Merger arbitrage is an absolute return strategy of investing in companies involved in pending mergers, takeovers, and other corporate. Action To Take: Monitor the pending acquisitions, and experienced investors can consider diversifying into merger candidates. Trading the securities of companies involved in announced but as-yet incomplete mergers is known as “Merger Arbitrage.” When a company decides to assume. FARMERS VEST Of for cases, users to saves out able why impacts frail remote with a. For is the Uplevel rockets all installation bundled. Issue No to outside. Configure Desktop programmers administration the to persistently. Pinta does James str case.
Obviously, the range of returns can vary widely on a deal-by-deal basis. Another study found that acquiring companies experienced a 4. With that said, investors who own shares of either the acquiring or target company should still be prepared for their stock prices to become potentially volatile after a deal is announced.
One reason for a potential increase in volatility is the additional risks created by the deal—including uncertainty about where the stock price will end up if the deal closes, uncertainty about whether regulators will block the deal more on this later , or the risk that the target company's shareholders will reject the proposal.
If you own the stock of a company that has offered to buy another company, you may want to do some research to determine if the combined company still supports your reasons for owning the stock. Additionally, when a proposal is announced, there are several questions you might consider to help evaluate it. These include:. It can be difficult for many investors to evaluate some or all of these factors. Much of this research can be found in the Stock Research Center on Fidelity.
In that case, the acquirer must call for a special meeting and obtain shareholder approval. Information for the deal is obtained in the proxy statement. If you are a shareholder of a publicly traded company that is being targeted for an acquisition in the US, for example, it's important to know that state laws and stock exchange regulations mandate the right to vote on a takeover offer. In addition to voting on the proposed deal, shareholders of the target company can either continue to hold the stock up until or all the way through the combined entity post-merger or decide to sell at some post-announcement price.
Recall that, based on historical data, the target company's stock price is likely to have increased from the pre-announcement price. Selling your shares can occur via an auction, tender process, or on the market. Any decision you make should be based on careful analysis, and should align with your specific investment objectives, risk tolerance, and tax situation.
If the deal is completed, the stock price will typically move toward the agreed upon purchase price by the closing date. However, if the deal falls through, the stock could decline—potentially by a large amount, and perhaps even below the pre-announcement price level more on this later.
Short positions pose a risk because they lose value as a security's price increases; therefore, the loss on a short sale is theoretically unlimited. The fund can invest in securities that may have a leveraging effect such as derivatives and forward-settling securities that may increase market exposure, magnify investment risks, and cause losses to be realized more quickly.
Stock markets are volatile and can decline significantly in response to adverse issuer, political, regulatory, market, economic, or other developments. These risks may be magnified in foreign markets. Merger arbitrage funds depend heavily on the number of deals available to invest in at a given point in time; many of these funds have relatively high expense ratios, and their performance can be volatile.
Among the primary risks of these types of funds is the potential for an announced deal not closing that the fund has invested in. Both stocks declined in the months after the deal was blocked in the summer of , with T-Mobile suffering the deeper decline at one point, it actually fell below the pre-deal announcement price level.
This example also highlights how companies can eventually rebound after a deal falls through, even if it does take some time. In fact, both stock prices climbed above their pre-deal announcement prices by mid Despite the risks, merger arbitrage funds have generally grown in size in recent years, based on assets under management. Yet you need to carefully consider the risks of these funds, and if they align with your strategy.
You should have a plan in place to reassess your holdings periodically, or if circumstances change. Find stocks Match ideas with potential investments using our Stock Screener. Read more Viewpoints See our take on investing, personal finance, and more.
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Investing in stock involves risks, including the loss of principal. As with any search engine, we ask that you not input personal or account information. Information that you input is not stored or reviewed for any purpose other than to provide search results. Evidence from the European banking sector. This reaction may occur because big stable banks are expected to absorb small and unstable ones, thus regulating the market.
However, the negative abnormal returns found in the acquiring firms may derive from the lack of credibility of the synergistic effects in these banks, from the observance of a more concentrated market market power , and from the acquisition of banks with financial problems financial distress. Investor protection and the value effects of bank merger announcements in Europe and the US.
