US Equity Research Articles

LEET: stock market forecast with long-term emotional change enhanced temporal model.

The stock market serves as a macroeconomic indicator, and stock price forecasting aids investors in analysing market trends and industry dynamics. Several deep learning network models have been proposed and extensively applied for stock price prediction and trading scenarios in recent times. Although numerous studies have indicated a significant correlation between market sentiment and stock prices, the majority of stock price predictions rely solely on historical indicator data, with minimal effort to incorporate sentiment analysis into stock price forecasting. Additionally, many deep learning models struggle with handling the long-distance dependencies of large datasets. This can cause them to overlook unexpected stock price fluctuations that may arise from long-term market sentiment, making it challenging to effectively utilise long-term market sentiment information. To address the aforementioned issues, this investigation suggests implementing a new technique called Long-term Sentiment Change Enhanced Temporal Analysis (LEET) which effectively incorporates long-term market sentiment and enhances the precision of stock price forecasts. The LEET method proposes two market sentiment index estimation methods: Exponential Weighted Sentiment Analysis (EWSA) and Weighted Average Sentiment Analysis (WASA). These methods are utilized to extract the market sentiment index. Additionally, the study proposes a Transformer architecture based on ProbAttention with rotational position encoding for enhanced positional information capture of long-term emotions. The LEET methodology underwent validation using the Standard & Poor's 500 (SP500) and FTSE 100 indices. These indices accurately reflect the state of the US and UK equity markets, respectively. The experimental results obtained from a genuine dataset demonstrate that this method is superior to the majority of deep learning network architectures when it comes to predicting stock prices.

Defining and Controlling Information Leakage in US Equities Trading

We present a new framework for defining information leakage in the setting of US equities trading, and construct methods for deriving trading schedules that stay within specified information leakage bounds. Our approach treats the stock market as an interactive protocol performed in the presence of an adversary, and draws inspiration from the related disciplines of differential privacy as well as quantitative information flow. We apply a linear programming solver using examples from historical trade and quote (TAQ) data for US equities and describe how this framework can inform actual algorithmic trading strategies.

Drivers of S&P 500’s Profitability: Implications for Investment Strategy and Risk Management

The financial markets, shaped by dynamic forces, including macroeconomic trends and technological advancements, are influenced by a multitude of factors impacting the S&P 500 stock index, a pivotal indicator in the US equity markets. This paper highlights the significance of understanding the exogenous variables affecting the index’s profitability for academics, portfolio managers, and investment professionals. Amid the global ramifications of the S&P 500, particularly in combating the eroding purchasing power caused by inflation, investing in stock indexes emerges as a means to safeguard wealth. The study employs various statistical techniques, emphasizing a methodical approach to uncover influential variables, and using static regression and autoregressive models for immediate and time-lagged effects. In conclusion, the findings have broad practical implications beyond investment strategy, extending to portfolio construction and risk management. Acknowledging inherent uncertainties in financial market forecasts, future research endeavors should target long-term trends, specific influences, and the impact of exchange rate fluctuations on index evolution. Collaboration across regulatory bodies, academia, and the financial industry is underscored, holding the potential for effective risk monitoring and bolstering overall economic and financial market stability. This research serves as a foundational step towards enhancing market understanding and facilitating more efficient investment decision-making approaches.

What causes US equity market sector volatilities during the coronavirus pandemic?

ABSTRACT This paper seeks to identify key variables contributing to sectoral stock market volatilities in the US under the enduring pressure of the COVID-19 pandemic, using a broad array of candidate factors. We adopt a Beta-Skew-t-EGARCH model to capture the time-varying dynamics of the individual sectoral return volatilities. The empirical analysis is performed via an elastic-net regularized regression model. We find that trading volume, volatility of broad US dollar exchange rates, coronavirus infection rates, VIX, Google search trends, US economic policy uncertainty, and the initiation of vaccination programmes are the most common determinants of sectoral volatility.

