Empirical Finance Research Articles

Integrating Structural and Reduced-Form Methods in Empirical Finance

Integrating Structural and Reduced-Form Methods in Empirical Finance

The Contribution of Spatial Econometrics in the Field of Empirical Finance

The Contribution of Spatial Econometrics in the Field of Empirical Finance

Um Tutorial sobre o Método Generalizado dos Momentos (GMM) em Finanças

ABSTRACT Context: empirical problems in which the researcher is faced with a model that is partially specified. In these cases, the GMM method is the natural alternative for estimating the parameters of interest. Objective: the goal of this paper is to offer a tutorial that allows the researcher to understand both the theory and empirical aspects of the GMM method. Methods: we discuss the GMM concepts, forms of estimation, and limitations associated with the method. As a way of illustrating the method, we use two applications in the area of empirical finance. The first application is the estimation of the parameters of a consumption-based asset pricing models; the second is the estimation of the parameters of the evolution of the interest rate in continuous time. The data and codes in R are provided as online appendices. Conclusion: the GMM method can be used in problems where other methods such as maximum likelihood are not feasible, or even when the researcher wants to estimate a model partially specified.

Twenty-nine years of the Journal of International Review of Economics and Finance: A scientometric overview (1992–2020)

This paper aims to conduct a comprehensive retrospective analysis of the International Review of Economics and Finance (IREF). Using the bibliometric approach, the study analyses the journal's performance, citation structure, network connection, and mapping analysis through which a conceptual structure is developed. The Scopus database is used to retrieve data and a wide range of bibliometric tools are utilized for analysis such as RStudio “Biblioshiny” package, VOSviewer, and Microsoft Excel. Analysing IREF performance over the last twenty-nine-years (1992–2020), the findings reveal that the most prolific authors have USA and China affiliations; the journal's authors derive their information and build on research from sources that are ranked (Q1, A*, 4, 4* and SSCI), which indicates high connectedness between IREF content and published work in top sources in economics and finance. Moreover, the citation of the journal is surging in an unprecedented way during the last five years. Thus, IREF has positioned itself as one of the most influential sources, especially in finance. Its citation performance is close to the Journal of Finance, Journal of Banking and Finance, and Journal of Corporate Finance, while being above many esteemed finance journals, such as Review of Finance, Emerging Markets Finance and Trade, and Journal of Empirical Finance. The most prominent themes discussed in the journal are capital structure, economic growth, corporate governance, monetary policy, foreign direct investment, exchange rates, and emerging markets. This article presents the trending topics in IREF over the last three decades, which is helpful for the journal's board of editors in drawing a successful strategy based on a bibliometric study of the economics and finance field, where it is likely to garner interest.

Optimal characteristic portfolios

Characteristic-sorted portfolios are the workhorses of modern empirical finance, deployed widely to evaluate anomalies and construct asset pricing models. We propose a new method for their estimation that is simple to compute, makes no ex-ante assumption on the nature of the relationship between the characteristic and returns, and does not require ad hoc selections of percentile breakpoints or portfolio weighting schemes. Characteristic portfolio weights are implied directly from data, through maximizing a Mean–Variance objective function with mean and variance estimated non-parametrically from the cross-section of assets. To illustrate the method, we evaluate the size, value and momentum anomalies and find overwhelming empirical evidence of the outperformance of our methodology compared to standard methods for constructing characteristic-sorted portfolios.

Investor Experience and Portfolio Choice

MiFID II forces banks and wealth managers to ask clients for their investment knowledge and experience. The implied regulatory view is that less experience should result in less risk taking. While this is neither shared in theoretical nor in empirical finance, it becomes a source of legal risk for asset managers and banks. How do banks react? So far this question was impossible to answer. The relevant data have not been available as they are not shared by banks. We circumvene this problem by using publicly available portfolio recommendations from robo-advisory firms. These firms fall under the same regulations as banks and wealth managers with respect to MiFID II investor profiling and are often owned by traditional banks. It is therefore reasonable to assume that their treatment of investor experience is similar to traditional banks' approaches.

