Journal of Asset Management

Published by Springer Nature
Online ISSN: 1479-179X
Print ISSN: 1470-8272
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Efficiency measures for the TOPIX and Nikkei 225 indices. This figure plots the efficiency measures for the TOPIX (A–C) and Nikkei 225 (D–F). EG is the Hurst exponent less 0.5 and D1 and D2 are the first and second price delay measures of Hou and Moskowitz (2005). The red vertical line on 15 December 2010 denotes the commencement of the BOJ’s ETF purchase programme
Efficiency measures for the MSCI Asia ex Japan index. This figure plots the efficiency measures for the MSCI Asia ex Japan index. EG is the Hurst exponent less 0.5 (A) and D1 and D2 are the first and second price delay measures of Hou and Moskowitz (2005) (B and C, respectively)
We examine the impact of the Bank of Japan’s exchange traded fund (ETF) purchases on two aspects of market efficiency—long-range dependence and price delay—of the TOPIX and Nikkei 225 indices. An increase in ETF purchases results in lower long-range dependence for both indices while the impact on the price delay varies according to index and measure. A sub-period analysis shows that the impact on market efficiency varies over time, with the dominant pattern being a delayed harmful effect, followed by a positive impact and thereafter a negative effect. The implications of these findings are discussed.
 
In this article, we aim to explain what causes the depth of a stock market drawdown using the discretionary global macro approach. Our key finding is that the increase in credit risk to high/very high level after the beginning of a drawdown significantly explains the depth of the drawdown. An expected aggressive monetary policy tightening can trigger a correction, especially if accompanied with a high recession probability. Further, an expected aggressive monetary policy easing, as a sign of an imminent recession, can deepen the total drawdown. However, the depth of the total drawdown depends of whether the drawdown transitions to the ultimate credit crunch stage.
 
Major emerging market countries issue significant amounts of local currency bonds in order to finance their budget deficits. As liquidity is a substantial feature of the financial markets, understanding bond liquidity dynamics is essential. The bid-ask spread is an important measure of bond liquidity and reflects explicit transaction costs. We apply a panel regression model in order to analyze bond-level and country-level characteristics’ effects on bond liquidity and bid-ask spread. Results show that volatility, credit risk and duration have significant effects on emerging market bond liquidity. Emerging market sovereign bonds with lower volatility, lower credit risk and shorter duration have narrower bid-ask spreads, on average.
 
Sharpe ratios per decades
Box plot of the maximum weights in %, 1968–2020
Box plot of the number of invested stocks, 1968–2020
Box plots of the weights in % in sub-portfolios for signal-based strategies, 1968–2020. Notes: this exhibit reports box plots of the maximum weights, the number of invested stocks, and the weights in sub-portfolios for signal-based strategies from July 1968 to June 2020. We display the minimum, the maximum, the mean, the median, and the first and third quartiles.
Using a partially revealing dynamic equilibrium model, investors adjust their estimates of the expected returns through the price discovery process (past price dynamics) and consequently implement price contingent portfolios based on these estimates. We implement the price contingent portfolio on the U.S. stock market and compare its performance with other common portfolio strategies. We also consider the price-volume contingent strategy, estimating the expected return and covariance matrix from both the past price and observed volume dynamics. We find that these signal-based portfolios outperform the capitalization and equal weighted strategies. They also provide appealing diversification benefits compared to common optimization-based portfolios.
 
Growth in Indian mutual funds, this figure depicts the value of Asset Under Management (AUM) by mutual fund companies. The bar clearly shows that AUM has followed an increasing trend across the study period that, affirms the growth in the Indian mutual fund industry. Note: *Conversion rate for presenting in US dollars is ₹ 75= $1. **Incomplete year different period.
Source: Association of Mutual Funds of India (AMFI), (Association of Mutual Funds in India (AMFI) Statistics, https://www.amfiindia.com/research-information/aum-data/aum-aaum-disclosure. Accessed on September 24, 2022.)
We evaluate investors' learning from past fund performance and subsequent capital allocation decisions in mutual funds in an emerging market setup. We find that investors in India learn more about funds' ability to generate excess returns from past funds’-family performances. We explore two possible channels, common skill effect and negative correlation effects, through which investors observe fund-family performance and its impact on future fund flows. We observe that fund-family performance dominates individual fund performance when the common skill effect is stronger than the negative correlation effect. Our findings suggest that investors can use fund-family resources to understand funds' alpha-generating skills. Overall, our result highlights the new learning perspective of Indian fund investors.
 
Accounting and economic evaluation criteria play an important role in assessing the performance of the firms. Choosing proper criteria for such evaluation has been reported in reviews of literature on financial management. The present study aims to find the relationship between economic value added (EVA) as the criterion for evaluating economic performance, and return on assets and stock returns as indicators of economic accomplishment. For this, our statistical community includes 1104 companies from Iran (between 2011 and 2018) and 1058 companies from Venezuela (between 2011 and 2017). Results of analysis showed that there was a reverse significant relationship between EVA and stock returns in two stock exchanges of Iran and Venezuela because of the significance level less than 0.05 and coefficient by − 1.2. On the other hand, there was no such relationship observed between EVA and return on assets because the significance level was found higher than 0.05.
 
Lift charts
ROC curves
This study provides an applicable methodological approach applying artificial intelligence (AI)-based supervised machine learning (ML) algorithms in risk assessment of post-pandemic household cryptocurrency investments and identifies the best performed ML algorithm and the most important risk assessment determinants. The empirical findings from analyzing 13 determinants from 1,000 dataset collected from major cryptocurrency communities online suggest that the logistic regression (LR) algorithm outperforms the remaining six ML algorithms by using performance metrics, lift chart, and ROC chart. Moreover, to make the ML algorithm results explainable and tackle the “black box” issue, the top five most important determinants are discovered, which are the interaction between investment amount and investment duration, investment amount, perception of traditional investments, cryptocurrency literacy, and perception of cryptocurrency volatility. The present study contributes to the literature on risk assessment, especially on the household cryptocurrency investments in the post-pandemic era and the body of knowledge on explainable supervised ML algorithms.
 
