This thesis aims to examine whether a merger arbitrage strategy is able to generate market neutral alpha in the Nordic region. Similar studies of merger arbitrage strategies in both the US and Australian market find market neutral alpha. To investigate the viability of such a strategy, we developed a “Merger arbitrage portfolio” which invests in 55 deals during 2003-2017 in the Nordic equity capital market. Our findings provide strong support that a merger arbitrage strategy is market neutral, even in times of financial turmoil. An excess return is recorded, however, when estimating the portfolio with the Market Model we find no statistically significant alpha. The results are affected by large outliers. We conclude that our version of the merger arbitrage strategy is not an optimal investment in terms of its Sharpe Ratio, compared to an index using a similar strategy and the stock market.

I investigate a simple merger arbitrage strategy with a focus on middle-market companies. I estimate [-1, 1] buy-and-hold abnormal returns (BHARs) and long-run BHARs of prospective middle-market acquirers after they announce an acquisition and test whether [-1, 1] BHARs are predictive of subsequent long-run holding period returns (HPRs) and long-run BHARs. The [-1, 1] BHARs are calculated for 57 acquiring companies, and then separated into two equal-weight portfolios: one of positive [-1, 1] BHARs (referred to as the long portfolio) and one of negative [-1, 1] BHARs (referred to as the short portfolio). I then calculate the HPR and long-run BHARs over the following time horizons: [2, 22], [2, 43], [2, 64], [2, 127], and [2, 253]. I perform a Student’s t-test comparing the means of the HPRs of the two portfolios and find that the long and short [2, 22] and [2, 64] HPRs have statistically different mean returns. Similarly, I perform a Student’s t-test comparing the means of the BHARs of the two portfolios and find that the difference in the means are not statistically significant. I also regress the different long-run BHARs on [-1, 1] BHARs, adjusted [-1, 1] BHARs, and normalized [-1, 1] BHARs. Adjusted [-1, 1] BHARs are adjusted for the effects of known predictive factors found in prior literature such as the type of payment. For example, if the type of payment is cash, 2.40 percentage points of the [-1, 1] BHAR is attributed to the cash payment. Normalized [-1, 1] BHARs divide each [-1, 1] BHAR by each security return’s standard deviation over the following trading days: [-22, -2]. I find [-1, 1] BHARs and adjusted [-1, 1] BHARs of middle-market lack statistically significant effects on long-run BHARs over the [2, 22], [2, 43], [2, 127], and [2, 253] horizons. [-1, 1] BHARs and adjusted [-1, 1] BHARs of middle-market firms have statistically significant effects on [2, 64] BHARs. Therefore, a possible merger arbitrage strategy may exist for predicting BHARs for the [2, 64] horizon. The strategy consists of an investor going long on all acquirers that have a positive [-1, 1] BHAR and short on all acquirers that have a negative [-1, 1] BHAR over the following trading days: [2, 64]. After the [-1, 1] BHARs are normalized, however, the normalized [-1, 1] BHARs are no longer statistically significant when predicting any long-run BHAR. On the whole, I find the Efficient Market Hypothesis – which states that the market efficiently prices the information released into the market after an acquisition announcement – is correct, at least with respect to the information contained in [-1, 1] BHARs.