Working with CRSP/COMPUSTAT in R: Reproducible Empirical Asset Pricing

It is common to come across SAS or Stata manuals while working on academic empirical finance research. Nonetheless, given the popularity of open-source programming languages such as R, there are fewer resources in R covering popular databases such as CRSP and COMPUSTAT. The aim of this article is to bridge the gap and illustrate how to leverage R in working with both datasets. As an application, I illustrate how to form size-value portfolios with respect to Fama and French (1993) and study the sensitivity of the results with respect to different inputs. Ultimately, the purpose of the article is to advocate reproducible finance research and to contribute to the recent idea of Open Source Cross-Sectional Asset Pricing'', proposed by Chen and Zimmermann (2020).

Nonlinearity everywhere: implications for empirical finance, technical analysis and value at risk

We show that expected returns on US stocks and all major global stock market indices have a particular form of non-linear dependence on previous returns. The expected sign of returns tends to reverse after large price movements and trends tend to continue after small movements. The observed market properties are consistent with various models of investor behaviour and can be captured by a simple polynomial model. We further discuss a number of important implications of our findings. Incorrectly fitting a simple linear model to the data leads to a substantial bias in coefficient estimates. We show through the polynomial model that well-known short-term technical trading rules may be substantially driven by the non-linear behaviour observed. The behaviour also has implications for the appropriate calculation of important risk measures such as value at risk.

Predictability of stock market indexes following large drawdowns and drawups

The efficient market hypothesis is one of the most popular subjects in the empirical finance literature. Previous studies of the stock markets, which are mostly based on fixed-time price variations, have inconclusive findings: evidence of short-term predictability varies according to different samples and methodologies. We propose a novel approach and use drawdowns and drawups as triggers, to investigate the existence of short-term abnormal returns in the stock markets. As these measures are not computed within a fixed time horizon, they are flexible enough to capture subordinate, time-dependent processes that could drive market under- or overreaction. Most estimates in our results support the efficient market hypothesis. The underreaction hypothesis receives stronger support than does overreaction, with higher prevalence of return continuations than reversals. Evidence for the uncertain information hypothesis is present in some markets, mainly after lower-magnitude events.

Investor sentiment by relative strength index and stock return: Empirical evidence on Vietnam's stock market

This article examines how investor sentiment affects stock returns on Vietnam's stock market. Investor sentiment index is measured by a relative strength index (RSI) of 57 companies listed on the Ho Chi Minh Stock Exchange from January 1, 2015 to July 31, 2020. Control variables include investors' stock trading behavior, firm size, and cash flow per share. Using Fama-MacBeth regression estimation and general least square estimation (GSL) on a daily basis, both methods find the sentiment of high investors producing higher stock returns, on the contrary, the sentiment of low investors erodes stock returns. Different from the results of Brown and Cliff (2004) [Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of empirical finance, 11(1), 1-27], the article found that the investor sentiment factor plays the most important role in explaining the return of the stock market compared to the rest of the factors.

Working with CRSP/COMPUSTAT in R: Reproducible Empirical Asset Pricing

It is common to come across SAS or Stata manuals while working on academic empirical finance research. Nonetheless, given the popularity of open-source programming languages such as R, there are fewer resources in R covering popular databases such as CRSP and COMPUSTAT. The aim of this article is to bridge the gap and illustrate how to leverage R in working with both datasets. As an application, I illustrate how to form size-value portfolios with respect to Fama and French (1993) and study the sensitivity of the results with respect to different inputs. Ultimately, the purpose of the article is to advocate reproducible finance research and to contribute to the recent idea of Open Source Cross-Sectional Asset Pricing'', proposed by Chen and Zimmermann (2020).

