US Equity Market Research Articles

Quantile dependencies between discontinuities and time-varying rare disaster risks

We study the role of rare disaster risks in discontinuities (jumps) in the US equity market. To this end, we use data from Dow Jones Industrial Average and International Crisis Behavior database (as a proxy for rare disaster risks) over the period January 1918 – December 2013. We apply a quantile dependence approach in order to detect directional predictability from rare disaster risks to various types of jumps, realized skewness and realized kurtosis risks at different quantiles and lags. We find an asymmetric relationship between jumps and rare disaster risks, as we report a heterogenous dependency across different quantiles and lag orders. Although rare disaster risks can significantly help in the predictability of jumps, large jumps due to large price movements happened in the market do not associate with rare disasters.

What Explains Expected Value of Price Changes of US Equity Markets in a Long Run?

I study if changes in total value of debt ten years earlier explain expected value or mean of changes of value of US equity markets in a long run. I find that, when total value of debt is controlled over time, mean of quarter, annual, five-year and ten-year changes of Russell 3000 and S&P 500 price indices decreases near to zero. Results are statistically significant at 5 to 0.1 % level. I thus conclude that changes in total value of debt ten years earlier do explain expected changes in value of US equity markets in a long run.

The COVID-19 Crash in the US Stock Market

This paper studies the US equity market during the COVID-19 period in the first half of 2020. There is a record rise, then a record fall in prices and then a record recovery. Throughout the period there was extreme volatility and much short term momentum with fear and greed alternating. The VIX index signalled the market direction. The roles of the 10 year government bond, the S&P500, Apple Computer stock are studied as well as the massive Fed and government stimuli which fuelled the rebound, despite extremely weak fundamentals.

Risk Management and the Optimal Combination of Equity Market Factors

Managing the intertemporal risk of optimally constructed multifactor portfolios adds to performance. The increases in Sharpe ratios are in addition to the utility that investors gain from controlling how much active risk they are exposed to over time. We derive a simple closed-form formula for security weights in optimal multifactor portfolios with an active-risk target. We test the risk control of five well-known factors—value, momentum, small size, low beta, and profitability—and the optimal multifactor portfolio. Our empirical research was carried out on the large-capitalization US equity market for 1966 through 2019. We conclude that for the equity market, more active factors are better than fewer if each subportfolio is “pure” as to factor, anchored to the benchmark, and combined on the basis of forecastable risks. Our portfolio construction methodology allows for transparent performance attribution and replication of the process in other markets and time periods.

Business conditions, uncertainty shocks and Bitcoin returns

Using a causality test in the frequency domain and a quantile regression model, we examine the impact of the US business conditions and uncertainty shocks (the US equity market uncertainty and global geopolitical risk) on Bitcoin returns. We find that (1) there exists significant causality from the US business condition and uncertainty shocks to Bitcoin returns, and (2) the effects of the US business condition and uncertainty shocks on Bitcoin returns depend on frequency and vary across different market states of Bitcoin.

Using CAPE to Forecast Country Returns for Designing an International Country Rotation Portfolio

The cyclically adjusted price-to-earnings ratio (CAPE), though originally derived for the US equity market, has now been calculated for various national equity markets outside the United States. In all its various adaptations, the measure has been used only to express valuations of those markets in terms of being overvalued, undervalued, or fairly valued. None of the adaptations of the measure have been used to derive finite equity return expectations from those markets. This article explores the approach of applying CAPE to develop a computable forecast measure, called the medium-term country yield forecast (CY-M), to express the medium-term real return expectations of national equity markets. In almost all of its past adaptations, CAPE has mostly been used to express only long-term (periods greater than 10 years) valuations of the equity markets, but the author here has explored its application in the medium term (periods between 2 and 10 years). Drawing conclusions from empirical studies on countries in the MSCI All Countries World Index ex. USA (ACWX) for the period 1969 through 2016, the author has amended the CAPE of a national equity market by coalescing it with the cyclically adjusted real exchange rate (computed in the same manner as CAPE) of the country and the long-term price return momentum (adjusted for inflation) of the market. The author also has applied CY-M derived from the adapted CAPE as a comparative measure to screen and rank countries in the ACWX to construct an international country rotation equity portfolio. TOPICS:Portfolio theory, portfolio construction, equity portfolio management, international investing Key Findings • The article illustrates how to adapt CAPE (also known as Shiller’s PE), a popular long-term US equity valuation measure, to be an equally proficient medium-term valuation measure for countries outside the United States. • It elucidates further a mechanism of applying the modified CAPE of a country to frame its medium-term return expectations. • The article also demonstrates how to rank countries based on their medium-term return expectations to construct a country rotation international equity portfolio.

