Oil shocks,policy uncertainty and stock returns in China

This paper examines the interdependence of China's policy uncertainty, the global oil market and stock market returns in China. A structural VAR model is estimated that shows that a positive shock to economic policy uncertainty in China has a delayed negative effect on global oil production, real oil prices and real stock market returns. Shocks to oil market‐specific demand significantly raise China's economic policy uncertainty and reduce the real stock market returns. As measured by a spillover index, the interdependence between these variables has been rising since 2003 as China's influence in the oil market has increased. An equivalent spillover index calculated for the US is smaller and has been largely flat over time.     

OIL PRICE SHOCKS AND STOCK MARKET BOOMS IN AN OIL EXPORTING COUNTRY

This paper analyses the effects of oil price shocks on stock returns in Norway, an oil-exporting country, highlighting the transmission channels of oil prices for macroeconomic behaviour. To capture the interaction between the different variables, stock returns are incorporated into a structural VAR model. I find that following a 10% increase in oil prices, stock returns increase by 2.5%, after which the effect gradually dies out. The results are robust to different (linear and non-linear) transformations of oil prices. The effects on the other variables are more modest. However, all variables indicate that the Norwegian economy responds to higher oil prices by increasing aggregate wealth and demand. The results also emphasize the role of other shocks; monetary policy shocks in particular, as important driving forces behind stock price variability in the short term.     

Financial Market Data Versus Accounting Data: Which Better Explains Stock Returns?

This paper presents the comparison of how financial market and accounting data affect stock prices and returns. The goal was to ascertain whether financial information or accounting data dominate in evaluating stock prices. Most valuation techniques used by firms are based on models using either accounting variables (earnings, book value, cash flows, research and development expenses) or financial market data (e.g. beta, market value, interest). The answer is of great importance for valuators and investors as it will help them focus on the most important variables and make better valuations and choices. This answer is also important for accounting standard setters as the preferred method will serve as an indicator for the quality of financial statements and their importance to users. The paper contributes to the existing literature in the fields of value relevance of accounting information and firm valuation and accounting standards (e.g. International Financial Reporting Standards, United States General Accepted Accounting Principles). To answer this question, share prices were estimated based on financial data using the capital asset pricing model and for accounting data, using Ohlson’s model. The results were tested for both methodologies by comparing estimated share prices with actual ones. The greater the correlation between the two variables the better the explanatory power of the model. The focus was on S&P 500 firms for the period 2002–2017.

     

Can M&A activities act as a predictor of the performance of economic growth or stock prices?

One of the important issues with regard to the relationship between M&As (mergers and acquisitions) and economic growth or stock prices is whether such activities can act as a predictor of these two variables' performance, or whether these variables have resulted in significant impacts on M&A activities. The aim of this paper is to use the method proposed in Kónya (2006) to carry out a causality test among M&A activities, economic growth and stock prices, because the causal relationships that may be uncovered by this would be meaningful for both policymakers and stockholders. This paper uses quarterly data from six OECD countries for the period from April 1980 to March 2010. The bootstrap panel Granger causality test that this work applies also considers cross-sectional dependency and slope heterogeneity simultaneously. The findings of the paper are as follows. There is significant, one-way causality from stock prices to M&A activities, and thus changes in stock prices lead M&A activities. With real GDP as the control variable, for all the countries surveyed, except Australia, stock prices lead M&A activities. As for the impact that economic growth has on M&A activities, we conclude that, when using stock prices as the control variable, there is almost no lead-lag relationship between economic growth and M&A activities, except for in Japan.     

Housing prices,stock prices and the US economy

This paper studies the relationship between housing prices, stock prices, interest rates and aggregate output in the US using monthly data from 1993 to 2014. Evidence from causality tests and a variance decomposition procedure suggest that stock prices have a much larger effect on aggregate output in the US economy than do either housing prices or interest rates. Instead, the wealth effect created by changes in stock prices has a relatively large impact on US aggregate output. Separate estimations and variance decompositions for the sample periods 1993–2001, 2002–2008 and 2009–2014 show that the impact of housing prices relative to stock prices has been waning over time.     