Slope Factors Outperform: Evidence from a Large Comparative Study

Does the method used to construct long-short factors from firm-level characteristics change the assessment of the factor risks (risk-factors) that are incorporated in the cross-section of expected returns? Is the method of construction important for the pricing performance of those risk- factors? We compare factors constructed by three methods: pure-play slope factors (estimated OLS slopes from cross-sectional regressions), differential factors (difference in returns of 2 by K sorts of stocks) and rank factors (normalized rank weighted returns). Starting from 62 firm level characteristics, we construct 62 such factors from each method. For each set of 62 factors plus the market factor, we examine the posterior distributions of market prices of factor risks to infer risk-factors, and apply a Bayesian pricing criteria to determine the evidence in favor of pricing for a large collection of portfolios, ETFs and stocks. The evidence shows that the slope risk-factors strongly dominate, with important implications for empirical finance.

Are stable cryptocurrencies exempted from impossible trinity?

PurposeWhile monetary autonomy is self-explanatory for cryptocurrencies such as Bitcoin with predetermined supply path, it is of great interest to probe into the monetary structures of Stablecoins. In these supply contracts and expands and capital restrictions apply due to the existence of reserves as the exchange rate arrangement adheres to a price rule.Design/methodology/approachEver since the launch of Bitcoin and its offspring, examination of cryptocurrencies' trading activity from the empirical finance viewpoint has received much attention and continues to do so. The particular monetary arrangements found in Stable cryptocurrencies (colloquially referred to as Stablecoins), however, have not been properly (1) classified and (2) studied within an empirical international finance and banking context. This paper provides an empirical framework analogous to Impossible Trinity for exploring monetary arrangements across Stablecoins wherein reserves are held as price stability is targeted.FindingsThe study findings of existence of the degree of achievement along the three dimensions of the Impossible Trinity hypothesis, namely monetary independence, exchange rate stability and financial openness for a representative sample able to cover all varieties of Stablecoins, provide fresh empirical insights and arguments to this growing literature with respect to the success of their embedded exchange rate stabilization mechanisms. While the hypothesis can be supported for all cryptocurrencies in question, the trade-off combination among exchange rate stability, capital openness and monetary independence varies with the categorical types of Stablecoins.Research limitations/implicationsIf Stable cryptocurrencies, therefore, claim the role of global monetary assets freed from sovereign limits and national boundaries, it is critical to explore whether they adhere to traditional monetary frameworks. It goes without saying that in this work the author does not use a complete catalogue of all the available Stablecoins, rather a complete catalogue of all the possible asset classes of Stablecoins. While there is a significant difficulty in finding Algorithmic Stablecoins and, so far, there is plethora of Stable Token initiatives, a broader sample to further examine these under this paper's empirical framework is suggested. Enrichment of the robustness analysis by constructing additional proxies, possibly building time series for the proposed cmo1 subindex and using additional estimation methods is encouraged.Practical implicationsStablecoins have been developed aiming to address the issue of excessive price variation in cryptocurrencies such as Bitcoin. Holders of Stablecoins enjoy the combined advantages of using a blockchain-based digital infrastructure in fulfilling the functions of store of value and media of exchange and of using a traditional currency, which merely plays the role of the unit of account (and in some circumstances the trusted reserve to which is convertible to). Understanding the varieties of Stablecoins and quantifying the components for success of their price stabilization may result in designing better Stablecoins.Social implicationsBlockchain and cryptocurrencies have introduced new challenges to money and banking. Cryptocurrencies, which independently float such as Bitcoin, have gained the interest so far due to price variation that allows for gains. But these should be by far not considered to be a substitute to traditional means of payment. Lately, Stablecoins have increasingly gained attention for that USD Tether/Bitcoin pair (a Stablecoin pegged to the US dollar at parity) has outrun the US dollar/Bitcoin pair as the most traded pair in digital exchanges marking the strong position and high demand for Stablecoins.Originality/valueThis approach uncovers the varieties of Stablecoins with respect to their monetary constraints compared to the rest of the cryptocurrencies, which independently float. In this paper, the author provides a conceptual framework for the analysis of the exchange rate mechanisms conditional on Stablecoin asset classes accompanied with an empirical study from the monetary viewpoint. This is the first work in this attempt. The empirical framework employed is analogous to the traditional theory of international monetary economics referred to as Impossible Trinityz.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/JES-06-2020-0279

Large sample size bias in empirical finance

The vast majority of empirical studies in finance employ large enough sample sizes and use the conventional thresholds for statistical significance. This routine practice can potentially lead to spurious statistically significant results. The primary aim of this paper is to present a rule of thumb that can be used to determine the appropriate thresholds for statistical significance for a given sample size. The paper argues that the list of statistically significant findings in the broader finance literature is likely to be much shorter after accounting for large sample size bias.