İki Dinamik Koşullu Korelasyon (DCC) Modeli Arasındaki Tahmin Doğruluğunun Karşılaştırılması

This study compares two commonly used DCC-family models to predict the linkage between the US equity and REIT markets within a global minimum-variance portfolio. Equity and REIT portfolios are constructed using variance-covariance matrices, which represent forward-looking covariance information. These matrices are constructed out-of-sample with ex-ante forecasting. By assessing the predictive precision of each model, the study aims to determine which one produces the lowest forecasting errors and performs better economically. According to a statistical comparison, ex-ante correlation forecasts based on the Asymmetric DCC model were more accurate than those based on the standard DCC model. An empirical comparison of the economic performance of these two models in a dynamic portfolio allocation framework reveals that, despite its complexity, the Asymmetric DCC model exhibits similar economic performance characteristics to the standard DCC model. Despite the lack of emphasis on the economic overperformance of the Asymmetric DCC model, investors who recalibrate their portfolios weekly will benefit from reduced forecast errors and the ability to create more efficient portfolios by using an asymmetric model instead of a standard model.

Riding the waves: A study of return spillovers and inter-sector linkages in US equity markets during the COVID-19 pandemic

In times of crisis, stock markets experience a significant increase in return volatility, which leads to spillovers across equity sectors. The purpose of this study is to investigate the asymmetric spillovers across ten U.S. equity sectors, representing different industries. Daily prices of sector indices were collected from 02 January 2018 to 22 October 2021 for the analysis. In addition, the study applied Diebold and Yilmaz's (2012) dynamic spillover methodology, along with the static and rolling windows phenomena, to examine the daily returns spillovers across sector indices. The results indicate that 82 % of volatility forecast error variance in U.S sector indices is due to the spillover effect. Moreover, both industrials and financials exhibit the highest gross spillovers to other sectors, while they also receive the highest spillovers from other sectors. Furthermore, the oil and gas sector and utilities sector receive the highest net returns spillovers. These empirical findings provide crucial information regarding the interdependence of U.S. sector indices during the COVID-19 pandemic, which is relevant for investors and practitioners.

Extreme connectedness between NFTs and US equity market: A sectoral analysis

This study examines the returns connectedness between NFTs and US sector stock markets. For this purpose, we use the recently developed technique of quantile-based regression to explore the dependence structure under various conditions. Our results support the view that connectedness between NFTs and sectoral markets is characterized by asymmetry and heterogeneity in the extreme conditions compared to the median quantile and mean-based approach. Under normal conditions, all NFTs except the ENJ are net recipients of the return spillover from the sectoral stock markets, whereas, financial, consumer staple, and industrial stocks are major net transmitters to the system. However, we observe heterogeneity at both tails, as all NFTs act as net transmitter (recipient) at the higher (lower) quantiles. This confirms the asymmetric dependence structure. We also compute the static optimal weights and hedge ratios using TVP-VAR model for the stocks/NFTs portfolios and show that investors and portfolio managers may consider including NFTs in their holdings to achieve diversification benefits.

Impact of COVID-19 on Factor-Based Investment Strategies in the US Equity Market

Impact of COVID-19 on Factor-Based Investment Strategies in the US Equity Market

Quantitative Models for US Equity Trading Strategies Using Complex Derivatives in Wealth Management

Quantitative Models for US Equity Trading Strategies Using Complex Derivatives in Wealth Management

How macroeconomic conditions affect systemic risk in the short and long-run?

This study quantifies the effects of macroeconomic variables on various market-based systemic-risk measures in 24 European banks over the 2008–2019 period. In a first step, I measure daily systemic risk for banks based on ΔCoVaR, MES, and SRISK frameworks, and examine the contributions of individual banks to aggregate systemic risk during specific stress events. Systemic risk in European banks has risen in the wake of the global financial crisis and the Brexit referendum result. In a second step, I investigate how macroeconomic conditions affect systemic risk in the short and long-run. I find that three systemic risk measures have a long-run stable relationship with EU industrial production, EU inflation, Euribor, and US equity market volatility, but some variables have opposite effects in the short and long-run.

An adaptive long memory conditional correlation model

We propose a conditional correlation model with long memory dependence and smooth structural change. Previous literature has considered correlation and covariance models with structural change or long memory, but this is the first paper to jointly model both features. The correlation matrix is decomposed into long and short run components. Short run correlations converge hypergeometrically towards a slow moving long run correlation matrix that evolves according to one or more flexible Fourier forms. The model is applied to two data sets: a US equity portfolio; and a US equity, bond, gold and oil portfolio. Model fit and out of sample forecasts over 1 to 60 day horizons support the proposed approach.