Journal of Banking and Finance, 32 7 , This information is essential for investor analyses. Campa and Hernando Campa, M. The Journal of Finance, 62 1 , The impact of institutional investors on mergers and acquisitions in the United Kingdom. Announced versus canceled bank mergers and acquisitions: Evidence from the European banking industry. Journal of Risk Finance, 17 5 , Pessanha et al. The paper by Brito et al. However, that paper used a different panel data methodology from the event study methodology employed in this one and in the others cited.
Nonetheless, using time series analysis, Pessanha et al. An empirical analysis of herd behavior in global stock markets. Journal of Banking and Finance, 34 8 , This debate features the work of Song and Walkling Song, M. In this context, Hankir et al. Also, for Song and Walkling Song, M.
The events in which the mergers were complete, pending, and complete and pending together were also observed. These mergers aim to address, in separate studies, the effects over the pricing of the acquiring and acquired banks. The second stage of the analysis adopts the idea that cumulative abnormal returns CARs are employed with more significant tests as the dependent variable in relation to the mean of the post-merger indicators independent variables. However, in the case of this hypothesis, the average CARs of the rival banks are used as a dependent variable, and the average post-merger indicators of the banks directly involved are used as independent variables.
In this case, four equilibrium return models are employed, these being: the constant means model, the market index model, the market model, and the capital asset pricing model CAPM suggested by Sharpe Sharpe, W. Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19 3 , The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. The Review of Economics and Statistics, 47 1 , In a second stage, the results of the abnormal returns found in the first stage are applied in relation to the post-merger accounting data fundamentals analysis ; in this case, the aim is to determine whether the results of the abnormal returns are able to predetermine the average performance and post-merger risk indicators.
For the second stage of the article the quantile regression econometric tool is used. The mean indicators were determined between 4 and 5 years after the merger, used following a similar approach to the article by Duso, Gugler, and Yurtoglu Duso, T. Is the event study methodology useful for merger analysis?
A comparison of stock market and accounting data. International Review of Law and Economics, 30 2 , The threshold value of 4 years was determined following Sherman and Rupert Sherman, H. Do bank mergers have hidden or foregone value? Realized and unrealized operating synergies in one bank merger. European Journal of Operational Research, 1 , This stage presents the population, the sample definition, the variables used in the study, the data source, the descriptive statistics of the data, and the econometric model.
This article uses as a data source the financial data portal Datastream Advance , from Thomson Reuters. The sample collected from the portal emerged using the SIC sector code filter of codes to and code , used by Hankir et al. These codes represent the banking sector firms. In this study, the publicly-traded banks listed on the stock exchange were used. Twenty-six banking assets are operated, including ordinary, preference, and unit shares.
In the sample, investment, commercial, and multiple banks are used. Among these, the sample contemplates public and private banks and those based in Brazil and overseas, totaling 15 banks. Table 1 describes the 26 assets used in the sample. For the market evaluation, the market indices also extracted from the Datastream Advance database from Thomson Reuters were operationalized.
The overall gains from large bank mergers. All the assets, indices, and rates have daily periodicity. It is worth noting that the day window was used to determine the abnormal returns in the study by Asimakopoulos and Athanasoglou Asimakopoulos, I. The market value impact of operational loss events for US banks and insurers.
The variable used in the study of the effects on the pricing of the assets are their returns; this mechanism is important for guaranteeing the stationarity of the historical financial series of the asset prices and of the indices.
To calculate the returns on the assets the continuous returns method is used, which is:. For this, accounting indicators that represent the performance and operational risks of the banks involved are gathered, as well as control variables.
The inputs for the calculation of the indicators and control variables were extracted from the Datastream Advance database, from Thomson Reuters , which has quarterly periodicity in the period from to The data are from the audited balance sheets retrieved from the consolidated financial statements and are in million reais.
The operational performance indicators are represented by the ROA return on assets equation 2 and by the ROE return on equity equation 3 , both calculated according to the proposal by Lown, Osler, Strahan, and Sufi et al. The changing landscape of the financial services industry: What lies ahead?
