nep-fmk New Economics Papers
on Financial Markets
Issue of 2019‒09‒16
eleven papers chosen by

  1. Gold, Platinum and the Predictability of Bond Risk Premia By Elie Bouri; Riza Demirer; Rangan Gupta; Mark E. Wohar
  2. Models for expected returns with statistical factors By Cascos Fernández, Ignacio; Grané Chávez, Aurea; Cueto, J.M.
  3. Detecting stock market bubbles based on the cross-sectional dispersion of stock prices By Takayuki Mizuno; Takaaki Ohnishi; Tsutomu Watanabe
  4. Validating Weak-form Market Efficiency in United States Stock Markets with Trend Deterministic Price Data and Machine Learning By Samuel Showalter; Jeffrey Gropp
  5. The U.S. term structure and stock market volatility: Evidence from emerging stock markets By SADETTIN AYDIN YUKSEL; ASLI YUKSEL; RIZA DEMIRER
  6. Emerging Markets and the Conditional CAPM By Ahmed, M. F.; Satchell, S.
  7. An Anomaly in Hong Kong Stock Market By David Chui
  8. Automatic Financial Trading Agent for Low-risk Portfolio Management using Deep Reinforcement Learning By Wonsup Shin; Seok-Jun Bu; Sung-Bae Cho
  9. IPO underpricing phenomenon: the evidence from the Warsaw Stock Exchange By Dorota Podedworna-Tarnowska; Daniel Kaszy?ski
  10. Tehran Stock Exchange Prediction Using Sentiment Analysis of Online Textual Opinions By Arezoo Hatefi Ghahfarrokhi; Mehrnoush Shamsfard
  11. Herding Behaviours in Poland and Tanzania By DORIKA JEREMIAH MWAMTAMBULO

  1. By: Elie Bouri (USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon); Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Mark E. Wohar (College of Business Administration, University of Nebraska at Omaha, 6708 Pine Street, Omaha, NE 68182, USA, and School of Business and Economics, Loughborough University, Leicestershire, LE11 3TU, UK)
    Abstract: We show that the ratio of gold to platinum prices (GP) contains significant predictive information for excess U.S. government bond returns, even after controlling for a large number of financial and macro factors. Including GP in the model improves the predictive accuracy, over and above the standard macroeconomic and financial predictors, at all forecasting horizons for the shortest maturity bonds and at longer forecasting horizons for bonds with longer maturities beyond 2 years. The findings highlight the predictive information captured by commodity prices on bond market excess returns with significant investment and policy making implications.
    Keywords: Bond Premia, Predictability, Gold-Platinum Price Ratio, Out-of-Sample Forecasts
    JEL: C22 C53 G12 G17 Q02
    Date: 2019–08
  2. By: Cascos Fernández, Ignacio; Grané Chávez, Aurea; Cueto, J.M.
    Abstract: In this paper we propose factor-models assembled out of three new factors and evaluate them on European Equities. The new factors are built from statistical measurements on stock prices, in particular, coefficient of variation, skewness and kurtosis. Data come from Reuters, correspond to nearly 2000 EU companies and span from Jan-2008 to Feb-2018. Regarding methodology, we propose a non-parametric resampling procedure that accounts for time dependency in order to test the validity of the model and the significance of the parameters involved. We compare our bootstrap- based inferential results with classical proposals (based on F-statistics). Methods under assessment are Time-series regression, Cross-Sectional regression and the Fama-MacBeth procedure. The main findings indicate that the two factors that better improve the CAPM-model with regard to the adjusted R2 in the time-series regressions are the skewness and the cofficient of variation. For this reason, a model including those two factors together with the market is thoroughly studied.