Cross-sectional IC estimates for the 12-month momentum factor
Cross-sectional IC estimates for the Book-to-Price Ratio factor
Theoretical and simulated information ratios as functions of N under time-varying cross-sectional information coefficients
Simulated IR using empirical factors
Simulated IR using multifactor combination
The information coefficient (IC), defined as the correlation coefficient between a stock return and its factor exposures predictor variables, is one of the most commonly used statistics in quantitative financial analysis. In this paper, we establish consistency and asymptotic normality of the time series average of cross-sectional sample ICs when the true underlying ICs between the risk-adjusted residual return and the standardized factor exposures are time varying. We use those results to show that the time series average of the cross-sectional sample ICs divided by its sample standard deviation converges to the ex ante expected portfolio information ratio (IR) as derived in Ding and Martin (2017). A simulation study based on a true factor model shows that the finite sample results are strikingly close to what the theory suggests. We also conduct empirical simulations using actual stock returns and quantitative factor exposures, and we find that the logarithm of the estimated IR can be explained very well by a function of the IC mean, the IC standard deviation, and the sample size in exactly the same way as predicted by our theory built on a linear factor model with time varying ICs.
 
Weekly average returns (Rm\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}_{m}$$\end{document}) and Cross-Sectional Absolute Deviations (CSAD) for the different stock markets from 01/03/2010 to 03/31/2019
Estimation of the time-varying herding coefficient β2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{2}$$\end{document} for the six stock markets. The parameter is statistically significant if the upper and down bonds of the confidence interval have the same sign
This study investigates the asymmetric effect of oil price on herding behavior in three oil-exporting (Saudi Arabia, Canada, and Russia) and three oil-importing countries (USA, Japan, and China) stock markets. Due to the negative relationship between oil-exporting and oil-importing stock markets, the effect of oil price on herding behavior in those stock markets may differ. Based on the static approach, results reveal no evidence of herding in all stock markets, particularly investors in oil-importing stock markets trade away from the market consensus. Our findings support a dynamic herding pattern by conducting a time-varying parameter model, which seems to be more prevalent in the Chinese stock market. While we do not detect a significant impact of oil price on the level of herding in all stock markets, the rise in oil price boosts investors to herd in Russia, Japan, and China.
 
Portfolio allocation decisions over the life cycle depend, among many factors, on the retirement income as well as risks and choices faced in retirement. However, due to computational complexity, many retirement factors are typically assumed away. By building on the standard life cycle investment-consumption model that includes a more realistic progressive retirement income program, I discuss how each aspect of retirement income affects optimal portfolio allocation over the life cycle. I find that all investors across all scenarios maintain high levels of stocks in their portfolios at a young age. However, investors who face low net replacement rates, risk of forced retirement, or retirement income uncertainty hedge these risks by accumulating higher private savings and reducing risky portfolio shares at an earlier age. In a realistic setting with early forced retirement risk and endogenous retirement timing, optimal equity portfolio share temporarily increases once the investor becomes eligible for retirement. Retirement income’s dependency on workers’ lifetime labor earnings is not, however, an important factor in portfolio allocation over the life cycle.
 
Counties exposed to sea level rise. This figure presents counties exposed to sea level rise, as computed by Hallegatte et al. (2013). Expected mean annual losses as a percentage of a city’s GDP are obtained assuming a 40 cm rise in the sea level as of 2050 and that cities attempt to adapt to this rise. Cities are then mapped to counties following (Painter 2020). All counties for which there is no measure computed by Hallegatte et al. (2013) are assigned a SLR of zero.
Using a firm’s geographic footprint to measure its exposure to sea level rise (SLR), I find that corporate bonds bear a climate risk premium upon issuance. A one standard deviation increase in firms’ SLR exposure is associated with a 7 basis point premium, representing a 3% increase in average yield spread. This effect is more pronounced for geographically concentrated firms, within industries vulnerable to extreme weather conditions, and after the Paris Agreement. I do not find evidence that credit rating agencies account for SLR exposure at bond issuance. Results are robust to placebo tests and inverse propensity weighting to address possible endogeneity.
 
Growth indicator
Inflation indicator
Two regime plots
Portfolio weights
This paper presents a practical investment framework for dynamic asset allocation strategies based on changes in the macro-environment. To identify economic regimes, we use macro-indicators that track monthly growth and inflation of the US economy. We then demonstrate that the regimes divided by changes in growth and inflation trends successfully partition the historical performance of asset classes, and construct a regime-based dynamic strategy for shifting exposures toward attractive assets according to economic regimes. Out-of-sample analysis suggests that the dynamic approach outperforms the static approach after accounting for transaction costs, leading to a higher risk-adjusted return and information ratio. These results have crucial implications for portfolio managers seeking to develop a dynamic asset allocation strategy throughout economic cycles to enhance long-term portfolio performance.
 
Total dynamic connectedness. Note In the figure above, the total volatility spillovers among treasury inflation-protected securities, short-term treasury bonds, medium-term treasury bonds, long-term treasury bonds, gold, real estate, oil, equities, volatility index (VIX) and economic policy uncertainty index (EPU) are illustrated for the period 1/1/2010–3/31/2022. y-axis depicts total volatility spillovers estimated with TVP-VAR method and x-axis time
Total directional connectedness “to” others. Note In the figure above, the total directional connectedness “to” others is illustrated for treasury inflation-protected securities, short-term treasury bonds, medium-term treasury bonds, long-term treasury bonds, gold, real estate, oil, equities, volatility index (VIX) and economic policy uncertainty index (EPU) for the period 1/1/2010–3/31/2022. y-axis depicts directional volatility spillovers to other markets estimated with TVP-VAR method and x-axis time
Total directional connectedness “from” others. Note In the figure above, the total directional connectedness “from” others is illustrated for treasury inflation-protected securities, short-term treasury bonds, medium-term treasury bonds, long-term treasury bonds, gold, real estate, oil, equities, volatility index (VIX) and economic policy uncertainty index (EPU) for the period 1/1/2010–3/31/2022. y-axis depicts directional volatility spillovers from other markets estimated with TVP-VAR method and x-axis time
“Net” total directional connectedness. Note In the figure above, the net total directional connectedness is illustrated for treasury inflation-protected securities, short-term treasury bonds, medium-term treasury bonds, long-term treasury bonds, gold, real estate, oil, equities, volatility index (VIX) and economic policy uncertainty index (EPU) for the period 1/1/2010–3/31/2022. y-axis depicts net directional volatility spillovers estimated with TVP-VAR method and x-axis time
Documenting the interlinkages among assets that are widely used to hedge against inflation is crucial for investors, as the necessity to protect the investment portfolio is stronger under inflationary conditions. For this purpose, we investigate the volatility spillovers between treasury inflation-protected securities (TIPS) and a battery of other assets perceived as inflation hedges, including bonds, gold, real estate, oil and equities. The applied methodology comprehends the time-varying parameter vector autoregressive (TVP-VAR) extension of the Diebold and Yilmaz (Int J Forecast 28:57–66, 2012, 10.1016/j.ijforecast.2011.02.006) approach for the period 1/1/2010–3/31/2022. Our results indicate that the assets under consideration are moderately interconnected and subjected to several exogenous shocks, such as the US–China trade war, the COVID-19 pandemic and the Russia–Ukraine war. Furthermore, we assess the hedging effectiveness of TIPS against each asset by estimating hedge ratios and optimal portfolios weights, before and after the spread of COVID-19 pandemic, by using conditional variance estimations (DCC-GARCH). The empirical findings show that the short position in the volatility of TIPS is proved to be an excellent hedge for all the sampled assets, with the exception of short-term Treasury bonds, and their hedging ability was improved during COVID-19.
 