Spillovers and hedging effectiveness between oil and US equity sectors: Evidence from the COVID pre- and post-vaccination phases

We examine the return and volatility transmission between oil and US equity sectors during three periods: the pre-COVID-19 phase, COVID-19 pre-vaccination phase, and COVID-19 post-vaccination phase. We show that the return and volatility spillovers between oil and US equity sectors vary across these three phases. The highest return connectedness is observed during the post-vaccination phase. The volatility spillover is highest from the majority of sectors to oil market in the COVID-19 pre-vaccination phase. The volatility transmission from oil to US equities during COVID-19 is weak. Moreover, our optimal weights estimations suggest that investors should decrease their exposures in US equities for the portfolio of oil-equity sectors in the COVID-19 post-vaccination phase. Finally, hedging cost is lowest, and hedging effectiveness is highest during the post-vaccination phase. Our findings provide valuable insights to portfolio managers and investors regarding portfolio diversification, hedging, and forecasting during health crises and subsequent recovery phases.

Quantile coherency across bonds, commodities, currencies, and equities

This paper examines quantile coherency in bonds, commodities, currencies, and equities using a novel quantile coherency approach. While recent literature has explored single-frequency tail- and time-frequency dependence in asset returns, we provide fresh evidence on asset return dependence across quantiles (proxying business cycles or market conditions) at different frequencies (representing investment horizons). Considering sixty-seven individual asset return series in four asset classes, we observe that low frequency (yearly) dependence is stronger in the bond, foreign exchange, and equity markets. Specifically, we find strong dependence between the German and French bond markets, heating oil and crude oil, gold and silver, British Pound, and Euro, French and German and Canadian and US equities. As we report asset return interdependence in different business cycles and at different time horizons, these results have important implications for portfolio allocation and investment strategy formulation.

Partially egalitarian portfolio selection

We propose a new portfolio optimization framework, partially egalitarian portfolio selection (PEPS). Inspired by the celebrated LASSO regression and its recent variant partially egalitarian LASSO (PELASSO) developed in [1] in the context of the forecast combinations problem in econometrics in [1], we regularize the mean-variance portfolio optimization of Markowitz by adding two regularizing terms that essentially zero out portfolio weights of some of the assets in the portfolio and select and shrink portfolio weights of the remaining assets towards equal weights to hedge against parameter estimation risk. We solve our PEPS formulations by applying Gurobi 9.0 mixed integer optimization (MIO) solver that allow us to tackle large-scale portfolio problems. We test our PEPS portfolios against an array of classical portfolio optimization strategies on a number of datasets in the US equity markets. The PEPS portfolios exhibit the highest out-of-sample Sharpe ratios in all instances considered.

Interconnectedness dynamic spillover among US, Russian, and Ukrainian equity indices during the COVID-19 pandemic and the Russian–Ukrainian war

We examine the dynamics and spillover effects of interconnectedness among equity indices during the COVID-19 pandemic and the Russian–Ukrainian war. Using daily data from June 2010 to May 2023, we consider five major equity indices, namely, the Dow Jones Industrial Average (DJIA), National Association of Securities Dealers Automated Quotations (Nasdaq), Standard and Poor's 500 (S&P 500), PFTS (Ukraine), and Moscow Exchange (MOEX; Russia). The results demonstrate a 45 % level of the total connectedness index to explain the interconnectedness among these indices. The findings indicate that the DJIA, Nasdaq, Small Cap 100, and MOEX are net receivers of spillovers, whereas the S&P 500 and PFTS are significant net transmitters. Moreover, analysis of different periods, including before and during COVID-19 and before the Russian–U war, provides insights into the changing patterns of interconnectedness and spillover effects during these events. This study contributes to a better understanding of the interdependency and transmission mechanisms among equity markets and highlights the importance of considering external factors and geopolitical events in formulating risk management and investment strategies.

