Spotting the Predictive Dynamics of Cyclically‐Adjusted Financial Ratios in the US Stock Market

In the contemporary era, people have strong incentives to explore the underlying principles of stock markets and China and the US are the 2 largest economies across the world. So, it is the stock markets in these two countries that we need to explore and study in this paper. In order to test whether the trends of the US and Chinese stock market are predictable and identify the difference between these two markets, we employed various models to study the S&P 500 and CSI 300 indexes' trends. Specifically, in this paper, we included the Markov chain, hidden Markov model (HMM), logistical regression with lasso, autoregressive integrated moving average (ARIMA) and support vector machine (SVM) to achieve our target.Therefore, we obtained several interesting key findings in our paper. We found that the Chinese stock market is more likely to be affected by technical indicators instead of historical information, as logistical regression with lasso selected most of the technical indicators and the estimated order of the Markov chain is zero when modelling the CSI 300 trends, which is different from the US stock market. Also, the AUC value of SVM outperformed other models used in the US stock market, at 0.731, while ARIMA model resulted in high AUC values in both markets, at 0.606 and 0.622 for the US and Chinese stock markets respectively. So, we confirmed that the Chinese stock market is less efficient than the US stock market. What's more, to predict the future trends in the US stock market, SVM could be the best choice, while ARIMA model works effectively for both markets.

Which fear index matters for predicting US stock market volatilities: Text-counts or option based measurement?

This study investigates the interdependent structure between Nigeria and developed stock market using directed acyclic graph with Bayesian estimation techniques for the period of March 11, 2020 to July 31, 2021. The dynamic Granger causal test of both the contemporaneous and autoregressive estimation were done using the Markov Chain Monte Carlo (MCMC) simulation method with Metropolis-Hasting (MH) sampling technique. The graphical structure of the instantaneous and autoregressive results shows returns correlation for Nigeria’s stock market and both China and India’s stock market while absence of return correlation are found for both US and UK stock market. The result implies that Nigeria’s stock market has potential diversification benefit for the US and UK investors during pandemic crises. In addition, US stock market returns is found to depend on China stock market returns while volatility spill over runs from the US stock market to China’s stock market. Last, UK stock market tends to depend on India’s stock market. The implication of the study is that Nigeria’s stock market is integrated with China and India’s stock market while China’s stock market is linked to the US stock market such that any financial shock from the lead stock markets is transmitted to the lagged stock markets.

Stock market return predictability has long been one of the key and unsolved areas of research in finance. Although the stock market has been argued to follow a random walk, researchers have struggled to improve the accuracy of predicting stock market returns through extensively examining forecasting variables such as financial ratios, economic indicators, and behaviour factors. Pollet and Wilson (2010) have recently developed a new predicator and claimed that average correlation reveals the movement of the systematic component of the market return and it predicts the stock market returns. This thesis uses the newly developed predictor, average correlation, to predict stock market returns, both in the US and across a number of developed countries and emerging countries. Three interrelated studies are sequentially undertaken to examine the predictive power of average correlation for future stock market returns. The first study uses the average correlation of the 48 Fama-French industry portfolio returns in the US stock market to predict the US stock market returns. To juxtapose average correlation with conventional predictors, a number of forecasting variables, including term spread, default spared, dividend price ratio, the cyclically adjusted price-to-earnings ratio and investor sentiment, are incorporated in the model. The second study uses 27 non-US financial markets and extends the analysis to the relatively less explored area relating to the predictability of the international stock market returns. The average correlation of industry portfolio returns in each financial market, including more forecasting variables such as industrial production, gross domestic production and financial crisis dummies, is used to predict the stock returns of the financial markets under study. The third study further extends the analysis and uses both the US average correlation from the first study and the local average correlation from the second study as predictors for the stock market returns of each financial market. The US average correlation is posited as capturing the global influence on a particular financial market, while the local average correlation is used to represent the domestic influence within that financial market. The key findings of the thesis are summarised as follows. First, average correlation is a significant predictor for the US stock market returns at a two-month lag and for the returns of other stock markets with a one-month lag. Second, average correlation outperforms all predictors conventionally used in the US stock market, as well as in most other international stock markets. Third, the US and local average correlations predict the local stock market returns, indicating that the global influence has an impact on the local stock market returns and that the US average correlation successfully captures such an influence. The research findings suggest that the average correlation is closely related to stock market returns. The findings of the thesis would be of interest to policymakers as well as stock market practitioners who wish to formulate effective trading strategies.

