Platform mediated networks enable networks of customers, or users, and intermediaries to interact with each other. Examples of PMNs are Ebay and Airbnb. Within these platforms, intermediaries provide the platform, encompass the infrastructure, and set up rules to facilitate users to interact. Nowadays many startups aspire to either build new platforms or to profit by offering complements that leverage platforms. The most valuable features of PMNs are not their products but their communities.

PMNs have made an impact on the financial industry. Specifically, the security trading platforms that allow investors and companies to connect. Analyzing the characteristics of this industry by using Porter’s five forces framework one sees that the threat of new entrants is low, as trading platforms have a large legislative burden to abide by. The buyer power enabled an environment where buyers have plenty of choice and power over brokers, which are usually subsidiaries of large banks or other financial institutions. The suppliers on the other hand like BUX and Interactive Brokers have limited power. Switching costs are low as there are many trading platforms in this industry hence the threat of being substituted by a rival’s platform is considerable. Differentiation is limited as most platforms offer the same functionality and switching costs can even be negative. This causes for high competitive rivalry where marketing is paramount for the success of the platform.

In evaluating the business models of the two companies, the canvas model is used. When comparing the business model canvas for both platforms, we see the following key differences: while BUX offers a simple and accessible trading product, IB offers their customers a wide range of comprehensive products. This results in different customer segments where BUX applies to everybody interested in investing, even without any knowledge of the financial market. While the other platform aims mostly at more educated investors. This translates in a difference in key resources where IB offers around the clock, global trading access while BUX is limited to the national stock market of the customer. Both companies have a platform, however, IB is far more extensive than BUX and benefits from the network effect. BUX focuses only on a set amount of companies per country and does not benefit from this cycle.

If BUX keeps focusing on the current value proposition and customer segments, we predict that the growth of BUX will remain or increase as we expect BUX to roll out their platform in other countries. For interactive brokers we predict it will remain a steady professional player in the industry and will grasp some small sustainable growth due to the network effects. For the security trading industry we predict that the market will become more volatile. Due to the financial crisis, especially regulatory, security, and accounting changes will impact the volatility of the market. Furthermore, the increase in available data used by technologies and models like high frequency trading and artificial intelligence trading will increase volatility in the trading industry even more.

URL: https://www.youtube.com/watch?v=sbcdP4n4BBM

Group 52
Dennis Beers 418224
Daan Wiedenhof 359452
Max Slagt 460633
Jurjen op de Weegh 401907

Around the beginning of the 1980s around 14 Leveraged buyouts (LBOs) took place accounting for 1.3% of the merger and acquisition (M&A) deals. The market for LBOs really started to lift of mid 1980s due to recovery of the economy, declining interest rates, a rising stock market and the invention of a new financial product called the junk bond. The invention of the junk bond resulted in a flow of money that was followed by more aggressive deals, more defaults, lower returns, and falling interests. At the end of the 1980s LBO activity peaked for the first time, with 478 deals accounting for five to seven percent of the M&A deals and nine to twenty percent of the value of M&A deals. Over this period the number of LBOs increased with a factor of 34 and the overall market value of LBOs in the 1980s amounted between $50- and $75 billion. At the end of the 1990s another LBO wave occurred. Between 1998 and 2000 the number of LBO deals almost doubled. Many saw the doubling as a result of a failing market for corporate governance.
After the second wave, and before the subprime mortgage crisis of 2007, a third wave rose. In this period the LBO market increased once again as there was a significant period of easy access to debt. Starting in the 1980s, the total LBO deal value increased until it hit its peak in 2007 when it amounted to $700 billion. After the crisis the total deal value decreased and amounted to $150-200 billion in the post crisis era (Bruner, 2004) (Kaplan & Stromberg, 2008) (Wang, 2012).
Now after the financial crisis there is a new LBO trend, the tech buyout. Historically, LBO firms have preferred to buy more-mature companies with more-stable cash flows or predictable business cycles, so that the large debt loads associated with these kinds of buyouts could be more easily supported. But now tech firms are getting targeted. Tech firms are targeted because they are now more stable businesses then they were in the past 30 years. New successful tech LBOs like Michael Dell who together with Silver Lake Partners bought Seagate Technology, an investment that returned over 700% show that it is doable and also very lucrative if done right (Bloomberg, 2016). Besides the fact that some LBOs have shown it is possible to take tech firms private in a profitable way and not kill them, the historically low financing costs and rare opportunities for LBOs in other industries also helped to spark a new LBO wave. But where there are opportunities, there are risks too, as the number of tech firms available for investment will decline it will become harder and harder to select valuable firms that allow for successful LBOs. Furthermore, the potential increase in competition from other private equity and LBO firms will enable bigger risks and potential fall backs in estimated profits. Lastly with an increase in M&A and LBO activity, a potential consolidation of the tech industry will change the market considerable, and were change is, there is risk. For example tech firms will increase protective measures or change share rights to prevent being bought by outside firms.
I’m curious to see what the next decade will bring when the increasing M&A activity and LBO deals in the tech industry will reveal what kind of effect they had on the market.