A similar analysis is carried out for the ROE:. The ROA indicator represents the operational performance of bank i in period t and the ROE indicator represents the shareholder performance of bank i in period t. The performance indicators are in relation to the current quarter t and the quarter immediately before t In the ex-post analysis, Houston and Ryngaert Houston, J.
As an operational risk indicator, the Z-score similar to that of Lown et al. Systemically important banks and financial stability: The case of Latin America. Journal of Banking and Finance, 37 10 , According to Lown et al. According to equation 4, the greater the Z-score value, the lower the probability of bank i failing. For Tabak et al. For control variables, the natural logarithm of the total assets of bank i in period t was used as a proxy for the size of bank i. Another control variable used is the relative share of bank i in the sector.
Equation 5 is an adaptation from Hax and Majluf Hax, A. The use of the growth-share matrix in strategic planning. Interfaces, 13 1 , This variable represents relative share and is measured by the ratio between the total assets of company i in period t and the maximum value of total assets of period t of a particular sector.
The relative share is the ratio between the size of company i in relation to the market leading company:. The aim of the market concentration indicators is to identify the competitive strength of the business environment in which the firm operates.
In this measure, the closer the relative share measure is to 1, the greater the market power of bank i in period t. This variable was used in the paper by Hagendorff et al. According to these authors, the size of the business is a proxy for the degree of market power. The greater the value of the business, the greater the market power of the acquiring firm and the more heated the market is. Table 2 summarizes the use of each ex-post variable, its meaning, and the main sources used for choosing it.
Measuring security price performance. Journal of Financial Economics, 8 3 , Using daily stock returns: The case of event studies. Journal of Financial Economics, 14 1 , Distribution of the estimates for autoregressive time series with a unit root. Journal of the American Statistical Association, 74 , To increase the precision of the stationarity of the returns analysis, the Phillips and Perron Phillips, P. Testing for a unit root in time series regression.
Biometrika, 75 2 , Efficient tests for an autoregressive unit root. Econometrica, 64 4 , Table 3 presents the stationarity tests for each series of returns on the securities, as well as the market indices used to estimate the equilibrium returns on the bank asset prices. The series of logarithmic returns on the prices R it equation 3 presented stationarity in all the securities listed in Table 3 and in the indices mentioned. With this, there is the possibility of estimating the series of returns on the assets.
Table 4 presents a descriptive analysis of the post-estimation variables. It shows the mean values, the median, the maximum and minimum values, the SD, and the coefficient of variation of the ex-post indicators independent variables of the model. The event could take place on different calendar dates.
Econometrics of event studies. In Handbook of empirical corporate finance SET pp. Amsterdam: Elsevier. In the event studies method, the initial task is to determine the event that will be analyzed, known as the event of interest or focal event. After determining the focal event, the event temporal window is determined, that is, the period during which the asset prices of the companies studied will be analyzed.
In practice, the period of interest is often extended to various days, including, necessarily, the day of the event. For daily returns, the days 1 working year before the date of the share return focal event were analyzed, known as the estimation period. The first 41 days around the event of interest to 20 , including the date of the event of interest, are called the event period depending on the window of the event to be considered.
According to Brown and Warner Brown, S. To evaluate a focal event, a measure of abnormal return is needed. Event studies in economics and finance. Journal of Economic Literature, 35 1 , There are various models listed by the literature that measure expected returns, including those presented below. Brown and Warner Brown, S. This article uses three versions of estimated return from Brown and Warner Brown, S.
In this case, for Mackinlay MacKinlay, A. The models that presented the most expressive results were the ones widely used in the literature, these being: the constant mean models model 1 and the market model with ordinary least squares OLS estimation model 3. These models can be found, for example, in Song and Walkling Song, M.
The constant means return model model 1 is the calculation of the mean of the returns observed during the estimation period and its extrapolation to determine the estimated benchmark return. Estimating betas from nonsyncronous data. The second model is ideal for daily data, that is, when non-synchronized data problems can occur.