We investigate the interdependence between U.S., Japan, China and Korea stock markets using daily return data covered from September 14, 2005 to September 30, 2011. To effectively study the short-term information transmission among 4 major stock markets we separated the whole sample period into two sub-sample period, before and after 2007 financial crisis. For this purpose we introduced the Granger causality test and variance decomposition analysis based on VECM(vector error correction model). The main results of empirical tests are as follows;First, we test the stationarity of 4 countries’ stock market index using ADF and PP model. According to the empirical results we find that there are unit roots in the level variables of 4 countries’ stock market data but not in the returns data of the 4 countries stock market both before and after financial crisis. Second, we also try to estimate the long-run relationship among S&P500, Nikkei 225, Shanghai stock index and KOSPI 200 stock index. For this we introduce the Johansen co-integration model and come to the conclusion that there is a long-run relationship among the level variables of four national stock markets. Third, in order to the short-term information transmission mechanism among the four national stock market index, we employ the Granger causality test based on the VECM(3). According to the empirical test, we find that during the whole sample period, S&P 500 and Nikkei225 stock index have an impact on the KOSPI 200 stock index in the statistically significant level but there is no information transmission effect from Shanghai stock market to Korea stock market. The impact of S&P500 stock index on the KOSPI200 stock index is relatively greater than that of Nikkei225 stock index on the KOSPI200 stock index. Fourth, in case of the empirical result on before and after financial crisis period, we find that the interdependence among the four national stock markets after financial crisis period is much greater than that of before financial crisis period. Fifth, we also compare the influence of US and Japan stock market on China stock market and we find that the US stock market has an greater impact on China stock market than Japan stock market. we also find that after financial crisis of 2008 the comovements between four stock markets are greater than before financial crisis. After the financial crisis the impact of China stock market to other countries is increased but that of Japan is decreased. Sixth, in case of information transmission US and Japan stock markets, there is a feed information transmission between the two national stock market but the influence of US stock market on other countries' stock is more dominant since the financial crisis. The empirical results by variance decomposition analysis are consistent with those of Granger causality test. From these empirical results, we infer that market integration among stock markets is increasing over time and these empirical results are informative to stock market investors and regulators to set up a investment strategies and government policy.

The aim of this paper is to examine the interdependence revulsion of Indonesia Stock Markets (JCI) with the changes in US Monetary policy and Stock Markets (DJCI). The methodology used in this study is time series econometric techniques i.e. the unit root test, cointegration test, Granger’s causality and Vector Error Correction Model (VECM). The result reveals a short-term and long-term dynamic relationship between the US stock markets and the Indonesia one. A 1 percent increase in US stock markets contributes to Indonesia stock markets by 0.4 percent over the next 10 months. One of the policy implications is that the authority of Indonesia stock markets should strengthen and improve their regulations so that the susceptible of the stock markets can be minimized.

(Study on the Impact of Us Stock Market's Shock Due to Global Financial Crisis on Korean Financial Markets)

This article, using the daily returns of the indices of US (S&P 500) and the Indian stock markets (CNX S&P Nifty), examines the impact of the global financial crisis on the level of financial integration between the US and Indian stock markets from March 2005 to November 2010. The article also analyses the existence of cointegration and dynamic relationship between the two indices during the pre-crisis, crisis and post-crisis periods, and in the last five years, using the Johansen Cointegration analysis and the Vector Auto Regression (VAR) Model. The article finds no cointegration between the two indices in all the four periods. The returns from the Indian stock market with reference to the previous day’s return of the US stock market show a lot of feedback effect from the US to India, whereas the returns from the US stock market show no significant reaction. However, in terms of its own past, there is a strong negative reaction due to the overreaction or mean reversion of the stock market during the post-crisis period.