Simulated impacts, Note The figure shows the impact measured in terms of Spearman’s correlation between the categories xk\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x_{k}$$\end{document} and the score S\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$S$$\end{document} (first subplot) and between xk\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x_{{\text{k}}}$$\end{document} and the adjusted score S~\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tilde{S}$$\end{document}. Simulation by the authors
Refinitiv case, scores in 2020, Note ESG Data from Refinitiv. ESG scores are displayed in the upper-left subplot. The Y-axis corresponds to the number of companies. The X-axis corresponds to the scores ranging from 0 to 100 and distributed over 12 buckets split in three pillars: the environmental pillar (ENV) in the upper-right subplot, the social pillar (SOS) in the bottom-left subplot, and the governance pillar (GOV) in the bottom-right subplot. ENV, SOS and GOV scores are directly obtained from the data provider for two markets: the Eurostoxx 600 in blue and the S&P500 in orange
Bloomberg case, scores in 2020, Note ESG Data from Bloomberg. ESG scores are displayed in the upper-left subplot. The Y-axis corresponds to the number of companies. The X-axis corresponds to the scores ranging from 0 to 100 and distributed over 12 buckets split in three pillars: the environmental pillar (ENV) in the upper-right subplot, the social pillar (SOS) in the bottom-left subplot, and the governance pillar (GOV) in the bottom-right subplot. ENV, SOS and GOV scores are directly obtained from the data provider for two markets: the Eurostoxx 600 in blue and the S&P500 in orange
Refinitiv case, ESG rankings in 2020, Note ESG Data from Refinitiv. The X-axis corresponds to the number of companies. The Y-axis corresponds to the scores ranging from 0 to 100 and distributed over 12 buckets. Scores are based on the following weights: 23% for the environmental pillar, 49.35% for the social pillar, and 27.65% for the governance pillar. Black dots refer to non-adjusted scores while blue dots refer to adjusted scores for cross-dispersion bias
Bloomberg case, ESG rankings in 2020, Note ESG Data from Bloomberg. The X-axis corresponds to the number of companies. The Y-axis corresponds to the scores ranging from 0 to 100 and distributed over 10 topics. Scores are based on the following weights: 23% for the environmental pillar, 49.35% for the social pillar, and 27.65% for the governance pillar. Black dots refer to non-adjusted scores while blue dots refer to adjusted scores for cross-dispersion bias.
We study the formation of ESG scores and rankings. In particular, we investigate the impact of aggregation rules when combining information on firms across categories, notably the E, S and G categories, into single ESG scores. Usual aggregation rules may bias scores toward the smost dispersed category. We suggest a correction for this dispersion bias. We apply this correction to scores provided by two of the main score providers: Refinitiv and Bloomberg. We also provide simulation evidences. We show that the cross-dispersion bias may have a significant impact on ESG scores formation and that our proposed adjustment tends to weather it.
 
This paper offers cross-sectional and data-intensive insights into Robo-advisory portfolio structures. For this purpose, we scrape portfolio recommendations for 16 German Robo-advisors. Our sample accounts for about 78% of assets in the German Robo-advisory market. We analyze about 243.000 pairs of recommended portfolios and their corresponding client characteristics. Our results show that current Robo-advice offers limited individualization. Variables that matter in modern portfolio choice like the amount and nature (beta) of human capital or shadow assets are largely ignored. Instead, portfolio recommendations are designed to meet investor preconceptions or the regulator’s understanding of portfolio choice. While ensuring consumer trust and regulatory approval makes business sense, it also limits the economic benefits of Robo-advisors.1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^1$$\end{document}
 
A two-step iterative estimation of a risk model, alternating between a cross-sectional and time-series regression, aims to achieve an in-sample consistent representation of risk factors, such that the security exposure matrix input of the crosssectional step is equal to the output exposure matrix estimated in the subsequent time-series step. The sequence of estimated exposure matrices is proven to converge to a fxed point. The condition for a fxed point is identifed and proven necessary and sufcient. The presented mathematical proof of viability of the two-step iterative estimation is complementary to earlier research in this area.
 
Evolution of Euro Stoxx 50 Index closing values and ETFs NAVs per share from Panel A scaled to 100 at the beginning of the sample
Evolution of Euro Stoxx 50 Index closing values and ETFs NAVs per share from Panel B scaled to 100 at the beginning of the sample
The main goal of the article is to examine the tracking efficiency of a homogenous sample of 14 ETFs listed on European exchanges, replicating the performance of Euro Stoxx 50 Index—a benchmark index for blue chips from the euro area. This study provides some insights into the tracking quality of European ETFs over the long time horizon (2012–2021 period) including data from entire business cycle: both economic prosperity and COVID-19 crisis. The study has been made applying different tracking error calculation techniques and return intervals—daily, weekly and monthly. Passive investing may be a highly desirable, cheap and accurate method for long or short term investments in the largest 50 cap companies in the euro zone. Hence, this unique research may help to succeed in ETF selection process. The study reveals that ETFs are very effectively managed by keeping the TEs below 0.3% (for ETFs with accumulating share classes) and below 1% (for ETFs with distributing share classes). This shows that the ETFs with accumulating share classes perform much better—the average TE for three different methods is 0.11% for accumulating share classes ETFs and 0.33% for distributing share classes ETFs. It proofs, that it is not important whether to use the standard deviation of the difference between the return of an ETF and that of its benchmark index, or the standard error of regression in TE assessment, both methods give very similar results. However, TE calculation method signifies, if the average of the absolute difference between the return of an ETF and that of the index is used. Additionally, it is found that time intervals used in TE calculations matter—the shift from monthly to daily intervals results in reduction of TE levels. Using shorter intervals brings lower TE values of European ETFs.
 
