US Equity Market Research Articles

How Investors Use Passive for Active

Index mutual funds and ETFs have seen significant asset inflows over the past decades. What is often overlooked about index fund investing, however, is that investors use these products to build active exposures in aggregate. To empirically explore this conclusion, we analyze the characteristics of the asset-weighted aggregate portfolio of US domiciled US equity index mutual funds and exchange-traded funds whose goal is to track an index other than a total US market index. Our results suggest that this portfolio of index funds is active in nature. This conclusion rests on (1) the non-trivial variability of excess returns relative to the total US equity market, (2) statistically significant style factor exposures, and (3) industry-level asset weights that are not market capitalization proportional to the total US equity market. In other words, index fund investing should not be considered equivalent to passive investing.

How Investors Use Passive for Active

Index mutual funds and ETFs have seen significant asset inflows over the past decades. What is often overlooked about index fund investing, however, is that investors use these products to build active exposures in aggregate. To empirically explore this conclusion, we analyze the characteristics of the asset-weighted aggregate portfolio of US domiciled US equity index mutual funds and exchange-traded funds whose goal is to track an index other than a total US market index. Our results suggest that this portfolio of index funds is active in nature. This conclusion rests on (1) the non-trivial variability of excess returns relative to the total US equity market, (2) statistically significant style factor exposures, and (3) industry-level asset weights that are not market capitalization proportional to the total US equity market. In other words, index fund investing should not be considered equivalent to passive investing.

Reassessing the Predictability of the Investor Sentiments on US Stocks: The Role of Uncertainty and Risks

We examine the predictive power of US investor sentiments on US sectoral returns and aggregated S&P 500 index in the presence of different risk and uncertainty indices. Investor sentiments are measured using the sentiment index proposed by Baker and Wurgler (2006). We also use bearish and bullish investor sentiment indices i.e., AAII sentiment indices, published by the American association of individual investors. We also use different risk and uncertainty indices i.e., economic policy uncertainty, financial uncertainty, geopolitical risk, and US equity market volatility. Results of the baseline model (i.e., the single model where the only predictor is sentiment index) show that the forecasting power of the predictor is weak. Augmenting the baseline model to account for uncertainty measures shows that the uncertainty's transmission impact is more accurate for out-of-sample forecasts than the single-factor model. We also highlight that the performance of the multi-factor predictive model incorporating USS B&W is superior to the benchmark model. Policy implications of the results are also discussed.

The Correlation Risk Premium: International Evidence

In this paper we carry out a cross-country analysis of the correlation risk premium. We examine the statistical properties of the implied and realized correlation in European equity markets and relate the resulting premium to US equity market correlation risk and a global correlation risk premium. We find evidence of strong co-movement of correlation risk premiums in European and US equity markets. Our results support the existence of a strong empirical relationship between the global correlation risk premium and international equity market option returns. We document the dependence of the correlation risk premium on macroeconomic uncertainty and related variables.

Style Drift and Alphas: A Case Study in International Retail Funds

This paper examines style drift and alphas for a sample of 110 international retail funds offered to individual investors. We show that when fund managers “deviate” from their stated categories, alphas are biased upward. While previous studies in the international stock arena typically employ theoretical constructs to benchmark fund performance, we employ an actual investable vehicle (tradeable ETFs) in the same categories as the funds. For the period 2002-2020, we show empirically that managers do indeed deviate from their stated fund categories with subsequent upward bias to their fund alphas. For over half of the funds in our sample, we find significant drift to emerging markets and to the US equity market. We observe that alpha is biased upward an average of 86 basis points for the retail funds examined in this study.

Practical Applications of The Best of Both Worlds: Forecasting US Equity Market Returns Using a Hybrid Machine Learning–Time Series Approach

In <b><i>The Best of Both Worlds: Forecasting US Equity Market Returns Using a Hybrid Machine Learning–Time Series Approach</i></b>, published in the Spring 2021 issue of <b><i>The Journal of Financial Data Science,</i></b><b>Haifeng Wang, Harshdeep Singh Ahluwalia, Roger Aliaga-Díaz, and Joseph Davis</b> (all at<b> Vanguard</b>) revisit the long-term equity return forecasting performance of a popular time-series model based on Shiller’s cyclically adjusted price-to-earnings (CAPE) ratio. Noting that its performance had deteriorated due to structural economic changes, they considered additional variables, econometric methods, and machine learning (ML) techniques. Using their domain knowledge in the data science arena, the authors sought to improve the out-of-sample predictive accuracy of long-run stock return forecasts. They compared the performance of various approaches, including linear regressions, vector autoregressive (VAR) models, random forests, gradient boosting machines, and gated recurrent units. They found that a two-step approach that estimates a VAR model with ML techniques and uses outputs in an additive component model of returns works best. Using an ensemble of ML techniques within a robust economic framework yields more accurate long-run return predictions than using any of the methods alone.

