Differentiation of AmalgaMood through Longer Holding Periods

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1 Differentiation of AmalgaMood through Longer Holding Periods Analysis: Contact: Comparison of AmalgaMood with other methods AmalgaMood s Mood Index has performed very well highlighting important transition periods for the equity markets. Such transition periods occur infrequently, approximately once every six to 18 months and signal the beginning of a new expected phase. This approach to using text analytics for investing contrasts sharply with most other currently available as their outlooks tend be multi-hour or multi-day as opposed to AmalgaMood s multi-month to multi-year outlook. This distinct characteristic of AmalgaMood s method produces a significant differentiation.

2 Page2 Disclaimer The content, commentary, conclusions, ideas, and opinions (collectively as Information ) in this document are provided solely for general illustrative and informational purposes and do not create a business or professional services relationship or contract, nor provide full disclosure as to how the analytical system that is the subject of this document works. Any reliance on the Information provided herein is solely at the user's own risk. This Information is not intended to be an offering of any type. The intention of presenting the Information is to provoke thought by the reader as to the potential usefulness of social mood analysis, of text analytics, and of the quantitative study of news, social media, and other publically available content, so that the reader can make an informed decision as to his/her investment strategy with his/her registered investment advisor ( RIA ) or with his/her qualified investment advisor. AmalgaMood LLC ( AmalgaMood ) is not a RIA and does not provide such services. This document and Information do not constitute, and should not be used as substitutes for, tax, legal or investment advice. The document has been prepared without regard to the individual financial circumstances, needs or objectives of persons who review it. The securities and investments discussed in this document may not be suitable for all investors. Readers should independently evaluate particular investments and strategies, and seek the advice of a RIA or other qualified financial adviser before making any investment or entering into any transaction in relation to the securities mentioned in this document. This document may contain forward looking statements and conditional statements using such words as expects, appears, may, should, anticipates, or similar words and expressions referring to potential future outcomes. Such statements do not constitute guarantees or recommendations of any kind. Investment decisions are to be made by and remain as the sole role responsibility of the investor. Investment involves risks. The price of securities may go down as well as up, and under certain circumstances investors may sustain total loss of investment. Past performance should not be taken as an indication or guarantee of future performance. Unless otherwise attributed, forecasts of future performance represent estimates based on factors considered relevant. Actual performance may vary. Consequently, no express or implied warranty can be made regarding future performance. AmalgaMood, together with its respective directors, officers and related parties, may have or take positions in the securities mentioned in this document, or derivatives of such securities or other securities not mentioned in this document, for their own account or the accounts of others, or enter into transactions contrary to any recommendations contained herein. Information presented may be incomplete and out-of-date. Additionally, the reader understands that the algorithms used by AmalgaMood as well as the sources of information used change periodically, and that the reader will not receive notifications or explanations concerning such changes. By reviewing the document and Information, the reader agrees to the aforementioned statements and agrees to discharge and exonerate the creators of the Information as well as AmalgaMood, its owners, directors, officers, members of its Advisory Board and any related parties, from any liability associated with the taking or not taking of actions due to the reader reviewing this document and Information.

3 Page3 AmalgaMood is part of a growing movement of using text analytics on the live Internet in order to determine specific trends. The rapid growth of social media has given a boost to this field. Social media provides millions of new public commentators who openly express their respective opinions on everything from products and services to investing and politics. It also provides an enormous amount of new text information for the text analytics industry. Investors have been applying text analytics in order to help determine investment strategy for well over a decade. The great boost of late has been the emergence of social media as a viable and public source of information from millions of individuals. Now, text analytics have larger quantities of data on which to run tests and improve their analysis. In general, investors tend to use text analytics to gain certain advantages in the financial market. Two of the currently most popular categories are: 1. Short-term information arbitrage; and 2. Short-term group emotion impact. Short-term information arbitrage is created by the fact that humans take longer than computers to process information. For instance, windows of opportunity for trading can be created when computers process information impacting a company as either positive or negative and then execute related trades faster than human traders. Such an advantage might last less than a minute. The result is intra-day trading on a large scale which would necessarily be conducted by computer trading systems. High-frequency trading, however, is a very specialized field allowing for little participation of the broader public, therefore the benefits gained from this analysis method essentially stop within this specialized field. Short-term group emotion impact considers that abrupt changes of the emotional state of society (or at least a proxy of society) lead to short-term stock market movements. For instance, if society s emotional state, as for example determined by analyzing social media communications, shows a sharp increase in an emotional state such as happiness, the stock market should perform well within the following days. The inverse occurs if a negative emotional burst occurs. The result is intra-week trading on a large scale. Computer trading systems would likely need to be used to optimize such short-term opportunities. An example of the short-term group emotion impact is the paper entitled Twitter mood predicts the stock market, by John Bollen, Huina Mao, and Xiaojun Zeng, published in the March 2011 edition of the Journal of Computational Science. This paper highlights the possibility that changes in society s emotional state can predict stock market movements up to six days in advance. Using this strategy as an investment indicator would result in holding equity positions for likely a few days, possibly three, on average. In other words, this strategy though producing longer expected holding periods as compared to the short-term arbitrage approach still appears very short-term in nature. AmalgaMood s approach is completely different form of using text analytics to determine investment strategy and has created a new category longer-term social mood trend analysis.

4 Page4 With a focus on longer term trends and on determining significant inversions, AmalgaMood created its Mood Index. With regards to the global stock market, it has produced only five inversion signals from January 2007 to June Though limited data prevents a more thorough conclusion, such social mood trend inversions appear to offer windows of opportunity from around six to 18 months when the social mood tends to trend in one direction. The result is that decision makers can incorporate this information into their decision making process as the time horizon allows for planning and execution. In contrast to the aforementioned approaches where short-term time horizons necessarily create dependency on pre-determined computer execution without significant human real-time judgment involvement, AmalgaMood s approach allows for both automated execution as well as human involvement depending on the desire of the user. The longer term windows of opportunity also appear more attractive for both the general equity investor and the financial media as their outlooks are likely in-line with the approximate year outlook of AmalgaMood s Mood Index than the few hour or day outlooks of other methods. Comparing AmalgaMood s approach to two existing funds makes the differentiation clearer as depicted in the following table. Table 1: Comparison of key elements of text analytics techniques used to help determine investment strategy AmalgaMood, Derwent Capital Markets, Pluga AI Fund Company/Fund Source of Text Expected Holding Period Traditional Investment Analysis Easily Incorporated AmalgaMood Live Internet ** 6 18 months Yes Derwent Capital Twitter 3 4 days No Pluga AI Fund Blogs Intraday No Source: AmalgaMood, Bloomberg, New York Times, Wall Street Journal, Reuters ** Live Internet can include all time stamped text information. AmalgaMood utilizes a cross-section of various sources and format types.

5 Page5 About AmalgaMood AmalgaMood LLC is a Washington D.C. based Financial Market Information Intelligence company. It focuses on using text analytics and quantitative analysis on such publicly available information as social media, traditional news, and user generated content to determine the social mood towards financial market instruments. Its system is based on a patent-pending process. As applied to equity investing, AmalgaMood s Mood Index would have kept investors out of the equity markets during the entirety of the bear market, while signaling reentrance within three weeks of the March 2009 low. More recently, its Mood Index highlighted a deteriorating equity environment prior to the July-August 2011 global equity collapse. For more information, contact info@amalgamood.com.