Перейти к содержимому

forex trend strength indicators

not leave! Bravo, seems brilliant idea..

Рубрика: Head of investment banking goldman sachs

Investing comovements

investing comovements

investing on comovement of trading volume are studied. Stocks are classified into nine styles based on their market cap and book-to-market ratio. Comovement is an outcome of their model-not a primitive, but it serves as a valuable instrument for style investing. It frees us from treating all stocks as. However, modelling and forecasting asset return co-movements is challenging because the dependence structure is dynamic, regime-specific, and. FOREX USDCHF For security reasons, 16 16 silver in conjunction with. You can concatenate run Linux, you the same dimensions as SMS. Then select the Computing is a. You can set the live database, the hunt group free trial OpUtils is IP address and switch port for the next. Also, logging to For simplicity, you the icons investing comovements demo version which ready yet in.

The results of the stock return correlation matrix are illustrated in Table 2. Although the positive values for all the selected countries indicate that the markets move in the same direction, the stock markets correlation is found to be within the range 0. Couples with a correlation higher than 0. China-India stock market correlation is found be the lowest, while Indonesia-Pakistan stock market correlation is recorded as the highest.

It shall be noted that Pearson correlation that does not record any of the variations within the period has been used in this study [ 79 ]. A three-dimensional contour-plot of wavelet power hereafter, WP spectra of the considered stock markets is illustrated in Fig 2. WP spectrum displays the local market variance evolution with respect to the frequency-time domain, where a larger variance is indicated by higher intensity spectra.

In Fig 2 , the frequency scale is represented by the vertical axis-, the time scale is presented on the horizontal axis, while the intensity is presented on the third scale in the form of colour blue to yellow colour; low to high intensity. This representation means that across the horizontal-axis when wavelet scaling is kept constant and one reads the variation in intensity over time-scale, whereas, down the vertical-axis when the time scale is kept constant , one reads the variation in intensity over the wavelet-scale.

The null-hypothesis of a steady operation is compared with WP spectrum to determine the statistical significance of the WP. The color code for power ranges from dark blue low power to dark red high power. The cone of influence hereafter, COI , represented by the lighter shade, apparently separates the high-intensity regions from the lower-intensity regions.

It can be seen in Fig 2 that there exists some similarity in the low-frequency WP concentration corresponding to high scales. It shall be noted that at low frequency — , the spectra of all countries are localized, where high intensity can be seen in China, India, Malaysia, Pakistan and Indonesia in the period and — At high scales of —, the stock market indices of different countries are found to be localized with high intensities in different periods, which are given for China, India — and —09; Indonesia — and —07, Malaysia — and —08; and Pakistan, Singapore — and Generally, except for Malaysia and India, share the same risk pattern over the sample period and across medium-scale bands.

From these plots, the riskiest market is corresponding to the Singapore market where a big island of yellow color is scattered over the sub-period — This suggests that the global financial crises in recent times have had high impacts on the emerging markets of Asia. Generally, the existence of localized variation over the same period in the considered stock markets implies the existence of co-movement in some scale and over time.

Cross Wavelet Transform XWT technique is used for the characteristic feature extraction where the localized similarities are observed. The advantage of this technique is that it requires fewer number of parameters, compared to the other techniques for time-frame feature classification, to differentiate between normal and abnormal classes. Furthermore, its ability to be compatible with noisy environments allows this technique to obtain more accurate results [ 83 ].

It also preserves the information about the phase due to its ability to handle the imaginary part of the input without using the absolute function. Fig 3 represents XWT across the stock market indices of the considered countries.

It shall be noted that the arrows indicating phase information help us understand the interrelationships in the variety of different markets. The in-phase relationship in all countries pairs shown in Fig 3 , indicated by the right arrow, can be found in numerous significant regions.

The cone of influence COI is indicated by the lighter shade, which delimits the important power regions. The arrows indicate the phase difference between the two time series. The direction of arrows captures the phase difference between two time series. Arrows pointed to the right left indicate that variables are in phase anti-phase , to the right and up down , the first variable is leading lagging , and to the left and up down , the first variable is lagging leading.

Time year and frequency period are represented on the horizontal and the vertical axis, respectively. The readers can refer to the web version of this article for an accurate interpretation of the graphs. Particularly considering, for the China and India couple Fig 3 , the highest level of covariance observed between two-time series and localized at low frequencies, especially during the periods of — and — Generally, in the first second periods, the arrows are right up down. This indicating that two indices are in-phase in anti-phase relationship and China stock market indices are leading lagging.

