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on Financial Markets |
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Issue of 2023‒02‒13
thirteen papers chosen by |
| By: | Ozili, Peterson K; |
| Abstract: | This paper presents a concise review of the existing digital finance research in the literature, and highlight some of the developments in digital finance around the world. The paper reached several conclusions. Firstly, it showed that digital finance has become an important part of modern finance and the major application of digital finance can be found in Fintech, embedded finance, open banking and decentralized finance, central bank digital currencies, among others. Secondly, it identified some international determinants of digital finance which includes the need for efficiency in financial services delivery, the need to achieve the United Nations sustainable development goals using existing digital technologies, the need to increase financial inclusion through digital financial inclusion and the need for efficient payments and payment settlement finality. The paper also finds that digital finance research is growing fast, and recent studies have investigated contemporary issues in digital finance that are relevant for policy and practice. Regarding the digital finance developments around the world, the paper shows that the Fintech and mobile money industries are the largest beneficiary of investments in digital finance with the total number of users of mobile money services surpassing 1 billion globally. Also, the paper predicts that the future of digital finance is to create a digital environment that permits the offering of all kinds of financial product and services that can be customized and personalized to meet the unique needs of all users on a single digital platform and without requiring any form of human assistance or intermediary. The paper then suggest some areas for future research which include the need for more research on how regulators can keep pace with emerging digital finance transformation, the need for more research on user information security and compliance, the need for more research on how to deal with bias caused by bad data, the need for more research on how to deal with algorithmic bias, and the need for more research on how to combine a risk-conscious culture with a higher risk appetite for digital finance transformation. |
| Keywords: | digital finance, artificial intelligence, machine learning, financial inclusion, fintech, access to finance, financial stability, economic growth, blockchain, central bank digital currency, robotics, cryptocurrency. |
| JEL: | G21 O32 O33 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:115780&r=fmk |
| By: | John B. Guerard (McKinley Capital Management, LLC); Dimitrios D. Thomakos (Department of Business Administration, National and Kapodistrian University of Athens, Athens, 10559 Greece; International Centre for Economic Analysis, Canada); Foteini Kyriazi (Department of Agribusiness and Supply Chain Management, Agricultural University of Athens); Konstantinos Mamais (Department of Business Administration, National and Kapodistrian University of Athens, Athens, 10559 Greece) |
| Abstract: | We obtained from Standard and Poor's Corporation, the complete 126-year history of the Dow Jones Industrial Average (DJIA) daily closing prices. We are applying rolling window averaging and adaptive learning methodologies, coupled with robust estimation methods, to examine which are the best forecasting models over a broad range of economic and financial conditions during the life of the index, based on daily and monthly stock index prices and daily, monthly, and semi-annual stock returns. Why is an AR(1) model a reasonable benchmark of stock prices? Why do we have it? What should be our forecasting benchmarks? Let us briefly re-visit the history of stock price research and efficient markets. Do we find forecasting improvements from the Hendry-Castle-Doornik-Clements approach using robust forecasting methodologies and saturation variables in the prices of the index? Given that the DJIA fell over 15% during the first half of 2022, is this one of the worst six-month periods ever? What has happened to the Dow, historically, during such periods in the past with regards to six-month, one-year, and three-year-ahead stock returns? Is capitalism dead or doomed? We report statistically significant forecasting improvement from saturation and robust forecasting techniques during the 1896 -June 2022 period. We report forecasted stock returns for the next 6 months and three years that are bullish. In the King's English, June 30, 2022 was another excellent common stock buying opportunity and capitalism is not dead. |
| Keywords: | forecasting financial prices, forecasting financial returns, leading economic indicator, return volatility, rolling window averaging |
| JEL: | C53 C52 C58 G11 G14 |
| Date: | 2023–01 |
| URL: | http://d.repec.org/n?u=RePEc:gwc:wpaper:2023-001&r=fmk |
| By: | Carina Burs (Paderborn University) |
| Abstract: | We study the effect of heterogeneous beliefs about asset prices on the long-term behavior of financial markets. Starting from the ideas of Abreu and Brunnermeier (2003), we develop a two-dimensional system of differential equations. The first dynamic variable is the asset price growth rate. The second dynamic variable is the number of investors who believe that asset prices are abnormally high. In a phase plane analysis we find both stable and unstable equilibria, depending on the spread of information and the response to other agents’ beliefs. If individuals try to increase their returns while perceiving more overpricing, these equilibria can be spirals or even approach limit cycles. Although we intend to study general price patterns, abnormally high asset prices can be caused by financial bubbles. In our model, bubbles can emerge and deflate both in cycles or directly, or they can grow until they burst. Further, we analyze market behavior after a central bank increases the interest rate. This can lead to new stable equilibria, but the emergence and bursting of bubbles cannot be prevented. |