The model’s interpretation. a: The importance ranking of the variables according to the mean; b: Order of highest influence according to SHAP value. Red and blue dots indicate when each factor is high or low, respectively, to determine the direction of influence on ROA output.
The model’s interpretation. a: The importance ranking of the variables according to the mean (SHAP value); b: Order of highest influence according to SHAP value. Red and blue dots indicate when each factor is high or low, respectively, to determine the direction of influence on Tobin’s Q output.
The model’s interpretation. a: The importance ranking of the variables according to the mean (|SHAP value|); b: Order of highest influence according to SHAP value. Red and blue dots indicate when each factor is high or low, respectively, to determine the direction of influence on CSR output.
SHAP dependence plots. a Effect of CSR on ROA output; b Effect of CSR on Tobin’s Q.
SHAP dependence plots of the all features for CSR output.
In this paper, we examine the relation between corporate social responsibility and corporate financial performance in a bullish market. Previous studies have heterogeneous results, mainly due to differences in the samples and statistical approaches used. To resolve these issues, we use an innovative approach through explainable artificial intelligence (XAI). To reflect the recent expansions of CSR practices, we propose a longitudinal analysis of the US market from 2014–2019. We find that in a bullish market, CSR is negatively related to financial market performance. Through the use of XAI, we show that CSR exclusively improves the financial performance of the most sustainable companies. We also highlight the existence of thresholds that modify the relation between the level of CSR and our financial variables.
 
This study examines whether downside risk matters in the Indian equity market. We observe a strong negative relationship between standard variance-based risk measures (variance, beta, and idiosyncratic variance) and the expected stock returns. After controlling for traditional risk measures, analytically and statistically orthogonalized forms of downside risk measures present a positive risk–return relationship. In cross-sectional regressions, the downside beta shows a positive risk–return trade-off after controlling for the effect of traditional beta. It implies that investors avoid the “probability of loss” but look at the higher variance as a potential to earn higher returns. The desire to make speculative profits dominates over the need for safety while investing in the equity market. Investors seeking higher returns invest in high volatility or high beta stocks resulting in the overvaluation of these stocks. Preference for the lottery stocks (proxied by Max and idiosyncratic volatility) emerges as the strong determinant of cross-sectional variation of stock returns. After controlling for the lottery effect, the relationship between traditional beta and expected returns becomes flat.
 
This paper examines the risk and returns of classic car price indices over the 1994–2021 period. We calculate the central tendency, dispersion, shape of risk and returns, the unit root tests, and correlations. The results indicate a moderated volatility, a low range of returns, and a weak expectation of financial gain given the ancillary costs related to the auction, transport, insurance, guarding, maintenance, and restoration. There are low correlations among the classic car markets. These results provide a better understanding of the risk and returns of the classic car market for many actors such as individual and professional investors, collectors, and wealth managers. Investing in a classic car is more of a passion and emotional investment than a simple desire for financial gain.
 
This paper investigates the dynamics of cross-listing and dividend policy. Using a sample of 19,200 firm-year observations for the period 1990–2019, we find that cross-listed firms are less likely to distribute dividends, adopt more stable policy and pay more cash compared to their non-cross-listed peers. We also show that firms originated from poor legal environment have a stable policy and pay more dividend. Finally, we find that cross-listed firms with more dividend payment exhibit higher valuations.
 
Trends of bid prices for firms with different market experience. Notes: We plot the trends of normalized bid prices for firms with different levels of market experience. The normalized bid price is defined as the bid price divided by the midpoint of the IPO price range and subtract one. “Frequent Bidders” corresponds to mutual funds that are above the 50th percentile of the distribution of auction participation times before April 28, 2012. “Nonfrequent Bidders” corresponds to mutual funds that are below the 50th percentile of the distribution of auction participation times before April 28, 2012.
This paper explores whether market experience exacerbates or mitigates institutional investors’ precautionary bidding behavior. Using an IPO reform in China as an exogenous shock, we apply a difference-in-difference approach to identify a causal relationship between willingness to bid and market experience. The mutual funds’ willingness to bid for the IPOs decreased by 13.53 percentage points after the removal of the three-month IPO lockup period. A one-standard-deviation increase in market experience in terms of IPO participation mitigated 4.36 percent of the decline. The mitigation effect of market experience on precautionary bidding is more pronounced for IPO firms with disadvantaged geographical locations, is attenuated for IPOs certified by reputable underwriters, and is attenuated in mutual funds that have strong business ties with the lead underwriters. Furthermore, we find investors with more market experience help to improve the efficiency of IPO pricing.
 
Improving ESG can boost investment returns. The reason is simple: better ESG means healthier firms. Healthier firms trade at higher prices. Hence, improving ESG can boost firm valuation—and investment returns. We formalize this intuition. We estimate how eight key E, S, and G variables influence firm valuation. Our data cover over 2200 firms globally. These variables have a significant impact, which can vary across sectors. Enhancing ESG can unlock significant shareholder value. For example, firms adopting top decile practices across all eight variables would boost their equity valuation by 35% on average. Which ESG improvement(s) can boost share price mostly depends on firms. More than half the gains come from just one or two ESG variables. Our research allows identify such improvement(s) for each firm, and hence prioritize ESG engagement. Focusing on creating shareholder value should prove persuasive with firms, creating a virtuous circle between impact and returns.
 
Investment horizons of the representative investors
a shows the required return (p.a.), y1L,EQ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y_1^{L,EQ}$$\end{document} and y2L,EQ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y_2^{L,EQ}$$\end{document}, of Investor L for Stock 1 and Stock 2, defined as the annualized logarithmic expected return for each stock in equilibrium according to Eq. (21). The skewness premium skprem\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$sk^{prem}$$\end{document} in b is the logarithmic price difference between Stock 1 and Stock 2 in equilibrium as defined in Eq. (22)
In this paper, we analyze how tail risk impacts both asset prices and the optimal asset allocation. For this purpose, we consider an equilibrium model with investors exhibiting an empirically well-justifiable decreasing relative risk aversion (DRRA) and different investment horizons. In contrast to the seminal CAPM, two fund separation does no longer hold, and investors not only regard one risk measure such as the standard deviation but additionally care for the size of tail risk. The shorter the investment period, the more prone they are to negatively skewed returns. In particular, short-term investors not only hold a lower equity ratio than (else equal) long-term investors do, but they also reduce the fraction of assets with negative tail risk. Consistently, the more short-term investors are in a market, the higher the tail risk premium is, i.e., the additional expected return due to skewness beyond a given standard deviation. Consequently, these theoretical findings allow us to draw empirical predictions about (i) the drivers of the skewness premium, (ii) characteristics for markets in which the premium is especially severe, and (iii) the optimal investors’ asset allocation.
 