The Mystery of Zero-Leverage Firms: Evidence from Nigerian Quoted Firms

Objective – Empirical finance literature has added a new twist to the debt conservatism puzzle within the broader capital structure puzzle, namely the phenomenon of zero leverage. Motivated by Strebulaev and Yang (2013), this study investigates the attributes of zero leverage firms in Nigeria in an attempt to add a developing country perspective to the zero-leverage phenomenon observed in firms. Methodology/Technique – The non-financial corporations quoted on the Nigerian Stock Exchange (NSE) for the period 1999-2014 constitute the population of the study. Firms with market leverage ratios ranging from 0% to 5% met the criteria for inclusion. Panel data regression techniques such as the generalized method of moments (GMM) and two stage least squares (2SLS) were used in the study. Findings – Zero leverage is persistent across 13 industries and is a declining function of the marginal tax rate, firm size, profitability, and liquidity. Firms that follow a zero-leverage (and almost zero-leverage) policy have higher growth opportunities, more tangible assets, pay higher dividends, are older, and have access to debt markets. Non-debt tax shields do not explain zero-leverage behaviour. Originality/Value – This study addresses the gaps related to the questions of why and how firm-specific attributes affect zero leverage behaviour among Nigerian quoted firms. It sheds light on the economic mechanisms driving zero leverage phenomenon within firms with high debt capacity. Type of Paper: Empirical. JEL Classification: G30, G32. Keywords: Capital Structure; Zero Leverage Puzzle; Tax Benefits; Debt Capacity; Financing Decisions. Reference to this paper should be made as follows: Paseda, O; Adedeji, B.S. 2020. The Mystery of Zero-Leverage Firms: Evidence from Nigerian Quoted Firms, Acc. Fin. Review 5 (2): 44 – 71. https://doi.org/10.35609/afr.2020.5.2(2)

Is Financial Literacy Associated with Investment in Financial Markets in the United States?

Lack of investment in financial markets is one of the enduring puzzles in empirical finance. Although recent studies ascribe the lack of investment in stocks to financial literacy, the association between financial literacy and investment in financial markets remains inconclusive. We examine whether financial literacy is associated with investment in financial markets in the United States. We use investment in stocks, futures/options, investment trusts, corporate bonds, foreign currency deposits, and government bonds of foreign currency as a proxy for investment in financial markets. Using data from the Preference Parameter Study, a nationwide panel survey conducted by Osaka University of Japan, we provide evidence that financial literacy has a significantly positive association with investment in financial markets even after controlling for demographic, socioeconomic, and psychological factors. We check the robustness of our results by using an alternative proxy for investment in financial markets. Our study has far-reaching policy implications and we conclude by suggesting the introduction of financial literacy programs into the academic curriculum. Improving financial literacy could positively impact the mobilization of household funds and contribute to capital formation.

Measuring liquidity in Indian stock market: A dimensional perspective.

Market liquidity ensures the marketability of security and is an indispensable feature of stock markets. Previous studies have emphasized the role of stock market liquidity in empirical finance. However, they have inadequately explored its multidimensional nature. This study eliminates the ambiguities related to market liquidity by precisely measuring it by using popular and proven liquidity measures. As such, the present study aims to evaluate market liquidity in terms of depth, breadth, tightness, and immediacy in the Indian equity market and also identifies crucial interdependencies between liquidity dimensions. The study selects 500 stocks constituting the NIFTY 500 index of the National Stock Exchange, India, as of 26th May 2019. The data on trading volume, bid price, ask price, the number of shares outstanding, closing share prices were retrieved for the period from 1st April 2009 to 31st March 2019. The study employs Share Turnover, Amihud Illiquidity Ratio, Relative Quoted Spreads, and Coefficient of Elasticity of Trading for liquidity measurement. The Vector Auto-Regressive (VAR) model is used to establish the simultaneous relationships between liquidity dimensions. The analysis is conducted at the aggregate market level as well as across turnover based stock groups divided based on their rankings in terms of stock specific share turnover. The empirical results evidenced the presence of consistent depth, strong breadth, and immediacy but lower tightness in the Indian equity market. The market depth and tightness appear to be relevant in determining dimensional interdependencies. Also, less frequently traded stocks exhibit higher illiquidity in the wake of lower tightness. The findings of this study will assist the investors to wisely understand the multifaceted nature of market liquidity and base their trading decisions accordingly. Moreover, the regulators of the stock exchange can devise liquidity enhancing policies based on the directional movements among liquidity dimensions.

