nep-fmk New Economics Papers
on Financial Markets
Issue of 2018‒11‒05
eight papers chosen by

  1. Using Deep Learning for price prediction by exploiting stationary limit order book features By Avraam Tsantekidis; Nikolaos Passalis; Anastasios Tefas; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis
  2. Mutual Fund Flows and Seasonalities in Stock Returns By Moritz Wagner; John Byong-Tek Lee; Dimitris Margaritis
  3. Measurement of Volatility Spillovers and Asymmetric Connectedness on Commodity and Equity Markets By Tereza Palanska
  4. The pricing of FX forward contracts: Micro evidence from banks' dollar hedging By Abbassi, Puriya; Bräuning, Falk
  5. Post-FOMC Announcement Drift in U.S. Bond Markets By Jordan Brooks; Michael Katz; Hanno Lustig
  6. Price Discovery and the Accuracy of Consolidated Data Feeds in the U.S. Equity Markets By Brian F. Tivnan; David Slater; James R. Thompson; Tobin A. Bergen-Hill; Carl D. Burke; Shaun M. Brady; Matthew T. K. Koehler; Matthew T. McMahon; Brendan F. Tivnan; Jason Veneman
  7. A Temporal Analysis of Intraday Volatility of Nifty Futures on the National Stock Exchange By Singh, Ritvik; Gangwar, Rachna
  8. Herding behavior of Dutch pension funds in asset class investments By I. Koetsier; J.A. Bikker

