nep-fmk New Economics Papers
on Financial Markets
Issue of 2018‒11‒19
nine papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Macroeconomics determinants of the correlation between stocks and bonds By Marcello Pericoli
  2. Do Survey Expectations of Stock Returns Reflect Risk-Adjustments? By Adam, Klaus; Maatvev, Dmitry; Nagel, Stefan
  3. Long Run Returns Predictability and Volatility with Moving Averages By Chang, C-L.; Ilomäki, J.; Laurila, H.; McAleer, M.J.
  4. Using Stock Prices as Ground Truth in Sentiment Analysis to Generate Profitable Trading Signals By Ellie Birbeck; Dave Cliff
  5. Volatility Spillovers and Systemic Risk Across Economies: Evidence from a Global Semi-Structural Model By Javier G. Gómez-Pineda
  6. Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market By Arthur le Calvez; Dave Cliff
  7. Reframing the S\&P500 Network of Stocks along the \nth{21} Century By Tanya Ara\'ujo; Maximilian G\"obel
  8. A Temporal Analysis of Intraday Volatility of Nifty Futures on the National Stock Exchange By Singh, Ritvik; Gangwar, Rachna

  1. By: Marcello Pericoli (Bank of Italy)
    Abstract: We analyze the correlation between the stock and bond markets in Germany and the US. We use a standard no-arbitrage affine model to decompose the correlation between these two assets into its main drivers. The correlation between bond yields and stock returns is a key determinant of asset allocation. Our results show that the correlation is primarily influenced by the uncertainty about inflation and real interest rates as well as by co-movement between inflation, real interest rates and dividend growth. Shocks to inflation, real interest rates and dividend growth can explain the correlation’s temporary deviation from its long-term dynamics.
    Keywords: bond market, stock market, macroeconomic shocks, money illusion
    JEL: C32 E43 G12
    Date: 2018–11
  2. By: Adam, Klaus; Maatvev, Dmitry; Nagel, Stefan
    Abstract: Motivated by the observation that survey expectations of stock returns are inconsistent with rational return expectations under real-world probabilities, we investigate whether alternative expectations hypotheses entertained in the asset pricing literature are consistent with the survey evidence. We empirically test (1) the notion that survey forecasts constitute rational but risk-neutral forecasts of future returns, and (2) the notion that survey forecasts are ambiguity averse/robust forecasts of future returns. We find that these alternative hypotheses are also strongly rejected by the data, albeit for different reasons. Hypothesis (1) is rejected because survey return forecasts are not in line with risk-free interest rates and because survey expected excess returns are predictable. Hypothesis (2) is rejected because agents are not always pessimistic about future returns, instead often display overly optimistic return expectations. We speculate as to what kind of expectations theories might be consistent with the available survey evidence.
    Date: 2018–10
  3. By: Chang, C-L.; Ilomäki, J.; Laurila, H.; McAleer, M.J.
    Abstract: The paper examines how the size of the rolling window, and the frequency used in moving average (MA) trading strategies, affect financial performance when risk is measured. We use the MA rule for market timing, that is, for when to buy stocks and when to shift to the risk-free rate. The important issue regarding the predictability of returns is assessed. It is found that performance improves, on average, when the rolling window is expanded and the data frequency is low. However, when the size of the rolling window reaches three years, the frequency loses its significance and all frequencies considered produce similar financial performance. Therefore, the results support stock returns predictability in the long run. The procedure takes account of the issues of variable persistence as we use only returns in the analysis. Therefore, we use the performance of MA rules as an instrument for testing returns predictability in financial stock markets.
    Keywords: Trading strategies, Risk, Moving average, Market timing, Returns predictability, Volatility, Rolling window, Data frequency
    JEL: C22 C32 C58 G32
    Date: 2018–09–01
  4. By: Ellie Birbeck; Dave Cliff
    Abstract: The increasing availability of "big" (large volume) social media data has motivated a great deal of research in applying sentiment analysis to predict the movement of prices within financial markets. Previous work in this field investigates how the true sentiment of text (i.e. positive or negative opinions) can be used for financial predictions, based on the assumption that sentiments expressed online are representative of the true market sentiment. Here we consider the converse idea, that using the stock price as the ground-truth in the system may be a better indication of sentiment. Tweets are labelled as Buy or Sell dependent on whether the stock price discussed rose or fell over the following hour, and from this, stock-specific dictionaries are built for individual companies. A Bayesian classifier is used to generate stock predictions, which are input to an automated trading algorithm. Placing 468 trades over a 1 month period yields a return rate of 5.18%, which annualises to approximately 83% per annum. This approach performs significantly better than random chance and outperforms two baseline sentiment analysis methods tested.
