nep-fmk New Economics Papers
on Financial Markets
Issue of 2021‒07‒26
twelve papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Analyzing stock market signals for H1N1 and COVID-19: The BRIC case. By Sepúlveda Velásquez, Jorge; Tapia Griñen, Pablo; Pastén Henríquez, Boris
  2. Pricing Without Mispricing By Jianan Liu; Tobias J. Moskowitz; Robert F. Stambaugh
  3. A Unified Formula of the Optimal Portfolio for Piecewise HARA Utilities By Zongxia Liang; Yang Liu; Ming Ma
  4. Predicting Risk-adjusted Returns using an Asset Independent Regime-switching Model By Nicklas Werge
  5. Dynamic Spending Responses to Wealth Shocks: Evidence from Quasi-Lotteries on the Stock Market By Asger Lau Andersen; Niels Johannesen; Adam Sheridan
  6. The Role of Binance in Bitcoin Volatility Transmission By Carol Alexander; Daniel Heck; Andreas Kaeck
  7. Effectiveness of Artificial Intelligence in Stock Market Prediction based on Machine Learning By Sohrab Mokhtari; Kang K. Yen; Jin Liu
  8. Stock price prediction using BERT and GAN By Priyank Sonkiya; Vikas Bajpai; Anukriti Bansal
  9. Time Varying Risk in U.S. Housing Sector and Real Estate Investment Trusts Equity Return By Masud Alam
  10. Do CDS maturities matter in the evaluation of the information content of regulatory banking stress tests? Evidence from European and US stress tests By Amavi Agbodji; Emmanuelle Nys; Alain Sauviat
  11. Emotions in Macroeconomic News and their Impact on the European Bond Market By Sergio Consoli; Luca Tiozzo Pezzoli; Elisa Tosetti
  12. Application of deep reinforcement learning for Indian stock trading automation By Supriya Bajpai

  1. By: Sepúlveda Velásquez, Jorge; Tapia Griñen, Pablo; Pastén Henríquez, Boris
    Abstract: In this study, stock performance of the BRIC countries is examined and compared with regards to the announcement of the H1N1 and COVID-19 pandemics. With the use of the event study methodology, we have found evidence that the reactions of these stock markets when faced with these pandemics are diverse, even though they belong to the same group. Apparently, the assimilation of previous experiences improves the actions of the financial market, by reducing the duration and magnitude of the drop in returns when faced with these events, and promoting the semi-strong form of the Efficient Market Hypothesis (EMH).
    Keywords: COVID-19, H1N1, BRIC, event study, pandemic.
    JEL: G14 G15
    Date: 2021–06–20
  2. By: Jianan Liu; Tobias J. Moskowitz; Robert F. Stambaugh
    Abstract: We offer a novel test of whether an asset pricing model describes expected returns in the absence of mispricing. Our test assumes such a model assigns zero alpha to investment strategies using decade-old information. The CAPM satisfies this condition, but prominent multifactor models do not. While multifactor betas help capture current expected returns on mispriced stocks, persistence in those betas distorts the stocks' implied expected returns after prices correct. These results are most evident in large-cap stocks, whose multifactor betas are the most persistent. Hence, prominent multifactor models distort expected returns, absent mispricing, for even the largest, most liquid stocks.
    JEL: G12 G14 G40
    Date: 2021–07
  3. By: Zongxia Liang; Yang Liu; Ming Ma
    Abstract: We propose a general family of piecewise hyperbolic absolute risk aversion (PHARA) utility, including many non-standard utilities as examples. A typical application is the composition of an HARA preference and a piecewise linear payoff in hedge fund management. We derive a unified closed-form formula of the optimal portfolio, which is a four-term division. The formula has clear economic meanings, reflecting the behavior of risk aversion, risk seeking, loss aversion and first-order risk aversion. One main finding is that risk-taking behaviors are greatly increased by non-concavity and reduced by non-differentiability.
