nep-fmk New Economics Papers
on Financial Markets
Issue of 2019‒07‒22
seventeen papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Are Analyst Trade Ideas Valuable? By Birru, Justin; Gokkaya, Sinan; Liu, Xi; Stulz, Rene M.
  2. Regularities in stock markets By Abhin Kakkad; Harsh Vasoya; Arnab K. Ray
  3. Asset Pricing vs Asset Expected Returning in Factor Models By Carlo A. Favero; Alessandro Melone
  4. Time scales in stock markets By Ajit Mahata; Md Nurujjaman
  5. Tracking VIX with VIX Futures: Portfolio Construction and Performance By Tim Leung; Brian Ward
  6. Identification of short-term and long-term time scales in stock markets and effect of structural break By Ajit Mahata; Debi Prasad Bal; Md Nurujjaman
  7. Speculative Bubbles in Segmented Markets By Efthymios Pavlidis; Konstantinos Vasilopoulos
  8. Optimistic Bull or Pessimistic Bear: Adaptive Deep Reinforcement Learning for Stock Portfolio Allocation By Xinyi Li; Yinchuan Li; Yuancheng Zhan; Xiao-Yang Liu
  9. Benchmarking Operational Risk Stress Testing Models By Filippo Curti; Marco Migueis; Rob T. Stewart
  10. Learning from Others in the Financial Market By Matthias Feiler; Thibaut Ajdler
  11. A long short-term memory stochastic volatility model By Nghia Nguyen; Minh-Ngoc Tran; David Gunawan; R. Kohn
  12. The Case for Long-Only Agnostic Allocation Portfolios By Pierre-Alain Reigneron; Vincent Nguyen; Stefano Ciliberti; Philip Seager; Jean-Philippe Bouchaud
  13. Mean and Volatility Spillovers between REIT and Stocks Returns A STVAR-BTGARCH-M Model By Das, Mahamitra; Kundu, Srikanta; Sarkar, Nityananda
  14. The Effects of the Introduction of Bitcoin Futures on the Volatility of Bitcoin Returns By Wonse Kim; Junseok Lee; Kyungwon Kang
  15. Impact of oil prices on stock return: evidence from G7 countries By Omar Masood; Manuela Tvaronavičienė; Kiran Javaria
  16. Deep learning calibration of option pricing models: some pitfalls and solutions By A Itkin
  17. Reach for Yield by U.S. Public Pension Funds By Lina Lu; Matthew Pritsker; Andrei Zlate; Kenechukwu E. Anadu; James Bohn

  1. By: Birru, Justin (Ohio State University (OSU) - Department of Finance); Gokkaya, Sinan (Department of Finance, Ohio University; Ohio State University (OSU) - Department of Finance); Liu, Xi (Miami University of Ohio - Richard T. Farmer School of Business Administration); Stulz, Rene M. (Ohio State University (OSU) - Department of Finance; National Bureau of Economic Research (NBER); European Corporate Governance Institute (ECGI))
    Abstract: Using a novel database, we show that the stock-price impact of analyst trade ideas is at least as large as the impact of stock recommendation, target price, and earnings forecast changes, and that investors following trade ideas can earn significant abnormal returns. Trade ideas triggered by forthcoming firm catalyst events are more informative than ideas exploiting temporary mispricing. Institutional investors trade in the direction of trade ideas and commission-paying institutional clients do so earlier than non-clients. Analysts generating trade ideas are more established and are more likely to produce ideas for stocks with high dollar trading commissions in their coverage universe.
    JEL: G11 G12 G14 G20 G23 G24
    Date: 2019–07
  2. By: Abhin Kakkad; Harsh Vasoya; Arnab K. Ray
    Abstract: From the stock markets of six countries with high GDP, we study the stock indices, S&P 500 (NYSE, USA), SSE Composite (SSE, China), Nikkei (TSE, Japan), DAX (FSE, Germany), FTSE 100 (LSE, Britain) and NIFTY (NSE, India). The daily mean growth of the stock values is exponential. The daily price fluctuations about the mean growth are Gaussian, but with a finite asymptotic convergence. The growth of the monthly average of stock values is statistically self-similar to their daily growth. The monthly fluctuations of the price follow a Wiener process, with a decline of the volatility. The mean growth of the daily volume of trade is exponential. These observations are globally applicable and underline regularities across global stock markets.
