nep-fmk New Economics Papers
on Financial Markets
Issue of 2021‒11‒29
eleven papers chosen by

  1. Central Banking in the Time of Covid By Alan S. Blinder
  2. Stock Price Prediction Using Time Series, Econometric, Machine Learning, and Deep Learning Models By Ananda Chatterjee; Hrisav Bhowmick; Jaydip Sen
  3. IQ from IP: simplifying search in portfolio choice By Chen, Huaizhi; Cohen, Lauren; Gurun, Umit; Lou, Dong; Malloy, Christopher
  4. FinTech Lending By Tobias Berg; Andreas Fuster; Manju Puri
  5. Momentum, Reversals, and Investor Clientele By Andy C.W. Chui; Avanidhar Subrahmanyam; Sheridan Titman
  6. Yield curve momentum By Sihvonen, Markus
  7. The contribution of Economic Policy Uncertainty to the persistence of shocks to stock market volatility By Paraskevi Tzika; Theologos Pantelidis
  8. DEEDP DIVING INTO THE S&P 350 EUROPE INDEX NETWORK ANS ITS REACTION TO COVID-19 By Ariana Paola Cortés à ngel; Mustafa Hakan Eratalay
  9. Brexit: Cyclical dependence in market neutral hedge funds By Julio A. Crego; Julio Gálvez
  10. U.S. Housing as a Global Safe Asset: Evidence from China Shocks By William Barcelona; Nathan L. Converse; Anna Wong
  11. Profit warnings and stock returns: Evidence from moroccan stock exchange By Ilyas El Ghordaf; Abdelbari El Khamlichi

