nep-fmk New Economics Papers
on Financial Markets
Issue of 2023‒01‒23
twelve papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Using Intermarket Data to Evaluate the Efficient Market Hypothesis with Machine Learning By N'yoma Diamond; Grant Perkins
  2. Peer-reviewed theory does not help predict the cross-section of stock returns By Andrew Y. Chen; Alejandro Lopez-Lira; Tom Zimmermann
  3. Why do investors buy shares of actively managed equity mutual funds? Considering the Correct Reference Portfolio from an Uninformed Investor's Perspective 1, 2 By Radu Burlacu; Patrice Fontaine; Sonia Jimenez-Garcès
  4. Retail Fund Flows and Performance: Insights from Supervisory Data By Martin Hodula; Milan Szabo; Josef Bajzik
  5. Optimal Capital structure and financial stability By Firano, Zakaria; Filali adib, Fatine
  6. Cross-Domain Shopping and Stock Trend Analysis By Aditya Pandey; Haseeba Fathiya; Nivedita Patel
  7. Do Consumption-based Asset Pricing Models Explain Own-history Predictability in Stock Market Returns? By Ashby, M.; Linton, O. B.
  8. Machine learning methods in finance: Recent applications and prospects By Hoang, Daniel; Wiegratz, Kevin
  9. Leverage and Stablecoin Pegs By Gary B. Gorton; Elizabeth C. Klee; Chase P. Ross; Sharon Y. Ross; Alexandros P. Vardoulakis
  10. CBDC, Fintech and cryptocurrency for financial Inclusion and financial stability By Ozili, Peterson K
  11. Crypto Wash Trading By Lin William Cong; Xi Li; Ke Tang; Yang Yang
  12. Return Volatility, Correlation, and Hedging of Green and Brown Stocks: Is there a Role for Climate Risk Factors? By Haohua Li; Elie Bouri; Rangan Gupta; Libing Fang

  1. By: N'yoma Diamond; Grant Perkins
    Abstract: In its semi-strong form, the Efficient Market Hypothesis (EMH) implies that technical analysis will not reveal any hidden statistical trends via intermarket data analysis. If technical analysis on intermarket data reveals trends which can be leveraged to significantly outperform the stock market, then the semi-strong EMH does not hold. In this work, we utilize a variety of machine learning techniques to empirically evaluate the EMH using stock market, foreign currency (Forex), international government bond, index future, and commodities future assets. We train five machine learning models on each dataset and analyze the average performance of these models for predicting the direction of future S&P 500 movement as approximated by the SPDR S&P 500 Trust ETF (SPY). From our analysis, the datasets containing bonds, index futures, and/or commodities futures data notably outperform baselines by substantial margins. Further, we find that the usage of intermarket data induce statistically significant positive impacts on the accuracy, macro F1 score, weighted F1 score, and area under receiver operating characteristic curve for a variety of models at the 95% confidence level. This provides strong empirical evidence contradicting the semi-strong EMH.
    Date: 2022–12
  2. By: Andrew Y. Chen; Alejandro Lopez-Lira; Tom Zimmermann
    Abstract: To examine whether theory helps predict the cross-section of returns, we combine text analysis of publications with out-of-sample tests. Based on the original texts, only 18% predictors are attributed to risk-based theory. 58% are attributed to mispricing and 24% have uncertain origins. Post-publication, risk-based predictability decays by 65%, compared to 50% for non-risk predictors. Out-of-sample, risk-based predictors fail to outperform data-mined accounting predictors that are matched on in-sample summary statistics. Published and data-mined returns rise before in-sample periods end and fall out-of-sample at similar rates. Overall, peer-reviewed research adds little information about future mean returns above naive back testing.
    Date: 2022–12
  3. By: Radu Burlacu (CERAG - Centre d'études et de recherches appliquées à la gestion - UGA [2016-2019] - Université Grenoble Alpes [2016-2019]); Patrice Fontaine (EUROFIDAI - Institut Européen de données financières - Essec Business School - CNRS - Centre National de la Recherche Scientifique); Sonia Jimenez-Garcès ((Axe de recherche : Finance) - CERAG - Centre d'études et de recherches appliquées à la gestion - UPMF - Université Pierre Mendès France - Grenoble 2 - CNRS - Centre National de la Recherche Scientifique)
    Abstract: We use the Grossman & Stiglitz (1980) framework to build a reference portfolio for uninformed investors and employ this portfolio to assess the performance of actively managed equity mutual funds. We propose an empirical methodology to construct this reference portfolio using the information on prices and supply. We show that mutual funds provide, on average, an insignificant alpha of 23 basis points per year when considering this portfolio as a reference. With the stock market index as a proxy for the market portfolio, the average fund alpha is negative and highly significant, −128 basis points per year. The results are robust when considering various subsets of funds based on their characteristics and their degree of selectivity. In line with rational expectations equilibrium models considering asymmetrically informed investors and partially revealing equilibrium prices, our study supports that active management adds value for uniformed investors.