    Date: 2019–06
  3. By: Carlo A. Favero; Alessandro Melone
    Abstract: This paper proposes a new approach to factor modeling based on the long-run equilibrium relation between prices and related drivers of risk (integrated factors). We show that such relationship reveals an omitted variable in standard factor models for returns that we label as Equilibrium Correction Term (ECT). Omission of this term implies misspecification of every factor model for which the equilibrium (cointegrating) relation holds. The existence of this term implies short-run mispricing that disappears in the long-run. Such evidence of persistent but stationary idiosyncratic risk in prices is consistent with deviations from rational expectations. Its inclusion in a traditional factor model improves remarkably the performance of the model along several dimensions. Furthermore, the ECT -being predictive- has strong implications for risk measurement and portfolio allocation. A zero-cost investment strategy that consistently exploits temporary idiosyncratic mispricing earns an average annual excess return of 6.21%, mostly unspanned by existing factors. Keywords: Asset Pricing, Asset Returns, Equilibrium Correction Term, Dynamic Factor Structure JEL Codes: G11, G17
    Date: 2019
  4. By: Ajit Mahata; Md Nurujjaman
    Abstract: Different investment strategies are adopted in short-term and long-term depending on the time scales, even though time scales are adhoc in nature. Empirical mode decomposition based Hurst exponent analysis and variance technique have been applied to identify the time scales for short-term and long-term investment from the decomposed intrinsic mode functions(IMF). Hurst exponent ($H$) is around 0.5 for the IMFs with time scales from few days to 3 months, and $H\geq0.75$ for the IMFs with the time scales $\geq5$ months. Short term time series [$X_{ST}(t)$] with time scales from few days to 3 months and $H~0.5$ and long term time series [$X_{LT}(t)$] with time scales $\geq5$ and $H\geq0.75$, which represent the dynamics of the market, are constructed from the IMFs. The $X_{ST}(t)$ and $X_{LT}(t)$ show that the market is random in short-term and correlated in long term. The study also show that the $X_{LT}(t)$ is correlated with fundamentals of the company. The analysis will be useful for investors to design the investment and trading strategy.
    Date: 2019–06
  5. By: Tim Leung; Brian Ward
    Abstract: We study a series of static and dynamic portfolios of VIX futures and their effectiveness to track the VIX index. We derive each portfolio using optimization methods, and evaluate its tracking performance from both empirical and theoretical perspectives. Among our results, we show that static portfolios of different VIX futures fail to track VIX closely. VIX futures simply do not react quickly enough to movements in the spot VIX. In a discrete-time model, we design and implement a dynamic trading strategy that adjusts daily to optimally track VIX. The model is calibrated to historical data and a simulation study is performed to understand the properties exhibited by the strategy. In addition, comparing to the volatility ETN, VXX, we find that our dynamic strategy has a superior tracking performance.
    Date: 2019–06
  6. By: Ajit Mahata; Debi Prasad Bal; Md Nurujjaman
    Abstract: The paper presents the comparative study of the nature of stock markets in short-term and long-term time scales with and without structural break in the stock data. Structural break point has been identified by applying Zivot and Andrews structural trend break model to break the original time series (TSO) into time series before structural break (TSB) and time series after structural break (TSA). The empirical mode decomposition based Hurst exponent and variance techniques have been applied to the TSO, TSB and TSA to identify the time scales in short-term and long-term from the decomposed intrinsic mode functions. We found that for TSO, TSB and TSA the short-term time scales and long-term time scales are within the range of few days to 3 months and greater than 5 months respectively, which indicates that the short-term and long-term time scales are present in the stock market. The Hurst exponent is $\sim 0.5$ and $\geq 0.75$ for TSO, TSB and TSA in short-term and long-term respectively, which indicates that the market is random in short-term and strongly correlated in long-term. The identification of time scales at short-term and long-term investment horizon will be useful for investors to design investment and trading strategies.
    Date: 2019–07
  7. By: Efthymios Pavlidis; Konstantinos Vasilopoulos
    Abstract: We propose a novel approach for testing for rational speculative bubbles in segmented capital markets. The basic idea is that, under capital controls, heterogeneity of speculative expectations across international equity markets causes financial assets with identical cash flow promises to trade at different prices. Because these deviations from the law of one price inherit the properties of the speculative bubble process, they display periods of explosive dynamics and have predictive power for future movements in equity prices in sample. These two hypotheses can be examined empirically using sequential unit root tests and predictive regressions. An attractive feature of this approach for bubble detection is that it does not require the specification of a model for market fundamentals, thus mitigating the well-known joint hypothesis problem. The focus of the paper is on mainland Chinese companies that cross list shares in Hong Kong. China is an ideal setting for our analysis because of the significant restrictions on capital movements imposed by the authorities and the turbulent behaviour of its stock market over the last decades.