  1. By: Alan S. Blinder (Princeton Unviersity)
    Keywords: Covid, banks, central banking
    JEL: E50 E58 G01
    Date: 2021–09
  2. By: Ananda Chatterjee; Hrisav Bhowmick; Jaydip Sen
    Abstract: For a long-time, researchers have been developing a reliable and accurate predictive model for stock price prediction. According to the literature, if predictive models are correctly designed and refined, they can painstakingly and faithfully estimate future stock values. This paper demonstrates a set of time series, econometric, and various learning-based models for stock price prediction. The data of Infosys, ICICI, and SUN PHARMA from the period of January 2004 to December 2019 was used here for training and testing the models to know which model performs best in which sector. One time series model (Holt-Winters Exponential Smoothing), one econometric model (ARIMA), two machine Learning models (Random Forest and MARS), and two deep learning-based models (simple RNN and LSTM) have been included in this paper. MARS has been proved to be the best performing machine learning model, while LSTM has proved to be the best performing deep learning model. But overall, for all three sectors - IT (on Infosys data), Banking (on ICICI data), and Health (on SUN PHARMA data), MARS has proved to be the best performing model in sales forecasting.
    Date: 2021–11
  3. By: Chen, Huaizhi; Cohen, Lauren; Gurun, Umit; Lou, Dong; Malloy, Christopher
    Abstract: Using a novel database that tracks web traffic on the Security Exchange Commission's EDGAR server between 2004 and 2015, we show that institutional investors gather information on a very particular subset of firms and insiders, and their surveillance is very persistent over time. This tracking behavior has powerful implications for their portfolio choice and its information content. An institution that downloaded an insider trading filing by a given firm last quarter increases its likelihood of downloading an insider trading filing on the same firm by more than 41.3 percentage points this quarter. Moreover, the average tracked stock that an institution buys generates annualized alphas of over 12% relative to the purchase of an average non tracked stock. We find that institutional managers tend to track top executives and to share educational and locational commonalities with the specific insiders they choose to follow. Collectively, our results suggest that the information in tracked trades is important for fundamental firm value and is only revealed following the information-rich dual trading by insiders and linked institutions.
    Keywords: tracked trades; return predictability; institutional trading; insider trading
    JEL: G11 G14 G23
    Date: 2020–10–01
  4. By: Tobias Berg; Andreas Fuster; Manju Puri
    Abstract: In this paper, we review the growing literature on FinTech lending – the provision of credit facilitated by technology that improves the customer-lender interaction or lenders’ screening and monitoring of borrowers. FinTech lending has grown rapidly, though in developed economies like the U.S. it still only accounts for a small share of total credit. An increase in convenience and speed appears to have been more central to FinTech lending’s growth than improved screening or monitoring, though there is certainly potential for the latter, as is the case for increased financial inclusion. The COVID-19 pandemic has shown potential vulnerabilities of FinTech lenders, although in certain segments they have displayed rapid growth.
    JEL: G2 G20 G21 G23
    Date: 2021–10
  5. By: Andy C.W. Chui; Avanidhar Subrahmanyam; Sheridan Titman
    Abstract: Different share classes on the same firms provide a natural experiment to explore how investor clienteles affect momentum and short-term reversals. Domestic retail investors have a greater presence in Chinese A shares, and foreign institutions are relatively more prevalent in B shares. These differences result from currency conversion restrictions and mandated investment quotas. We find that only B shares exhibit momentum and earnings drift, and only A shares exhibit monthly reversals. Institutional ownership strengthens momentum in B shares. These patterns accord with a setting where momentum is caused by informed investors who underreact to fundamental signals, and short-term reversals represent premia to absorb the demands of noise traders. Overall, our findings confirm that clienteles matter in generating stock return predictability from past returns.
    JEL: G02 G12 G14
    Date: 2021–11
  6. By: Sihvonen, Markus
    Abstract: I analyze time series momentum along the Treasury term structure. Past bond returns predict future returns both due to autocorrelation in bond risk premia and because unexpected bond return shocks increase the premium. Yield curve momentum is primarily due to autocorrelation in yield changes rather than autocorrelation in bond carry and can largely be captured using a single bond return or yield change factor. Because yield changes are partly induced by changes in the federal funds rate, yield curve momentum is related to post-FOMC announcement drift. The momentum factor is unspanned by the information in the term structure today and is hence inconsistent with standard term structure, macrofinance and behavioral models. I argue that the results are consistent with a model with unpriced longer term dependencies.
    JEL: G12 E43 E47
    Date: 2021–11–16
  7. By: Paraskevi Tzika (Department of Economics, University of Macedonia); Theologos Pantelidis (Department of Economics, University of Macedonia)
    Abstract: This paper examines the contribution of Economic Policy Uncertainty (EPU) to the persistence of shocks to stock market volatility. The study applies an innovative approach that compares the half-life of a shock in the context of a bivariate V AR model that includes the volatility of stock returns and EPU, with the half-life of the equivalent univariate ARMA model for the stock return volatility. Based on daily data for the UK and the US, the empirical results corroborate that EPU contributes to the persistence of shocks to stock market volatility for both countries. This contribution is higher for the US, where 14.3% of the persistence of shocks to stock market volatility can be attributed to the EPU index.
    Keywords: Economic Policy Uncertainty, Stock Market Volatility, Persistence, Half-Life
    JEL: C22 C32 E44
    Date: 2021–09
  8. By: Ariana Paola Cortés à ngel; Mustafa Hakan Eratalay
    Abstract: In this paper, we analyse the dynamic partial correlation network of the constituent stocks of S&P Europe 350. We focus on global parameters such as radius, which is rarely used in financial networks literature, and also the diameter and distance parameters. The first two parameters are useful for deducing the force that economic instability should exert to trigger a cascade effect on the network. With these global parameters, we hone the boundaries of the strength that a shock should exert to trigger a cascade effect. In addition, we analysed the homophilic profiles, which is quite new in financial networks literature. We found highly homophilic relationships among companies, considering firms by country and industry. We also calculate the local parameters such as degree, closeness, betweenness, eigenvector, and harmonic centralities to gauge the importance of the companies regarding different aspects, such as the strength of the relationships with their neighbourhood and their location in the network. Finally, we analysed a network substructure by introducing the skeleton concept of a dynamic network. This subnetwork allowed us to study the stability of relations among constituents and detect a significant increase in these stable connections during the Covid-19 pandemic.
    Keywords: Financial Networks, Centralities, Homophily, Multivariate GARCH, Networks Connectivity, Gaussian graphical model, Covid-19
    Date: 2021
  9. By: Julio A. Crego (Tilburg University); Julio Gálvez (Banco de España)
    Abstract: We examine linear correlation and tail dependence between market neutral hedge funds and the market portfolio conditional on the financial cycle. We document that the low correlation between these funds and the S&P 500 consists of a negative correlation during bear periods and a positive one during bull periods. In contrast, the remaining styles present a positive correlation across cycles. We also find that these funds present tail dependence only during bull periods. We study their implications for market timing and risk management.
    Keywords: hedge funds, market neutrality, market timing, tail dependence, risk management
    JEL: G11 G23
    Date: 2021–11
  10. By: William Barcelona; Nathan L. Converse; Anna Wong
    Abstract: This paper demonstrates that the measured stock of China's holding of U.S. assets could be much higher than indicated by the U.S. net international investment position data due to unrecorded historical Chinese inflows into an increasingly popular global safe haven asset: U.S. residential real estate. We first use aggregate capital flows data to show that the increase in unrecorded capital inflows in the U.S. balance of payment accounts over the past decade is mainly linked to inflows from China into U.S. housing markets. Then, using a unique web traffic dataset that provides a direct measure of Chinese demand for U.S. housing at the zip code level, we estimate via a difference-in-difference matching framework that house prices in major U.S. cities that are highly exposed to demand from China have on average grown 7 percentage points faster than similar neighborhoods with low exposure over the period 2010-2016. These average excess price growth gaps co-move closely with macro-level measures of U.S. capital inflows from China, and tend to widen following periods of economic stress in China, suggesting that Chinese households view U.S. housing as a safe haven asset.
    Keywords: China; Housing and real estate; Capital flows; Safe assets
    JEL: F30 F60 R30
    Date: 2021–11–12
  11. By: Ilyas El Ghordaf (UCD, IAE - UCA); Abdelbari El Khamlichi (UCD, IAE - UCA)
    Abstract: There is an important literature focused on profit warnings and its impact on stock returns. We provide evidence from Moroccan stock market which aims to become an African financial hub. Despite this practical improvement, academic researches that focused on this market are scarce and our study is a first investigation in this context. Using the event study methodology and a sample of companies listed in Casablanca Stock Exchange for the period of 2009 to 2016, we examined whether the effect of qualitative warning is more negative compared to quantitative warnings in a short event window. Our empirical findings show that the average abnormal return on the date of announcement is negative and statistically significant. The magnitude of this negative abnormal return is greater for qualitative warnings than quantitative ones.
    Date: 2021–11

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