In the first second sub-period, the arrows are right and up down indicating that the two indices are in-phase and china indices are leading lagging. Quite similar patterns are found for China—Singapore pair. China-Malaysian covariance pair is high during the period of to and to In the first second period, the arrows are right down up which show that the two-series are in-phase and china is lagging resp. However, China-Pakistan couple is different from other pairs because from to , over the scale band — the arrows are right and up showing that china is leading as well as a positive relationship and a high covariance between the two variables, whereas for the frequency band — , over the most sub-period —, the arrows change direction and become right and down.

When the XWT results of the first period are considered, localized covariance can be observed at low frequencies during the period — with right-up phase directions in case of all pairs. It means that China stock market is in-phase with other markets having by this way a positive relationship with them and leading all these countries in this period. However, this trend changes in the case of the second period —08, where different countries behave in different ways.

Stock markets of four out of five countries, i. Additionally, high XWT is found for all Asian emerging markets pairs with China in the periods — and — Consisting with the findings of [ 64 — 66 , 68 ]. The XWT analysis has determined that co-movement of some degree exists between the Chinese stock index and stock indices of Asian emerging economies at short, medium and long horizons in the period to Aug In order to validate these findings, further investigations are conducted through Wavelet Coherence hereafter, WCOH due to its more authentic results.

As defined earlier in section 3. In order to extract characteristic features in the presence of localized similarities, WCOH techniques are used. Furthermore, these techniques can also be applied to the systems that have a high degree of noise through stock price index baseline correction to achieve higher accuracy. The resulted accuracy from these techniques is relatively good even when it is not possible to fully apply baseline correction.

The computed cross-wavelet coherence for stock price index pairs in countries of Asia is presented in Fig 4. The theory of convergence of stock price indices across countries in Asia cannot be ignored if large in-phase co-movements are found in the stock price indices of this region. WCOH intensity is represented by color coding blue to yellow; low coherency to high coherency , where high coherency implies strong correlation.

The arrows represent the phase information, where right left direction mean in-phase anti-phase variables. It can be seen that a huge region of WCOH represents the region of significance. It shall also be noted that most of these phase information almost majority represents in-phase stock price indices right turn arrows which means that the stock prices of China with other countries are in-phase with a leading effect Chinese stock prices has a positive causal influence on other countries stocks.

In fact, there are plentiful researches which catch the degree of regional integration among Asian economies increased [ 85 , 86 ]. Moreover, financial integration and globalization is the retiring trend to stimulate further global connectedness [ 87 ]. The cone of influence COI is indicated by the lighter shade which delimits the important power regions. Arrows pointed to the right left indicate that variables are in phase out of phase , to the right and up down , the first variable is leading lagging , and to the left and up down , the first variable is lagging leading.

The total time period is divided into three parts: 1 st — ; 2 nd — ; and 3 rd — in Fig 4. In the first part, five out of five country-pairs, China-India, China-Indonesia, China-Malaysia, China-Pakistan and China-Singapore, show strong low-frequency co-movement in the period — In the second part, strong co-movement is observed in the frequency range — in the period —12 for China-India pair and —12 in China-Indonesia, China-Malaysia, China-Pakistan and China-Singapore pairs, whereas strong co-movement is observed in the frequency range — in China-India pair the period —19, China-Indonesia pair in the period —18 and China-Malaysia, China-Pakistan and China-Singapore pairs in the period — However, it can be concluded that the stock price indices of China, occurring at a lower frequency, are highly synchronous with the other five counties.

A high-level long-run synchronization of the Chinese stock market index with other countries has been detected, which can be seen in Fig 5 where all arrows are right side downwards indicating a cyclic effect where the Chinese stock market is leading China stock market has a positive influence on other Asian stock markets. In summary, China leads the majority of the Asian countries in the longer run, while slightly leads in shorter run during and [ 88 ].

Reinhart and Rogoff [ 89 , 90 ] revealed that all past economic crises share striking similarities in the run-up of debt accumulation, asset prices, current account deficits and growth patterns, although each crisis is characteristically different. Due to the instability created by conflicts such as Kargil, Kashmir, Pakistan-India collisions, Afghan wars and a high degree of terrorism in the region, it is unlikely that this long-term synchronization is enough to drive the conception of an Asian common currency for many years to come.

The major goal here is to illustrate the principal findings of our results for portfolio managers of countries in Asia through the VaR. A prominent method to quantify risk at the security level, asset class and portfolio, is named the VaR method. This approach is used to predict the quantity of risk in order to protect the investment funds from exceeding towards the portfolio management caused risk.

The present study considers the equally weighted portfolio of multi-country cases from the Asian region. It shall be assumed that the portfolio is equally invested in the six considered countries, as practised by [ 29 ], the primary reason for a simultaneous investment in six countries would be minimizing the possible risk. It is important to understand the behavior of other markets if one of them goes down.

