US Stock Research Articles

Diversification, hedging, and safe-haven characteristics of cryptocurrencies: A structural change approach

This study investigates the influence of structural change on the diversification, hedging, and safe-haven characteristics of Bitcoin and Ethereum against various financial assets such as gold, the US Dollar Index, stock indices, oil, and commodity indices from August 7, 2015, to August 15, 2022, using the DCC–ARMA–GARCH models with the CUSUM test. Our results indicate that cryptocurrencies have the same characteristics vis-à-vis financial markets during the entire sample period and periods tied to the date of major international events (COVID-19 and the early-2022 Russia–Ukraine War). However, we find that cryptocurrencies play different roles against specific asset markets in different periods separated by structural change models. Our findings suggest that incorporating structural changes into a model accounts for higher volatility and may better describe the real-world capabilities of cryptocurrencies against financial assets.

Detecting Change in a Volatile Curve United State Stock Market (US SM) with the Use of Automated Decomposition for Time Series Components

The main reason for this investigation is to use manual process of identification of time series components with two types of automated decomposition for time series known as automated BFTSC (break for time series components) and, automated GFTSC (Group for time series components) in detecting change in a volatile curve united states stock market (US SM). In identification of components of time series present in the seasonal data of US stock market. The data US Stock Market was a monthly data from January 2001 until December 2018 and a total of 18 years. The stock market of US is also available as a secondary data at the DataBank of University Utara Malaysia Library. The weaknesses of BFAST were corrected by the extension of BFAST to BFTSC and GFTSC. Both were created to capture the cyclical and irregular components that were not captured by BFAST technique and it was included in the methodology of this study. BFTSC and GFTSC were considered to provide a combined image of all the four components of time series while GFTSC had additional advantage of providing equations to the components automated. Evaluation using simulation data and empirical data vindicated the accuracy of BFTSC and GFTSC based on linear trend less volatile data. They are effective and better than BFAST because it was able to identify 100% of the data with the basic four time series components monthly. Both techniques detects 99 % of the entire components in the time series data in a linear trend data.

Price effects after one-day abnormal returns and crises in the stock markets

We investigate price effects after one-day abnormal returns during crises in US, Japanese, Chinese, Russian and Brazilian stock markets, using the ANOVA, Mann–Whitney, t-tests, the modified cumulative abnormal return approach, regression analysis with dummy variables, and the trading simulation approach. The results suggest that the momentum effect is the most typical case of price behaviour after the days with positive abnormal returns, especially in emerging markets in pre and post crisis periods. Interestingly the momentum effect in developed markets changes into contrarian during crisis periods. However, in emerging markets the momentum effect prevails even in crisis periods. However, the power of the detected effects is weak. These effects do not provide opportunities to beat the market and might result from prevailing positive returns in these stock markets.

Can Intraday Return Reversals Predict Future Stock Returns?

Based on the research of the US stock market, it is found that the fierce long-short battle between heterogeneous investors with different views is manifested as the high-frequency inversion between overnight returns and daytime returns over a period of time. This kind of long-short battle has an impact on asset prices over time, resulting in pricing anomaly on a cross-section. Based on the research of A-share market, this paper finds that after controlling the characteristics of scale, book-to-market ratio, reversal effect, profitability, turnover rate, etc., the abnormal frequency of opening high and closing low (AB_NR) is significantly positively correlated with the future return of stocks. The hedge portfolio constructed according to AB_NR has excess returns in time series (opening high and closing low anomaly) which cannot be explained by the existing asset pricing model. Further testing shows that the anomaly is significant in the period of high arbitrage cost and high investor sentiment. The empirical results show that the anomaly is due to mispricing rather than risk premium.

Global financial risk and market connectedness: An empirical analysis of COVOL and major financial markets

This study presents a novel investigation into the dynamic connectedness between the recently developed global risk measure, COVOL, created by Engle and Campos-Martins (2023), and the major international stock, gold, and crude oil markets in China, the US and Europe. Applying conditional and aggregated connectedness approaches, we systematically uncover the multi-dimensional interlinkages to understand volatility transmissions within these markets. Results reveal that COVOL predominantly receives volatility transmissions, with the US and European stock markets acting as principal transmitters of shocks across all three regional systems. The research also highlights the crucial role of significant events, such as the Global Financial Crisis, the Chinese stock market crash, and the COVID-19 pandemic, when increasing the intensity of connectedness. The volatility transmission of the COVOL-gold pairing is comparatively weaker than the COVOL-stock and COVOL-oil pairs. At the same time, the US and Europe consistently transmit stronger shocks to global financial risk, contributing significantly to the global risk transmission channel, whereas, in contrast, China’s financial markets transmit less international uncertainty. Robustness testing verifies these findings.