Optimal asset allocations for the U.S. regulatory framework for varying planning horizon (top), expected stock market return (middle), and risk-free rate (bottom). The general features of the optimal curve, in particular its hump-shape, are stable for a large parameter range
Optimal asset allocations for the U.S. regulatory framework for varying benefits from saved contribution Bonus (top) and varying levels of contribution to liabilities CL. As Bonus increases, the hump shape remains. As CL decreases, the hump shape disappears
Optimal asset allocations for the Swiss regulatory framework for varying planning horizon (top), expected stock market return (middle), and risk-free rate (bottom). The optimal proportion of stocks is monotonically decreasing as the funding ratio increases
What percentage of its assets should a defined benefit pension plan invest into stocks as its funding ratio varies? We show that the answer to this question depends on the institutional setting and in particular on the extent to which the sponsoring company contributes to the fund as the funding ratio varies. We consider two settings: in one setting, the sponsoring company contributes to its pension fund only if the funding ratio is below the target level (as is the case, for example, in the US); in the other setting, the sponsoring company always contributes to its pension fund (as is the case, for example, in Switzerland). We show that these two institutional frameworks lead to two different dynamics, conditional distributions of the funding ratios, and relationships between the current funding ratio and investment into stocks. For settings like the US, that relation is non-monotonic while for settings like in Switzerland, it is monotonically decreasing. Previous empirical findings point towards a similar pattern.
 
Using Granger causality test, we investigate the lead-lag relation between volume and volatility in 14 Chinese ADRs and those of their underlying H-shares. We consider volume as denoting liquidity. We model and forecast volatility using a TARCH model and find evidence of leverage effect and persistence in volatility among the ADRs and H-shares. We document significant but asymmetric bidirectional Granger causality between volume and volatility in ADRs and their underlying H-shares. The asymmetry seems to have declined in recent years, during the latter half of the sample period. We conclude that the relation between liquidity denoted by volume and volatility are time- varying and asymmetric between ADRs and their underlying H-shares.
 
Evidence from many developed markets suggests that fundamental indices outperform capitalisation-weighted indices. Existing studies suspect a story of market mispricing, yet a mechanism has not been identified. Using Australian data, we study the relation between analyst forecast errors and the performance of various fundamental indices. We find that fundamental indices contain a relatively higher exposure to stocks with low analyst long-term growth forecasts. Valuations for these stocks are ex ante overly pessimistic and drive the statistical significance of alphas produced by fundamental indexation. We show how hedging against analyst forecast errors can generate additional alpha for investors using fundamental indexation.
 
The present value of expected savings (human capital), TDF, and total assets over time. This figure displays the present value of expected savings (human capital), the TDF, and total assets from age 20 to age 60 in value. The bar in gray represents the annual value of the TDF. The black bar shows that the value of expected savings (Human capital) peaks in the mid-40s and declines afterward. The gray line indicates the value of total assets over time
The equity allocation (%) of TDF with the human capital. This figure shows the TDF’s asset allocation over time. The gray line represents the equity allocation in the TDF. The black line represents the equity allocation for total assets. The black dotted line represents the equity allocation for the traditional TDF with 100-age approach
The equity allocation (%) of TDF with bond like human capital. This figure shows the TDF’s asset allocation over time for the scenario analysis. It assumes that human capital is more bond-like, consisting of 30% stocks and 70% bonds. The gray line represents the equity allocation in the TDF. The black dotted line represents the equity allocation for total assets
In this paper, we propose a new target date fund that incorporates human capital. The proposed glide path for the target date fund is based on the capital asset pricing model in a way such that the asset allocation of the target date fund with human capital is matched with that of the global market portfolio with human capital. This new target date fund provides a customized retirement solution satisfying different human capital risk profiles.
 
Average Daily Volume (ADV).
Source: by authors based on African Market database
In this empirical investigation, we examine the relationship between trading volume, return and volatility for eleven African Stock Exchanges. This study covers the period from September 24, 2010 to September 24, 2020, i.e., a total of 3037 daily observations per country. The relationship between trading volume and return is examined using the Granger causality test. For the relationship between trading volume and returns volatility, we use an asymmetric EGARCH (1, 1) model. The results indicate that returns do not cause volume while volume causes return in some countries’ Stock Exchanges. Regarding the volatility of the daily return, the study shows on the one hand that the persistence in the volatility is low and the trading volume increases this persistence on the majority of Stock Exchanges. On the other hand, lag trading volume affects the daily volatility of the markets. In this empirical investigation, we examine the relationship between trading volume, return and volatility for eleven African Stock Exchanges. This study covers the period from September 24, 2010 to September 24, 2020, i.e., a total of 3037 daily observations per country. The relationship between trading volume and return is examined using the Granger causality test. For the relationship between trading volume and returns volatility, we use an asymmetric EGARCH (1, 1) model. The results indicate that returns do not cause volume while volume causes return in some countries’ Stock Exchanges. Regarding the volatility of the daily return, the study shows on the one hand that the persistence in the volatility is low and the trading volume increases this persistence on the majority of Stock Exchanges. On the other hand, lag trading volume affects the daily volatility of the markets.
 
Plots of return series
Correlation matrix
This paper addresses two key issues relating to the interactions among the North American hedge funds industry, the equity and treasury bond markets during the COVID-19 pandemic. First, we examine the market-timing ability of North America hedge fund managers using eight strategies as well as the composite hedge fund index. Secondly, we analyze both the short- and long-term effects of both the North American equity and bond markets on the performance of the regional hedge funds industry while accounting for the effects of COVID-19 pandemic. Our results show no significant evidence of market return-timing ability of hedge fund managers across all the funds strategies during the pandemic. However, we document a strong evidence of the effects of the pandemic on the performance of fund managers, except for the Managed Futures and the Relative Value funds strategies. Secondly, we demonstrate that the COVID-19 pandemic may have significantly altered the long-term effects of the North American equity market on the performance of the hedge fund industry while the effects of the bond market is only significant in the short-term. We outlined some crucial implications of these findings for the decision-making process of hedge fund managers, investors as well as market makers during a health crisis-induced financial market turbulence.
 
