed-form solutions for the optimal terminal wealths; then via representation problem the optimal portfolio strategies are achieved. We compare the performances of these measures on the terminal wealths and optimal strategies of such constrained investors. Finally, we present some numerical results to compare them in several respects to give light to further studies.
    Date: 2019–12
  4. By: Thomas Gomez; Giulia Piccillo
    Abstract: We propose a heuristic switching model of an asset market where the agents’ choice of heuristic is consistent with their individual risk aversion. They choose between a fundamentalist and a trend-following rule to form expectations about the price of a risky asset. Given their risk aversion, agents make a deterministic trade-off between mean and variance both in choosing a forecasting heuristic and determining the number of risky assets to buy. Heterogeneous risk preferences can lead to diverse choices of heuristic. Using empirical estimates for the distribution of risk aversion, simulations show that the resulting time-varying heterogeneity of expectations can give rise to chaotic dynamics: irregular booms and busts in the asset price without exogenous shocks. Small, stochastic price shocks lead to larger asset price bubbles, and can make stable solutions explosive. We prove that a representative agent cannot capture our model.
    Keywords: heterogeneous risk aversion, bounded rationality, heterogeneous expectations, heuristic switching, asset pricing
    JEL: D81 D84 G11 G12
    Date: 2019
  5. By: Ian Martin; Stefan Nagel
    Abstract: Modern investors face a high-dimensional prediction problem: thousands of observable variables are potentially relevant for forecasting. We reassess the conventional wisdom on market efficiency in light of this fact. In our model economy, which resembles a typical machine learning setting, N assets have cash flows that are a linear function of J firm characteristics, but with uncertain coefficients. Risk-neutral Bayesian investors impose shrinkage (ridge regression) or sparsity (Lasso) when they estimate the J coefficients of the model and use them to price assets. When J is comparable in size to N, returns appear cross-sectionally predictable using firm characteristics to an econometrician who analyzes data from the economy ex post. A factor zoo emerges even without p-hacking and data-mining. Standard in-sample tests of market efficiency reject the no-predictability null with high probability, despite the fact that investors optimally use the information available to them in real time. In contrast, out-of-sample tests retain their economic meaning.
    JEL: C11 G12 G14
    Date: 2019–12
  6. By: Boyue Fang; Yutong Feng
    Abstract: Based on iterative optimization and activation function in deep learning, we proposed a new analytical framework of high-frequency trading information, that reduced structural loss in the assembly of Volume-synchronized probability of Informed Trading ($VPIN$), Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Support Vector Machine (SVM) to make full use of the order book information. Amongst the return acquisition procedure in market-making transactions, uncovering the relationship between discrete dimensional data from the projection of high-dimensional time-series would significantly improve the model effect. $VPIN$ would prejudge market liquidity, and this effectiveness backtested with CSI300 futures return.
    Date: 2019–12
  7. By: Thanasis Stengos (Department of Economics and Finance, University of Guelph, Guelph ON Canada); Theodore Panagiotidis (Department of Economics, University of Macedonia); Orestis Vravosinos (Department of Economics, New York University)
    Abstract: We examine the significance of fourty-one potential covariates of bitcoin returns for the period 2010–2018 (2,872 daily observations). The principal component-guided sparse regression is employed, introduced by Tay et al. (2018). We reveal that economic policy uncertainty and stock market volatility are among the most important variables for bitcoin. We also trace strong evidence of bubbly bitcoin behavior in the 2017-2018 period.
    Keywords: bitcoin; cryptocurrency; bubble; sparse regression; LASSO; PC-LASSO; principal component; flexible least squares
    JEL: G12 G15
    Date: 2020
  8. By: Antoine Jacquier; Chloe Lacombe
    Abstract: We provide a thorough analysis of the path-dependent volatility model introduced by Guyon, proving existence and uniqueness of a strong solution, characterising its behaviour at boundary points, and deriving large deviations estimates. We further develop a numerical algorithm in order to jointly calibrate SP500 and VIX market data.
    Date: 2020–01
  9. By: Zineb Lanbouri; Saaid Achchab
    Abstract: The stock market prediction has always been crucial for stakeholders, traders and investors. We developed an ensemble Long Short Term Memory (LSTM) model that includes two-time frequencies (annual and daily parameters) in order to predict the next-day Closing price (one step ahead). Based on a four-step approach, this methodology is a serial combination of two LSTM algorithms. The empirical experiment is applied to 417 NY stock exchange companies. Based on Open High Low Close metrics and other financial ratios, this approach proves that the stock market prediction can be improved.
    Date: 2020–01
  10. By: Jianhao Su
    Abstract: Our main task is to study the effect of corporate governance on the market liquidity of listed companies' stocks. We establish a theoretical model that contains the heterogeneity of investors' beliefs to explain the mechanisms by which corporate governance improves liquidity of the corporate stocks. In this process we found that the existence of noise traders who are semi-informed in the market is an important condition for corporate governance to have the effect of improving liquidity of the stocks. We further find that the strength of this effect is affected by the degree of noise traders' participation in market transactions. Our model reveals that corporate governance and the degree of noise traders' participation in transactions have a synergistic effect on improving the liquidity of the stocks.