Stock Market Fluctuations and Money Demand in Italy, 1913–2003

This paper examines the impact of stock market fluctuations on money demand in Italy from a long‐run perspective. The money demand function estimated by Muscatelli and Spinelli (2000) for a long time span is utilized as a benchmark, adding to the specification information on share prices from the Milan Stock Exchange Reform of 1913 to recent years. For a shorter time period (1938–2003), annual observations on stock market capitalization and turnover velocity are also considered. The empirical findings suggest that stock market fluctuations help to explain temporary movements in liquidity preference, rather than its secular patterns. Overall, a positive association emerges between an index of stock market prices that includes dividends and real money balances; however, the estimated long‐run relationship is unstable. In a dynamic, short‐term specification of money demand, the estimated coefficient of deflated stock prices is positive, and therefore compatible with a wealth effect, in the years 1913–1980, while in the last two decades a substitution effect has prevailed and the correlation between money and share prices has been negative. This is likely to reflect a change in financial structure and the increasing role of opportunity costs defined over a wider range of assets. These results are confirmed by data on stock market capitalization. Moreover, in the recent period, stock market turnover and money growth are positively correlated .     

The macroeconomic effects of fiscal policy

We investigate the macroeconomic effects of fiscal policy using a Bayesian Structural Vector Autoregression (B-SVAR) approach. We identify fiscal policy shocks via a partial identification scheme, but also: (i) include the feedback from government debt; (ii) look at the impact on the composition of output; (iii) assess the effects on asset markets; (iv) use quarterly data; and (v) analyse empirical evidence from the US, the UK, Germany and Italy. The results show that government spending shocks, in general, have a small effect on Gross Domestic Product (GDP); lead to important ‘crowding-out’ effects; have a varied impact on housing prices and generate a quick fall in stock prices. Government revenue shocks generate a mixed effect on housing prices and a small and positive effect on stock prices. The empirical evidence also suggests that it is important to explicitly consider the government debt dynamics in the model.     

The impact of large-scale asset purchases on the S&P 500 index,long-term interest rates and unemployment

After the bankruptcy of Lehman Brothers in September 2008 and the financial panic that ensued, the Federal Reserve moved rapidly to reduce the federal funds rate to .25%. It was quickly judged that additional measures were needed to stabilize the US economy. Beginning in December 2008, the Federal Reserve Bank initiated three rounds of unconventional monetary policies known as quantitative easing (QE). These policies were intended to reduce long-term interest rates when the short-term federal funds rates had reached the zero lower bound and could not become negative. It was argued that the lowering of longer-term interest rates would help the stock market and thus the wealth of consumers. This article carefully investigates three hypotheses: QE impacting long-term interest rates, QE impacting the stock market and QE impacting unemployment using a Markov regime switching methodology. We conclude that QE has contributed significantly to increases in the stock market but less significantly to long-term interest rate and unemployment.     

The cross-section and time-series effects of individual stock sentiment on stock prices

In this article, we construct an individual stock sentiment index by using the principal component analysis method. We empirically study the cross-section and time-series effects of investor sentiment on the stock prices based on the panel data model with dummy variable. The results indicate that individual stock sentiment has greater impact on small-firm stock prices than big-firm stock prices, which presents obvious cross-section effect. Moreover, individual stock sentiment leads to much sharper ?uctuations of stock prices in the stock market downturn than in the stock market expansion, which shows obvious time-series effect. Specifically, the individual stock sentiment has the greatest impact on small-firm stock prices under the stock market downturn, exerting significant dual asymmetric effect. Our results are helpful to understanding the micro-mechanism of sentiment effect.     

Modeling the Taiwan Stock Market and International Linkages

This paper analyzes the Taiwan stock market and examines its price and volatility linkages with those of the United States. In particular, it tests the hypothesis that the short-term volatility and price changes spill over from the developed markets, mainly the United States, to the emerging Taiwan stock market. The model and the test are built upon Engle's ARCH (autoregressive conditional heteroskedasticity) and Engle and Kroner's M-GARCH (multivariate generalized ARCH) models. The paper differs from previous studies on the Taiwan stock market in three respects. First, instead of using daily closing prices, it uses close-to-open and open-to-close returns to avoid the problem of overlapping samples. It carefully models the day-of-the-week effect in daily data to avoid misspecification of the model. Second, to circumvent the generated regressor problem arising from the two-step estimation procedure, it also employs the M-GARCH model where all parameters are estimated simultaneously. Third, the misspecification test is carried out on various kinds of asymmetric ARCH factors. A substantial volatility spillover effect is found from the US stock market to the Taiwan stock market, especially for the model using close-to-open returns. There is also evidence supporting a spillover effect in price changes. The findings can be explained by the recent gradual opening of the Taiwan stock market to foreign investors.     