Eigenvalues and factor weights: original factors vs risk-adjusted factors. On the left panel, the figure compares the eigenvalues of the covariance matrix of the returns of the original factors vs the eigenvalues of the covariance matrix of the returns of the risk-adjusted factors. The covariance matrices are estimated over the full-sample period 1929–2018. On the right panel, the figure shows the original and the risk-adjusted factor weights for the mean-variance optimal (MVO) and the risk-parity (RP) portfolios. The weights are estimated over the full-sample period 1929-2018. In both panels, the factors considered are Mkt-RF, SMB, HML and MOM
Mean-variance frontier: original factors vs risk-adjusted factors. The figure shows the mean-variance frontier for both the standard and the risk-adjusted factors over the full sample period. The corresponding MV-optimal portfolios are marked on each of the frontiers
Cumulative autocorrelation function: original factors vs risk-adjusted factors. The figure shows for each original and risk-adjusted factor the cross-autocorrelation function of the returns along with the 90% coverage bands of the bootstrap null distribution of no autocorrelation. The factors considered are Mkt-RF, SMB, HML and MOM
Rolling cumulative autocorrelation: original factors vs risk-adjusted factors. The figure shows for each original and risk-adjusted factor the cumulative autocorrelation function of the returns estimated with 12 month lags and based on a rolling-window of ten years of return data. The factors considered are Mkt-RF, SMB, HML and MOM
Decomposition of autocorrelation functions for all factors. The figure shows for each factor the cross-autocorrelation function using the risk adjustment for the timing-signal or for the returns earned or both. This decomposes the components of the improvements of the timing strategy. ’org>org’ denotes corr(ri,t,ri,t+s)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {corr}(r_{i,t}, r_{i,t+s})$$\end{document}, ’org>adj’ denotes corr(ri,t,Ri,t+s)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {corr}(r_{i,t}, R_{i,t+s})$$\end{document}, ’adj>org’ denotes corr(Ri,t,ri,t+s)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {corr}(R_{i,t}, r_{i,t+s})$$\end{document} and ’adj>adj’ denotes corr(Ri,t,Ri,t+s)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {corr}(R_{i,t}, R_{i,t+s})$$\end{document}
This paper investigates the effects of volatility scaling on factor portfolio performance and factor timing. We focus on the four equity factors analyzed by Carhart (1997) and find that volatility scaling may lead to higher diversification benefits for a long-horizon investor when equity factors are combined into a portfolio. Depending on the portfolio formation methodology, we also discover a substantial time-variation in portfolio performance. In addition, our results show that volatility scaling improves factor return predictability, but this does not necessarily translate into a profitable factor rotation strategy.
 
Turn of the month (TOM) is a widely recognized anomaly and studied majorly in the context with equity markets. However, the global mutual fund market has not been much exposed to empirical testing of the TOM anomaly and the implication thereof. This study has dual objectives of not only investigating if the TOM effect persists in the world of equity mutual funds but also proposing an investment strategy to exploit the TOM anomaly to mutual fund investors. The study examines 40 equity mutual funds across 6 different geographies and 2 multi-geographic segments. For the sample period of 15 years (2005–2020), crucially covering financial crisis as well as an outbreak of the Covid-19 pandemic this study confirms a statistically significant effect of TOM for 23 out of 40 funds. Based on findings, the paper proposes a staggered investment strategy to investors in mutual funds for entry and exit to exploit the TOM effect for return enhancement.
 
Portfolio optimization approaches inevitably rely on multivariate modeling of markets and the economy. In this paper, we address three sources of error related to the modeling of these complex systems: 1. oversimplifying hypothesis; 2. uncertainties resulting from parameters’ sampling error; 3. intrinsic non-stationarity of these systems. For what concerns point 1. we propose a \(L_0\)-norm sparse elliptical modeling and show thatsparsification is effective. We quantify the effects of points 2. and 3. by studying the models’ likelihood in- and out-of-sample for parameters estimated over different train windows. We show that models with larger off-sample likelihoods lead to better performing portfolios only for shorter train sets. For larger train sets, we found that portfolio performances deteriorate and detaches from the models’ likelihood, highlighting the role of non-stationarity. Investigating the out-of-sample likelihood of individual observations we show that the system changes significantly through time. Larger estimation windows lead to stable likelihood in the long run, but at the cost of lower likelihood in the short term: the “optimal” fit in finance needs to be defined in terms of the holding period. Lastly, we show that sparse models outperform full-models and conventional GARCH extensions by delivering higher out of sample likelihood, lower realized volatility and improved stability, avoiding typical pitfalls of conventional portfolio optimization approaches.
 
This paper examines whether adding expected dividend yields implied by analyst dividend forecasts to expected capital gains implied by analyst target prices improves the portfolio strategy of buying stocks with the highest expected returns and selling stocks with the lowest expected returns. We find that the strategy based on the expected total returns performs only slightly better at the 1-month horizon because the short-term return predictability of the expected dividend yield is weak. We find that the strategy generates significant abnormal returns regardless of sorting the stocks universally or within industries, although sorting stocks within industries improves the performance.
 
Relationship between OPV and RPI. Note: This figure provides a non-parametric way of visualizing the relationship between OPV and RPI. The details on the construction of RPI are provided in "Appendix B: Construction of RPI" appendix. We use the binscatter command in Stata to plot this graph. It groups the variable OPV into equal-sized bins and computes the mean of OPV and RPI variables within each bin and then creates a scatterplot of these data points.
This study shows that the use of private information obtained during company visits is related to managerial skills. We construct a novel measure of a mutual fund’s capability of using such private information by considering the overlap between its stockholdings and its site visits. We find that the allocation of stocks of visited companies in a fund portfolio significantly improves its performance. The impact is more pronounced for mutual funds that hold relatively neglected stocks or stocks with inadequate information disclosure. Our findings suggest that communications with company managers provide significant information advantages for fund managers.
 
Monthly dispersion of the DSN-based size premium. Note The green-shaded area corresponds to the interquartile range in January. The three red-shaded areas correspond to October, November and December
Illustration of the DSN portfolio sorting procedure. Note The upper and lower face of the cube correspond to the nine large cap and small cap sets, respectively
Cumulative returns of the long/short size-calendar strategies. Notes The abbreviation “FF” refers to the Fama-French (1993) sorting method, whereas the abbreviation “DSN” refers to the dependent, symmetric on all names breakpoint sorting method. The vertical axis uses a logarithmic scale. The portfolios invested only in Q1 are represented with dotted gray lines. The portfolios that are short the size premium in Q4 are represented with dotted black lines
Rolling 5-year Sharpe ratios for the long-short size strategies. Note The dotted straight line reflects the linear time trend on each graph
Design of the long-short size-calendar strategies
This paper employs the DSN portfolio sorting procedure introduced by Lambert et al. (J Banking Finance 114:105811, 2020) to factor size characteristics into returns. The US size anomaly boils then down to a pure seasonal effect, fully supporting the “tax-loss-pruning” hypothesis. We build a long-short calendar trading strategy, easily reproducible by an asset manager, being long the Small-minus-Big (SMB) portfolio in January (or in Q1), staying in cash in Q2 and Q3, and shorting SMB in Q4. The strategy achieves a mean yearly return close to 11% from 1963 to 2019. It remains steady over time, across a variety of subperiods, and resists to the detection of false discoveries. The abnormal returns of the long-short calendar trading strategy withstands realistic transaction costs and short sales limitations.
 
