nep-fmk New Economics Papers
on Financial Markets
Issue of 2023‒05‒15
sixteen papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Stock Market Responses to COVID-19: The Behaviors of Mean Reversion, Dependence and Persistence By Yener, Coskun; Akinsomi, Omokolade; Gil-Alana, Luis A.; Yaya, OlaOluwa S
  2. Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models By Alejandro Lopez-Lira; Yuehua Tang
  3. Equity Term Structures without Dividend Strips Data By Stefano Giglio; Bryan T. Kelly; Serhiy Kozak
  4. Is the research agenda for calendar anomalies “much do about nothing”? By Sproule, Robert; Gosselin, Gabriel
  5. Four Facts About ESG Beliefs and Investor Portfolios By Stefano Giglio; Matteo Maggiori; Johannes Stroebel; Zhenhao Tan; Stephen Utkus; Xiao Xu
  6. Technological Shocks and Stock Market Volatility Over a Century: A GARCH-MIDAS Approach By Afees A. Salisu; Riza Demirer; Rangan Gupta
  7. Managing Portfolio for Maximizing Alpha and Minimizing Beta By Soumyadip Sarkar
  8. Disposed to Be Overconfident By Katrin Gödker; Terrance Odean; Paul Smeets
  9. Optimal Portfolio Rebalancing with Sweep Under Transaction Cost By Arjmandi, Nabi
  10. Optimal Trading in Automatic Market Makers with Deep Learning By Sebastian Jaimungal; Yuri F. Saporito; Max O. Souza; Yuri Thamsten
  11. Asset Pricing with Optimal Under-Diversification By Vadim Elenev; Tim Landvoigt
  12. Fintech, investor sophistication and financial portfolio choices By Leonardo Gambacorta; Romina Gambacorta; Roxana Mihet
  13. Short-Term Volatility Prediction Using Deep CNNs Trained on Order Flow By Mingyu Hao; Artem Lenskiy
  14. Crash risk in the Nordic Stock Market - a cross-sectional analysis By Fjærvik, Thomas
  15. Does the Adaptive Market Hypothesis Exist in Equity Market? Evidence from Pakistan Stock Exchange By Siddique, Maryam
  16. Credit Risk and Financial Performance of Commercial Banks: Evidence from Vietnam By Ha Nguyen

  1. By: Yener, Coskun; Akinsomi, Omokolade; Gil-Alana, Luis A.; Yaya, OlaOluwa S
    Abstract: We examine stock market responses during the COVID-19 pandemic period using fractional integration techniques. The evidence suggests that stock markets generally follow a synchronized movement before and the stages of the pandemic shocks. We find while mean reversion significantly declines, the degree of persistence and dependence has been increased in the majority of the stock market indices in whole sample analysis covering the period of 02.08.2019 and 09.07.2020. This outcome implies increasing integration and possibly declining benefits of diversification for the global stock portfolio management.
    Keywords: Coronavirus; stock markets; fractional integration; long memory; mean reversion.
    JEL: C2 C22
    Date: 2023–04–09
  2. By: Alejandro Lopez-Lira; Yuehua Tang
    Abstract: We examine the potential of ChatGPT, and other large language models, in predicting stock market returns using sentiment analysis of news headlines. We use ChatGPT to indicate whether a given headline is good, bad, or irrelevant news for firms' stock prices. We then compute a numerical score and document a positive correlation between these ``ChatGPT scores'' and subsequent daily stock market returns. Further, ChatGPT outperforms traditional sentiment analysis methods. We find that more basic models such as GPT-1, GPT-2, and BERT cannot accurately forecast returns, indicating return predictability is an emerging capacity of complex models. Our results suggest that incorporating advanced language models into the investment decision-making process can yield more accurate predictions and enhance the performance of quantitative trading strategies.
    Date: 2023–04
  3. By: Stefano Giglio; Bryan T. Kelly; Serhiy Kozak
    Abstract: We use a large cross-section of equity returns to estimate a rich affine model of equity prices, dividends, returns and their dynamics. Using the model, we price dividend strips of the aggregate market index, as well as any other well-diversified equity portfolio. We do not use any dividend strips data in the estimation of the model; however, model-implied equity yields generated by the model match closely the equity yields from the traded dividend forwards reported in the literature. Our model can be used to extend the data on the term structure of aggregate (market) discount rates over time (back to the 1970s) and across maturities, since we are not limited by the maturities of actually traded dividend claims. Most importantly, the model generates term structures for any portfolio of stocks (e.g., small and value portfolios, high and low investment portfolios, etc). The novel cross-section of term structure data estimated by our model, covering a span of 45 years that includes several recessions, represents a rich set of new empirical moments that can be used to guide and evaluate asset pricing models, beyond the aggregate term structure of dividend strips that has been studied in the literature.