| Keywords: | Financial Markets, Heterogeneous Beliefs, Asset Pricing, Financial Bubbles, Monetary Policy |
| JEL: | E44 E58 D83 G12 |
| Date: | 2023–02 |
| URL: | http://d.repec.org/n?u=RePEc:pdn:ciepap:154&r=fmk |
| By: | Vitaly Orlov (University of St. Gallen - School of Finance; Swiss Finance Institute); Stefano Ramelli (University of St. Gallen - School of Finance; Swiss Finance Institute); Alexander F. Wagner (University of Zurich - Department of Banking and Finance; Centre for Economic Policy Research (CEPR); European Corporate Governance Institute (ECGI); Swiss Finance Institute) |
| Abstract: | What do asset managers believe regarding the financial performance of Environmental, Social, and Governance (ESG) investment strategies? We address this question by exploring the relationship between fund managers’ co-ownership and portfolio ESG performance. Managers with more “skin in the game” exhibit significantly lower ESG performance in funds they manage than their peers. ESG performance is sensitive to changes in managerial ownership. Co-investing managers were less likely to increase their stake in high-ESG stocks after an exogenous shock in ESG-driven fund flows. Moreover, the negative effect of managerial ownership on ESG performance is stronger for managers paid to maximize assets under management, and weaker for managers paid exclusively to maximize financial returns. Overall, the results are contrary to what one would expect if managers really considered ESG strategies an enhanced form of portfolio management. |
| Keywords: | ESG, portfolio management, investor beliefs, mutual funds, skin-in-the-game, sustainability |
| JEL: | G11 G23 |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2298&r=fmk |
| By: | Brière, Marie; Huynh, Karen; Laudy, Olav; Pouget, Sébastien |
| Abstract: | Using textual data extracted by Causality Link platform from a large variety of news sources (news stories, call transcripts, broker re-search, etc.), we build aggregate news signals that take into account the tone, the tense and the prominence of various news statements about a given firm. We test the informational content of these signals and examine how news is incorporated into stock prices. Our sample covers 1, 701, 789 news-based signals that were built on 4, 460 US stocks over the period January 2014 to December 2021. We document large and significant market reactions around the publication of news, with some evidence of return predictability at short horizons. News about the future drives much larger reactions than news about the present or the past. Stock returns also react more to high-coverage news, fresh news and purely financial news. Finally, firms’ size matters: stocks that are not components of the Russell 1000 index experience larger reactions to news compared to those that are Russell 1000 components. Implications of our results for financial analysts and investors are of-fered and related to the links between news, firms’ market value and investment strategies. |
| Keywords: | Natural Language Processing; Textual Analysis; Efficient Market Hypothesis; ESG |
| Date: | 2023–01–19 |
| URL: | http://d.repec.org/n?u=RePEc:tse:wpaper:127755&r=fmk |
| By: | Xia Han; Liyuan Lin; Ruodu Wang |
| Abstract: | The diversification quotient (DQ) is a recently introduced tool for quantifying the degree of diversification of a stochastic portfolio model. It has an axiomatic foundation and can be defined through a parametric class of risk measures. Since the Value-at-Risk (VaR) and the Expected Shortfall (ES) are the most prominent risk measures widely used in both banking and insurance, we investigate DQ constructed from VaR and ES in this paper. In particular, for the popular models of multivariate elliptical and multivariate regular varying (MRV) distributions, explicit formulas are available. The portfolio optimization problems for the elliptical and MRV models are also studied. Our results further reveal favourable features of DQ, both theoretically and practically, compared to traditional diversification indices based on a single risk measure. |
| Date: | 2023–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2301.03517&r=fmk |
| By: | Laurent Millischer; Tatiana Evdokimova; Oscar Fernandez |
| Abstract: | Financial markets can support the transition to a low-carbon economy by redirecting funds from highly emissive to clean investments. We study whether European stock markets incorporate carbon prices in company valuations and to what degree they discriminate between firms with different carbon intensities. Using a novel dataset of stock prices and carbon intensities of 338 European publicly traded companies between 2013 and 2021, we find a strongly statistically significant relationship between weekly carbon price changes and stock returns. Crucially, this relationship depends on firms’ carbon intensity: the higher the carbon costs a firm faces, the poorer its stock performance during the periods of carbon price increases. Emissions covered with free allowances however do not affect this relationship, illustrating how both carbon pricing and disclosures are needed for financial markets to foster climate change mitigation. The relationship we identify can provide an incentive for firms to decarbonize. We argue in favor of more ambitious carbon pricing policies, as this would strengthen the stock-market incentive channel while causing only limited financial stability risk for stocks. |
| Keywords: | European Union Emissions Trading Scheme; Carbon price; Stock price valuation; Climate Finance; Climate Change Mitigation; Multifactor Market Model; stock-market incentive channel; firm face; IMF working papers; carbon intensity; Greenhouse gas emissions; Asset prices; Non-wage benefits; Inflation; Europe |
| Date: | 2022–11–18 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2022/231&r=fmk |
| By: | Watanabe, Toshiaki; Nakajima, Jouchi |