The source of financial contagion and spillovers: An evaluation of the covid-19 pandemic and the global financial crisis.

This study investigates the reaction of stock markets to the Covid-19 pandemic and the Global Financial Crisis of 2008 (GFC) and compares their influence in terms of risk exposures. The empirical investigation is conducted using the modified ICSS test, DCC-GARCH, and Diebold-Yilmaz connectedness analysis to examine financial contagion and volatility spillovers. To further reveal the impact of these two crises, the statistical features of tranquil and crisis periods under different time intervals are also compared. The test results show that although the outbreak’s origin was in China, the US stock market is the source of financial contagion and volatility spillovers during the pandemic, just as it was during the GFC. The propagation of shocks is considerably higher between developed economies compared to emerging markets. Additionally, the results show that the COVID-19 pandemic induced a more severe contagious effect and risk transmission than the GFC. The study provides an extensive examination of the COVID-19 pandemic and the GFC in terms of financial contagion and volatility spillovers. The results suggest the presence of strong co-movements of world stock markets with the US equity market, especially in periods of financial turmoil.

Value relevance and market valuation of assets measured using IFRS and US GAAP in the US equity market

PurposeStudies show firms reporting using Generally Accepted Accounting Principles in the United States (US GAAP) and International Financial Reporting Standards (IFRS) are similarly valued in the market, however, these studies are limited due to the noise present in international studies from regulatory differences. This study aims to eliminate much of this noise by using a cleaner sample of all listings with the Securities Exchange Commission (SEC). This paper also looks at more detailed book value figures.Design/methodology/approachThere have been previous studies on the differences in market valuation of firms reporting using IFRS vs US GAAP. Most of this research is confounded with difficulties due to different regulatory environments and volatile time periods. The study uses cleaner data following the SEC’s acceptance of IFRS financials without a 20-F Reconciliation. The authors use a large sample of non-US firms trading on US exchanges choosing to use either US GAAP or IFRS for SEC reporting purposes. The sample period starts two years after the SEC’s acceptance of IFRS financials without a 20-F reconciliation and is larger than earlier samples.FindingsThe authors show that there is no difference between IFRS and US GAAP firms’ overall value relevance, however, earnings are more value relevant when measured using IFRS and book value is more value relevant when measured using US GAAP. The authors find that the difference between US GAAP and IFRS can be explained, at least in part, by greater market multiples being placed on inventories and goodwill using US GAAP. This is offset in part by greater multiples being placed on other assets under IFRS.Originality/valueThe authors replicate earlier studies but also extend with a better sample and more detailed finings.

US Equity Market Volatility Index Components, Impact on the Industrial Production Index and Post Covid-19 Forecasting: Relevance to Mexico

US Equity Market Volatility Index Components, Impact on the Industrial Production Index and Post Covid-19 Forecasting: Relevance to Mexico

Trending Fast and Slow

This article develops a methodology to combine fast and slow time-series momentum signals using machine learning techniques based on market volatility. Starting with the US equity market, the authors find that the performance of a time-series momentum strategy is determined by both its responsiveness and the market volatility regime, among other factors. A decision tree gives a simple and insightful way to determine the threshold in characterizing low- and high-volatility regimes. A slow time-series momentum strategy tends to outperform a fast time-series momentum strategy when market volatility is low. The opposite tends to occur when volatility is high. This pattern of relative performance can be attributed to market-timing alpha and exists in most global equity markets, including both developed and emerging markets.

The cost of overconfidence in public information

We investigate the effects of investor overconfidence in public information on cross-sectional asset returns. The results show that investors in the US equity market are overconfident about public signals for mature firms that are relatively easy to price—old, large, and dividend-paying firms, value firms, and firms with a higher proportion of tangible assets, little external financing, and low sales growth. However, the effects of the overconfidence on cross-sectional stock returns are reversed quickly and comprise more than half of the short-term return reversals. The risk-adjusted cost of being overconfident about the noisy public signals, measured by return reversals of hedge portfolios formed on unexpected responses, is over 1.1% per month in the first month after portfolio formation, and is still significant despite the active arbitrage trading in the 2000s.