  1. By: Avraam Tsantekidis; Nikolaos Passalis; Anastasios Tefas; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis
    Abstract: The recent surge in Deep Learning (DL) research of the past decade has successfully provided solutions to many difficult problems. The field of quantitative analysis has been slowly adapting the new methods to its problems, but due to problems such as the non-stationary nature of financial data, significant challenges must be overcome before DL is fully utilized. In this work a new method to construct stationary features, that allows DL models to be applied effectively, is proposed. These features are thoroughly tested on the task of predicting mid price movements of the Limit Order Book. Several DL models are evaluated, such as recurrent Long Short Term Memory (LSTM) networks and Convolutional Neural Networks (CNN). Finally a novel model that combines the ability of CNNs to extract useful features and the ability of LSTMs' to analyze time series, is proposed and evaluated. The combined model is able to outperform the individual LSTM and CNN models in the prediction horizons that are tested.
    Date: 2018–10
  2. By: Moritz Wagner (University of Canterbury); John Byong-Tek Lee; Dimitris Margaritis
    Abstract: We propose a flow-based explanation for two long-standing anomalies in empirical finance – the Sell in May effect and the January effect. We find that the aggregate mutual fund flows exhibit similar seasonal patterns as stock returns. The Sell in May effect becomes insignificant in standard statistical tests after controlling for the impact of mutual fund flows on returns, with flow explaining about 54% of the variation in excess returns over the winter months. We also find that flow helps explaining the abnormally high returns of small-cap stocks in January. The Sell in May and January effects appear to be primarily a retail money effect. Similarly, the well-known co-movement between flow and market return is only present in retail fund flow. Overall, the evidence suggests that unanticipated rather than expected flow drives our results.
    Keywords: Mutual funds, Fund flows, Return seasonality
    JEL: G12 G14 G23
    Date: 2018–10–01
  3. By: Tereza Palanska (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic)
    Abstract: We study volatility spillovers among commodity and equity markets by employing a recently developed approach based on realized measures and forecast error variance decomposition invariant to the variable ordering from vector-autoregressions. This enables us to measure total, directional and net volatility spillovers as well as the asymmetry of responses to positive and negative shocks. We exploit high-frequency data on the prices of Crude oil, Corn, Cotton and Gold futures, and the S&P 500 Index and use a sample which spans from January 2002 to December 2015 to cover the entire period around the global financial crisis of 2008. Our empirical analysis reveals that on average, the volatility shocks related to other markets account for around one fifth of the volatility forecast error variance. We find that shocks to the stock markets play the most important role as the S&P 500 Index dominates all commodities in terms of general volatility spillover transmission. Our results further suggest that volatility spillovers across the analyzed assets were rather limited before the global financial crisis, which then boosted the connectedness between commodity and stock markets. Furthermore, the volatility due to positive and negative shocks is transmitted between markets at different magnitudes and the prevailing effect has varied. In the pre-crisis period, the positive spillovers dominated the negative ones, however, in several years following the crisis, the negative shocks have had a significantly higher impact on the volatility spillovers across the markets, pointing to an overall increase in uncertainty in the commodity and equity markets following a major crisis. In recent years, the asymmetric measures seem to have returned to their pre-crises directions and magnitudes.
    Keywords: Volatility, Spillovers, Relized Semivariance, Asymmetric effects, Commodity markets, Equity markets
    JEL: C18 C58 G01 G15 Q02
    Date: 2018–10
  4. By: Abbassi, Puriya; Bräuning, Falk
    Abstract: Using transaction-level data on foreign exchange (FX) forward contracts, we document large demand-driven heterogeneity in banks' dollar hedging costs. For identification, we exploit regulatory end-of-quarter reporting that penalizes banks' currency exposure with capital surcharges. Contracts that reduce quarter-end currency exposure trade at higher prices, specifically for banks with high dollar funding gaps and high leverage, while access to internal dollar capital markets and bargaining power reduces prices. Spreads between similar contracts with and without initial margin widen with leverage. Our results suggest that banks' shadow costs of capital are important for the international propagation of shocks through FX derivatives markets.
    Keywords: FX markets,hedging,price determination,global banks,international finance
    JEL: D40 E43 F30 F31 G15
    Date: 2018
  5. By: Jordan Brooks; Michael Katz; Hanno Lustig
    Abstract: The sensitivity of long-term rates to short-term rates represents a puzzle for standard macro-finance models. Post-FOMC announcement drift in Treasury markets after Fed Funds target changes contributes to the excess sensitivity of long rates. Mutual fund investors respond to the salience of Fed Funds target rate increases by selling short and intermediate duration bond funds, thus gradually increasing the effective supply to be absorbed by arbitrageurs. Using FOMC-induced variation in bond fund flows, we estimate short-run demand for Treasurys to be inelastic, especially for longer maturities. The gradual increase in supply, combined with the low demand elasticity, generate post-announcement drift in Treasurys, which spills over to other bond markets. Our findings shed new light on the causes of time-series-momentum in Treasury markets.
    JEL: E43 G12
    Date: 2018–10
  6. By: Brian F. Tivnan; David Slater; James R. Thompson; Tobin A. Bergen-Hill; Carl D. Burke; Shaun M. Brady; Matthew T. K. Koehler; Matthew T. McMahon; Brendan F. Tivnan; Jason Veneman
    Abstract: Both the scientific community and the popular press have paid much attention to the speed of the Securities Information Processor, the data feed consolidating all trades and quotes across the US stock market. Rather than the speed of the Securities Information Processor, or SIP, we focus here on its accuracy. Relying on Trade and Quote data, we provide various measures of SIP latency relative to high-speed data feeds between exchanges, known as direct feeds. We use first differences to highlight not only the divergence between the direct feeds and the SIP, but also the fundamental inaccuracy of the SIP. We find that as many as 60 percent or more of trades are reported out of sequence for stocks with high trade volume, therefore skewing simple measures such as returns. While not yet definitive, this analysis supports our preliminary conclusion that the underlying infrastructure of the SIP is currently unable to keep pace with the trading activity in today's stock market.
    Date: 2018–10
  7. By: Singh, Ritvik; Gangwar, Rachna
    Abstract: This paper aims to establish trends in intraday volatility in context of the Indian stock market and analyze the impact of development in the Indian economy on its stock market volatility. One minute tick data of Nifty 50 futures from Jan 1, 2011 to Aug 31, 2018 was used for the purpose of this research. Volatility was computed for each day of week and various time intervals. Our analysis shows evidence of the expected U-shaped pattern of intraday volatility (higher at the beginning and end of the day). We also observed a decline in the hourly volatility over the time period studied. However, sufficient evidence to determine the impact of development in the Indian economy on volatility in the stock market was not found.
    Keywords: Risk Analysis,Intraday Volatility,National Stock Exchange of India,Nifty Futures,Temporal Analysis
    JEL: G10 G13 G15
    Date: 2018
  8. By: I. Koetsier; J.A. Bikker
    Abstract: This study investigates asset herd behavior for Dutch pension funds from 1999 to 2014 using quarterly data. We find considerable asset class herd behavior, which is more intensive for the more ‘exotic’ sub-asset classes, such as private equity and emerging market shares. We find higher buy herd behavior in sub-asset class markets, which are affected by the stock market and debt crises. The extent of pension fund’s herd behavior is affected by financial market, macroeconomics circumstances and returns. We find destabilizing effects of herd behavior for shares and private equity on the sell side, for fixed-interest investments on the buy side and for real estate on both the buy and sell side. We find stabilizing effects of herd behavior for shares and private equity on the buy side, for fixed interest investments on the sell side and for other investments on both the buy and the sell side. For crises, we find evidence that destabilizing behavior is concentrated on the buy side, whereas sell herd behavior mostly has a stabilizing effect.
    Keywords: Herd behaviour, (de)stabilising, pension funds, asset classes
    Date: 2018

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