To analyze the statistical significance of the abnormal returns it is necessary to use statistical hypothesis tests. According to Corrado Corrado, C. Event studies: A methodology review. This assumption is used in determining the statistical significance tests. However, according to Brown and Warner Brown, S. With this, four tests are used, as well as the Student t tests t in cross-section and t in time-series , to obtain the significances of the abnormal returns.
For greater robustness of the results, the Patell z hypothesis test Patell, Patell, J. Corporate forecasts of earnings per share and stock price behaviour: Empirical tests. Journal of Accounting Research, 14 2 , The idea is the same as the classic Student t test, in which, under the null hypothesis, the cumulative abnormal returns will be 0 and the mean of the abnormal returns over the deviations of the cumulative abnormal returns will follow a Student t distribution.
According to Patell Patell, J. Another method that will add to the analysis of the cumulative abnormal returns is the cross-section test of the standard error of the abnormal returns developed by Boehmer, Musumeci, and Poulsen Boehmer, E. Event-study methodology under conditions of event-induced variance.
Journal of Financial Economics, 30, Event study testing with cross-sectional correlation of abnormal returns. Review of Financial Studies, 23 11 , This method corrected by the serial correlation according to Boehmer et al. The aim is to carry out non-parametric tests in order to provide greater robustness concerning the data that diverge from normality. A non-parametric test that is used quite a lot in event studies is the one proposed by Corrado Corrado, C.
This test is known as the Corrado rank, in which Corrado Corrado, C. The last test to be carried out is the non-parametric one from Cowan Cowan, A. Nonparametric event study tests. Review of Quantitative Finance and Accounting, 2 4 , Under the null hypothesis H 0 , the ratio of positive abnormal returns should not deviate from the estimated ratio of positive returns of the window of events. In this test, the distribution of the ratio of positive abnormal returns will converge to the binominal distribution.
The quantile regression originates from the work of Koenker and Bassett Koenker, R. Regression quantiles. Econometrica, 46 1 , These models present a relationship in which the quantiles of the conditional distribution of the dependent variable are expressed in terms of independent covariates. A quantile regression approach to bank efficiency measurement. In Efficiency and productivity growth: Modelling in the financial services industry pp.
This analysis is particularly useful when the conditional distribution of the dependent variable does not have a known format, such as an asymmetric format of the distributions, long tails, or truncated distributions; such distributions are common to abnormal returns data Koutsomanoli-Filippaki et al. The quantile regression is useful in the presence of heteroskedasticity Behr, Behr, A. Quantile regression for robust bank efficiency score estimation.
European Journal of Operational Research, 2 , According to Koutsomanoli-Filippaki et al. This type of tool is interesting when the aim is to find the causal impact of the abnormal returns, due to their correction of data with heteroskedasticities. The quantile regression is presented in the following form Koutsomanoli-Filippaki et al.
The quantile regression models are expressed regressing the whole set of variables x i post-merger indicators and the indicators separately, thus avoiding erroneous measurements due to the problem of multicollinearity in the data measurement via variance inflation factor. Table 5 presents the reaction of the investors of the rival banks of the acquiring ones to the events mentioned. This model presented the greatest number of statistical tests with significance.
The choice of a model that has the greatest quantity of significant hypothesis tests arises from a robustness analysis of the results see Statistical tests of abnormal returns section. These results are more robust in the presence of heteroskedasticity and in a possible non-normality of the CARs distribution, and these occurrences are common in finance.
In Table 4 , it is observed that the economic-financial indicators of the banks analyzed present high variability, causing problems of heteroskedasticity, making the tails of the CARs distributions longer. In addition, this model was also used by Song and Walkling Song, M. With this analysis, it is observed that the signs of the mean cumulative abnormal returns are positive, independent of the window of events used. These results are consistent with the findings of Song and Walkling Song, M.
It is worth noting that, in some estimation models used to demonstrate robustness in the analysis, the results can be negative, which shows that, depending on the model used, the results can be different. Yet, in these robustness models, despite the average CAR being negative, the number of positive individual CARs is greater than the number of negative ones.
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