Multivariate stochastic dominance applied to sector-based portfolio selection

AbstractIn this study, we investigate whether sector-weighted portfolios based on alternative parametric assumptions are consistent with multivariate stochastic dominance (MSD) conditions for a class of non-satiable risk-averse investors. Focusing specifically on stable symmetric and Student’s t distributions, we propose and motivate an MSD rule to determine a partial order among sectors, based on a comparison between (i) location, (ii) dispersion parameters and (iii) either stability indices (for stable symmetric distributions) or degrees of freedom (for Student’s t distributions). The proposed MSD rule is applied to the US equity market to evaluate whether and how the derived stochastic dominance conditions are relevant to investors’ decisions. The empirical study confirms that the proposed MSD rule is effective and that the tail behaviour of returns is relevant to the optimization of portfolios for non-satiable investors.

The influence of oil prices on the banking sector in oil-exporting economies: Is there a psychological barrier?

This paper examines the relationship between crude oil prices and banking sector market indices in the oil-exporting economies of the Gulf Cooperation Council (GCC), using daily frequency data over 2010–2017. Controlling for global banking impacts (S&P500 Banking Index) and interest rates (T-bills), dynamic ordinary least squared (DOLS) and fully modified ordinary least squared (FM-OLS) analysis indicates that oil prices positively affect bank indices until the $95 per barrel mark, after which the impact becomes negative, close to the psychological barrier found in the US equity market. The S&P500 Banking Index positively affects the GCC banking sector, whereas the interest rate affects it negatively. The validity of an inverse U-shaped relationship between crude oil price and banking sector indices is demonstrated. Causality analysis reveals the existence of bidirectional causalities between the prices of crude oil, GCC banking sectors, and the US banking sector. This paper demonstrates a vital non-linear relationship for oil-banking portfolio management and hedging strategies with oil price risk.

Dynamic Risk Management of Equity Market Factors

Managing the intertemporal risk of optimally constructed multifactor portfolios adds to performance. The increases in Sharpe ratios are in addition to the utility that investors gain from controlling how much active risk they are exposed to over time. We derive a simple closed-form formula for security weights in optimal multifactor portfolios with an active-risk target. We test the risk control of five well-known factors—value, momentum, small size, low beta, and profitability—and the optimal multifactor portfolio. Our empirical research was carried out on the large-capitalization US equity market for 1966 through 2019. We conclude that for the equity market, more active factors are better than fewer if each factor subportfolio is “pure” as to factor, anchored to the benchmark, and combined on the basis of forecastable risks. Our portfolio construction methodology allows for transparent performance attribution and replication of the process in other markets and time periods.

Deep Learning for Asset Bubbles Detection

We develop a methodology for detecting asset bubbles using a neural network. We rely on the theory of local martingales in continuous-time and use a deep network to estimate the diffusion coefficient of the price process more accurately than the current estimator, obtaining an improved detection of bubbles. We show the outperformance of our algorithm over the existing statistical method in a laboratory created with simulated data. We then apply the network classification to real data and build a zero net exposure trading strategy that exploits the risky arbitrage emanating from the presence of bubbles in the US equity market from 2006 to 2008. The profitability of the strategy provides an estimation of the economical magnitude of bubbles as well as support for the theoretical assumptions relied on.

Asymmetric Momentum Contingency in Gain and Loss: A Wavelet Approach

The authors explore the separate momentum effect in the positive and negative return environments at both the time domain and the frequency domain. Using the power spectrums and daily returns of sixteen representative exchange-traded funds (ETFs), the authors attempt to answer whether the momentum strategies have equal endurance gain and loss markets with assets that represent different market capitalizations, value, and industry. The authors’ results provide evidence that the US equity market carries strong negative-return resistance. The probability of realizing positive return is increasingly higher when the market carries longer consecutive negative returns. Small-cap stocks exhibit momentum effect in positive return environment but mean reversion effect in negative returns. For value stocks, the probabilities of continuing positive returns are 16.83% higher; for growth stocks, the probabilities of flipping over from negative returns are 16.4% higher. The power spectrums from the Merlot wavelet transformation suggest that the density of transaction activities is high at the high-frequency level, proving that the momentum strategy is less effective within a month-wide window. TOPICS:Exchange-traded funds and applications, mutual fund performance Key Findings • The average probability of the equity market realizing another positive return after the previous positive return is 51.80%, the probability of the third positive return after two positive days is 50.64%, 47.98% after three days, and 45.98% after four days. • The US equity market carries negative return resistance. The average probabilities of the market realizing positive return after one, two, three, four, and five days of consecutive negative returns are 52.88%, 54.63%, 56.55%, 57.17%, and 60.09% across the 11 sectors. • Small-cap stocks exhibit momentum effect in positive return environment but mean reversion effect in negative returns. The probabilities of continuing positive trend are higher for value stocks; yet the probabilities of stopping negative trend are higher for growth stocks.