    Keywords: Time Series; Factor Models; Cross-Sectional Regression; Bootstrap; Asset Pricing
    Date: 2019–09–04
  3. By: Takayuki Mizuno (National Institute of Informatics); Takaaki Ohnishi (Graduate School of Information Science and Technology, The University of Tokyo); Tsutomu Watanabe (Graduate School of Economics, The University of Tokyo)
    Abstract: A statistical method is proposed for detecting stock market bubbles that occur when speculative funds concentrate on a small set of stocks. The bubble is defined by stock price diverging from the fundamentals. A firm’s financial standing is certainly a key fundamental attribute of that firm. The law of one price would dictate that firms of similar financial standing share similar fundamentals.We investigate the variation in market capitalization normalized by fundamentals that is estimated by Lasso regression of a firm’s financial standing. The market capitalization distribution has a substantially heavier upper tail during bubble periods, namely, the market capitalization gap opens up in a small subset of firms with similar fundamentals. This phenomenon suggests that speculative funds concentrate in this subset. We demonstrated that this phenomenon could have been used to detect the dot-com bubble of 1998-2000 in different stock exchanges.
    Date: 2019–09
  4. By: Samuel Showalter; Jeffrey Gropp
    Abstract: The Efficient Market Hypothesis has been a staple of economics research for decades. In particular, weak-form market efficiency -- the notion that past prices cannot predict future performance -- is strongly supported by econometric evidence. In contrast, machine learning algorithms implemented to predict stock price have been touted, to varying degrees, as successful. Moreover, some data scientists boast the ability to garner above-market returns using price data alone. This study endeavors to connect existing econometric research on weak-form efficient markets with data science innovations in algorithmic trading. First, a traditional exploration of stationarity in stock index prices over the past decade is conducted with Augmented Dickey-Fuller and Variance Ratio tests. Then, an algorithmic trading platform is implemented with the use of five machine learning algorithms. Econometric findings identify potential stationarity, hinting technical evaluation may be possible, though algorithmic trading results find little predictive power in any machine learning model, even when using trend-specific metrics. Accounting for transaction costs and risk, no system achieved above-market returns consistently. Our findings reinforce the validity of weak-form market efficiency.
    Date: 2019–09
    Abstract: Decomposing the term structure of U.S. treasury yields into two components, the expectations factor and the maturity premium, we examine whether the U.S. term structure contains predictive information over emerging stock market volatility. Based on data from 20 emerging markets, we provide positive evidence that holds even after controlling for country specific factors including turnover and market size. Our findings indicate the market?s expectation of future short term rates, implied by the expectations factor, serves as a stronger predictor of stock market volatility compared to the maturity premium component of the yield spread. Moreover, the predictive power of the U.S. term structure increases following the global financial crisis.
    Keywords: Term structure of interest rates, Stock market volatility, Expectations factor, Maturity premium.
    JEL: G14 G15
    Date: 2019–07
  6. By: Ahmed, M. F.; Satchell, S.
    Abstract: Emerging Market equity returns have proved challenging to model using conventional statistical tools. In this paper we use the conditional capital asset pricing model (CCAPM) together with an explicit expectations structure to arrive at a framework which can be easily estimated. We take the perspective that US equity corresponds to the market and that our investors are US dollar investors and use this approach to explain emerging market country index equity returns. Different choices of US equity index provide, unsurprisingly, different results. A noteworthy finding is that the Russell 2000 seems a better explanatory variable than the Russell 1000 suggesting that it is the small to medium capitalised US companies that help us understand emerging market returns.
    Keywords: Emerging Market Equities, conditional CAPM, asset pricing
    JEL: C22 G11 G15
    Date: 2019–09–02
  7. By: David Chui (The Hang Seng University of Hong Kong)
    Abstract: The Eastern market wisdom of ?May is poor, June is bleak, and July will turn around? unveils an international stock markets dynamic that lower returns in May followed by even worse return in June but rebounding back to an upward trend in July. This wisdom is termed as ?Eastern Halloween? effect in this paper which has some similarities with the traditional Halloween Effect but differing in duration and timing. This paper examines the Eastern Halloween effect on Hong Kong stock market and the results show that May and June period returns are superior than the returns on the rest of the calendar months in Hong Kong Stock Market.