    JEL: G11 G12 G13
    Date: 2023–04
  4. By: Sproule, Robert; Gosselin, Gabriel
    Abstract: Calendar anomalies are a class of financial market phenomena which links periodic, time-specific dummy variables and variations in the market price of an asset. Prior studies which report a calendar anomaly are seen by some as refutations of the efficient market hypothesis. In this paper, we estimate, test for the presence of, and find no evidence of, the day-the-week effects in the S&P 500, 2013-2023. That is, in this paper, we show that the daily-dummy variables (both individually and collectively) are independent of the S&P 500. This finding supports those who have argued that the day�the-week effects, and (by extension) all calendar anomalies, are “chimera delivered by intensive data mining” or, quite simply, such anomalies are “much ado about nothing.”
    Keywords: Efficient market hypothesis, Behavioral finance, Calendar anomalies, Day�of-the-week effects, Ordinary least-squares estimation, Newey-West (1987) standard error correction, S&P 500 Index
    JEL: C1 C12 C13 C22 G12 G14
    Date: 2023–04–02
  5. By: Stefano Giglio; Matteo Maggiori; Johannes Stroebel; Zhenhao Tan; Stephen Utkus; Xiao Xu
    Abstract: We analyze survey data on ESG beliefs and preferences in a large panel of retail investors linked to administrative data on their investment portfolios. The survey elicits investors’ expectations of long-term ESG equity returns and asks about their motivations, if any, to invest in ESG assets. We document four facts. First, investors generally expected ESG investments to underperform the market. Between mid-2021 and late-2022, the average expected 10-year annualized return of ESG investments relative to the overall stock market was –1.4%. Second, there is substantial heterogeneity across investors in their ESG return expectations and their motives for ESG investing: 45% of survey respondents do not see any reason to invest in ESG, 25% are primarily motivated by ethical considerations, 22% are driven by climate hedging motives, and 7% are motivated by return expectations. Third, there is a link between individuals’ reported ESG investment motives and their actual investment behaviors, with the highest ESG portfolio holdings among individuals who report ethics-driven investment motives. Fourth, financial considerations matter independently of other investment motives: we find meaningful ESG holdings only for investors who expect these investments to outperform the market, even among those investors who reported that their most important ESG investment motives were ethical or hedging reasons.
    JEL: G4 G5 Q50 Q54
    Date: 2023–04
  6. By: Afees A. Salisu (Centre for Econometrics & Applied Research, Ibadan, Nigeria; Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)
    Abstract: This paper provides a novel perspective to the innovation-stock market nexus by examining the predictive relationship between technological shocks and stock market volatility using data over a period of more than 140 years. Utilizing annual patent data for the U.S. and a large set of economies to create proxies for local and global technological shocks and a mixed-sampling data (MIDAS) framework, we present robust evidence that technological shocks capture significant predictive information regarding future realizations of stock market volatility, both in- and out-of-sample and at both the short and long forecast horizons. Further economic analysis shows that investment portfolios created by the volatility forecasts obtained from the forecasting models that incorporate technological shocks as predictors in volatility models experience significantly lower return volatility in the out-of-sample horizons, which in turn helps to improve the risk-return profile of those portfolios. Our findings present a novel take on the nexus between technological innovations and stock market dynamics and paves the way for several interesting avenues for future research regarding the role of technological innovations on asset pricing tests and portfolio models.
    Keywords: Patents, Technology shocks, Stock market volatility, Forecasting
    JEL: C32 C53 E37 G15 O33
    Date: 2023–04
  7. By: Soumyadip Sarkar
    Abstract: Portfolio management is an essential component of investment strategy that aims to maximize returns while minimizing risk. This paper explores several portfolio management strategies, including asset allocation, diversification, active management, and risk management, and their importance in optimizing portfolio performance. These strategies are examined individually and in combination to demonstrate how they can help investors maximize alpha and minimize beta. Asset allocation is the process of dividing a portfolio among different asset classes to achieve the desired level of risk and return. Diversification involves spreading investments across different securities and sectors to minimize the impact of individual security or sector-specific risks. Active management involves security selection and risk management techniques to generate excess returns while minimizing losses. Risk management strategies, such as stop-loss orders and options strategies, aim to minimize losses in adverse market conditions. The importance of combining these strategies for optimizing portfolio performance is emphasized in this paper. The proper implementation of these strategies can help investors achieve their investment goals over the long-term, while minimizing exposure to risks. A call to action for investors to utilize portfolio management strategies to maximize alpha and minimize beta is also provided.