Monetary policy and herd behavior: International evidence

This paper is motivated by the recent discussion on the need of market supervisors, regulators, and policy makers, to take into account the behavioral elements of market participant attitudes and psychological and cognitive biases when taking policy decisions. We contribute to the discussion by studying, for the first time, the relationship between conventional and unconventional central bank monetary policy and herd behavior in equity markets, and argue that the transmission channel, through which monetary policy may affect herd behavior, is economic expectations and investor sentiment. We combine a range of research methodologies to measure monetary policy, herd behavior, and their possible relation, and our results indicate that conventional and unconventional Fed monetary policy explains a significant percentage of US equity market herd behavior variance, while ECB monetary policy explains a lower percentage of Eurozone herding variance. Impulse Response Functions indicate that Fed's conventional expansionary policy and non-standard policy reduces the levels of herding in the US equity market, while conventional ECB expansionary policy induces higher levels of herding in Spain and Italy. We also detect spill-over effects from Fed monetary policy to EU market herd behavior.

Analyzing the Impact of Corporate Stock Buybacks in China and the US Equity Markets

This paper contributes to the comparative analysing the impact of corporate stock buybacks in China and the US equity markets. The reason to compare two equity markets with buybacks - in the US equity market, the largest source of the US equity demand has consistently been buybacks. However, in the China equity market, China's A-share market conducted a surging number of buybacks that began in 2018, it believes showed corporations' strong intention of bottom fishing and would support stock value. Based on that, this paper analyses the motivation and status analysis of buybacks in China and the US equity markets. Further, according to the financial assessment of two big corporate buybacks in China and the US equity markets as case studies. Therefore, demand for buybacks in the equity market still large, it suggests confidence in a corporation's performance buybacks is one way for corporations to reward shareholders and rejuvenate bull market.

Safe Haven Currencies

As other safe haven assets, safe haven currencies are sought by investors to mitigate financial risk when economic turbulence hits. Three major safe haven currencies are the US dollar (USD), the Japanese yen (JPY) and the Swiss franc (CHF). The euro is now in competition as an alternative safe haven currency. US dollar will remain the best safe haven currency in the short term and the best investment currency in the medium term. In every uncertainty of the US equity market as well as in the case of a decline of the US dollar, the investor may consider investing in a safe haven currency like the yen or the Swiss franc. Given the stability of Swiss government and financial system of the country, the increased foreign demand for the currency usually pushes the Swiss franc upward. There are number of factors, characterizing the dynamics in which the investors fall, rushing to the Japanese yen during periods of global risk aversion. Traders looked for refuge in the cryptocurrency because they cannot find refuge elsewhere.

Does Uncertainty Affect Investor Behaviors and Returns in Stock Exchanges? A New Generation Analysis for the US Equity Markets

Does Uncertainty Affect Investor Behaviors and Returns in Stock Exchanges? A New Generation Analysis for the US Equity Markets

Stress Testing and Systemic Risk Measures Using Multivariate Conditional Probability

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. Specific examples are developed 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.

Deaths, Panic, Lockdowns and US Equity Markets: The Case of COVID-19 Pandemic

Deaths, Panic, Lockdowns and US Equity Markets: The Case of COVID-19 Pandemic

The Pricing of Volatility Risk in the US Equity Market

We analyse 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.

Beta Dispersion and Market Timing

The beta dispersion, which is the spread of betas on a stock market, can be interpreted as a measure of market vulnerability. This study examines the economic idea of the beta dispersion and its application as a market return predictor. Based on the empirical beta dispersion observed in the US equity market, the study develops measures to predict future market returns. These dispersion measures have substantial predictive power for future market movements. Moreover, I show that the informational content of beta dispersion can be successfully exploited by market timing strategies with the help of distributional regressions. This is an innovative application of this novel way of modeling the relationship between multiple variables and appears to be quite useful for timing strategies.

Hedging and diversification across commodity assets

ABSTRACTWe investigate the conditional cross effects and volatility spillover between equity markets and commodity markets (oil and gold), Fama and French HML and SMB factors, volatility index (VIX) and bonds using different multivariate GARCH specifications considering the potential asymmetry and persistence behaviours. We analyse the dynamic conditional correlation between the US equity market and a set of commodity prices and risk factors to forecast the transmission of shock to the equity market firstly, and to determine and compare the optimal hedge ratios from the different models based on the hedging effectiveness of each model. Our findings suggest that all models confirm the significant returns and volatility spillovers. More importantly, we find that GO-GARCH is the best-fit model for modelling the joint dynamics of different financial variables. The results of the current study have implications for investors: (i) the equity market displays inverted dynamics with the volatility index suggesting strong evidence of diversification benefit; (ii) of the hedging assets gold appears the best hedge for the US equity market as it has a higher hedge effectiveness than oil and bonds over time; and (iii) despite these important results, a better hedge may be obtained by using well-selected firm sized and profitability-based portfolios.