    Keywords: JEL Classification: G11 G12 G14 information asymmetry reference portfolio performance actively managed equity mutual funds rational expectations equilibrium models,JEL Classification: G11,G12,G14 information asymmetry,reference portfolio,performance,actively managed equity mutual funds,rational expectations equilibrium models
    Date: 2022
  4. By: Martin Hodula; Milan Szabo; Josef Bajzik
    Abstract: This paper explores flow patterns in retail equity mutual funds related to past and future performance. We employ supervisory data of monthly fund inflows and outflows in the Czech Republic and produce several key findings that shed light on the behavior of households as investors in an emerging market economy. First, we show that investor flows chase past performance and tend to underreact to poor performance - a typical finding in the literature. However, we find that retail investors are very sensitive to poor performance in times of aggregate illiquidity and when investing in funds that hold more illiquid assets. Second, we document that when facing illiquidity and a deteriorating performance, underperforming equity-investing funds experience lower investor purchases and a larger share of redemption requests. We observe similar investor behaviour in periods when retail investors face constraints on their disposable income. At such times, mutual fund inflows are found to decrease significantly and fund outflows to increase. Third, we document the presence of the smart money effect, while finding that it is caused by the buying (but not selling) decisions of retail investors.
    Keywords: Equity funds, liquidity, retail investors, smart money
    JEL: G11 G23
    Date: 2022–12
  5. By: Firano, Zakaria; Filali adib, Fatine
    Abstract: This paper attempts to answer the fundamental question of the choice of capital structure. The financial structure in Morocco raises several questions about the behaviour of firms, especially in relation to the banking system and the financial market. We tried to determine the factors that explain the choice of financial structure. In addition to the traditional known factors, we were able to introduce the effects of financial stability on the financial structure. The results obtained affirm that Moroccan companies are in a hierarchical conception of the choice of financing and they prefer the use of internal financing with a particularity where companies with long experience are less and less attracted by external financing. In addition, financial stability significantly affects the choice of financing method. Indeed, when the financial system is stable, companies prefer to use external financing, which results in over-indebtedness that negatively affects the stability of the Moroccan financial system in a second rank. We generalize this theoretical conception to assert that the degree of financial stability can have effects on the choice of the financial structure of companies.
    Keywords: financial structure; banking system; pecking order; financial stability
    JEL: G2 G3
    Date: 2022
  6. By: Aditya Pandey; Haseeba Fathiya; Nivedita Patel
    Abstract: This paper presents a cross-domain trend analysis that aims to identify and analyze the relationships between stock prices, stock news on Twitter, and users' behaviors on e-commerce websites. The analysis is based on three datasets: a US stock dataset, a stock tweets dataset, and an e-commerce behavior dataset. The analysis is performed using Hadoop, Hive, and Tableau, allowing for efficient and scalable processing and visualizing large datasets. The analysis includes trend analysis of Twitter sentiment (positive and negative tweets) and correlation analysis, including the correlation between tweet sentiment and stocks, the correlation between stock trends and shopping behavior, and the understanding of data based on different slices of time. By comparing different features from the datasets over time, we hope to gain insight into the factors that drive user behavior as well as the market in different categories. The results of this analysis can provide valuable insights for businesses and investors to inform decision-making. We believe that our analysis can serve as a valuable starting point for further research and investigation into these topics.
    Date: 2022–12
  7. By: Ashby, M.; Linton, O. B.
    Abstract: We show that three prominent consumption-based asset pricing models - the Bansal-Yaron, Campbell-Cochrane and Cecchetti-Lam-Mark models - cannot explain the own-history predictability properties of stock market returns. We show this by estimating these models with GMM, deriving ex-ante expected returns from them and then testing whether the difference between realised and expected returns is a martingale difference sequence, which it is not. Furthermore, semi-parametric tests of whether the models' state variables are consistent with the degree of own-history predictability in stock returns suggest that only the Campbell-Cochrane habit variable may be able to explain return predictability, although the evidence on this is mixed.
    Keywords: consumption-based asset pricing models, martingale difference sequence, MIDAS, power spectrum, predictability, quantilogram, rescaled range, serial correlation, variance ratio
    JEL: C52 C58 G12
    Date: 2022–10–20
  8. By: Hoang, Daniel; Wiegratz, Kevin
    Abstract: We study how researchers can apply machine learning (ML) methods in finance. We first establish that the two major categories of ML (supervised and unsupervised learning) address fundamentally different problems than traditional econometric approaches. Then, we review the current state of research on ML in finance and identify three archetypes of applications: i) the construction of superior and novel measures, ii) the reduction of prediction error, and iii) the extension of the standard econometric toolset. With this taxonomy, we give an outlook on potential future directions for both researchers and practitioners. Our results suggest large benefits of ML methods compared to traditional approaches and indicate that ML holds great potential for future research in finance.