Log-returns of indices
Conditional correlations generated by the Copula-GARCH model
Efficient Frontier
Weights of robust MCD and COV MV portfolios
This paper analyzes advantages of investing in catastrophe bonds (CATs) in terms of portfolio diversification. Indeed, the increase in environmental disasters and their economic and financial consequences are still poorly covered by insurance and reinsurance companies. As a result, there is a rapid growth in the use of catastrophe bonds on the financial markets, which can allow the transfer of risks to the capital market. We use copula-GARCH models to test the time-varying dependence of CATs, in a portfolio composed of six stock markets (CAC 40, DJIA, EUROSTOXX 50, FTSE 100, HANGSENG, and NIKKEI 225). Our results reveal that the CATs display the highest risk-adjusted performer. This security may be a good complement to a portfolio for investors seeking to optimize their risk-adjusted returns. In addition, the CATs are one of the best diversifiers. Finally, the CATs are the asset that increases the lowest the probability of extreme co-variations with its benchmark portfolio.
 
Index Value of the TASI from 31 December 2019 to 28 July 2020
Returns series for TASI (TADAWUL) and all sectoral indices
The aim of this study is to investigate the effect of the coronavirus health crisis (COVID-19) on the performance of the Saudi stock market, the Tadawul. Prices of the Tadawul All-Shares Index (TASI) and all sector indices are collected from December 2019 to end of July 2020. Analysis of the pandemic on the return and volatility is carried out using the GARCH (1, 1) model. The results show that the pandemic has a positive impact on the mean returns of all indices except for the REITS sector, but the impact is mostly insignificant. Analysis of the pandemic on the volatility shows that the TASI itself experienced lower volatility during the pandemic period but the impact is insignificant, while out of 21 sectors only 9 experienced significant impacts on volatility. Out of the 9 sectors, 5 experienced significantly increased volatility, while 4 experienced significantly lower volatility. Analysis of the impact of trading volume on the volatility shows stronger investor sentiment influencing volatility for the sectors that experienced higher volatility only. This study provides further understanding of how various market participants around the world react to the COVID-19 pandemic and the need for portfolio diversification to reduce risk during crisis periods. Sharedit link: https://rdcu.be/cOVTh
 
Shown is the procedure to identify and separate a core of some CCs within the whole market. The remaining CCs belong to a set that encloses the satellite
Critical step of the modeling process for the segmentation of the CC market using correlations
Cryptocurrencies (CCs) have become increasingly interesting for institutional investors’ strategic asset allocation and will therefore be a fixed component of professional portfolios in the future. However, this asset class differs from established assets primarily in that it has a higher standard deviation and tail risk. The question then arises whether CCs with similar statistical key figures exist. On this basis, a core market incorporating CCs with comparable properties enables the implementation of a tracking error approach. A prerequisite for this is the segmentation of the CC market into a core and a satellite, with the latter comprising the accumulation of the residual CCs remaining in the complement. Using a concrete example, we segment the CC market into these components based on modern methods from image/pattern recognition.
 
Cross-sectional absolute deviation (CSAD) from 2013 to2020. Note: This figure plots cross-sectional absolute deviation (CSAD) of daily stock return from July 2013 to January 2020. CSADt = 1N∑i=1NRi,t-Rm,t\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{1}{N}\mathop \sum \nolimits_{i = 1}^{N} \left| {R_{i,t} - R_{m,t} } \right|$$\end{document} where Rm,t is the market return at time t. Return is the percentage log return which is 100*ln(price[_n]/price[_n−1])
Cross-sectional squire deviation (CSSD) from 2013 to2020. Note: This figure plots cross-sectional squire deviation (CSSD) of daily stock return from July 2013 to January 2020. CSSDt = ∑iNRi,t-Rm,t2N-1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt {\frac{{\mathop \sum \nolimits_{i}^{N} \left( {R_{i,t} - R_{m,t} } \right)^{2} }}{N - 1}}$$\end{document}. where Rm,t is the market return at time t. Return is the percentage log return which is 100*ln(price[_n]/price[_n−1])
Cross-sectional absolute deviation (CSSD) and market return from 2013 to2020. Note: This figure plots cross-sectional absolute deviation (CSAD) of daily stock return from July 2013 to January 2020 with respect market return in the horizontal axis. CSADt = 1N∑i=1NRi,t-Rm,t\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{1}{N}\mathop \sum \nolimits_{i = 1}^{N} \left| {R_{i,t} - R_{m,t} } \right|$$\end{document} where Rm,t is the market return at time t. Return is the percentage log return which is 100*ln(price[_n]/price[_n−1])
In this paper, we provide an in-depth analysis of the herding nature in the cryptocurrency market. We use the first 200 crypto coins data ranked based on market capitalization on January 1, 2020, to show the analysis. We illustrate the crypto investors' herding nature and intensity in different terms (by using daily, weekly, and monthly frequency data) and various states (high vs. low EPU states and high vs. low VIX states). We also demonstrate the magnitude of the herding effect on the next day's market returns in the cryptocurrency market.
 
Spatial effect of a unit shock to a country on CDS spreads of other countries. Figure 1 illustrates the spatial effect of a unit shock to a country on CDS spreads of other countries with trade weight matrix. For the jth country these values correspond to jth column values of the V matrix given in Eq. (3) for i ≠ j
The impact of a 10% increase on government debt to GDP ratio on CDS of other countries. This figure illustrates the impact of a 10% increase in the government debt to GDP ratio on CDS of other countries with trade weight matrix. For the jth country these values correspond to jth column values of the 10*Sdebt given in Eq. 3 for i ≠ j
This paper examines the interactions among CDS spreads across 13 European countries using spatial econometrics techniques. Our model allows for the estimation of direct and indirect transmission of sovereign risk and feedback effects across the network of these countries. The novelty of this paper is to link macroeconomic variables and CDS spreads in a new context of analysis to uncover new channels affecting sovereign risk across countries during the European debt crisis. We show that the key channel in driving sovereign risk spillovers is trade linkages between the countries. Our results also reveal that a country’s CDS spread is approximately 7 basis points (bps) higher for a 1% increase in public debt-to-GDP levels while that increase in indebtedness is associated with roughly 2 bps higher spreads in all other countries.
 
In this article, we assess whether German private investors gamble in the stock market. Other studies that have analyzed private investors’ preferences with regard to lottery-like characteristics have used retail or discount brokerage data. They have shown that stock trading has common entertainment features with traditional gambling. In particular, clients of discount brokers may invest for speculative purposes and thus have disproportional preferences for lottery-like characteristics. In consequence, assessing preferences by solely using a subset of investors—associated brokerage clients—may lead to substantially biased results. We assess this issue by using SHS-base data from Deutsche Bundesbank which captures the aggregate holdings of the German private sector. In line with the research, we find that German private investors overinvest in stocks with lottery-like features. Yet, when assessing the economic significance of the aggregate overinvestment, the effect is negligible. Further, we do not find consistent evidence of skewness that positively affects the aggregate holdings of the private sector. As studies have identified preferences for skewness as a driving force for retail investors’ stock purchases, our results challenge the preconceived notion of which characteristics actually induce (disproportional) private sector investments.
 