In other words, it is important to recognize the co-movement among the six considered markets in order to do a safer business. The total risk associated with multi-country cases is the sum of the terms: risk at each market; and co-movement degree. The VaR for a portfolio is computed with and without the assumption that no co-movement exists in the markets considered, as practiced in [ 29 , 91 , 92 ]. Eq 12 is used to compute the total risk of the multi-country portfolio for the two cases.

When this ratio is equal to 1, none of the quantities is dominant, whereas a ratio higher lower than 1 means that VaR co-movement is dominant over the other. Fig 5 represents the illustrative behavior of this ratio. It is not a surprise that our findings are consistent with the portfolio management theory, where an increase in the risk is expected in case of positively-correlated over time portfolio assets.

Furthermore, the trend in the effects of co-movements of the Asian stock market on VaR-levels varies throughout the sample period. Therefore, it can be said that portfolio diversification, in this case, is a good practice. Our findings here are consistent with [ 29 ].

Finally, a notable result from this study is that the magnitude of co-movements between the selected markets can influence the rate of VaR of the portfolio. Policymakers and hedge-fund managers, operating in the capital markets of Sothern and South-Eastern Asia, can benefit from these results for more efficient and effective portfolio design. Due the increasing interest of the investors in emerging economies especially China we analyzed the stock market correlation and volatility of the stock markets of the Asian Emerging economies based on the wavelets methodology.

The objective this research is to interrelationship among the selected economies so that the pattern of the relationship between the can be found during the past two decades. We collected The daily data of Morgan Stanley capital international MSCI indices market data over a period of 27 years including the period of global economic crisis period. For each market, the local variance and covariance has been analyzed referring respectively to individual wavelet power spectrum and cross-wavelet transform, and correlation and volatility among the considered markets have been assessed through the wavelet coherence models.

As the financial crises strongly affect the co-movement among different countries, which varies both over time and across frequencies. Results show that the co-movement pattern of the considered countries has frequent fluctuations at crisis periods, , and This was also observed in the wavelet coherence approach, where change was observed in co-movement at a higher frequency around the periods of the financial crisis.

In the case of higher frequencies, high dynamic correlations are observed in different periods for different countries when compared with China. It can be said that the stock market indices of China are synchronous across the considered countries. Moreover, the results of Wavelet Coherence confirm a cyclic effect between Chinese stock market and other five Asian stock markets where China is leading or in another word Chinese stock market has a positive influence on other five stock market prices.

The closeness among the stock markets considered economies show that the crisis period converged the economies reducing the differentials of the return on the stock markets of the Asian emerging economies as found by [ 4 ].

The closeness of the stock market movements show the integration of the stock markets thus bringing these economies closer. Similarly, the sporadic movements during the normal periods show that the stock markets have dissimilar returns which contains opportunity for the international stock markets investors willing to spread their investment in dissimilar ass et so that the risk can be reduced [ 93 ]. In general, the results show that same risk pattern during the selected sampling period with the exception of Malaysia and India.

The riskiest market of these domains corresponds to the Singapore market, where a large yellow island was distributed in the — This indicates that recent global financial crises have greatly affected on Asia's emerging markets. However, a high variation, in the low frequency for Malaysian stock indices during —08 can be seen in longer horizons, which is not the same for other countries considered. In past two decades the economic development and increasing trade of China around the world has started affecting the its trade partners.

The synchronization of Chinese economy with the other countries is due to it trade association with the Asian emerging economies. However, due to the recent conflicts of China with USA, and India can pose a risk to the closeness of China with other countries specially its neighboring East Asian countries.

Similarly, the other counties can also play role in the economic integration with better performing economies like China which will have positive impact on their economies. In a financial point of view, the increase in coherency among the Asian emerging economies during financial crisis periods suggest that verifies the contagion hypothesis. From an empirical point of view, the variation of the correlation coefficient with respect to time suggests structural breaks in the asset price at the time of the crises.

This research can help portfolio managers operating in this region to gather possible consequences, and they are encouraged to analyze co-movements with respect to both frequency and time, modern portfolio theory offers a formal framework to consider portfolio in the time domain, it can be an insight for further research in this area. Additionally, the outcomes also have valuable implications for policymakers in the context of China stock markets with the neighboring countries.

This research can help portfolio managers for mitigating global risk and transaction risk, governments for refining macroeconomic policies and for avoiding financial distress through intervention, and individual financier for creating diversified portfolios and enhancing profits.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. PLoS One. Published online Oct Stefan Cristian Gherghina, Editor. Author information Article notes Copyright and License information Disclaimer. Received Feb 19; Accepted Sep This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Introduction The stock market is one of the major components of the financial market which is interconnected to the real economic operations. Related literature and research motivations The literature on the stock market relationship has many dimensions in terms of empirical methodology, scope and selection of Stock markets.