| Abstract: | A new high-frequency realized stochastic volatility model is proposed. Apart from the standard daily-frequency stochastic volatility model, the high-frequency stochastic volatility model is fit to intraday returns by extensively incorporating intraday volatility patterns. The daily realized volatility calculated using intraday returns is incorporated into the high-frequency stochastic volatility model by considering the bias in the daily realized volatility caused by microstructure noise. The volatility of intraday returns is assumed to consist of the autoregressive process, the seasonal component of the intraday volatility pattern, and the announcement component responding to macroeconomic announcements. A Bayesian method via Markov chain Monte Carlo is developed for the analysis of the proposed model. The empirical analysis using the 5-minute returns of E-mini S&P 500 futures provides evidence that our high-frequency realized stochastic volatility model improves in-sample model fit and volatility forecasting over the existing models. |
| Keywords: | Bayesian analysis, High-frequency data, Markov chain Monte Carlo, Realized volatility, Stochastic volatility model, Volatility forecasting |
| JEL: | C22 C53 C58 G17 |
| Date: | 2023–01 |
| URL: | http://d.repec.org/n?u=RePEc:hit:hiasdp:hias-e-127&r=fmk |
| By: | Vanya Horneff; Raimond Maurer; Olivia S. Mitchell |
| Abstract: | This paper investigates retirees’ optimal purchases of fixed and variable longevity income annuities using their defined contribution (DC) plan assets and given their expected Social Security benefits. As an alternative, we also evaluate using plan assets to boost Social Security benefits through delayed claiming. We determine that including deferred income annuities in DC accounts is welfare enhancing for all sex/education groups examined. We also show that providing access to well-designed variable deferred annuities with some equity exposure further enhances retiree wellbeing, compared to having access only to fixed annuities. Nevertheless, for the least educated, delaying claiming Social Security is preferred, whereas the most educated benefit more from using accumulated DC plan assets to purchase deferred annuities. |
| JEL: | D91 G11 G14 G22 G53 |
| Date: | 2023–01 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:30853&r=fmk |
| By: | Igor V. Evstigneev (University of Manchester - Economics, School of Social Sciences); Thorsten Hens (University of Zurich - Department of Banking and Finance; Norwegian School of Economics and Business Administration (NHH); Swiss Finance Institute); Mohammad Javad Vanaei (University of Manchester) |
| Abstract: | Evolutionary Finance (EF) explores financial markets as evolving biological systems. Investors pursuing diverse investment strategies compete for the market capital. Some "survive" and some "become extinct". A central goal is to identify evolutionary stable (in one sense or another) investment strategies. The problem is analyzed in a framework combining stochastic dynamics and evolutionary game theory. Most of the models currently considered in EF assume that asset payoffs are exogenous and depend only on the underlying stochastic process of states of the world. The present work develops a model where the payoffs are endogenous: they depend on the share of total market wealth invested in the asset. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2296&r=fmk |
| By: | Thomas Dierckx; Jesse Davis; Wim Schoutens |
| Abstract: | In this study, we predict next-day movements of stock end-of-day implied volatility using random forests. Through an ablation study, we examine the usefulness of different sources of predictors and expose the value of attention and sentiment features extracted from Twitter. We study the approach on a stock universe comprised of the 165 most liquid US stocks diversified across the 11 traditional market sectors using a sizeable out-of-sample period spanning over six years. In doing so, we uncover that stocks in certain sectors, such as Consumer Discretionary, Technology, Real Estate, and Utilities are easier to predict than others. Further analysis shows that possible reasons for these discrepancies might be caused by either excess social media attention or low option liquidity. Lastly, we explore how our proposed approach fares throughout time by identifying four underlying market regimes in implied volatility using hidden Markov models. We find that most added value is achieved in regimes associated with lower implied volatility, but optimal regimes vary per market sector. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2301.00248&r=fmk |
| By: | Tomoo Kikuchi; Satoshi Tobe |
| Abstract: | We investigate the elasticity of portfolio investment to geographical distance in a gravity model utilizing a bilateral panel of 86 reporting and 241 counterparty countries/territories for 2007-2017. We find that the elasticity is more negative for ASEAN than OECD members. The difference is larger if we exclude Singapore. This indicates that Singapore's behavior is very different from other ASEAN members. While Singapore tends to invest in faraway OECD countries, other ASEAN members tend to invest in nearby countries. Our study also shows the emergence of China as a significant investment destination for ASEAN members. |
| Date: | 2023–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2301.05443&r=fmk |
| By: | Tunio, Mohsin Waheed |
| Abstract: | This paper, using a combination of volatility models i.e. GARCH, TGARCH, and EGARCH, tries to explain the domestic and external factors, responsible for volatility in Pakistan’s sovereign bond yield-to-maturity of various bond tenors. The paper finds out that within domestic factors, apart from the macroeconomic fundamentals, political changes such as the one that took place in April 2022 did also significantly impact the yields. In addition to the domestic factors, the general riskiness perception of emerging market bonds as measured by Emerging Market Bond Index (EMBI) spreads does also have meaningful repercussions on the yields of Pakistani bonds. Besides, sovereign defaults in the regional economies such as Sri Lanka do also greatly influence the yields by causing an uptick in them. |
| Keywords: | Volatility, Sovereign bonds, Yield, Eurobond, Sukuk, Spreads |
| JEL: | B26 C01 C12 C32 C58 G12 G24 |
| Date: | 2023–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:116030&r=fmk |