    Keywords: EMH, Stock Market Anomaly, Empirical, Eastern Halloween
    JEL: G14 G31 G02
    Date: 2019–06
  8. By: Wonsup Shin; Seok-Jun Bu; Sung-Bae Cho
    Abstract: The autonomous trading agent is one of the most actively studied areas of artificial intelligence to solve the capital market portfolio management problem. The two primary goals of the portfolio management problem are maximizing profit and restrainting risk. However, most approaches to this problem solely take account of maximizing returns. Therefore, this paper proposes a deep reinforcement learning based trading agent that can manage the portfolio considering not only profit maximization but also risk restraint. We also propose a new target policy to allow the trading agent to learn to prefer low-risk actions. The new target policy can be reflected in the update by adjusting the greediness for the optimal action through the hyper parameter. The proposed trading agent verifies the performance through the data of the cryptocurrency market. The Cryptocurrency market is the best test-ground for testing our trading agents because of the huge amount of data accumulated every minute and the market volatility is extremely large. As a experimental result, during the test period, our agents achieved a return of 1800% and provided the least risky investment strategy among the existing methods. And, another experiment shows that the agent can maintain robust generalized performance even if market volatility is large or training period is short.
    Date: 2019–09
  9. By: Dorota Podedworna-Tarnowska (SGH Warsaw School of Economics); Daniel Kaszy?ski (SGH Warsaw School of Economics)
    Abstract: The existence of underpricing effect in IPO has been investigated by a several studies conducted on the basis of stock exchanges in numerous countries. This phenomenon has been explained in the literature with the help of agency theory, signaling, cascading, behavioral theories among others. Numerous exogenous and endogenous factors of IPO underpricing has been identified in several empirical researches. The influence of these various determinants mostly depends upon different level of the capital market development, different structures of the markets, countries? specific regulation. The aim of this article is to present and investigate the degree of underpricing depending on the form of IPO: an issue of new shares in the shape of a public subscription, a sale of existing shares in the shape of a public subscription, a combination of both previous variants or an introducing shares into trading without sale offering. The research is based on the historical data available from the Warsaw Stock Exchange. The analysis is conducted among IPOs that took place over the period 2005-2018. The numerical results indicate the differential effect on the degree of underpricing effect in IPO resulting from various forms of IPO.
    Keywords: IPO, underpricing, public subscription, share sale, share issue, listing
    JEL: G11 G23 G32
    Date: 2019–06
  10. By: Arezoo Hatefi Ghahfarrokhi; Mehrnoush Shamsfard
    Abstract: In this paper, we investigate the impact of the social media data in predicting the Tehran Stock Exchange (TSE) variables for the first time. We consider the closing price and daily return of three different stocks for this investigation. We collected our social media data from for about three months. To extract information from online comments, we propose a hybrid sentiment analysis approach that combines lexicon-based and learning-based methods. Since lexicons that are available for the Persian language are not practical for sentiment analysis in the stock market domain, we built a particular sentiment lexicon for this domain. After designing and calculating daily sentiment indices using the sentiment of the comments, we examine their impact on the baseline models that only use historical market data and propose new predictor models using multi regression analysis. In addition to the sentiments, we also examine the comments volume and the users' reliabilities. We conclude that the predictability of various stocks in TSE is different depending on their attributes. Moreover, we indicate that for predicting the closing price only comments volume and for predicting the daily return both the volume and the sentiment of the comments could be useful. We demonstrate that Users' Trust coefficients have different behaviors toward the three stocks.
    Date: 2019–08
  11. By: DORIKA JEREMIAH MWAMTAMBULO (Wroclaw University of Economics)
    Abstract: Over the years the USA markets have shown a strong resilient to herding behaviours while mixed results or consistent herding behaviours have been observed in other markets around the world. This study aimed at providing the most recent evidence of herding behaviours in two of such markets. Using data from Poland and Tanzania and CSAD approach, the findings showed no significant market herding behaviours in Poland during the period and during the up and down markets. Except for Informatics, all other industries showed significant industry herding behaviours and during the up and down markets. On the other hand, no market herding behaviours were observed in Tanzania during the period, during the up and down markets. No industrial herding behaviours were observed in the two industries. During this period Poland had experienced an increase in industry herding behaviours while Tanzania have experienced a decline in market herding behaviours in the market.
    Keywords: Herding Behaviours, Behavioural Finance, Up and Down Markets, Trade Volume, Industry Portfolios, Poland Stock Market, Tanzania Stock Market
    JEL: G02 G10 G12
    Date: 2019–06

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