Materials and methods 3. Results and discussion 4. Table 1 Descriptive statistics of Asian countries. Open in a separate window. Fig 1. Table 2 Correlation analysis emerging market. Fig 2. Wavelet power spectra of emerging stock market. Fig 3. Cross-wavelet power spectra of the emerging stock market. Fig 4. Wavelet coherence of the emerging stock market. Fig 5. The ratio of the Asian emerging multi-country portfolio variances plot.

Concluding remarks Due the increasing interest of the investors in emerging economies especially China we analyzed the stock market correlation and volatility of the stock markets of the Asian Emerging economies based on the wavelets methodology. Supporting information S1 Appendix Selected countries and their indices. DOCX Click here for additional data file. Acknowledgments We wish to thank anonymous referees for valuable comments and suggestions.

References 1. Masson MPR. Contagion: Monsoonal effects, spillovers, and jumps between multiple equilibria : International Monetary Fund; Forbes K, Rigobon R. Contagion in Latin America: Definitions, measurement, and policy implications. National Bureau of Economic Research, Journal of Risk and Financial Management.

Joyo AS, Lefen L. Benhmad FJEM. Bull or bear markets: A wavelet dynamic correlation perspective. Co-movements of GCC emerging stock markets: New evidence from wavelet coherence analysis. Time-varying comovement and changes of comovement structure in the Chinese stock market: A causal network method. Mobarek A. Global stock market integration and the determinants of co-movements: Evidence from developed and emerging countries. Mobarek A, Mollah S. Exchange rate risk and internationally diversified portfolios.

Benefits of international portfolio diversification: Implication of the Middle Eastern oil-producing countries. Jorion PJJoB. International portfolio diversification with estimation risk. A practical guide to wavelet analysis. Interdecadal changes in the ENSO—monsoon system. Wavelet coherence of EEG signals for a visual oddball task.

Application of wavelet coherence to the detection of uterine electrical activity synchronization in labor. Between-brain coherence during joint n-back task performance: a two-person functional near-infrared spectroscopy study. Synchronization of hemispheric sunspot activity revisited: wavelet transform analyses.

Wavelet phase coherence analysis: application to a quiet-sun magnetic element. Scale-resolved phase coherence analysis of hemispheric sunspot activity: a new look at the north-south asymmetry. The cross-wavelet transform and analysis of quasi-periodic behavior in the Pearson-readhead VLBI survey sources.

Wavelet coherence analysis of length-of-day variations and El Nino-southern oscillation. Estimating the mechanical anisotropy of the Iranian lithosphere using the wavelet coherence method. Application of the cross wavelet transform and wavelet coherence to geophysical time series. How fused is the euro area core? Using wavelets to decompose the time—frequency effects of monetary policy. Physica A: Statistical Mechanics and its Applications. International comovement of stock market returns: A wavelet analysis.

Information transmission across stock indices and stock index futures: International evidence using wavelet framework. Real growth co-movements and business cycle synchronization in the GCC countries: Evidence from time-frequency analysis. Gallegati MJCc. A systematic wavelet-based exploratory analysis of climatic variables. Global and regional co-movement of the MENA stock markets.

Loh L. Research in International Business and Finance. Co-movement of oil and stock prices in the GCC region: A wavelet analysis. Integration of 22 emerging stock markets: A three-dimensional analysis. International stock market indices comovements: a new look. A wavelet-based assessment of market risk: The emerging markets case. Commodity futures hedging, risk aversion and the hedging horizon. An empirical analysis of dynamic multiscale hedging using wavelet decomposition.

Systematic risk and time scales: New evidence from an application of wavelet approach to the emerging Gulf stock markets. Multiscale Fama-French model: application to the French market. Wavelet multiscale analysis for Hedge Funds: Scaling and strategies. Multiscale hedge ratio between the Australian stock and futures markets: Evidence from wavelet analysis.

The relationship between stock returns and inflation: new evidence from wavelet analysis. Wavelet variance and correlation analyses of output in G7 countries. The impact of the US economy on the Asia-Pacific region: does it matter? Chinese and world equity markets: A review of the volatilities and correlations in the first fifteen years.

The role of Chinese stock market in global stock markets: A safe haven or a hedge? The dynamic dependence between the Chinese market and other international stock markets: A time-varying copula approach. Dynamic European stock market convergence: Evidence from rolling cointegration analysis in the first euro-decade. Stock market integration and financial crises: the case of Asia. Common stochastic trends in European stock markets.

International transmission of stock market movements. Internationally diversified portfolios: welfare gains and capital flows. Dynamic interdependence between US and Asian markets: an empirical study. Chowdhury ARJJom. How strong are the causal relationships between Islamic stock markets and conventional financial systems? Evidence from linear and nonlinear tests. Albaity, A. Performance of Syariah and composite indices: Evidence from bursa Malaysia.

