nep-fmk New Economics Papers
on Financial Markets
Issue of 2021‒06‒21
ten papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. The Global Determinants of International Equity Risk Premiums By Juan M. Londono; Nancy R. Xu
  2. Persistence in ESG and Conventional Stock Market Indices By Guglielmo Maria Caporale; Luis A. Gil-Alana; Alex Plastun; Inna Makarenko
  3. Price graphs: Utilizing the structural information of financial time series for stock prediction By Junran Wu; Ke Xu; Xueyuan Chen; Shangzhe Li; Jichang Zhao
  4. What Data Augmentation Do We Need for Deep-Learning-Based Finance? By Liu Ziyin; Kentaro Minami; Kentaro Imajo
  5. Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review By Tidor-Vlad Pricope
  6. Deep reinforcement learning on a multi-asset environment for trading By Ali Hirsa; Joerg Osterrieder; Branka Hadji-Misheva; Jan-Alexander Posth
  7. Adaptive Market Hypothesis: A Comparison of Islamic and Conventional Stock Indices By Akbar, Muhammad; Ali, Shahid; Ullah, Ihsan; Rehman, Naser
  8. Volatility Modeling of Stocks from Selected Sectors of the Indian Economy Using GARCH By Jaydip Sen; Sidra Mehtab; Abhishek Dutta
  9. Measuring Market Power in the IPO Underwriter By Yue Cai
  10. Commodity Prices and Forecastability of South African Stock Returns Over a Century: Sentiments versus Fundamentals By Afees A. Salisu; Rangan Gupta

  1. By: Juan M. Londono; Nancy R. Xu
    Abstract: We examine the commonality in international equity risk premiums by linking empirical evidence for the international stock return predictability of US downside and upside variance risk premiums (DVP and UVP, respectively) with implications from an international asset pricing framework, which takes the perspective of a US/global investor and features asymmetric global macroeconomic, financial market, and risk aversion shocks. We find that DVP and UVP predict international stock returns through different global equity risk premium determinants: bad and good macroeconomic uncertainties, respectively. Across countries, US investors demand lower macroeconomic risk compensation but higher financial market risk compensation for more-integrated countries.
    Keywords: Downside variance risk premium; Upside variance risk premium; International stock markets; Asymmetric state variables; Stock return predictability
    JEL: F36 G12 G13 G15
    Date: 2021–05–18
  2. By: Guglielmo Maria Caporale; Luis A. Gil-Alana; Alex Plastun; Inna Makarenko
    Abstract: This paper uses R/S analysis and fractional integration techniques to examine the persistence of two sets of 12 ESG and conventional stock price indices from the MSCI database over the period 2007-2020 for a large number of both developed and emerging markets. Both sets of results imply that there are no significant differences between the two types of indices in terms of the degree of persistence and its dynamic behaviour. However, higher persistence is found for the emerging markets examined (especially the BRICS), which suggests that they are less efficient and thus offer more opportunities for profitable trading strategies. Possible explanations for these findings include different type of companies’ ‘camouflage’ and ‘washing’ (green, blue, pink, social, and SDG) in the presence of rather lax regulations for ESG reporting.
    Keywords: stock market, ESG, persistence, long memory, R/S analysis, fractional integration
    JEL: C22 G12
    Date: 2021
  3. By: Junran Wu; Ke Xu; Xueyuan Chen; Shangzhe Li; Jichang Zhao
    Abstract: Stock prediction, with the purpose of forecasting the future price trends of stocks, is crucial for maximizing profits from stock investments. While great research efforts have been devoted to exploiting deep neural networks for improved stock prediction, the existing studies still suffer from two major issues. First, the long-range dependencies in time series are not sufficiently captured. Second, the chaotic property of financial time series fundamentally lowers prediction performance. In this study, we propose a novel framework to address both issues regarding stock prediction. Specifically, in terms of transforming time series into complex networks, we convert market price series into graphs. Then, structural information, referring to associations among temporal points and the node weights, is extracted from the mapped graphs to resolve the problems regarding long-range dependencies and the chaotic property. We take graph embeddings to represent the associations among temporal points as the prediction model inputs. Node weights are used as a priori knowledge to enhance the learning of temporal attention. The effectiveness of our proposed framework is validated using real-world stock data, and our approach obtains the best performance among several state-of-the-art benchmarks. Moreover, in the conducted trading simulations, our framework further obtains the highest cumulative profits. Our results supplement the existing applications of complex network methods in the financial realm and provide insightful implications for investment applications regarding decision support in financial markets.
    Date: 2021–06
  4. By: Liu Ziyin; Kentaro Minami; Kentaro Imajo
    Abstract: The main task we consider is portfolio construction in a speculative market, a fundamental problem in modern finance. While various empirical works now exist to explore deep learning in finance, the theory side is almost non-existent. In this work, we focus on developing a theoretical framework for understanding the use of data augmentation for deep-learning-based approaches to quantitative finance. The proposed theory clarifies the role and necessity of data augmentation for finance; moreover, our theory motivates a simple algorithm of injecting a random noise of strength $\sqrt{|r_{t-1}|}$ to the observed return $r_{t}$. This algorithm is shown to work well in practice.