    Keywords: speculative bubbles, law of one price, AH premium, recursive unit root tests, predictive regressions
    JEL: C22 C32 G15
    Date: 2019
  8. By: Xinyi Li; Yinchuan Li; Yuancheng Zhan; Xiao-Yang Liu
    Abstract: Portfolio allocation is crucial for investment companies. However, getting the best strategy in a complex and dynamic stock market is challenging. In this paper, we propose a novel Adaptive Deep Deterministic Reinforcement Learning scheme (Adaptive DDPG) for the portfolio allocation task, which incorporates optimistic or pessimistic deep reinforcement learning that is reflected in the influence from prediction errors. Dow Jones 30 component stocks are selected as our trading stocks and their daily prices are used as the training and testing data. We train the Adaptive DDPG agent and obtain a trading strategy. The Adaptive DDPG's performance is compared with the vanilla DDPG, Dow Jones Industrial Average index and the traditional min-variance and mean-variance portfolio allocation strategies. Adaptive DDPG outperforms the baselines in terms of the investment return and the Sharpe ratio.
    Date: 2019–06
  9. By: Filippo Curti; Marco Migueis; Rob T. Stewart
    Abstract: The Federal Reserve’s Comprehensive Capital Analysis and Review (CCAR) requires large bank holding companies (BHCs) to project losses under stress scenarios. In this paper, we propose multiple benchmarks for operational loss projections and document the industry distribution relative to these benchmarks. The proposed benchmarks link BHCs’ loss projections with both financial characteristics and metrics of historical loss experience. These benchmarks capture different measures of exposure and together provide a comprehensive view of the reasonability of model outcomes. Furthermore, we employ several approaches to assess the conservatism of BHCs’ stress loss projections and our estimates for the conservatism of loss projections for the median bank range from the 90th percentile to above the 99th percentile of the operational loss distribution.
    Keywords: Benchmarking ; Operational Risk ; Stress Testing
    JEL: G28 G21 G32
    Date: 2019–05–28
  10. By: Matthias Feiler; Thibaut Ajdler
    Abstract: Prediction problems in finance go beyond estimating the unknown parameters of a model (e.g of expected returns). This is because such a model would have to include knowledge about the market participants' propensity to change their opinions on the validity of that model. This leads to a circular situation characteristic of markets, where participants collectively create the target variables they wish to estimate. In this paper, we introduce a framework for generating expectation models and study the conditions under which they are adopted by a majority of market participants.
    Date: 2019–06
  11. By: Nghia Nguyen; Minh-Ngoc Tran; David Gunawan; R. Kohn
    Abstract: Stochastic Volatility (SV) models are widely used in the financial sector while Long Short-Term Memory (LSTM) models have been successfully used in many large-scale industrial applications of Deep Learning. Our article combines these two methods non trivially and proposes a model for capturing the dynamics of financial volatility process, which we call the LSTM-SV model. The proposed model overcomes the short-term memory problem in conventional SV models, is able to capture non-linear dependence in the latent volatility process, and often has a better out-of-sample forecast performance than SV models. The conclusions are illustrated through simulation studies and applications to three financial time series datasets: US stock market weekly index SP500, Australian stock weekly index ASX200 and Australian-US dollar daily exchange rates. We argue that there are significant differences in the underlying dynamics between the volatility process of SP500 and ASX200 datasets and that of the exchange rate dataset. For the stock index data, there is strong evidence of long-term memory and non-linear dependence in the volatility process, while this is not the case for the exchange rates. An user-friendly software package together with the examples reported in the paper are available at
    Date: 2019–06
  12. By: Pierre-Alain Reigneron; Vincent Nguyen; Stefano Ciliberti; Philip Seager; Jean-Philippe Bouchaud
    Abstract: We advocate the use of Agnostic Allocation for the construction of long-only portfolios of stocks. We show that Agnostic Allocation Portfolios (AAPs) are a special member of a family of risk-based portfolios that are able to mitigate certain extreme features (excess concentration, high turnover, strong exposure to low-risk factors) of classical portfolio construction methods, while achieving similar performance. AAPs thus represent a very attractive alternative risk-based portfolio construction framework that can be implemented in different situations, with or without an active trading signal.
    Date: 2019–06
  13. By: Das, Mahamitra; Kundu, Srikanta; Sarkar, Nityananda
    Abstract: In this study we have examined volatility spillovers as well as volatility-in-mean effect between REIT returns and stock returns for both the USA and the UK by applying a bivariate GARCH-M model where the conditional mean is specified by a smooth transition VAR model. Dynamic conditional correlation approach has been applied with the GJR-GARCH specification so that the intrinsic nature of asymmetric volatility in case of positive and negative shocks can be duly captured. The major findings that we have empirically found is that the mean spillover effect from stock returns to REIT returns is significant for both the countries while the same from REIT returns to stock returns is significant only in the UK. It is also evident from the results that own risk-return relationship of REIT market is positive and significant only in the bear market situation in both the countries while for the stock market own risk-return relationship is insignificant for both the bull and bear markets in the USA but it is negative in the bear market condition and positive in the bull market situation for the UK. We have also found that asymmetric nature of conditional variance and dynamic behavior in the conditional correlation holds as well. Finally, several tests of hypotheses regarding equality of various kinds of spillover effects in the bull and bear market situations show that these spillover effects are not the same in the two market conditions in most of the aspects considered in this study.