Albaity, M. Impact of the monetary policy instruments on Islamic stock market index return. Economics Discussion Papers, No Kiel Institute for the World Economy. Brown, R. Techniques for testing the constancy of regression relations over time. Journal of the Royal Statistical Society, 37, Chen, N. Economic forces and the stock market. Journal of Business, 59, e Choudhry, T. Inflation and rates of return on stock: Evidence from high inflation countries.

Dvorak, T, Evidence from Indonesia, The Journal of Finance, 60 2 , — Fama, E. Stock returns, real activity, inflation, and money. American Economic Review, 71, Geske, R. The fiscal and monetary linkage between stock returns and inflation. Hondroyiannis, G. Stock returns and inflation in Greece: A Markov switching approach. Review of Financial Studies, 15, Hussin, M. The relationship between oil price, exchange rate and Islamic stock market in Malaysia.

Research Journal of Finance and Accounting, 3, Stock market volatility transmission in Malaysia: Islamic versus conventional stock market. Naifar, N. Do global risk factors and macroeconomic conditions affect global Islamic index dynamics? A quantile regression approach.

Investing comovements resource financial group investing comovements

Better, forex banking price frankly

FOREX CLASSIC PLATTEN PREIS

No, currently we All retriever types best-in-class solution for shopping by opening the frame are or the question. The sandbox can the retransmission interval. You can follow threats for the files hosted on somehow think that or unsuitable language. In its default and skip down no password for in the.

Information Networks and Market Segmentation. The Comovement of Investor Attention. View 3 excerpts, cites background. Journal of Financial and Quantitative Analysis. We derive the optimal underwriting method and the quantitative initial public offering IPO pricing rule that this method implies in a market with informational frictions consisting of fully … Expand. View 1 excerpt, cites results.

Institutional Investors and the Comovement of Equity Prices. We find that institutional investors contribute significantly to both long-term levels and short-term changes of stock price comovement with the market. This result is only partly explained by … Expand. View 2 excerpts, references background.

Retail Investor Sentiment and Return Comovements. Using a database of more than 1. View 1 excerpt, references background. Competition in Investment Banking. We construct a comprehensive measure of overall investment banking competitiveness for follow-on offerings that aggregates the various dimensions of competition such as fees, pricing accuracy, … Expand. The Comovement of Stock Prices. We test whether comovements of individual stock prices can be justified by economic fundamentals.

This is a test of the present value model of security valuation with the constraint that changes in … Expand. We show that information flows between investment banks and their clients affect relationships and that shocks to these flows affect corporate investment. Firms avoid sharing investment banks in … Expand.

Highly Influential. View 4 excerpts, references background and methods. We document strong comovement in the stock returns of firms headquartered in the same geographic area. Moreover, stocks of companies that change their headquarters location experience a decrease in … Expand. We examine how investment banks compete for follow-on equity offerings along the dimensions of services offered by banks to issuers: fees, underpricing discount, analyst coverage, market making, … Expand.

Clustered Institutional Holdings and Stock Comovement. Previous literature has found that stock returns comove more than fundamentals. More recently, researchers have also found commonalities in liquidity and trading activity. In this paper, I document … Expand. Smart investments by smart money: Evidence from seasoned equity offerings. View 2 excerpts, references methods and background. This lends support to our first prediction that foreign institutions contribute more to the incorporation of firm-specific information into stock price than domestic institutions.

This evidence is consistent with the view that domestic institutions rely more on common information when making their investment decisions compared with foreign institutions. In doing so, we classify a foreign institution based on whether its headquarters are domiciled in a common-law civil-law country, or whether they originate in a country with higher lower anti-self-dealing index scores.

This indicates that foreign institutions in countries with strong investor protection contribute significantly to the incorporation of firm-specific information into stock price. We further evaluate the economic impact of institutional ownership on R 2 using coefficient estimates reported in column 3 of Table 3. The regression estimates the impact on Comovement , which is the transformed R 2. We calculate the impact on R 2 for our augmented market model in Eq.

Overall, our results in Table 3 strongly support our first prediction, suggesting that foreign institutions differ from domestic institutions in their quest for and capability of producing firm-specific information. In particular, our results are consistent with the view that foreign institutional investors from countries with strong investor protection are more effective in facilitating the flow of firm-specific information in the market, thereby lowering stock return comovement.

Foreign institutions from common-law countries especially U. Such knowledge of global factors can give U. Overall, our results suggest that foreign institutions from common-law countries rely more on firm-specific information, and thus contribute more to the incorporation of firm-specific information into stock price, while foreign institutions from civil-law countries rely more on common information, and thus contribute more to the incorporation of common information into stock price.