    Date: 2021–07
  4. By: Nicklas Werge
    Abstract: Financial markets tend to switch between various market regimes over time, making stationarity-based models unsustainable. We construct a regime-switching model independent of asset classes for risk-adjusted return predictions based on hidden Markov models. This framework can distinguish between market regimes in a wide range of financial markets such as the commodity, currency, stock, and fixed income market. The proposed method employs sticky features that directly affect the regime stickiness and thereby changing turnover levels. An investigation of our metric for risk-adjusted return predictions is conducted by analyzing daily financial market changes for almost twenty years. Empirical demonstrations of out-of-sample observations obtain an accurate detection of bull, bear, and high volatility periods, improving risk-adjusted returns while keeping a preferable turnover level.
    Date: 2021–07
  5. By: Asger Lau Andersen; Niels Johannesen; Adam Sheridan
    Abstract: How much and over what horizon do households adjust their consumption in response to stock market wealth shocks? We address these questions using granular data on spending and stock portfolios from a large bank and exploiting lottery-like variation in gains across investors with similar portfolio characteristics. Consistent with the permanent income hypothesis, spending responses to stock market gains are immediate and persistent. The responses cumulate to a marginal propensity to consume of around 4% over a one-year horizon. The estimates differ substantially by household liquidity, but not by financial attention, as measured by the frequency of account logins.
    Keywords: wealth shocks, household consumption, marginal propensity to consume, permanent income hypothesis
    Date: 2021
  6. By: Carol Alexander; Daniel Heck; Andreas Kaeck
    Abstract: We analyse high-frequency realised volatility dynamics and spillovers in the bitcoin market, focusing on two pairs: bitcoin against the US dollar (the main fiat-crypto pair) and trading bitcoin against tether (the main crypto-crypto pair). We find that the tether-margined perpetual contract on Binance is clearly the main source of volatility, continuously transmitting strong flows to all other instruments and receiving only a little volatility. Moreover, we find that (i) during US trading hours, traders pay more attention and are more reactive to prevailing market conditions when updating their expectations and (ii) the crypto market exhibits a higher interconnectedness when traditional Western stock markets are open. Our results highlight that regulators should not only consider spot exchanges offering bitcoin-fiat trading but also the tether-margined derivatives products available on most unregulated exchanges, most importantly Binance.
    Date: 2021–07
  7. By: Sohrab Mokhtari; Kang K. Yen; Jin Liu
    Abstract: This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms are applied to classify the public sentiment based on news and social media. In the technical analysis, the historical price data is exploited from Yahoo Finance, and in fundamental analysis, public tweets on Twitter associated with the stock market are investigated to assess the impact of sentiments on the stock market's forecast. The results show a median performance, implying that with the current technology of AI, it is too soon to claim AI can beat the stock markets.
    Date: 2021–06
  8. By: Priyank Sonkiya; Vikas Bajpai; Anukriti Bansal
    Abstract: The stock market has been a popular topic of interest in the recent past. The growth in the inflation rate has compelled people to invest in the stock and commodity markets and other areas rather than saving. Further, the ability of Deep Learning models to make predictions on the time series data has been proven time and again. Technical analysis on the stock market with the help of technical indicators has been the most common practice among traders and investors. One more aspect is the sentiment analysis - the emotion of the investors that shows the willingness to invest. A variety of techniques have been used by people around the globe involving basic Machine Learning and Neural Networks. Ranging from the basic linear regression to the advanced neural networks people have experimented with all possible techniques to predict the stock market. It's evident from recent events how news and headlines affect the stock markets and cryptocurrencies. This paper proposes an ensemble of state-of-the-art methods for predicting stock prices. Firstly sentiment analysis of the news and the headlines for the company Apple Inc, listed on the NASDAQ is performed using a version of BERT, which is a pre-trained transformer model by Google for Natural Language Processing (NLP). Afterward, a Generative Adversarial Network (GAN) predicts the stock price for Apple Inc using the technical indicators, stock indexes of various countries, some commodities, and historical prices along with the sentiment scores. Comparison is done with baseline models like - Long Short Term Memory (LSTM), Gated Recurrent Units (GRU), vanilla GAN, and Auto-Regressive Integrated Moving Average (ARIMA) model.