Volatility spillovers and macroeconomic announcements: evidence from crude oil markets

The paper applies an event study methodologyaims to investigate the macroeconomic announcements effects on Standard&Poor’s500 and oil prices. Our results provide evidence for a significant impact of the US macroeconomic news on oil prices. This impact is split into two components, namely the direct effect (common response) and indirect effect (volatility transmission). Altogether our results show that the volatility transmission is bidirectional. Not only a significant volatility transmission from the oil market to the US stock market is revealed, but also a high volatility transmission is recorded from the oil market to the stock market especially after the release of consumption indicators.     

Long-run money demand in OECD countries: what role do common factors play?

This article examines the long-run money demand function for 11 OECD countries from 1983Q1 to 2006Q4 using panel data. The distinction between common factors and idiosyncratic components using principal component analysis allows for the detection of cross-member cointegration and the determination as to whether national or international sources are responsible for the non-stationarity of money and its determinants. Indeed, the finding that the common factors are I(1) while the idiosyncratic components are I(0) indicates that cross-member cointegration may exist and non-stationarity in the variables is primarily driven by common international trends. Furthermore, it is found that the impact of income on money demand is positive, whereas it is negative for the interest rate, exchange rate and stock prices. Except for the income elasticity of money demand, all estimated long-run coefficients are larger for the common factors of the variables than for the variables themselves. This article provides evidence that the exchange rate is an important determinant of money demand, whereas the results for the stock prices are ambiguous. Finally, the results of a panel-based error-correction model suggest that several domestic money stocks converge to a common international equilibrium relationship between the common factors.     

CROSS‐BORDER M&As AND FIRM VALUE: A COMPARISON OF CHINA‐ AND US‐JAPAN M&As

We examine the effect of M&As conducted by U.S. and Chinese bidders (US‐Japan and China‐Japan M&As) on the stock prices of Japanese targets. We find that both types of M&As tend to increase the stock prices of the Japanese targets and that market reactions are significantly greater for US‐Japan M&As than for China‐Japan M&As. Additionally, capital participation produces greater market reactions to China‐Japan M&As than other structures, while acquisition produces this effect in US‐Japan M&As. Our results are consistent with previous research indicating that market reactions increase for bidders operating in a developed country with high‐quality institutions and corporate governance. (JEL G32, G34)     

Improving the predictability of the oil–US stock nexus: The role of macroeconomic variables

In this study, we revisit the oil–stock nexus by accounting for the role of macroeconomic variables and testing their in-sample and out-of-sample predictive powers. We follow the approaches of Lewellen (2004) and Westerlund and Narayan (2015), which were formulated into a linear multi-predictive form by Makin et al. (2014) and Salisu et al. (2018) and a nonlinear multi-predictive model by Salisu and Isah (2018). Thereafter, we extend the multi-predictive model to account for structural breaks and asymmetries. Our analyses are conducted on aggregate and sectoral stock price indexes for the US stock market. Our proposed predictive model, which accounts for macroeconomic variables, outperforms the oil-based single-factor variant as well as the constant returns (historical average) model for both in-sample and out-of-sample forecasts. We find that it is important to account for structural breaks in our proposed predictive model, although asymmetries do not seem to improve predictability. In addition, we show that it is important to pre-test the predictors for persistence, endogeneity, and conditional heteroscedasticity, particularly when modeling with high-frequency series. Our results are robust to different forecast measures and forecast horizons and are useful for making effective hedging decisions in the US stock market.     

On the Causality Between Exchange Rates and Stock Prices: A Note

This study uses a new Granger non–causality testing procedure developed by Toda and Yamamoto (1995) to contribute to the debate on exchange rates and stock prices in Sweden. It examines a possible causal relation between these variables in a vector autoregression (VAR) model. The results show that Granger causality is unidirectional running from stock prices to effective exchange rates. The results also reveal that an increase in Swedish stock prices is associated with an appreciation of the Swedish krona. Special attention is given to the estimation methodology and the lag choosing process.     