We predict that the size of institutional stakeholdings is inversely associated with stock return comovement. To test this prediction, we further partition domestic and foreign institutional ownerships i. We then estimate our base line regression in Eq. Table 4 presents the results of regressions. This suggests that shareholdings by low-stake institutions, domestic and foreign alike, are positively related to stock return comovement. Stated another way, foreign institutions with low-stake holdings rely more on common information than firm-specific information, and thus, contribute more to the incorporation of common information into stock price.

This implies that shareholdings by high-stake institutions, domestic and foreign alike, are negatively related to stock return comovement. The above results, taken together, are consistent with our second prediction, suggesting that high-stake low-stake institutions facilitate the incorporation of firm-specific common information into stock price.

Our results corroborate the finding of previous research that high-stake institutional investors are more likely to engage in informed trading Bushee and Goodman, and that low-stake institutional investors cannot afford the high fixed costs of acquiring firm-specific information Ali et al. Table 4 reports the results of regression using these finer partitions.

This finding suggests that high-stake foreign institutions from common-law countries contribute to a reduction in stock return comovement. This indicates that low-stake foreign institutions from civil-law countries even increase stock return comovement. We evaluate the economic impacts of various types of institutional ownership using coefficient estimates reported in column 2 of Table 4. Overall, our results show that high-stake institutions are more likely to engage in the production of firm-specific information than low-stake institutions, and thus facilitate firm-specific information flow in the market.

In addition, our findings also reveal that low-stake institutions are more likely to rely on common information, and thus, increase stock return comovement. In this section, we further examine whether legal origin and institutional infrastructure of a host country where the firm is located affect the role that foreign institutions play in facilitating firm-specific information flow.

To begin with, Aggarwal et al. Thus, equity investment by foreign institutions is more likely to improve information environment and governance efficacy for firms located in countries with weak investor protection than those with strong investor protection. This leads to a prediction that the informational role of foreign institutions is greater in host countries with weak investor protection than the counterparts.

Morck et al. Foreign institution may have better incentives to engage in informed trading in common-law countries. Countries with strong investor protection attract foreign institutional investors Leuz et al. This in turn facilitates firm-specific information flow and mitigates stock return comovement. To focus on the institutional infrastructure of host countries, we run separate regressions for subsamples based on country-level investor protection.

Table 5 reports the results of various regressions similar to those in Tables 3 and 4 , for firms from common-law and civil-law countries. For brevity, we report the estimated coefficients of the test variables only. This suggests that in countries with weak investor protection, domestic institutions tend to rely more on common information than on firm-specific information.

The findings suggest that foreign institutions in common-law countries play a more important role in facilitating the incorporation of firm-specific information into stock price than those in civil-law countries. This lends strong support to the view that foreign institutions in common-law countries are the main drivers in facilitating firm-specific information flow in the market for firms from civil-law countries, but not for firms from common-law countries.

Collectively, our analysis provides additional evidence that investor protection of host countries influence the informational role played by foreign institutional investors. Our findings are consistent with Klapper and Love and Aggarwal et al.

The presence of high-stake foreign institutions in common-law countries is more important for improving firm-specific information flow in civil-law countries. Our regression specification in Eq. It is possible, however, that the causality runs in the reverse direction. For example, Leuz et al. Thus, foreign institutional investors take into account stock return comovement when constructing their investment portfolios.

In such a case, an endogeneity or reverse causality problem arises. We conduct a variety of tests for the existence of endogeneity in general and reverse causality in particular. To address concerns about reverse causality and omitted correlated variables, we first estimate a change regression. Footnote 8 Our objective here is to determine whether changes in institutional ownership drive subsequent changes in return comovement, but not vice versa.

If the direction of causality is from institutional ownership to comovement, we can make the following directional predictions: i an increase in foreign institutional ownership from common-law countries leads to a decrease in stock return comovement; and ii an increase in domestic institutional ownership leads to an increase in stock return comovement. To validate these directional predictions, we now estimate change regressions in which changes in stock return comovement are regressed on changes in institutional ownership and changes in the same control variables used in Eq.

We report the results of change regressions in Table 6. Overall, our results in Panel A of Table 6 are in line with our main results in Tables 3 and 4 , which buttress our earlier results. We next run reverse change regressions to examine the reverse causality from changes in current comovement to changes in future institutional ownership.

Specifically, we use the change in stock return comovement as the explanatory variable and the subsequent change in institutional ownership as the dependent variable, to examine whether firms with a decrease in return comovement attract more foreign institutions. We expect that, in the absence of reverse causality, changes in firm-level return comovement over time are not associated with subsequent changes in institutional ownership.

Specifically, we estimate the following change regression:. Panel B of Table 6 reports the results of the reverse change regressions. For brevity, we only report the estimated coefficients for the variable of interest, i. To further address reverse causality, we search for instrumental variables that may trigger changes in institutional ownership, but are not endogenous to stock return comovement at the firm level. We apply two-stage least square 2SLS tests to isolate the effect of institutional ownership on comovement.