International monetary spillovers to frontier financial markets: Evidence from Bangladesh

AbstractThis paper investigates international monetary spillovers to stock prices in Bangladesh, a frontier market that has been excluded from prior studies in the literature. Using daily stock price data for over 300 publicly traded firms in a high‐frequency event study framework, we find that contractionary monetary shocks originating from the US, euro area, and China lower stock prices, with Chinese monetary shocks having the largest impact. Contractionary shocks originating from India, on the other hand, lead to a statistically significant increase in stock returns. The positive response is driven by a small number of policy decisions. When these outlier decisions are removed from the sample, contractionary Indian monetary shocks lead to a decline in stock prices in line with spillovers from the other countries.

Real Effects of Markets on Politics: Evidence from US Presidential Elections

Despite the economic importance of the US stock market, there is strikingly little evidence of its impact on elections. Using county-level variation in stock market participation, we document the impact of market returns on election outcomes. High-participation counties are more likely to vote for the incumbent party when the market has performed well relative to low-participation counties. Our findings provide evidence of a novel channel through which stock market fluctuations could be transmitted into the real economy. (JEL D72, G12, G35, G41, G51)

Stock Prices and Exchange Rates under Monetary Policy Changes: The Cases of Japan and the US

This study examines exchange rates and stock prices in the context of US and Japanese monetary policy shifts. Also, the effects of announcements made by the monetary authorities are examined. The empirical results show that US stock prices have positive impacts on Japanese stock prices. As predicted, there is a negative correlation between Japanese interest rates and stock prices. Depreciation of the Japanese yen leads to rising stock prices. Although the Bank of Japan's announcement of “positive” news that the end of quantitative easing is expected to cause stock prices to decline, they instead increased. In the case of the US, however, appreciation of the US dollar promotes rising stock prices. This result is the opposite of the Japanese case. It may be concluded that there exists good communication between the monetary authorities and the markets, and that the news causes no unexpected effects or confusion on the financial markets.

Analyzing Recent Anomalies in the Impact of Interest Rates and Inflation on US Financial Markets

The traditional relationships between interest rates, inflation, and US stocks have experienced anomalies in recent years. Despite rising interest rates in 2023, the S&P 500 enjoyed significant gains, and high inflation did not dampen stock market enthusiasm. This paper delves into these anomalies, exploring potential explanations and their implications for understanding asset valuation dynamics.

Double Asymmetric Impacts, Dynamic Correlations, and Risk Management Amidst Market Risks: A Comparative Study between the US and China

Extreme shocks, including climate change, economic sanctions, geopolitical conflicts, etc., are significant and complex issues currently confronting the global world. From the US–China perspective, this paper employs the DCC-DAGM model to investigate how diverse market risks asymmetrically affect return volatility, and extract correlations between stock indices and hedging assets. Then, diversified and hedging portfolios, constructed by optimal weight and hedge ratio, are investigated using multiple risk reduction measures. The empirical results highlight that, first, diverse risks exhibit an asymmetric effect on the return volatility in the long term, while in the short term, the US stock market is more sensitive to negative return shocks than the Chinese market. Second, risks impact correlations differently across time horizons and countries. Short-term correlations are stronger than long-term ones for the US market, with the Chinese stock market displaying more stable correlations. Third, the hedging strategy is more effective in reducing volatility and risk for US stocks, while the diversification strategy proves more effective for Chinese stocks. These findings have implications for market participants striving to make their portfolios robust during turbulent times.

Application of artificial intelligence and machine learning in financial forecasting and trading

Artificial Intelligence (AI) and Machine Learning (ML) have been employed in the field of financial forecasting for many years. Existing scientific research has substantiated the effectiveness of AI/ML in the financial market. Currently, approximately 35% of the total capitalization of the US stock market is influenced by quantitative analysis, primarily consisting of AI/ML methodologies and their variants. This paper reviews classical advanced methodologies of AI/ML in financial forecasting and automated trading. Specifically, it focuses on discussing representative methods from three categories: statistical approach (including ARIMA-GARCH), machine learning approach (including SVM and LSTM), and the logistic approach (including the Fuzzy System). In detail, this paper delves into the fundamental aspects of each method and illustrates their effectiveness through existing results from relevant papers. The structure of this paper begins with the introduction of each method, followed by their applications, and a discussion of their pros and cons. Furthermore, this paper offers an outlook on the hotspots and prospects for the development of this research topic.