The pricing of volatility risk in the US equity market

We analyze whether the pricing of volatility risk depends on the asset pricing framework applied in the tests, the specified volatility proxies, and the portfolio sorts used for spanning the asset universe. For this purpose, we compare the results using a macroeconomic and fundamental based asset pricing model using three proxies of volatility and uncertainty, using size/value sorted and industry sector portfolios. Our results reveal that the marginal pricing effect of the VIX volatility factor is strong and statistically significant throughout the models and specifications, while the effect of an EGARCH-based volatility factor is mixed, mostly smaller but with the correct sign. In most cases, the EGARCH factor does not impair the pricing effect of the VIX. The portfolio sorts have a substantial impact on the volatility premiums in both model frameworks. The size of the volatility risk premium is more uniform across the models if the industry sector portfolio sort is used. Finally, the size/value portfolio sort generates larger volatility risk premiums for both models.

The Joint Impact of Fragmentation into the Dark and Algorithmic Trading on Implicit Trading Costs and Market Manipulation

The authors investigate the effects of algorithmic trading and lit/dark fragmentation on US equity market quality. Market quality refers to the efficiency of lower implicit trading costs as well as market integrity, which are addressed here as reduced end-of-day (EOD) trade-based manipulation. A simultaneous equations model of effective spreads, a proxy for algorithmic trading, and EOD manipulation is carefully specified, and the instrumental variables are pretested to confirm exogeneity and strength. They estimate how fragmentation of the lit and dark order flow in NYSE-listed firms pre- and post-Regulation National Market System (Reg NMS) along with high-frequency algorithmic trading (HFT/AT) jointly determine round-trip implicit transaction costs and EOD manipulation. The authors find that lit (dark) market fragmentation lowers (raises) effective spreads, reduces (increases) EOD manipulation, and increases (decreases) HFT/AT once Reg NMS establishes quote discipline.

Investors’ attention and information losses under market stress

The paper proposes a novel point-wise entropy approach to measure the time-varying losses in the value of information that investors associate with market signals, financial and economic indicators, and news. We cast our approach in a Bayesian framework and assume that market agents update their beliefs to incoming signals based on a prior information set. By exploiting the distribution rather than the time-series properties of information signals, our method is able to construct univariate signal-specific, but also composite proxies of information loss, with the latter being more efficient in reducing misleading effects and interpretation errors. As an empirical illustration, we construct information loss proxies for the US equity market from several mainstream information signals and find that the majority of information loss indicators can influence investors’ attention, which then intermediates the impact of information signals on market outcomes. Finally, we show that, by relying on composites rather than univariate proxies, market agents can diversify and thus reduce their information losses when interpreting signals associated with the same underlying event.

The lead of oil price rises on US equity market beliefs and preferences

AbstractWe find that oil price rises from a strengthening global economy have a state‐dependent lead on US equity market beliefs and preferences. When equity volatility is low, rising oil prices lead higher cashflow expectations, lower long‐run (LR) volatility, and increased LR risk aversion. When volatility is high, markets focus on the contractionary effects of higher input costs, with rising oil prices leading decreased cashflow expectations, higher LR volatility, and decreased LR risk aversion. Findings suggest important refinements for asset pricing, portfolio choice, and models that link financial markets to the macroeconomy.

A note on tweeting and equity markets before and during the Covid-19 pandemic

We investigate the differential effects of a new index of Twitter-based market uncertainty (TMU) and variables for the US equity market before and during the Covid-19 pandemic. We find that markets are significantly more sensitive to the uncertainty contained in tweets during the pandemic, the TMU is a leading indicator of returns only during the pandemic, and the effect of the TMU on the volatility and liquidity of equity markets is greater during the pandemic compared to the pre-pandemic period. Our results show that the information contained tweets are having a much larger effect on equity markets during the pandemic.