    Date: 2023–04
  8. By: Katrin Gödker; Terrance Odean; Paul Smeets
    Abstract: We show that the disposition effect–the tendency of investors to hold losers and sell winners–can be a source of overconfidence. We find experimental evidence that individuals update beliefs about their own investment ability based on realized gains and losses rather than the overall performance of their portfolio. We also find supporting field evidence. Dutch retail investors who realized more gains than losses believe they have higher portfolio performance relative to other investors, even after controlling for their actual portfolio performance. We develop a formal model demonstrating how the disposition effect leads to overconfidence and examine model implications for investors’ trading behavior and expected profit.
    Keywords: investor beliefs, disposition effect, overconfidence, experimental finance
    JEL: D01 G40
    Date: 2023
  9. By: Arjmandi, Nabi
    Abstract: This paper investigates the optimal portfolio rebalancing strategy for assets with cash distributions and proportional transaction costs. A sweep account is an account that is used as the default destination for coupon and dividend proceeds as they arrive. In this study, we incorporate this account and investigate the optimal strategy for the sweep account manager. Our results indicate that the "no-transaction" region is split into two sub-regions, where the cash proceeds are either invested entirely in the riskless asset or in the risky asset, depending on the transaction costs. Additionally, we analyze the impact of the assets' cash distributions and the investors' investment horizon on the demand for the assets. Our findings suggest that changes in the cash distribution of assets, depending on the relative magnitude of transaction costs for risky and riskless assets, can have a varying impact on asset demand. In particular, our results indicate that when the transaction cost for the riskless asset is low, an increase in the cash distributions from the risky asset and an increase in the investor's investment horizon have a positive impact on the liquidity premium of the risky asset.
    Keywords: Transaction cost, Sweep account, Liquidity premium, Portfolio optimization, Continuous-Time methods.
    JEL: D11 D61 G11 G23
    Date: 2023
  10. By: Sebastian Jaimungal; Yuri F. Saporito; Max O. Souza; Yuri Thamsten
    Abstract: This article explores the optimisation of trading strategies in Constant Function Market Makers (CFMMs) and centralised exchanges. We develop a model that accounts for the interaction between these two markets, estimating the conditional dependence between variables using the concept of conditional elicitability. Furthermore, we pose an optimal execution problem where the agent hides their orders by controlling the rate at which they trade. We do so without approximating the market dynamics. The resulting dynamic programming equation is not analytically tractable, therefore, we employ the deep Galerkin method to solve it. Finally, we conduct numerical experiments and illustrate that the optimal strategy is not prone to price slippage and outperforms na\"ive strategies.
    Date: 2023–04
  11. By: Vadim Elenev; Tim Landvoigt
    Abstract: We study sources and implications of undiversified portfolios in a production-based asset pricing model with financial frictions. Households take concentrated positions in a single firm exposed to idiosyncratic shocks because managerial effort requires equity stakes, and because investors gain private benefits from concentrated holdings. Matching data on returns and portfolios, we find that the marginal investor optimally holds 45% of their portfolio in a single firm, incentivizing managerial effort that accounts for 4% of aggregate output. Investors derive control benefits equivalent to 3% points of excess return, rationalizing low observed returns on undiversified holdings in the data. A counterfactual world of full diversification would feature higher risk free rates, lower risk premiums on fully diversified and concentrated assets, less capital accumulation, yet higher consumption and welfare. Exposure to undiversified firm risk can explain approximately 40% of the level and 20% of the volatility of the equity premium. A targeted subsidy that decreases diversification improves welfare by increasing managerial effort and reducing financial frictions.
    JEL: E21 G11 G12 G32
    Date: 2023–04
  12. By: Leonardo Gambacorta (Bank for International Settlements); Romina Gambacorta (Bank of Italy); Roxana Mihet (HEC Lausanne)
    Abstract: This paper analyses the links between advances in financial technology, investors' sophistication, and their financial portfolios' composition and returns. We develop a simple portfolio choice model under asymmetric information and derive some theoretical predictions. Using detailed micro data from the Bank of Italy, we test these predictions for Italian households over the period 2004-2020. In general, heterogeneity in portfolio composition and in returns between sophisticated and unsophisticated investors grows with improvements in financial technology. This heterogeneity is reduced only if financial technology is accessible by everyone and if investors have a similar capacity to use it.