Jump Risk Contagion and Determinants Driven by COVID-19 in Sino-US Stock Market: An Empirical Analysis from a Dependence Perspective

This paper examines the jump risk contagion between the US and China during the financial crisis driven by COVID-19, and the impact of a series of determinants to detect the transmission mechanisms. Specifically, we employ the ARJI–GARCH model to capture jump behavior and apply the Clayton Copula to construct lower tail jump contagion. Furthermore, we conduct regression analysis on variables related to economic fundamentals and investor behavior to explore the fundamental or pure contagion hypotheses. Our results suggest an increased jump dependence during major catastrophic crises such as COVID-19, but it is closer to a memory-less process. Besides, bilateral trade, similarity in fundamentals and investor behavior are all important determinants, and pure contagion hypothesis exists between the Chinese and US stock markets during the pandemic.

How would the war and the pandemic affect the stock and cryptocurrency cross-market linkages?

This paper studies the cross-market linkages between six international stock markets and the two major cryptocurrency markets during the Covid-19 pandemic and the Russian invasion of Ukraine. By employing the local (partial) Gaussian correlation approach, we find that during the Covid-19 pandemic period, both cryptocurrency markets possess limited diversification and safe haven properties, which further diminish during the war. Bootstrap tests for contagion suggest that during the Covid-19 pandemic, the East Asian markets lead the transmission of contagion towards the two cryptocurrency markets. During the Russian invasion, the US stock market emerges as the principal transmitter of contagion. Uncovering the role of pandemic (Infectious Disease EMV Index) and geopolitical risk (GPR index) induced uncertainties, we find that under conditions of high uncertainty and falling prices, the dependency between the US and UK stock markets with both cryptocurrency markets increases considerably. The latter is more profound during the Russian–Ukrainian conflict. Our findings are useful for investors in their search for understanding the differences in asymmetric connectedness between markets during extreme events.

The impact of climate change news on the US stock market

PurposeThis paper aims to investigate the impact of climate change news on the dynamics of US stock indices.Design/methodology/approachThe empirical basis of the study was 3,209 news articles. Sentiment analysis was performed by a pre-trained bidirectional FinBERT neural network. Thematic modeling is based on the neural network, BERTopic.FindingsThe results show that news sentiment can influence the dynamics of stock indices. In addition, five main news topics (finance and politics natural disasters and consequences industrial sector and Innovations activism and culture coronavirus pandemic) were identified, which showed a significant impact on the financial market.Originality/valueFirst, we extend the theoretical concepts. This study applies signaling theory and overreaction theory to the US stock market in the context of climate change. Second, in addition to the news sentiment, the impact of major news topics on US stock market returns is examined. Third, we examine the impact of sentimental and thematic news variables on US stock market indicators of economic sectors. Previous works reveal the impact of climate change news on specific sectors of the economy. This paper includes stock indices of the economic sectors most related to the topic of climate change. Fourth, the research methodology consists of modern algorithms. An advanced textual analysis method for sentiment classification is applied: a pre-trained bidirectional FinBERT neural network. Modern thematic modeling is carried out using a model based on the neural network, BERTopic. The most extensive topics are “finance and politics of climate change” and “natural disasters and consequences.”

Is the Korean green premium in equilibrium?

This study examines the Green premium in the Korean stock market using the two-factor equilibrium model developed by Pástor, Stambaugh, and Taylor (2021), which consists of the Market and Green factors. From 2012 to 2020, the Green factor premium in the Korean stock market is estimated to be negative, with a monthly average of −0.23%. This finding aligns with the expectations of the equilibrium model, which suggests a negative Green premium (indicating low Green and high Brown) due to the investors’ preference for green investments and the demand for hedging against climate risk. The negative sign of the Green factor premium holds robustly across different methods and data employed in the analyses. However, there has been a recent weakening in the magnitude of the negative Green factor premium. This suggests a potential impact of positive ESG shocks on the Korean stock market, similar to the case observed in the US stock market, where a positive realized Green premium was identified by Pástor, Stambaugh, and Taylor (2022).