Long-Term Investing and the Frequency of Investment Decisions

This article analyzes the impact of the frequency of investment decisions on long-term investment results. Long-term investment returns are shown to be a nonlinear function of skill, transaction costs, volatility, and frequency. The author uses compound returns to show the minimum skill level that is required to result in a positive expected return after costs and shows the skill level required to outperform the buy-and-hold investor. These can be high for markets that have high transaction costs per unit of volatility. The optimum frequency of investment decisions is derived as that frequency that maximizes the expected terminal value of wealth. Empirical estimates from the US equity and fixed-income markets show that this optimum frequency is considerably different between these markets, even when the investor has an equal amount of skill in each. The author also shows how long-horizon value-at-risk varies with the frequency of investment decision-making. <b>TOPICS:</b>Wealth management, equity portfolio management, fixed-income portfolio management, risk management, portfolio construction, performance measurement <b>Key Findings</b> ▪ The author derives the success rate that is required to result in a positive expected return after cost and applies it to directional strategies in the US equity and fixed-income markets. For fixed income, the investor requires a success rate of more than 51%, regardless of the frequency of those decisions. For directional strategies in the US equity market, this minimum success rate varies more with frequency, but it is higher than 53%. ▪ The optimum frequency of investment decisions is derived as that frequency that maximizes expected terminal wealth. The article shows that this frequency is equal to skill times volatility over costs, squared. Because neither of these parameters is likely to be constant through time, the optimum frequency of investment decision-making is also likely to change through time, which has important implications for how an investment process is to be structured. Empirical estimates show very different results for different markets. ▪ Even for highly skilled investment processes, downside risk is meaningful when frequency is low. Downside risks are meaningfully lower only for high-skill investment processes at high frequency. This is an important consideration for investors who place high importance on downside risks at long investment horizons.

Stress Testing and Systemic Risk Measures Using Elliptical Conditional Multivariate Probabilities

Systemic risk, in a complex system with several interrelated variables, such as a financial market, is quantifiable from the multivariate probability distribution describing the reciprocal influence between the system’s variables. The effect of stress on the system is reflected by the change in such a multivariate probability distribution, conditioned to some of the variables being at a given stress’ amplitude. Therefore, the knowledge of the conditional probability distribution function can provide a full quantification of risk and stress propagation in the system. However, multivariate probabilities are hard to estimate from observations. In this paper, I investigate the vast family of multivariate elliptical distributions, discussing their estimation from data and proposing novel measures for stress impact and systemic risk in systems with many interrelated variables. Specific examples are described for the multivariate Student-t and the multivariate normal distributions applied to financial stress testing. An example of the US equity market illustrates the practical potentials of this approach.

The Best of Both Worlds: Forecasting US Equity Market Returns Using a Hybrid Machine Learning–Time Series Approach

Predicting long-term equity market returns is of great importance for investors to strategically allocate their assets. The authors explore machine learning (ML) methods to forecast 10-year-ahead US stock returns and compare the results with the traditional Shiller regression-based forecasts more commonly used in the asset-management industry. The authors find that ML techniques can only modestly improve the forecast accuracy of a traditional Shiller cyclically adjusted price-to-earnings ratio model, and they actually result in worse performance than the vector autoregressive model (VAR)–based two-step approach. The authors then implement this approach with ML techniques and allow for unspecified nonlinear relationships (a hybrid ML-VAR approach). They find about 50% improvement in real-time forecast accuracy for 10-year annualized US stock returns. <b>TOPICS:</b>Security analysis and valuation, big data/machine learning, quantitative methods, statistical methods, performance measurement <b>Key Findings</b> ▪ Applying machine learning (ML) techniques within a robust economic framework such as Davis et al.’s (2018) two-step approach is superior than applying such techniques in isolation (directly forecasting equity returns). ▪ Using the two-step approach, integrating ML with the vector autoregressive model (ML-VAR) to dynamically forecast earning yields reduces dramatically out-of-sample forecast errors, yielding an improvement of about 50% in forecast accuracy for long-horizon U.S. stock market returns. ▪ Among the ML algorithms tested, the ensemble method, which averages all other model forecasts, consistently provides improved predictive power.

Portfolio Selection with Regularization

We study the Markowitz mean-variance portfolio selection model under three types of regularizations: single-norm regularizations on individual stocks, mixed-norm regularizations on stock groups, and composite regularizations that combine the single-norm and mixed-norm regularizations. With mixed-norm regularizations incorporated, our model can accomplish group and stock selections simultaneously. Our empirical results using both US and global equity market data show that compared to the classical mean-variance portfolio, almost all regularized portfolios have better out-of-sample risk-adjusted performance measured by Sharpe ratio. In addition, stock selection and group screening accomplished by adding [Formula: see text] and [Formula: see text] regularizations respectively can lead to decreased volatility, turnover rate, and leverage ratio. Yet there are instances in which diversifying across different groups is more favorable, depending on the grouping methods. Moreover, we find a positive correlation between portfolio turnover and leverage. Heavily leveraged portfolios also have high turnover rates and thus high transaction costs.