OWA-based ANFIS model for TAIEX forecasting

In stock market forecasting, high-order time-series models that use previous several periods of stock prices as forecast factors are more reasonable to provide a superior investment portfolio for investors than one-order time-series models using one previous period of stock prices. However, in forecasting processes, it is difficult to deal with high-order stock data, because it is hard to give a proper weight to each period of past stock price, reduce data dimensions without losing stock information, and produce a comprehensive forecasting result based on stock data with nonlinear relationships.Additionally, there are two more drawbacks to past time-series models: (1) some assumptions (Bollerslev, 1986; Engle, 1982) about stock variables are required for statistical methods, such as the autoregressive model (AR) and autoregressive moving average (ARMA); (2) numeric time-series models have been presented to deal with forecasting problems for stock markets, but they can not handle the nonlinear relationships within the stock prices.To address these shortcomings, this paper proposes a new time series model, which employs the ordered weighted averaging (OWA) operator to fuse high-order data into the aggregated values of single attributes, a fusion adaptive network-based fuzzy inference system (ANFIS) procedure, for forecasting stock price in Taiwanese stock markets.In verification, this paper employs a seven-year period of the TAIEX stock index, from 1997 to 2003, as experimental datasets and the root mean square error (RMSE) as evaluation criterion. The experimental results indicate that the proposed model is superior to the listing methods in terms of root mean squared error.     

Stock prices and long-run demand for money: evidence from Malaysia

This paper investigates the money demand function for Malaysia in the 1971-1996 period using the multivariate cointegration and error correction model methodology. The results suggest that a stable long-run relationship exist between real M2, the interest rate differential, income and stock prices. Stock prices have a significant negative substitute effect on long-run as well as short-run broad-money demand (M2) and its omission can lead to serious misspecification in the money demand function. The analysis from the vector error correction model (VECM) and the Toda & Yamamoto (1995) causality tests find that money is endogenous and that there is at least a unidirectional relationship between stock prices and real M2. Stock prices Granger cause real M2 indirectly through income between interest rates and stock prices and stock prices and money stock. This paper comes to the conclusion that due to the endogeneity of money, M2 cannot be completely controlled by Malaysia's central bank. Therefore, in formulating future monetary policy, the response of money demand to stock prices should be considered.     

EFFECT OF OIL PRICES ON STOCK MARKETS: EVIDENCE FROM NEW GENERATION OF STAR MODEL

This paper aims to introduce an evidence of new generations of smooth transition regression model (STAR). It proposes two different forms of STAR model. First: a time varying STAR model (TVSTAR), which identify the estimated coefficients at each point of time. Second: a full specification STAR model (FSSTAR) which provides a consistent estimate even in the existence of some measurement errors, omitted variables and even if the true functional form is unknown. This study will consider the two proposed models and the traditional STAR model to examine the nonlinear relation between oil price and stock market index for two countries (Egypt and Turkey). Our results confirm the existing of a non‐linear relation between oil prices and stock return for both countries. The suggested models gives more accurate information about the time varying effect of oil price changes on stock markets and robust forecasts.     

Time–frequency relationship between US output with commodity and asset prices

Commodity and asset prices have a well-documented effect on economic growth as manifested through various channels. At the same time, the business cycle influences the commodity and asset prices. Whereas empirical evidence on the effect of commodity and asset prices on the long-run economic growth is ambiguous, most of the previous researches highlight a positive correlation in the short run. The aim of this article is to disentangle the short- and long-run co-movements between US historical business cycles and commodity and asset prices over the period 1859–2013. For this purpose, we use a time–frequency approach and we test the historical influence of oil, gold, housing and stock prices over the output growth. In contrast to other studies, we control for the effect of other prices and monetary conditions, using the wavelet partial coherency. In line with the previous works, we discover that co-movements between economic growth and commodity and assets prices manifest especially in the short run. We also find that stock returns and housing prices have a more powerful effect on the US economic growth rate than the oil and gold prices. The long-run co-movements are documented especially around the World War II. Finally, when controlling for the influence of the interest rate, inflation and other commodity and asset prices, co-movements become weaker in the short run. In general, the oil and housing prices lead the GDP growth, the US output leads the gold prices, while there is no clear causality direction between business cycle and stock prices.     

The recent boom–bust cycle: The relative contribution of capital flows, credit supply and asset bubbles

We use an estimated open economy DSGE model with financial frictions for the US and the rest of the world to evaluate various competing explanations about the recent boom–bust cycle. We find that the savings glut hypothesis is insufficient for explaining all aspects of the boom in the US. Relatively strong TFP growth and expansionary monetary policy are also not able to explain fully the volatility of corporate and in particular residential investment. We identify bubbles in the stock and housing market as crucial. Concerning the downturn in 2008/2009, the fall in house prices and residential investment only plays a minor role. Mortgage defaults have more explanatory power, especially in a specification of the model with a segregated equity market. Finally, the bursting of the stock market bubble was at least as important in this recession as in 2001. Because of various negative shocks hitting the economy at the same time in 2008/2009 and continued positive technology growth, not only the real interest rate declined but inflation fell rapidly and left insufficient room for monetary policy to play a similar stabilising role as in previous recessions.