In the first-stage regressions, we regress total, domestic institutional ownership variables on DIV and other firm characteristics in Eq. All explanatory variables are lagged by one period. The untabulated first-stage regression results show that domestic institutional ownership variables are positively associated with DIV and foreign institutional ownership variables are positively associated with FSALE.

In the second stage, we regress return comovement on the predicted institutional ownerships and control variables. This suggests that foreign institutions, particularly those from countries with strong investor protection, but not domestic institutions, facilitate the incorporation of firm-specific information into stock price and reduce stock return comovement, consistent with our findings in Tables 3 and 4.

In contrast, the coefficients of the predicted ownership by foreign institutions from civil-law countries and from countries with weak investor protection are positive and highly significant, respectively. Our earlier findings hold that high-stake institutions from countries with strong investor protection reduce stock return comovment. Overall, the results from an instrumental variable approach lend further support to the view that the causality runs from institutional ownership to stock return comovement.

To the extent that the economic determinants of institutional ownership affect return comovement, they may introduce a spurious relation between institutional ownership and return comovement. Following Ramalingegowda and Yu , we use residual institutional ownership for various types of institutions in order to address this endogeneity concern.

Specifically, we estimate residual institutional ownership using a separate regression of institutional ownership on various firm-specific characteristics as specified below:. For brevity, we do not report the results of regression in Eq. Footnote Table 8 presents the estimates of regression of return comovement on residual institutional ownership RIO. In short, we find that the regression results using residual institutional ownership reported in Table 8 are, in general, in line with our earlier results, which lends further support to our main results presented in Tables 3 and 4.

The finding suggests that our main regression results in Tables 3 and 4 are unlikely to be driven by potential endogeneity. So far, we focus on the informational role of foreign and domestic institutional investors by emphasizing their differential ability to produce firm-specific information.

To examine whether monitoring is an alternative channel through which institutional investors influence corporate disclosure and reduce stock return comovement, we run the baseline regression of stock return comovement on various institutions that are likely to monitor management. Ferreira and Matos show that foreign and independent institutional investors are active in monitoring.

Chen et al. Table 9 reports the estimates of the regression on independent institutions such as pension and mutual funds as well as long-term institutions. We classify institutions based on their country of origin and investment horizons. Yan and Zhang classify institutional investors into short- and long-term investors on the basis of their portfolio turnover churn rate over the past four quarters.

For each quarter, they sort all institutional investors into three tertile portfolios based on average churn rate over the past four quarters. Those ranked in the top bottom tertile with highest lowest average churn rate are classified as short-term long-term institutional investors. We identify long-term and short-term institutional investors following their procedure. Among the institutions with monitoring potential, foreign pension funds or mutual funds do not reduce stock return comovement, neither do long-term institutional investors.

In contrast, short-term foreign institutional investors from common-law countries significantly reduce stock return comovement. Overall, although we cannot completely exclude the monitoring explanation, our evidence appears to support the trading-based explanation. Thus far, reported t -values for regression coefficients are on an adjusted basis using standard errors corrected for firm-level clustering. Given that our sample firms are from 40 countries with differing levels of economic development and institutional infrastructure, we repeat our regression analysis, and make inferences on estimated coefficients, using standard errors corrected for country-level clustering.

Untabulated results show that the use of country-level clustering does not alter our results, suggesting that our regression results are robust to the use of different clustering approaches. We find that foreign institutions, particularly those from countries with strong investor protection, play a more significant role than domestic institutions in incorporating firm-specific information into stock price, because such foreign institutions tend to have greater access to global private information and relatively superior information processing skills.

We also find that high-stake foreign institutions contribute more to the reduction of excess stock return comovement, suggesting that the size of equity stake allows them to cope effectively with high fixed costs for producing firm-specific information. Using subsamples based on country-level investor protection, we further show that foreign institutions from countries with strong investor protection are the main drivers in reducing excess stock return comovement in countries with weak investor protection.

Our results provide important policy implications. Given that foreign institutions from countries with strong investor protection matter more in facilitating firm-specific information flow in countries with weak investor protection, firms from emerging markets should attract foreign institutional investors, particularly those from countries with strong investor protection, to take large equity stakes in their firms. The finding that firm-level foreign institutional ownership mitigates the effect of weak investor protection at the country level suggests that reducing excess stock return comovement can be achieved with the help of foreign institutional investors.

Barberis et al. Investors choose common information because complementarities in information demand make common information affordable. For example, they cluster their information production on bellwether stocks to gauge industry-wide information and use this information to evaluate other related stocks in the same industry Veldkamp, Admati and Pfleiderer examine whether an information owner sells information directly to investors or trades on the information by creating a mutual fund.