    Keywords: REIT; Volatility Spillover; STVAR-BTGARCH_M
    JEL: C58 G1 G11
    Date: 2019–07–01
  14. By: Wonse Kim; Junseok Lee; Kyungwon Kang
    Abstract: This paper investigates the effects of the launch of Bitcoin futures on the intraday volatility of Bitcoin. Based on one-minute price data collected from four cryptocurrency exchanges, we first examine the change in realized volatility after the introduction of Bitcoin futures to investigate their aggregate effects on the intraday volatility of Bitcoin. We then analyze the effects in more detail utilizing the discrete Fourier transform. We show that although the Bitcoin market became more volatile immediately after the introduction of Bitcoin futures, over time it has become more stable than it was before the introduction.
    Date: 2019–06
  15. By: Omar Masood (University of Lahore); Manuela Tvaronavičienė (Vilnius Gediminas Technical University); Kiran Javaria (University of Lahore)
    Abstract: The aim of the study is to investigate the impact of oil prices on the stock market of G7 countries. Oil prices not only affect the economy of a country but also the country's stock market. The stock market affects the stock valuation or, to put in another way, the company's stock value. The stock value is associated with the discounted sum of predictable future cash flows and these flows may be distressed by macroeconomic variables including oil prices fluctuations. This study has researched the impact of oil prices' fluctuation on countries included G7, i.e.. For the analysis, the most recent data is collected. In this study, the real stock return has considered as a depended variable or predict variable, while oil prices, industrial production, and short-term interest rate are as independent, or predictor variables. The study is quantitative in nature. All data was collected from OECD website with the exception of oil prices, which were taken from oil intelligence report. The model, which has been used in the study is based on Arbitrage pricing theory-APT model, where financial assets are associated with macroeconomic variables. The results showed that Industrial production is positively associated with a real stock return in the case of Germany, Italy, Japan, the United Kingdom, and France, while the short-term interest rate is negatively connected with a real stock return in the case of Canada, the United Kingdom, and United States of America. Oil prices have an insignificant effect on real stock markets of all considered countries. The authors provide an economic interpretation of the obtained results.
    Keywords: oil prices,industrial production,short-term interest rate,real stock return,G7 countries,Arbitrage Pricing Theory
    Date: 2019–06–30
  16. By: A Itkin
    Abstract: Recent progress in the field of artificial intelligence, machine learning and also in computer industry resulted in the ongoing boom of using these techniques as applied to solving complex tasks in both science and industry. Same is, of course, true for the financial industry and mathematical finance. In this paper we consider a classical problem of mathematical finance - calibration of option pricing models to market data, as it was recently drawn some attention of the financial society in the context of deep learning and artificial neural networks. We highlight some pitfalls in the existing approaches and propose resolutions that improve both performance and accuracy of calibration. We also address a problem of no-arbitrage pricing when using a trained neural net, that is currently ignored in the literature.
    Date: 2019–06
  17. By: Lina Lu; Matthew Pritsker; Andrei Zlate; Kenechukwu E. Anadu; James Bohn
    Abstract: This paper studies whether U.S. public pension funds reach for yield by taking more investment risk in a low interest rate environment. To study funds’ risk-taking behavior, we first present a simple theoretical model relating risk-taking to the level of risk-free rates, to their underfunding, and to the fiscal condition of their state sponsors. The theory identifies two distinct channels through which interest rates and other factors may affect risk-taking: by altering plans’ funding ratios, and by changing risk premia. The theory also shows the effect of state finances on funds’ risk-taking depends on incentives to shift risk to state debt holders. To study the determinants of risk-taking empirically, we create a new methodology for inferring funds’ risk from limited public information on their annual returns and portfolio weights for the interval 2002-2016. In order to better measure the extent of underfunding, we revalue funds’ liabilities using discount rate s that better reflect their risk. We find that funds on average took more risk when risk-free rates and funding ratios were lower, which is consistent with both the funding ratio and the risk-premia channels. Consistent with risk-shifting, we also find more risk-taking for funds affiliated with state or municipal sponsors with weaker public finances. We estimate that up to one-third of the funds’ total risk was related to underfunding and low interest rates at the end of our sample period.
    Keywords: U.S. public pension funds ; reach for yield ; Value at Risk ; underfunding ; duration-matched discount rates ; state public debt
    JEL: E43 G11 G32 G23 H74
    Date: 2019–06–27

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