Bibliography
Bruner, R. F., 2004. Applied mergers and acquisitions. In: s.l.:s.n.
Kaplan, S. & Stromberg, P., 2008. Leveraged buyouts and private equity. journal of economic research, Issue 1420.
Wang, Y., 2012. Secondary buyouts: Why buy and at what price?. Journal of corporate finance, pp. 1306-1325.
https://www.bloomberg.com/view/articles/2016-06-01/michael-dell-bought- company

Due to lacking performance and increasing scrutiny regarding management fees, hedge funds are experiencing massive outflow of capital. To increase performance and to get an edge in a super competitive financial world a growing number of big investment firms and hedge funds are adopting artificial intelligence (AI) technology.

AI has become a more frequently sided technique of investing next to quantitative and human based investing in the hedge fund world. Nowadays the majority of human based investing hedge funds get help from computer models to make investment decisions and approximately 9 percent of all hedge funds are quantitative based funds. The latter manages about $197 billion in total. Quantitative based investing involves data scientists or “quants” in Wall Street terms, using machines to build large statistical models. These models are complex, but also quite static. As the market changes, they may not work as well as they worked in the past. This is where AI outperforms as it is a flexible model. But despite that, it is not so easy to tell the difference between AI and quantitative based investing. Both methods are dependent on computers and huge amounts of data to make investment decisions. What distinguishes AI from quantitative investing besides flexibility is that AI itself is also learning and that it tries to improve itself over time as it gains more and more experience at its function. On top of that AI is also capable of finding trades that humans can’t.  This is because computing is getting cheaper and more accessible. Algorithms have become more complex and capable of taking advantage of this immense accessible computing power. Furthermore, data is more available than ever before. These factors explain why machines are far better and faster than humans at identifying patterns and trends in data. For example: monitoring book orders, lists used by traders that show interested buyers and interested sellers of securities including their asking and bidding prices, is better done by computers as they can rapidly detect patterns of behavior in these books that humans could easily miss. And therein lies the power of AI right now. Today’s AI is better because it has more and better information than ever from which to learn. As the volume of available data grows, it provides more chances for artificial intelligence to become the dominant way of investing (Freedman, et al., 2016) (Wigglesworth, 2016).
One of the current problems for AI based funds is that they have no real world track record and therefore struggle in attracting big capital. But as time continues more and more AI based funds have somewhat proved they can perform.  As a result, over the past three years’ investments in AI based funds have tripled from 700 million to 2.4 billion dollar. The biggest AI funds reached a 12% positive return this year. Even in Japan, which has one of the most volatile markets, AI investing methods reached a 2% positive return. One specific example of how an AI fund-management program outperformed the market is Japan based Nomoura. During the months of political polling around Brexit many traders in japan signaled that the “remain” side would prevail. But Nomoura’s AI program initiated trades that day that went against the prevailing sentiment of human traders in Tokyo. Nomoura embraced himself for impact as he thought all went to hell. But when the day ended and citizens of Britain voted to leave, markets in Japan tumbled and Numoura’s fund was up 3% (Cawley, 2016).
Even though cases like this give way for a bright future for AI there are still serious hurdles. One of the future problems for AI investing is that even if one fund achieves success with AI, the risk is that others will duplicate the system and thus undermine its success. Subsequently If a large portion of the market behaves in the same way, it changes the market by making it either more volatile or by making it more fixed, making market conditions less favorable. It furthermore could mean only a handful of companies that successfully employ AI based funds survive and that financial oligopolies or monopolies arise. This subsequently could result in an increase in inequality as more and more capital flows to the people who designed the surviving programs and the ones investing in it (FLeury, 2016).    
Wheter AI will become a success on Wall street is for the future to decide but what is defined is that it will become more dominant in the financial world.

Bibliography

Cawley, C., 2016. Tech.co. [Online]
Available at: http://tech.co/artificial-intelligence-investment-tripled-2016-07
[Accessed 25 September 2016].
Chu, K. & Ho, K., 2016. Bloomberg. [Online]
Available at: www.bloomberg.com/news.artiles..2016-08-21/hedge-fund-robot-outsmarts-human-master-as-ai-passes-brexit-test
[Accessed 25 Septermber 2016].
FLeury, M., 2016. BBC. [Online]
Available at: http://www.bbc.com/news/business-34264380
[Accessed 25 September 2016].
Freedman, R., Klein, R. & Ledermand, J., 2016. Artificial Intelligence in the Capital Markets: State-of-the-Art Applications for Institutional Investors, Bankers and Traders.. Mcgraw Hill, Volume 1994.
Wigglesworth, R., 2016. Financial Times. [Online]
Available at: https://www.ft.com/content/9278d1b6-1e02-11e6-b286-cddde55ca122
[Accessed 25 September 2016].