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.

Mean reversion in the US stock market

Characteristics of spillovers between the US stock market and precious metals and oil

The US stock market is one of the largest and most influential financial markets in the world. Throughout the course of history, it has played an important role in US societal development. And as the world enters the digital age, the US stock market is adapting to the latest technological developments, which could generate some potential problems. This paper examines the issues within the US stock market and their possible solutions, reaching the conclusion that market fluctuations, insider trading, market manipulation, and lack of investor education are examples of major problems within the US stock market. After describing each problem, this paper proposes potential solutions to deal with the problem, including transaction taxes, stricter enforcement, enhancing penalties, and so on. Overall, the research presented in this paper could be viewed within the larger context of academic conversation regarding the US stock market. The results and conclusions could inform the general public of the current state of the US stock market and provide suggestions to governmental officials and policy makers.

Are Stock Markets Adaptive? Evidence from US, Hong Kong and India

This paper studies volatility transmission effects between the US stock market and the COVID-19. Using BEKK-multivariate GARCH model, we find the US stock market volatility depends both its own past shocks and past COVID-19 shocks. Further, we find the US stock market volatility is positively affected by the death rate (bad news) while the recovered rate (good news) has a negative impact on the US stock market volatility. In addition, we find there is an asymmetric volatility impact of COVID-19 on the US stock market: the bad news affects the current US stock market much more than the good news. Our fixed effect panel regression results support the volatility spillover effects.

This study aims to analyse the impact of force majeure events or exogenous shocks, characterized by a high level of uncertainty, on the US and European stock market behaviour through cross-regional and inter-industrial comparisons. Based on the sample of 832 largest stock-listed companies publicly traded in both regions, the authors investigate whether specific regional and industrial differences in the stock market reaction to exogenous shocks—as exemplified by the ongoing COVID-19 pandemic—and their recovery paths to be determined. The novelty of the research conducted by the authors is underpinned by the application of a dynamic network analysis method—supported by the exponential random graph modelling—for a data sample of publicly traded companies, encompassing major players and industrial leaders in both regions. Furthermore, the methods are applied quarterly for an identical set of industries in cross-market comparison, within a time span of periods before, during and beyond the exogenous shock. The results reveal significant differences in the reaction of US and European stock markets to exogenous shocks, despite globally integrated financial markets. The connectivity of the largest listed companies on the US market is higher than the European ones. Thus, publicly traded companies in the United States are more likely to be closely connected to their industry peers. As demonstrated by a cross-market comparison of the European and US stock market network configuration, the US network reveals higher probabilities of intra-industrial connections of its market participants. In contrast, the European stock market exhibits fewer connections and a less dense network of market participants in times of uncertainty or exogenous shocks. Except for the European financial industry, which behaves similarly to its US counterpart, the overall interconnectedness of European companies is weaker within the uncertainty-related timespan. The latest could be partially explained by numerous national regulations being implemented at a country level, which are partly deviating from the EU single-market policies. In contrast to a relatively homogeneous setting of the US market, a low level of interconnectedness of publicly traded companies in Europe could be further argued by the application of non-harmonized industrial policies, with the latest being expanded during the ongoing crisis in selected European locations. This difference could also be explained by the absence of a mutual strategy for alleviating the effects of the COVID-19 pandemic in Europe. The obtained results could be valuable for academics, conducting similar thematic research; for portfolio investors, and policymakers in forecasting, reacting and assessing stock market behaviour overall—and at the level of industrial sectors in particular—in response to events characterized as force majeure, exogenous shocks or periods of uncertainty.