We introduce a robust regression estimator for time series factor models called the mOpt estimator. This estimator minimizes the maximum bias due to outlier generating distribution deviations from a standard normal errors distribution model, and at the same time has a high normal distribution efficiency. We demonstrate the efficacy of the mOpt estimator in comparison with the non-robust least squares (LS) estimator in applications to both single factor and multifactor time series models. For the case of single factor CAPM models we compared mOpt and LS estimates for cross sections of liquid stocks from the CRSP database in each contiguous two-year interval from 1963 to 1980. The results show that absolute differences between the two estimates greater than 0.3 occur for about 18% of the stocks, and differences greater than 0.5 occur for about 7.5% of the stocks. Our application of the mOpt estimator to multifactor models focuses on fitting the Fama-French 3-factor and the Fama-French-Carhart 4-factor models to weekly stock returns for the year 2008, using both the robust t -statistics associated with the mOpt estimates and a new statistical test for differences between the mOpt and LS coefficients. The results demonstrate the efficacy of the mOpt estimator in providing better model fits than the LS estimates, which are adversely influenced by outliers. Finally, since model selection is an important aspect of time series factor model fitting, we introduce a new robust prediction errors based model selection criterion called the Robust Final Prediction Error (RFPE), which makes natural use of the mOpt regression estimator. When applied to the 4-factor model, the RFPE finds as the best subset model the one that contains the Market, SMB and MOM factors, not the three Fama-French factors Market, SMB and HML. We anticipate that RFPE will prove to be quite useful for model selection of time series factor models.
 
Mutual funds that claim hedge fund strategies (HMFs) have experienced large increases in assets under management, yet academic studies are nearly unanimous in their negative appraisal of HMF performance. This paper examines whether this inconsistency can be resolved through the ‘value added’ paradigm. We demonstrate instead that pockets of value production and skill coexist alongside a largely wasteful HMF space as a whole. We document a highly fractured market structure in which the top 10 firms control 48% assets, the bottom half only 4%, and nearly 40% of firms disappear within a brief window. We explore the implications for tests of performance under such conditions.
 
In this study, we investigate the nexus between foreign institutional investors (FIIs) and the dividend policy in a developing country. Using a dataset of 529 Indonesian publicly listed firms between 2010 and 2018, we find that the presence of FIIs has a significant and negative effect on firms’ dividend policy. However, we further find that the negative impact diminishes in firms with a low FII share. In the Indonesian case, although dividends could be used as a mechanism to reduce agency problems caused by information asymmetry, FIIs possibly prefer capital gains because they are subject to a higher dividend tax than domestic investors. Our study contributes to the discussion on the dividend payment puzzle, especially in developing countries.
 
Percentage of transcript presence
Transcript data sample
Shape of exponential smoothing weights (D = 250)
Performance decomposition of long-short strategy (“adjust”')
This paper proposes a new investment strategy by using earnings call transcripts in the global stock markets. For this study, we (i) conducted appropriate data-cleaning, (ii) adjusted announcements timing, and (iii) extracted the accurate tone of the management which is not affected by public financial information. An empirical analysis in the global stock markets confirmed a 7.07% annual return of the proposed strategy based on a long-short analysis. We also compared the proposed strategy with existing smart beta strategies and found out that its characteristics differ from those of existing factor strategies.
 
The research design process
In this paper, we investigate characteristic differences between Socially Responsible Investment (SRI) funds and conventional funds across 35 different categories, including previously unexplored areas, such as fund manager skills and investment strategies. Further, we examine SRI and conventional funds globally rather than from just one country (e.g., US) or one region (e.g., Europe), covering funds listed in 22 different countries. We also adopt a new Principal Component Analysis (PCA) methodology for matching SRI funds against their conventional counterparts that significantly increases the sample size from previous studies, reducing selection bias and possibly explaining contradictory findings in the prior literature. Contributing to the literature, our findings show that: (i) SRI funds have more diversified portfolios than conventional funds; (ii) SRI funds have lower cash holdings while investing more in US equities; and (iii) SRI fund managers charge a smaller fee and are more successful in managing their portfolios. This is reassuring for investors who invest in SRI funds and for the future health and sustainability of the planet.
 
Impact of sovereign wealth fund under an aggregate demand–aggregate supply framework
Descriptive statistics (within CARICOM Economies)
Trends in real per capita GDP (Trinidad and Tobago vs. Synthetic Trinidad and Tobago)
Real per capita GDP gap between Trinidad and Tobago and Synthetic Trinidad and Tobago
Real per capita GDP gaps in Trinidad and Tobago and placebo gaps in the control CARICOM economies
A synthetic control method in a comparative case study evaluates the potential effect of a sovereign wealth fund on the economic growth of a country. Trinidad and Tobago (T&T) is the focus of the case study. This is the first empirical research in the economic literature that attempts to evaluate the impact of a sovereign wealth fund on the economic growth of an emerging economy. The results provide evidence that the fund contributed to a higher real per capita GDP of T&T by an estimate of $5,104.57 (2010 US$) per year. The cumulative 30 years' welfare impact of the fund is approximately $107,196 (2010 US$) per capita. Small island economies should consider implementing similar programs to foster economic growth.
 
This paper highlights the long run, strategic benefits of factor premia as a complement (overlay) to an underlying exposure to equities and bonds. We provide a utility-based framework for evaluating alternative strategies and in particular account for the impact of extreme and undesirable events to long run wealth accumulation. We present evidence suggesting that an overlay of equity premia to a reference portfolio can enhance the likelihood of achieving wealth accumulation goals and can smooth the transition path to achieving those goals. These results can be attributed to both long positions and short positions in contrast to recent findings suggesting shorts fail to add value. The benefits of the factor premia overlay additionally extend to the decumulation or retirement stage as reflected in an enhancement to the coverage ratio. Taken together, these findings suggest that the equity factor premia strategies we present can be utilized to support welfare enhancing gains.
 
Top-cited authors
Marie Brière
  • Paris Dauphine University
Ariane Szafarz
  • Université Libre de Bruxelles
Kim Oosterlinck
  • Université Libre de Bruxelles
Gregor Dorfleitner
  • Universität Regensburg
Britta Hachenberg
  • Technische Hochschule Köln