Target date funds as asset market stabilizers: evidence from the pandemic

Abstract Target date funds (TDFs) provide retirement investors, many of whom are unsophisticated or inattentive, with age-appropriate exposures to different asset classes like stocks and bonds. To maintain exposures, TDFs trade actively against market returns, buying stock funds when the stock market does poorly, and selling when the market does well (Parker et al., 2023, Journal of Finance). This paper shows that trading by TDFs was a significant stabilizing force in US equity markets during the unprecedented economic volatility of the COVID-19 pandemic period. Specifically, TDFs – now comprising a quarter of all 401(k) plan assets – caused significant contrarian investment flows across asset classes, flows that were not undone by enrollment of TDF investors or by discretionary actions by TDF managers. Mutual funds with large ownership by TDFs had more stable funding through the pandemic, and stocks that had greater indirect ownership by TDFs had lower co-movement with the market and lower volatility during the pandemic period.

The power of ethical words

AbstractIn this research, we analyse the impact of the inclusion of ethical expressions in the prospectuses of socially responsible (SR) mutual funds on money flows. We contribute to the existing literature by proposing a text‐based measure that integrates three attributes that are relevant to whether clients are attracted: exclusiveness, intensity and lexical diversity. We analyse a sample formed of 266 SR US equity mutual funds in the period 1999–2019. Our findings show that both the proposed indicator and other alternative partial proxies based on textual data have a positive impact on the money flows of the SR funds. This effect is more relevant in the case of SR mutual funds belonging to smaller families. Besides, persistence in money flows is more intense for SR mutual funds that are more attractive because of their ethical expressions. Another finding shows that return‐chaser behaviour occurs among all SR investors, independently of the level of text attractiveness of the mutual funds in which they invest, revealing that they take into account both financial and non‐financial outcomes. Our results indicate that policymakers should control fund prospectus information, given its importance for investors' decisions. In addition, managers should be especially cautious with the information provided in prospectuses because of its impact on investor decisions.

Climate Change, Technology Shocks and the US Equity Real Estate Investment Trusts (REITs)

Given the renewed interest in Real Estate Investment Trusts (REITs), we are keenly focused on exploring the possible connection between climate change and return volatility of US equity REITs, as well as the role of technology innovation for environmental sustainability in the nexus. While climate change might pose some threat to the REIT business, it is necessary to know the direction in which technological innovation can mitigate this impact. As a way to validate our evidence, we offer some additional analyses with alternative measures of technology shocks and the replacement of technology shocks with global economic expansion, as improvement in global economic activity could offer more investment options for investors to diversify their investment portfolio away from climate-prone assets. For completeness, the analyses are replicated for US mortgage REITs. Overall, we show that climate change heightens the return volatility of US equity REITs and that the former contains some predictive content for the latter. When the role of technology is examined, our results show that technology shock indeed reverses the cheering impact of temperature anomaly on the return volatility of US equity REITs. We show that these results are robust to alternative measures of economic shock and that the results equally hold for mortgage REITs. We further document some important implications of our findings for investors and policymakers alike.

Mood seasonality around the globe

This paper examines the existence of mood seasonality, documented by Hirshleifer et al. (2020, JFE) for the cross-section of US equity returns, in an international setting. First, we confirm the results of the original study. Next, we extend these findings to non-US markets and show that they are not sample-specific. A stock's relative historical seasonal returns are positively correlated with its relative future seasonal returns during similar or congruent mood periods and negatively related with its relative future seasonal returns during dissimilar or non-congruent mood periods. Moreover, both regression and portfolio analyses indicate that mood beta, the sensitivity of equity returns to aggregate investor mood, helps explain these mood seasonality effects.

The paradox of closing mutual funds to new investors.

I provide an explanation for the well-established paradox that mutual funds that close to new investors fail to maintain their pre-closure positive abnormal performance after closing. Using a large sample of active US equity mutual funds, I find that the within-style performance ranks of closed funds revert to the mean as strongly as those of a control group of open funds. Furthermore, I show that funds that are closed do not drastically alter their investment allocations and hold portfolios that are indistinguishable from their portfolios prior to closing in terms of risk-adjusted performance. Therefore, mean reverting stock returns explain the deterioration in fund performance after closing to new investors. I also document that funds are not capable of better preserving their assets when anticipating a decline in performance by closing to new investors and thus adding exclusivity. Furthermore, the control group of open funds does not suffer from excessive flows. Closing to new investors is at best unnecessary from the stand-alone perspective of the fund.