nep-fmk New Economics Papers
on Financial Markets
Issue of 2021‒10‒04
eight papers chosen by

  1. Stock Price Prediction Under Anomalous Circumstances By Jinlong Ruan; Wei Wu; Jiebo Luo
  2. Stock Index Prediction using Cointegration test and Quantile Loss By Jaeyoung Cheong; Heejoon Lee; Minjung Kang
  3. Option return predictability with machine learning and big data By Bali, Turan G.; Beckmeyer, Heiner; Moerke, Mathis; Weigert, Florian
  4. Foreign Investors and U.S. Treasuries By Alexandra M. Tabova; Francis E. Warnock
  5. Banks’ equity stakes in firms: A blessing or curse in credit markets? By Falko Fecht; José-Luis Peydró; Günseli Tümer-Alkan; Yuejuan Yu
  6. Tail Risks and Stock Return Predictability: Evidence From Asia-Pacific By Ogbonna, Ahamuefula; Olubusoye, Olusanya E
  7. Emerging market capital flows: the role of fund manager portfolio allocation By Georgia Bush; Carlos Iván Cañón Salazar; Daniel Gray
  8. Stock index futures trading impact on spot price volatility. The CSI 300 studied with a TGARCH model By Marcel Ausloos; Yining Zhang; Gurjeet Dhesi