    Date: 2021–07
  9. By: Masud Alam
    Abstract: This study examines how housing sector volatilities affect real estate investment trust (REIT) equity return in the United States. I argue that unexpected changes in housing variables can be a source of aggregate housing risk, and the first principal component extracted from the volatilities of U.S. housing variables can predict the expected REIT equity returns. I propose and construct a factor-based housing risk index as an additional factor in asset price models that uses the time-varying conditional volatility of housing variables within the U.S. housing sector. The findings show that the proposed housing risk index is economically and theoretically consistent with the risk-return relationship of the conditional Intertemporal Capital Asset Pricing Model (ICAPM) of Merton (1973), which predicts an average maximum of 5.6 percent of risk premium in REIT equity return. In subsample analyses, the positive relationship is not affected by sample periods' choice but shows higher housing risk beta values for the 2009-18 sample period. The relationship remains significant after controlling for VIX, Fama-French three factors, and a broad set of macroeconomic and financial variables. Moreover, the proposed housing beta also accurately forecasts U.S. macroeconomic and financial conditions.
    Date: 2021–07
  10. By: Amavi Agbodji (LAPE - Laboratoire d'Analyse et de Prospective Economique - GIO - Gouvernance des Institutions et des Organisations - UNILIM - Université de Limoges); Emmanuelle Nys (LAPE - Laboratoire d'Analyse et de Prospective Economique - GIO - Gouvernance des Institutions et des Organisations - UNILIM - Université de Limoges); Alain Sauviat (LAPE - Laboratoire d'Analyse et de Prospective Economique - GIO - Gouvernance des Institutions et des Organisations - UNILIM - Université de Limoges)
    Abstract: This paper questions the relevance of using only the 5-year maturity CDS spreads to examine the CDS market response to the disclosure of a regulatory stress test results. Since the stress testing exercises are performed on short-term forward-looking stressed scenarios (1 to 3 years), we assume that short-term CDS maturities (from 6-month to 3-year) should better reflect the CDS market response compared to the 5-year maturity. Based on ten regulatory stress tests carried out in Europe and in the US in the time period from 2009 to 2017, we analyze the CDS market response by investigating its reaction through all the different CDS maturities. Our results show that after the results' disclosure, the CDS market reacts by correcting the CDS spreads of tested banks (upward or downward correction), at the level of all maturities. More precisely, we evidence that for a given stress test, the nature of the correction (upward or downward) is the same for all CDS maturities while the extent of the correction differs between shortterm maturities (from 6-month to 3-year) and the 5-year maturity or more. Indeed, we find that the extent is higher on short-term maturities and in most cases, the lower the maturity of the CDS, the higher the extent of the correction (i.e. the stronger the market reaction). We therefore argue that the only use of the 5-year maturity is not suitable. Short-term CDS maturities matter since they better reflect the CDS market response. Also, the use of these short-term maturities show that the information content of the different stress tests is more diverse than what is highlighted in the existing literature.
    Keywords: Regulatory stress tests,CDS maturities,Market reaction,Event study,Disclosure
    Date: 2021–06–22
  11. By: Sergio Consoli; Luca Tiozzo Pezzoli; Elisa Tosetti
    Abstract: We show how emotions extracted from macroeconomic news can be used to explain and forecast future behaviour of sovereign bond yield spreads in Italy and Spain. We use a big, open-source, database known as Global Database of Events, Language and Tone to construct emotion indicators of bond market affective states. We find that negative emotions extracted from news improve the forecasting power of government yield spread models during distressed periods even after controlling for the number of negative words present in the text. In addition, stronger negative emotions, such as panic, reveal useful information for predicting changes in spread at the short-term horizon, while milder emotions, such as distress, are useful at longer time horizons. Emotions generated by the Italian political turmoil propagate to the Spanish news affecting this neighbourhood market.
    Date: 2021–06
  12. By: Supriya Bajpai
    Abstract: In stock trading, feature extraction and trading strategy design are the two important tasks to achieve long-term benefits using machine learning techniques. Several methods have been proposed to design trading strategy by acquiring trading signals to maximize the rewards. In the present paper the theory of deep reinforcement learning is applied for stock trading strategy and investment decisions to Indian markets. The experiments are performed systematically with three classical Deep Reinforcement Learning models Deep Q-Network, Double Deep Q-Network and Dueling Double Deep Q-Network on ten Indian stock datasets. The performance of the models are evaluated and comparison is made.
    Date: 2021–05

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