The impact of the US stock market on the BRICS and G7: a GVAR approach

PurposeThe authors investigate the impact of the US stock market on the economies of the BRICS and major industrialized economies (G7).Design/methodology/approachThe authors construct the world economy and the vulnerability between economies using three economic integration variables: bilateral trade, bilateral direct investment and bilateral equity positions. Global vector autoregressive (GVAR) empirical studies usually adopt trade integration to estimate models. The authors complement these studies by using bilateral financial flows.FindingsThe authors summarize the results in four points: (1) financial integration variables increase the effect of the US stock market on the BRICS and G7, (2) the US shock produces similar responses in these groups regarding industrial production, stock markets and confidence but different responses regarding domestic currencies: in the BRICS, the authors detect appreciation of the currencies, while in the G7, the authors find depreciation, (3) G7 stock markets and policy rates are more sensitive to the US shock than the BRICS and (4) the estimates point out to heterogeneities such as the importance of industrial production to the transmission shock in Japan and China, the exchange rate to India, Japan and the UK, the interest rates to the Eurozone and the UK and confidence to Brazil, South Africa and Canada.Research limitations/implicationsThe results reinforce the importance of taking into account different levels of economic development.Originality/valueThe authors construct the world economy and the vulnerability between economies using three economic integration variables: bilateral trade, bilateral direct investment and bilateral equity positions. GVAR empirical studies usually adopt trade integration to estimate models. The authors complement these studies by using bilateral financial flows.

Macroeconomic shocks and stock market returns

ABSTRACT We examine how structural macroeconomic disturbances impact US stock market returns. Our analysis uncovers a significant negative correlation between stock market returns and both systematic and monetary shocks. Our findings indicate that monetary policy shocks exert a substantial influence on US stock market returns. Forecast error variance decomposition of stock market returns shows systematic and monetary shocks explain 95% of stock return variance. An examination of industry-level stock returns indicates industries engaged in essential goods have greater resilience to shocks, indicating a reduced sensitivity to market fluctuations.

A long-short dual-mode knowledge distillation framework for empirical asset pricing models in digital financial networks

The continuous combination of digital network technology and traditional financial services has given birth to digital financial networks, which explore massive economic data under the AI-driven models to achieve intelligent connections among financial institutions, markets, transactions, and instruments. Empirical asset pricing is a challenging task in financial analysis, which has attracted research attention. However, existing studies only focus on tackling the challenges of equity risk premium in the single stock market. Considering multiple economic linkages between the two countries, the transaction history of the US stock market as empirical knowledge is a powerful supplement to improve the prediction of equity risk premium in the China market. In this paper, we aim to fully leverage the prior information in two stock markets for empirical asset pricing models. Due to the rich financial domain knowledge, there may be various characteristic signals that partially overlap in different periods. To address these issues, we propose a framework based on long-short dual-mode knowledge distillation, termed as LSDM-KD, which incorporates US and China stock market models, and a shared characteristic signals model. The method effectively understands the relationships between assets and market behaviour, reducing reliance on expensive correlation databases and professional knowledge. Extensive experiments conducted on US and China stock market datasets demonstrate that our LSDM-KD can significantly improve the performance of empirical asset pricing.

Stock movement prediction: A multi‐input LSTM approach

AbstractGenerally, the nonlinear and non‐stationary financial time series becomes an obstacle in the process of stock movement prediction. But the recent theories of machine learning and deep learning have provided with some new solutions. Based on LSTM (long short‐term memory), we propose a hybrid model of wavelet transform (WT) and multi‐input LSTM to predict the trend of SSE composite index. It can mine valid data in time series and support different types of data as input. The whole model is divided into two stages. In the first stage, we adopt the level 1 decomposition with db4 mother wavelet to eliminate noise. In the second stage, combinative and qualitative analysis was made base on the data from Chinese stock market, US stock market, and technical indicators as input. According to the result, the proposed model, with the accuracy of 72.19%, performs better than single‐input LSTM, decision tree, random forest, Support Vector Machine (SVM), and XGBoost.

A Comparison of the Performance of Rainbow Options and Stocks under COVID-19 and Ukraine Conflict

This paper hopes to explore which is the better choice for investors under the different environments of the US stock market, rainbow option or buying a single stock. Due to the improvement of the COVID-19 situation in the United States in 2021 and the introduction of government policies to stimulate the economy, the US stock market showed an upward trend throughout 2021. In 2022, due to the conflict between Russia and Ukraine, the global economy will be impacted, and the US stock market is no exception. In these two long-term bear markets and bull markets, it hopes to find a mutation option to control risks and improve returns. In this case, it focuses on rainbow options, which are options that help investors screen out the better-performing stocks or futures included in them. First, this paper paired 14 stocks in five fields, calculated the option’s payoff according to the rainbow option's rules, and then compared it with the return of the stock alone to explore the superiority of the rainbow option. The return on the rainbow option is higher than the return on any single stock. Then the option is simulated and priced for 1000 future trading days so as to obtain the return of the rainbow option under the assumption of stable fluctuation in the US stock market environment, which is used as the reference for pricing. Finally, the sensitivity analysis of options is conducted to study the main factors affecting the volatility of option premiums. The result is that the moves in rainbow options are always correlated with the stock that is more active.