  1. By: Jinlong Ruan; Wei Wu; Jiebo Luo
    Abstract: The stock market is volatile and complicated, especially in 2020. Because of a series of global and regional "black swans," such as the COVID-19 pandemic, the U.S. stock market triggered the circuit breaker three times within one week of March 9 to 16, which is unprecedented throughout history. Affected by the whole circumstance, the stock prices of individual corporations also plummeted by rates that were never predicted by any pre-developed forecasting models. It reveals that there was a lack of satisfactory models that could predict the changes in stocks prices when catastrophic, highly unlikely events occur. To fill the void of such models and to help prevent investors from heavy losses during uncertain times, this paper aims to capture the movement pattern of stock prices under anomalous circumstances. First, we detect outliers in sequential stock prices by fitting a standard ARIMA model and identifying the points where predictions deviate significantly from actual values. With the selected data points, we train ARIMA and LSTM models at the single-stock level, industry level, and general market level, respectively. Since the public moods affect the stock market tremendously, a sentiment analysis is also incorporated into the models in the form of sentiment scores, which are converted from comments about specific stocks on Reddit. Based on 100 companies' stock prices in the period of 2016 to 2020, the models achieve an average prediction accuracy of 98% which can be used to optimize existing prediction methodologies.
    Date: 2021–09
  2. By: Jaeyoung Cheong; Heejoon Lee; Minjung Kang
    Abstract: Recent researches on stock prediction using deep learning methods has been actively studied. This is the task to predict the movement of stock prices in the future based on historical trends. The approach to predicting the movement based solely on the pattern of the historical movement of it on charts, not on fundamental values, is called the Technical Analysis, which can be divided into univariate and multivariate methods in the regression task. According to the latter approach, it is important to select different factors well as inputs to enhance the performance of the model. Moreover, its performance can depend on which loss is used to train the model. However, most studies tend to focus on building the structures of models, not on how to select informative factors as inputs to train them. In this paper, we propose a method that can get better performance in terms of returns when selecting informative factors using the cointegration test and learning the model using quantile loss. We compare the two RNN variants with quantile loss with only five factors obtained through the cointegration test among the entire 15 stock index factors collected in the experiment. The Cumulative return and Sharpe ratio were used to evaluate the performance of trained models. Our experimental results show that our proposed method outperforms the other conventional approaches.
    Date: 2021–09
  3. By: Bali, Turan G.; Beckmeyer, Heiner; Moerke, Mathis; Weigert, Florian
    Abstract: Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. Besides statistical significance, the nonlinear machine learning models generate economically sizeable profits in the long-short portfolios of equity options even after accounting for transaction costs. Although option-based characteristics are the most important standalone predictors, stock-based measures offer substantial incremental predictive power when considered alongside option-based characteristics. Finally, we provide compelling evidence that option return predictability is driven by informational frictions, costly arbitrage, and option mispricing.
    Keywords: Machine learning,big data,option return predictability
    JEL: G10 G12 G13 G14
    Date: 2021
  4. By: Alexandra M. Tabova; Francis E. Warnock
    Abstract: While foreigners are prominent in the Treasury market and in theoretical and empirical work, little is known about the nature of their Treasury portfolios. We provide novel evidence on foreigners' U.S. Treasury portfolios based on data not yet used by researchers: the security-level Treasury portfolios of foreigners and private U.S. investors. We find that private foreign investors earn above market returns and on a risk-adjusted basis both foreign private and foreign official investors outperform U.S. investors. Moreover, while foreign officials, with their broader objective functions, may well have inelastic demand, private foreign investors increase purchases of Treasuries and increase the duration of their Treasury portfolios when their sovereign yields are low or decrease relative to Treasury yields (that is, when CIP deviations decrease). Our results are so different from existing results that we close with a reconciliation exercise that provides a useful assessment of different sources of data on flows and holdings.
    JEL: F30 G11 G12
    Date: 2021–09
  5. By: Falko Fecht; José-Luis Peydró; Günseli Tümer-Alkan; Yuejuan Yu
    Abstract: We analyze how banks' equity stakes in firms influence their credit supply in crisis times. For identification, we exploit the 2008 Global Financial Crisis and merge unique supervisory data from the German credit register on individual bank-firm credit exposures with the security register data that include banks' equity holdings. We find that a large and ex-ante persistent equity position held by a bank in a firm is associated with a larger credit provision from the respective bank to that firm. In crisis times, however, equity stakes only foster credit supply to ex-ante riskier firms especially from relatively weak banks. This ex-ante risk-taking may be due to better (insider) information by the bank, including a traditional lending relationship over the crisis. However, this ex-ante riskier lending translates also into higher ex-post loan defaults, worse firm-level stock market returns and even more firm bankruptcy or restructuring cases. Our results therefore suggest that banks' equity stakes in their borrowers do not mitigate debt overhang problems of distressed firms in crisis times, but rather foster evergreening of banks' outstanding credit to those (zombie) firms.
    Keywords: Universal banks; credit supply; bank equity holdings; debt overhang; evergreening
    JEL: G01 G21 G28 G30
    Date: 2021–09
  6. By: Ogbonna, Ahamuefula; Olubusoye, Olusanya E
    Abstract: Hinging on the recently established relevance of tail thickness information, we examine the predictability of fifteen major stocks in the Asia-Pacific region using conditional autoregressive value at risk (CAViaR) model estimates of tail risks. We used a Westerlund and Narayan–type distributed lag model to examine the nexus between returns and tail risk under controlled global and US stocks spillover effects. Country-specific tail risks induce a near-term rise (completely disappears) in returns on “bad” (“good”) days. Our results are robust.
    Keywords: Conditional Autoregressive Value at Risk; Predictability; Returns; Tail Thickness
    JEL: C10 C53 G17
    Date: 2021–04–09
  7. By: Georgia Bush; Carlos Iván Cañón Salazar; Daniel Gray
    Abstract: We exploit individual security holdings data for global mutual funds to distinguish between two reasons why a fund's holdings of emerging market economy (EME) bonds might change: (i) the amount invested in the fund changes and (ii) the fund manager changes portfolio allocations. We find that funds' responsiveness to global macroeconomic conditions, ''push factors'', is explained by investor flow decisions. Conversely, funds' responsiveness to local macroeconomic conditions, ''pull factors'', is explained by manager reallocation decisions. We also identify other institutional factors which impact reallocation decisions: their leverage, their benchmark, and risk appetite (funds reallocate towards safer EMEs when global risk increases).
    JEL: F32 G11 G15 G23
    Date: 2021–09
  8. By: Marcel Ausloos; Yining Zhang; Gurjeet Dhesi
    Abstract: A TGARCH modeling is argued to be the optimal basis for investigating the impact of index futures trading on spot price variability. We discuss the CSI-300 index (China-Shanghai-Shenzhen-300-Stock Index) as a test case. The results prove that the introduction of CSI-300 index futures (CSI-300-IF) trading significantly reduces the volatility in the corresponding spot market. It is also found that there is a stationary equilibrium relationship between the CSI-300 spot and CCSI-300-IF markets. A bidirectional Granger causality is also detected. ''Finally'', it is deduced that spot prices are predicted with greater accuracy over a 3 or 4 lag day time span.
    Date: 2021–08

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