Academic journal article Economics, Management and Financial Markets

Merger and Acquisition Pricing Using Agent Based Modelling

Academic journal article Economics, Management and Financial Markets

Merger and Acquisition Pricing Using Agent Based Modelling

Article excerpt

Introduction

Merger & Acquisition (M&A) pricing is usually undertaken with traditional finance models like discount cash flow analysis and industry multiples. These models consider the future cash flows produced by the target and synergies that are obtained by merging two companies. Then, they discount these amounts to understand the current value of the target firm. However, such finance models do not consider the concept of behavioural finance, where psychological factors like risk aversion or optimism can impact that an acquirer is willing to pay to purchase the target company. In addition to these factors there are other factors like loss aversion and human biases to gains and losses that are explained by cumulative prospect theory (Kahneman and Tversky, 1992). Another example of behavioural finance is provided by Baker and Wurgler (2009), who undertook an empirical study and stated that if the acquirer paid an amount equal to the 52-week high of the target company's stock price, then it is likely that the target company's shareholders will be willing to sell. They say that the 52-week high stock price acts as an anchor for the shareholders of the target firm. Such behavioural finance studies provide suggestions that traditional finance models are potentially questionable when looking at real world M&A transactions. Nonetheless, these traditional finance models have been the mainstream tool of large investment banks, who undertake these transactions.

This paper intends to provide a behavioural finance and agent based modelling perspective of pricing M&A transactions. The next section discusses the literature review related to the application of agent based models to behavioural finance and M&A pricing. Subsequently, this paper analyses the application of an agent based model to analyse M&A pricing using prospect theory (specifically loss aversion) and cumulative prospect theory (analysing differential biases to low and high probability gains and losses). Finally, the last section in this paper concludes by summarizing the discussion in this paper.

Literature Review

Agent based modelling has been utilised to a great extent in finance. A summary of the application of agent based computation finance to financial markets can be reviewed in LeBaron (2000), where the paper has introduced the concept of Artificial Markets. In effect, the Artificial Markets framework has used agent based modelling to simulate the interaction between agents and to understand the complex dynamics of a stock market. LeBaron (2006) has used agent based models to subsequently delve further into understanding investor heterogeneity and its impact on changing asset prices in financial markets. Hommes (2002) states that agent based models can be developed to analyse rational agents in a financial markets framework with simple trading rules and stylized facts, including fat tails, volatility clustering, financial stress and long memory to understand price dynamics in a financial markets environment. Janssen and Ostrom (2006) discuss that there is an increased use of agent based models in combination with empirical methods in finance. They state that four types of empirical approaches including case studies, role playing games, lab experiments and stylized facts have been used, which allows agent based models to emulate real world scenarios to solve problems. A study by Marchesi (2000) is one of the examples where agent based modelling has been used to analyse volatility clustering in asset prices.

Kim and Kim (2014) used agent based modelling to analyse the relationship between the dynamic interactions and behaviours of rational agents in the financial market with monetary policy. The agent based model analyses agent behaviour at different levels of irrationality, the dynamics of the group of investors that behave rather irrationally in the market and the unpredictability of the behaviour of both rational and irrational investors. …

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