Underspecification of the Empirical Return-Factor Model and a Factor Analytic Augmentation as a Solution to Factor Omission

This empirical paper comprehensively sets out the impact of underspecification on a key foundational concept in empirical finance, the linear factor model. It places emphasis on the extensive consequences of factor omission for model estimation and interpretation. Factor omission in time-series models that relate asset returns to pre-specified factor sets is a common problem. A proposed standard and widely-used solution is the inclusion of a residual market factor which is assumed to be a catch-all proxy for omitted factors. This study shows that a specification that incorporates a set of carefully selected macroeconomic factors will be underspecified. The inclusion of residual market factors will alleviate but not eliminate the consequences of underspecification. Although the early use of factor analytically derived factor scores in factor models has been criticized, augmenting a model comprising pre-specified factors with statistical factors derived from the residuals results in an accurately specified model for which the diagonality assumption holds. Consequently, this paper shows that a factor analytic augmentation is an effective and readily implementable solution to the factor omission problem.

Which measure of systematic risk should we use? An empirical study on systematical risk and Treynor measure using the economic index of riskiness and operational measure of riskiness

AbstractThis paper empirically studies the differences among the systematic risks of three asset pricing models, namely; the mean–variance capital asset pricing model (MV‐CAPM), AS‐CAPM and FH‐CAPM. The last two are derived by replacing variance with the Aumann‐Serrano (AS) index and the Foster‐Hart (FH) as the risk measure in MV‐CAPM. We use the Dow Jones Industrial Average (DJIA) index as a proxy for the market portfolio, and its component stocks to check if the systematic risks and the Treynor measures are different. The monthly return data from January 1997 to October 2017 are used for empirical estimations. The results show that the three systematic risks are highly correlated. Similarly, high correlation is also found for the three Treynor measures. It seems that even though they are derived under different risk measures, they produce almost the same systematic risk and performance measure for individual stocks. Therefore the findings of the present study suggest that any of the above measures can be used in empirical finance in the area of risk management. As this finding is different from those of other studies in the existing literature in this area, this study makes a contribution to the finance literature.

Looking at Extremes without Going to Extremes: A New Self-Exciting Probability Model for Extreme Losses in Financial Markets.

Forecasting market risk lies at the core of modern empirical finance. We propose a new self-exciting probability peaks-over-threshold (SEP-POT) model for forecasting the extreme loss probability and the value at risk. The model draws from the point-process approach to the POT methodology but is built under a discrete-time framework. Thus, time is treated as an integer value and the days of extreme loss could occur upon a sequence of indivisible time units. The SEP-POT model can capture the self-exciting nature of extreme event arrival, and hence, the strong clustering of large drops in financial prices. The triggering effect of recent events on the probability of extreme losses is specified using a discrete weighting function based on the at-zero-truncated Negative Binomial (NegBin) distribution. The serial correlation in the magnitudes of extreme losses is also taken into consideration using the generalized Pareto distribution enriched with the time-varying scale parameter. In this way, recent events affect the size of extreme losses more than distant events. The accuracy of SEP-POT value at risk (VaR) forecasts is backtested on seven stock indexes and three currency pairs and is compared with existing well-recognized methods. The results remain in favor of our model, showing that it constitutes a real alternative for forecasting extreme quantiles of financial returns.