    Keywords: inequality, inclusion, fintech, innovation, Matthew effect
    JEL: G1 G5 G4 D83 L8 O3
    Date: 2023–04
  13. By: Mingyu Hao; Artem Lenskiy
    Abstract: As a newly emerged asset class, cryptocurrency is evidently more volatile compared to the traditional equity markets. Due to its mostly unregulated nature, and often low liquidity, the price of crypto assets can sustain a significant change within minutes that in turn might result in considerable losses. In this paper, we employ an approach for encoding market information into images and making predictions of short-term realized volatility by employing Convolutional Neural Networks. We then compare the performance of the proposed encoding and corresponding model with other benchmark models. The experimental results demonstrate that this representation of market data with a Convolutional Neural Network as a predictive model has the potential to better capture the market dynamics and a better volatility prediction.
    Date: 2023–04
  14. By: Fjærvik, Thomas (Dept. of Business and Management Science, Norwegian School of Economics)
    Abstract: This paper takes the viewpoint of an investor that can invest in the Nordic countries Norway, Sweden, Denmark and Finland. The four markets are treated as one integrated market. In the analysis we investigate whether there exists a risk premium for investing in stocks exhibiting high crash risk, as measured by their lower tail dependence with the rest of the market portfolio. We indeed find evidence that this is the case, and this evidence is in line with previous research done on American and German stocks markets, as well as theoretical predictions in the literature. However, the results are less clear than was the case for the abovementioned markets. Lower tail dependence is estimated using convex combinations of copulas exhibiting different tail dependence characteristics. The results are robust to different portfolio formations and copula selection criteria.
    Keywords: Crash risk premium; copulas; Pearson correlation
    JEL: G00 G01 G11 G12
    Date: 2023–04–28
  15. By: Siddique, Maryam
    Abstract: In this study we empirically check the adaptive market hypothesis in Pakistan stock Market by measuring the association among present stock returns and past stock returns over the time of 2001 to 2020. We divided the weekly data of returns into subsamples of equal length 0f 3 years of seven samples and last sample with two years gap. For this purpose, we applied the five (linear and nonlinear) tests, in linear test Autocorrelation test, Variance ratio test, runs test and in nonlinear BDS independence test and Lagrange Multiplier test was applied to explicate that in what way the efficiency of market varies from time to time and whether their existence of any relationship between market condition and return predictability. Our results showed that efficiency of stock market fluctuates among episodic periods of dependency (inefficient condition) and independencies (efficient condition) in full and each subsample thus it is conclude that PSX follow adaptive market and constant with AMH. Overall findings of the study concluded that AMH can better explain the stock return behavior then EMH. As the variation in market conditions can highly impress the trading activities and market efficiency so the investors can get the help from this study in making the investing decisions. It can be applied to practical setting such as asset allocation, investment consulting and risk management.
    Date: 2023–04–04
  16. By: Ha Nguyen
    Abstract: Credit risk is a crucial topic in the field of financial stability, especially at this time given the profound impact of the ongoing pandemic on the world economy. This study provides insight into the impact of credit risk on the financial performance of 26 commercial banks in Vietnam for the period from 2006 to 2016. The financial performance of commercial banks is measured by return on assets (ROA), return on equity (ROE), and Net interest margin (NIM); credit risk is measured by the Non-performing loan ratio (NPLR); control variables are measured by bank-specific characteristics, including bank size (SIZE), loan loss provision ratio (LLPR), and capital adequacy ratio (CAR), and macroeconomic factors such as annual gross domestic product (GDP) growth and annual inflation rate (INF). The assumption tests show that models have autocorrelation, non-constant variance, and endogeneity. Hence, a dynamic Difference Generalized Method of Moments (dynamic Difference GMM) approach is employed to thoroughly address these problems. The empirical results show that the financial performance of commercial banks measured by ROE and NIM persists from one year to the next. Furthermore, SIZE and NPLR variables have a significant negative effect on ROA and ROE but not on NIM. There is no evidence found in support of the LLPR and CAR variables on models. The effect of GDP growth is statistically significant and positive on ROA, ROE, and NIM, whereas the INF is only found to have a significant positive impact on ROA and NIM.
    Date: 2023–04

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