    Date: 2018–11
  5. By: Javier G. Gómez-Pineda (Banco de la República (the central bank of Colombia))
    Abstract: The paper presents some evidence on the overwhelming relevance of systemic risk and the lesser importance of US interest rates in the global transmission of shocks. This evidence suggests that the literature could benefit from incorporating global confidence variables into global frameworks in the study of the global transmission of shocks. As framework, we used a global semi-structural model (GSSM) augmented with common factors for country risk and country credit. We approximated country risk with historical stock volatility, a measure that is uniform and available across countries; in addition, we measured spillovers as the share of forecast error variance explained by different volatility factors. We found that systemic risk is the main volatility factor in all systemic economies, and also accounts for the bulk of spillovers into non systemic economies. Other volatility factors such as global credit, foreign interest rates and trade-related factors rarely accounted for shares of forecast error variance above one percent.
    Keywords: Spillovers, Systemic risk, Systemic Economies, Global semi structural model
    JEL: E58 E37 E43 Q43
    Date: 2018–11–01
  6. By: Arthur le Calvez; Dave Cliff
    Abstract: We report successful results from using deep learning neural networks (DLNNs) to learn, purely by observation, the behavior of profitable traders in an electronic market closely modelled on the limit-order-book (LOB) market mechanisms that are commonly found in the real-world global financial markets for equities (stocks & shares), currencies, bonds, commodities, and derivatives. Successful real human traders, and advanced automated algorithmic trading systems, learn from experience and adapt over time as market conditions change; our DLNN learns to copy this adaptive trading behavior. A novel aspect of our work is that we do not involve the conventional approach of attempting to predict time-series of prices of tradeable securities. Instead, we collect large volumes of training data by observing only the quotes issued by a successful sales-trader in the market, details of the orders that trader is executing, and the data available on the LOB (as would usually be provided by a centralized exchange) over the period that the trader is active. In this paper we demonstrate that suitably configured DLNNs can learn to replicate the trading behavior of a successful adaptive automated trader, an algorithmic system previously demonstrated to outperform human traders. We also demonstrate that DLNNs can learn to perform better (i.e., more profitably) than the trader that provided the training data. We believe that this is the first ever demonstration that DLNNs can successfully replicate a human-like, or super-human, adaptive trader operating in a realistic emulation of a real-world financial market. Our results can be considered as proof-of-concept that a DLNN could, in principle, observe the actions of a human trader in a real financial market and over time learn to trade equally as well as that human trader, and possibly better.
    Date: 2018–11
  7. By: Tanya Ara\'ujo; Maximilian G\"obel
    Abstract: Since the beginning of the new millennium, stock markets went through every state from long-time troughs, trade suspensions to all-time highs. The literature on asset pricing hence assumes random processes to be underlying the movement of stock returns. Observed procyclicality and time-varying correlation of stock returns tried to give the apparently random behavior some sort of structure. However, common misperceptions about the co-movement of asset prices in the years preceding the \emph{Great Recession}, is said to have even fueled the crisis' economic impact. Here we show how a varying macroeconomic environment influences stocks' clustering into communities. From a sample of 296 stocks of the S\&P 500 index, the periods 2000 to 2007 and 2007 through 2009 are used to develop networks of stocks. The Minimal Spanning Tree analysis of those time-varying networks of stocks demonstrates that the crisis drove the market from a star-like random network in the pre-crisis period, to a highly clustered community structure during the \emph{Great Recession}. A comparison with the \textit{General Industry Classification Standard} conveys the impression that the crisis, besides its devestating macroeconomic effect, helped to restore the stock market's ceased order of the pre-crisis era.
    Date: 2018–11
  8. 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: 2018–09–18
  9. By: M. Hakan Eratalay; Evgenii V. Vladimirov
    Abstract: In this article we use partial correlations to derive bidirectional connections between major firms listed in the Moscow Stock Exchange. We obtain coefficients of partial correlation from the correlation estimates of the Constant Conditional Correlation GARCH (CCC-GARCH) and the consistent Dynamic Conditional Correlation GARCH (cDCC-GARCH) models. We map the graph of partial correlations using the Gaussian Graphical Model and apply network analysis to identify the most central firms in terms of both shock propagation and connectedness with others. Moreover, we analyze some network characteristics over time and based on these we construct a measure of system vulnerability to external shocks. Our findings suggest that during the crisis interconnectedness between firms strengthens and becomes polarized and the system becomes more vulnerable to systemic shocks. In addition, we found that the most connected firms are the state-owned firms Sberbank and Gazprom and the private oil company Lukoil, while in the top most central in terms of systemic risk contributors Sberbank gave its place to NLMK Group.
    Keywords: Multivariate GARCH, Volatility Spillovers, Network connections, MICEX
    JEL: C01 C13 C32 C52
    Date: 2018

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