    Keywords: Machine Learning, Artificial Intelligence, Big Data
    JEL: C45 G00
    Date: 2022
  9. By: Gary B. Gorton; Elizabeth C. Klee; Chase P. Ross; Sharon Y. Ross; Alexandros P. Vardoulakis
    Abstract: Money is debt that circulates with no questions asked. Stablecoins are a new form of private money that circulate with many questions asked. We show how stablecoins can maintain a constant price even though they face run risk and pay no interest. Stablecoin holders are indirectly compensated for stablecoin run risk because they can lend the coins to levered traders. Levered traders are willing to pay a premium to borrow stablecoins when speculative demand is strong. Therefore, the stablecoin can support a $1 peg even with higher levels of run risk.
    JEL: G0 G1 G10
    Date: 2022–12
  10. By: Ozili, Peterson K
    Abstract: This article presents a discussion of the role of central bank digital currency (CBDC), Fintech and cryptocurrency for financial inclusion and financial stability. We show that Fintech, CBDC and cryptocurrency can increase financial inclusion by providing an alternative channel through which unbanked adults can access formal financial services. CBDC and Fintech services have the potential to preserve financial stability while cryptocurrency presents financial stability risks that can be mitigated through effective regulation. The paper also identified some problems of CBDC, Fintech and cryptocurrency for financial inclusion and financial stability. The paper offered some insight about the future of financial inclusion and the future of financial stability. Although CBDC, Fintech or cryptocurrency can extend financial services to unbanked adults and offer cost-efficient advantages, there are risk considerations that need to be taken into account when using CBDC, Fintech and cryptocurrency to increase financial inclusion and to preserve financial stability.
    Keywords: CBDC, Fintech, cryptocurrency, financial inclusion, financial stability, blockchain, central bank digital currency.
    JEL: E40 E51 E58 E59 G21 O31
    Date: 2023
  11. By: Lin William Cong; Xi Li; Ke Tang; Yang Yang
    Abstract: We introduce systematic tests exploiting robust statistical and behavioral patterns in trading to detect fake transactions on 29 cryptocurrency exchanges. Regulated exchanges feature patterns consistently observed in financial markets and nature; abnormal first-significant-digit distributions, size rounding, and transaction tail distributions on unregulated exchanges reveal rampant manipulations unlikely driven by strategy or exchange heterogeneity. We quantify the wash trading on each unregulated exchange, which averaged over 70% of the reported volume. We further document how these fabricated volumes (trillions of dollars annually) improve exchange ranking, temporarily distort prices, and relate to exchange characteristics (e.g., age and userbase), market conditions, and regulation.
    JEL: G18 G23 G29
    Date: 2022–12
  12. By: Haohua Li (School of Management and Engineering, Nanjing University, No. 5 Pingcang Lane, Gulou District of Nanjing, Jiangsu Province, China); Elie Bouri (School of Business, Lebanese American University, Byblos, Lebanon); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Libing Fang (School of Management and Engineering, Nanjing University, No. 5 Pingcang Lane, Gulou District of Nanjing, Jiangsu Province, China)
    Abstract: We examine the effects of three monthly climate risk factors, climate policy uncertainty (CPU), climate change news (CCN), and negative climate change news (NCCN) on the long-run volatilities and correlation of daily green and brown energy stock returns, and perform a hedging analysis. Given that our dataset combines daily and monthly data, we rely on mixed data sampling models such as GARCH-MIDAS and DCC-MIDAS in standard and asymmetric forms with a bivariate skew-t distribution, which also allows us to deal with volatility clustering, asymmetric effects, and negative skewness in innovation which characterize our dataset. Firstly, the results of the GARCH-MIDAS models show evidence that climate risk contains information useful to improve the prediction of return volatility of brown energy stocks. Secondly, the results of the DCCMIDAS model indicate that climate risk reduces the green-brown returns correlation, suggesting a negative effect and hedging opportunities. Thirdly, the results of the hedging analysis show that incorporating a climate risk factor, especially NCCN, into the long-run component of dynamic correlation significantly improves the hedging performance between green and brown energy stock indices, and this are robust to an out-of-sample analysis under various refitting window sizes. These results matter to portfolio and risk managers for energy transition and portfolio decarbonization.
    Keywords: Conditional volatility, dynamic correlation, GARCH-MIDAS, DCCMIDAS, climate change news (CCN), Climate policy uncertainty (CPU), hedging
    JEL: C32 G00 G11 Q54
    Date: 2023–01

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