    Date: 2020–01
  11. By: Zhengyang Jiang; Hanno Lustig; Stijn Van Nieuwerburgh; Mindy Z. Xiaolan
    Abstract: The market value of outstanding federal government debt in the U.S. exceeds the expected present discounted value of current and future primary surpluses by a multiple of U.S. GDP. When the pricing kernel fits U.S. equity and Treasury prices and the government surpluses are consistent with U.S. post-war data, a government debt valuation puzzle emerges. Since tax revenues are pro-cyclical while government spending is counter-cyclical, the tax revenue claim has a higher short-run discount rate and a lower value than the spending claim. Since revenue and spending are co-integrated with GDP, the long-run risk discount rates of both claims are much higher than the long Treasury yield. These forces imply a negative present value of U.S. government surpluses. Convenience yields for Treasurys are much larger than previously thought and/or U.S. Treasury markets have failed to enforce the no-bubble condition.
    JEL: E43 E62 G12 G15
    Date: 2019–12
  12. By: Vlastakis, Nikolaos; Triantafyllou, Athanasios; Kellard, Neil
    Abstract: In this paper we empirically examine the impact of oil price uncertainty shocks on US stock market volatility. We define the oil price uncertainty shock as the unanticipated component of oil price fluctuations. We find that our oil price uncertainty factor is the most significant predictor of stock market volatility when compared with various observable oil price and volatility measures commonly used in the literature. Moreover, we find that oil price uncertainty is a common volatility forecasting factor of S&P500 constituents, and it outperforms lagged stock market volatility and the VIX when forecasting volatility for medium and long-term forecasting horizons. Interestingly, when forecasting the volatility of S&P500 constituents, we find that the highest predictive power of oil price uncertainty is for the stocks which belong to the financial sector. Overall, our findings show that financial stability is significantly damaged when the degree of oil price unpredictability rises, while it is relatively immune to observable fluctuations in the oil market.
    Keywords: Stock market, Oil, Uncertainty, Realized Variance, Volatility
    Date: 2020–01–22
  13. By: Gifty Malhotra; R. Srivastava; H. C. Taneja
    Abstract: This paper focuses on the pricing of continuous geometric Asian options (GAOs) under a multifactor stochastic volatility model. The model considers fast and slow mean reverting factors of volatility, where slow volatility factor is approximated by a quadratic arc. The asymptotic expansion of the price function is assumed, and the first order price approximation is derived using the perturbation techniques for both floating and fixed strike GAOs. Much simplified pricing formulae for the GAOs are obtained in this multifactor stochastic volatility framework. The zeroth order term in the price approximation is the modified Black-Scholes price for the GAOs. This modified price is expressed in terms of the Black-Scholes price for the GAOs. The accuracy of the approximate option pricing formulae is established, and the model parameter is also estimated by capturing the volatility smiles.
    Date: 2019–12
  14. By: Joana Almeida; Raquel M. Gaspar
    Abstract: Equity researches are conducted by professionals, who also provide buy/hold/sell recommenda-tions to investors. Nowadays, target prices determined by financial analysts are publicly available to investors, who may decide to use them for investment purposes. Studying thea ccuracy of suchanalysts’ forecasts is, thus, of paramount importance. Based upon empirical data on 50 of the biggest (larger capitalisation) European stocks over a 15–year period, from 2004 to 2019 and using a panel data approach,this is the first study looking at overall accuracy in European stock markets. We find that Bloomberg’s 12-month consensus target prices have no predictive over future market prices. Panel results are robust to company fixed effects and sub-period analysis. These results are in line with the (mostly US-based) evidence in the literature. Extending common practice, we perform a comparative accuracy analysis, comparing the accu-racy of target prices with that of simple capitalisations of current prices. It turns out target pricesare not better in forecasting, than simple capitalisations. More interestingly, by analysing also the relationship between both measures – target prices and capitalised prices – we find evidence that capitalised prices partially explain how target prices are determined. Even when considering individual regressions, accuracy is still very low, but varies considerablyacross stocks.
    Keywords: Target prices, forecast accuracy, panel data analysis
    JEL: C33 G14 G17 G24
    Date: 2020–01
  15. By: Tevdovski, Dragan; Stojkoski, Viktor
    Abstract: This paper examines co-movement of extreme negative returns in the South Eastern European (SEE) stock markets during the period covering the recent financial crisis and sovereign debt crisis. The analysis is based on negative co-exceedances - joint occurrences of negative extreme returns in different countries stock markets. To provide a valuable insight on how persistence, asset class, volatility and liquidity effects are related with negative co-exceedances in SEE markets we utilize a multinomial logistic regression procedure. We find evidence in favor of the continuation hypothesis in SEE stock markets. However, the factors associated with the co-exceedances differ between the SEE EU member countries and SEE EU accession countries. The EU member countries are more dependent on the signals from major EU economies, while the accession countries are mainly influenced by the signals from the region.
    Keywords: co-movement, contagion, stock markets, emerging markets, South Eastern Europe.
    JEL: G10
    Date: 2020–01–20
  16. By: Raphaëlle BELLANDO; Laura-Dona CAPOTA; Sébastien GALANTI
    Keywords: , bond mutual funds, flows, flows-performance relationship
    Date: 2019

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