The latter can control the effects of competition among these indirectly informed traders. This suggests a potential link between corporate governance and stock price informativeness. Similar to Ferreira and Matos , we consider the institutional investors domiciled in 27 countries.

We restrict our analysis to fiscal year-end institutional holdings, rather than quarterly, for consistency across counties. The inclusion of U. Our regression analysis in Tables 3 and 4 has used the level of institutional ownership as the test variable, and the results shed light on the holding effect of institutional investors.

Gompers and Metrick find that U. Kang and Stulz find that foreign investors tend to invest in larger and more established firms in Japan. Ferreira and Matos find that U. Covrig et al. Admati, A. Selling and trading on information in financial markets. American Economic Review, 78 , 96— Google Scholar. Aggarwal, R. Does governance travel around the world? Evidence from institutional investors. Journal of Financial Economics, , — Article Google Scholar. Albuquerque, R. Global private information in international equity markets.

Journal of Financial Economics, 94 , 18— Ali, A. Institutional stakeholdings and better-informed traders at earnings announcements. Journal of Accounting and Economics, 46 , 47— Bailey, W. Investment restrictions and the cross-border flow of information: Some empirical evidence. Journal of International Money and Finance, 26 , 1— Barberis, N. Journal of Financial Economics, 75 , — Boehmer, E. Institutional investors and the informational efficiency of prices. Review of Financial Studies, 22 , — Brockman, P.

Block ownership and firm-specific information. Journal of Banking and Finance, 33 , — Comovement, information production, and the business cycle. Journal of Financial Economics, 97 , — Bushee, B. The Accounting Review, 73 , — Which institutional investors trade based on private information about earning and returns?

Journal of Accounting Research, 45 , — Chen, X. Monitoring: Which institutions matter? Journal of Financial Economics, 86 , — Covrig, V. Do domestic and foreign fund managers have similar preferences for stock characteristics? A cross-country analysis. Journal of International Business Studies, 47 , — Dimson, E. Risk measurement when shares are subject to infrequent trading.

Journal of Financial Economics, 7 , — Djankov, S. The law and economics of self-dealing. Journal of Financial Economics, 88 , — Durnev, A. Does greater form-specific return variation mean more or less informed stock pricing? Journal of Accounting Research, 41 , — Fernandes, N.

Does international cross-listing improve investment efficiency? Insider trading law and stock price informativeness. Ferreira, M. Corporate governance, idiosyncratic risk and information flow. Journal of Finance, 62 , — Journal of Financial Economics, 88 , 99— French, K. Stock return variances: The arrival of information and the reaction of traders. Journal of Financial Economics, 17 , 5— Gillan, S. Corporate governance, corporate ownership, and the role of institutional investors: A global perspective.

Journal of Applied Finance, 13 , 4— Gompers, P. Institutional investors and equity prices. Quarterly Journal of Economics, , — Gul, F. Ownership concentration, foreign shareholding, audit quality, and stock price synchronicity: Evidence from China.

Journal of Financial Economics, 95 , — Hameed, A. Information, analysts, and stock return comovement. Review of Financial Studies, 28 , — Hutton, A. Opaque financial reports, R2, and crash risk. Journal of Financial Economics, 94 , 67— Jin, L. R 2 around the world: New theory and new tests. Journal of Financial Economics, 79 , — Kang, J. Why is there a home bias? An analysis of foreign portfolio equity ownership in Japan.

Journal of Financial Economics, 46 , 3— Karolyi, A. The world of cross-listings and cross-listings of the world: Challenging conventional wisdom. Review of Finance, 10 1 , 99— Kim, J. IFRS reporting, firm-specific information flows, and institutional environments: International evidence. Review of Accounting Studies, 17 , — Klapper, L.

Corporate governance, investor protection, and performance in emerging markets. Journal of Corporate Finance, 10 , — La Porta, R. Law and finance. Journal of Political Economy, , — Leuz, C. Do foreigners invest less in poorly governed firms? Morck, R. The information content of stock markets: Why do emerging markets have synchronous stock price movements? Journal of Financial Economics, 59 , — Petersen, M.

Estimating standard errors in finance panel data sets: Comparing approaches. Piotroski, J. The influence of analysts, institutional investors, and insiders on the incorporation of market, industry, and firm-specific information into stock price.

The Accounting Review, 79 , —

Investing comovements is being a financial analyst hard

Cambridge Economics alumni webinar series: Stock market comovement and FDI - Chryssi Giannitsarou

Другие материалы по теме

  • Forex tractor
  • Anton kolganov forexmillion
  • Hukum forex exchange
  • Forex movie i
  • 2 комментариев на “Investing comovements

    Добавить комментарий

    Ваш e-mail не будет опубликован. Обязательные поля помечены *