    Date: 2021–06
  5. By: Tidor-Vlad Pricope
    Abstract: Algorithmic stock trading has become a staple in today's financial market, the majority of trades being now fully automated. Deep Reinforcement Learning (DRL) agents proved to be to a force to be reckon with in many complex games like Chess and Go. We can look at the stock market historical price series and movements as a complex imperfect information environment in which we try to maximize return - profit and minimize risk. This paper reviews the progress made so far with deep reinforcement learning in the subdomain of AI in finance, more precisely, automated low-frequency quantitative stock trading. Many of the reviewed studies had only proof-of-concept ideals with experiments conducted in unrealistic settings and no real-time trading applications. For the majority of the works, despite all showing statistically significant improvements in performance compared to established baseline strategies, no decent profitability level was obtained. Furthermore, there is a lack of experimental testing in real-time, online trading platforms and a lack of meaningful comparisons between agents built on different types of DRL or human traders. We conclude that DRL in stock trading has showed huge applicability potential rivalling professional traders under strong assumptions, but the research is still in the very early stages of development.
    Date: 2021–05
  6. By: Ali Hirsa; Joerg Osterrieder; Branka Hadji-Misheva; Jan-Alexander Posth
    Abstract: Financial trading has been widely analyzed for decades with market participants and academics always looking for advanced methods to improve trading performance. Deep reinforcement learning (DRL), a recently reinvigorated method with significant success in multiple domains, still has to show its benefit in the financial markets. We use a deep Q-network (DQN) to design long-short trading strategies for futures contracts. The state space consists of volatility-normalized daily returns, with buying or selling being the reinforcement learning action and the total reward defined as the cumulative profits from our actions. Our trading strategy is trained and tested both on real and simulated price series and we compare the results with an index benchmark. We analyze how training based on a combination of artificial data and actual price series can be successfully deployed in real markets. The trained reinforcement learning agent is applied to trading the E-mini S&P 500 continuous futures contract. Our results in this study are preliminary and need further improvement.
    Date: 2021–06
  7. By: Akbar, Muhammad; Ali, Shahid; Ullah, Ihsan; Rehman, Naser
    Abstract: We assess informational efficiency of nine Dow Jones Islamic market indices and their counterpart conventional Morgan Stanley indices using data from 1996 to 2020. We test the martingale difference hypothesis of no return predictability overtime and assess the adaptive market hypothesis over different market conditions. We find that the null is rejected in a number of periods in line with the adaptive market hypothesis for both Islamic and conventional stock indices. However, we do not observe any significant differences in return predictability between Islamic and conventional stocks over different market conditions including financial crisis of 2007-08 and COVID-19 pandemic.
    Date: 2021–06–16
  8. By: Jaydip Sen; Sidra Mehtab; Abhishek Dutta
    Abstract: Volatility clustering is an important characteristic that has a significant effect on the behavior of stock markets. However, designing robust models for accurate prediction of future volatilities of stock prices is a very challenging research problem. We present several volatility models based on generalized autoregressive conditional heteroscedasticity (GARCH) framework for modeling the volatility of ten stocks listed in the national stock exchange (NSE) of India. The stocks are selected from the auto sector and the banking sector of the Indian economy, and they have a significant impact on the sectoral index of their respective sectors in the NSE. The historical stock price records from Jan 1, 2010, to Apr 30, 2021, are scraped from the Yahoo Finance website using the DataReader API of the Pandas module in the Python programming language. The GARCH modules are built and fine-tuned on the training data and then tested on the out-of-sample data to evaluate the performance of the models. The analysis of the results shows that asymmetric GARCH models yield more accurate forecasts on the future volatility of stocks.
    Date: 2021–05
  9. By: Yue Cai (Waseda University)
    Abstract: This paper study underwriter’s competitive behavior in the Japanese IPO underwriting markets. We use demand estimation techniques to obtain marginal costs, and empirically compare several models of conduct. We find that differentiated product Bertrand, partially collusive models are rejected against the perfectly collusive models. We conclude that spreads in the Japanese IPO underwriting markets are consistent with the collusive pricing behavior. Underwriters seem to internalize the effect of their spread on their rivals.
    Date: 2021–05
  10. By: Afees A. Salisu (Centre for Econometric and Allied Research, University of Ibadan, Ibadan, Nigeria); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield, 0028, South Africa)
    Abstract: We forecast real stock returns of South Africa over the monthly period of 1915:01 to 2021:03 using real oil, gold and silver prices, based on an autoregressive type distributed lag model that controls for persistence and endogeneity bias. Oil price proxies for fundamentals, while gold and silver prices capture sentiments. We find that the metrics for fundamentals and sentiments both predict real stock returns of South Africa, with nonlinearity, modelled by decomposition of these prices into their respective positive and negative counterparts, playing an important role in terms of forecasting when a longer out-of-sample period spanning over three-quarters of a century is used. When compared to fundamentals, sentiments, particularly real gold prices, have a relatively more stronger role to play in forecasting real stock returns. Further, the predictability of stock returns emanating from fundamentals and sentiments is in line with the findings over the same period derived for two other advanced markets namely, the United Kingdom (UK) and the United States (US), but the stock market of another emerging economy, i.e., India covering 1920:08 to 2021:03, unlike South Africa, is found to be completely unpredictable. In other words, South Africa, in terms of its predictability, behaves like a developed stock market. Finally, given the importance of platinum and palladium for South Africa, our forecasting exercise based on their real prices over 1968:01 to 2021:03, depicts strong predictive content for real stock returns, thus again highlighting the importance of behavioral variables. However, these prices do not necessarily contain additional information over what is already available in gold, silver and oil real prices. Our results have important implications for academicians, investors and policymakers.
    Keywords: Commodity prices, real stock returns, emerging and developed markets, forecasting
    JEL: C22 C53 G15 G17 Q02
    Date: 2021–06

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