Prediction Market: A Study of Future Country Interest. And The Market Behavior

Transcription

1 UNIVERSITY COLLEGE LONDON DEPARTMENT OF COMPUTER SCIENCE Prediction Market: A Study of Future Country Interest And The Market Behavior Author: Xueyi Jiang ( ) MSc Financial Computing Project 2010/2011 Project Supervisors: Chris Clack, Michael Mainelli This report is submitted as part requirement for the MSc Degree in Financial Computing at University College London. It is substantially the result of my own work except where explicitly indicated in the text. The report will be distributed to the internal and external examiners, but thereafter may not be copied or distributed except with permission from the project host organisation.

2 ABSTRACT Prediction markets have proven high forecasting performance in many fields such as politics, sports and business-related fields compared to traditional methods such as polls or expert opinions. A web-based trading platform with a set of continuous double auction prediction markets, called ExtZy will be studied for testing ideas on the causal connection between future country interest and market behaviour. The market data collected during this study will be statistically analyzed in order to explain how market response to information in reality in prediction markets. Methods like technical analysis and the hypothesis testing methodology will be applied to understand traders behaviour in prediction markets. The empirical evidence shows that the market price in ExtZy implied relatively high prediction accuracy in predicting country interest by the collective wisdom of traders. ExtZy indeed can be used to predict search trends and spikes in some cases.

3 ACKNOWLEDGEMENTS This section acknowledges people without whose help and supporting, none of the current work would have been feasible. Firstly, I would like to express my thanks and appreciation to my supervisor Prof. Michael Mainelli, for his huge encouragement and guidance, and my colleague in Z/yen, Ben Morris, for his continuous patience and assistance. Whenever the problems came, they always gave the right direction and encourage me to solve it. That improved my research skills greatly and was really helpful in my further study. I need as well to appreciate my academic supervisor Dr. Chris Clack s significant suggestions and help. Specially, my appreciation goes to my dear parents. They gave me the chance to study abroad, and this experience would be greatly valuable in my whole life. Without their love and help, I would have no courage to face the troubles in study and life at all. Without the understanding and love of these people, I would not have thought it possible to complete a research project such as this one. They always offer me a helping hand whenever they see me in the struggle.

4 CONTENTS 1. Introduction Motivation Aims and Objectives Technical Objectives Experimental Objectives Research Questions Project Scope and Limitation Methodology Report Structure Literature Review Stock Markets Prediction Markets How Prediction Markets Work? Accuracy of Prediction Markets Benefits of Prediction Markets Empirical Findings of Prediction Markets...12

5 3. Market Analysis and Requirement Gathering Market Description Market Design Account Basics Contract Design Trading Rules Market Information Data Analysis and Empirical Results Data Classification Methodology Initial Data Collection Market Price and Trade Volume Future Interest Market Volatility Supply and Demand Distribution Prediction Accuracy Data Preparation Dividend Price-Earning Ratio Historical Volatility Pricing Options and Black-Scholes Formula... 27

6 4.5 Empirical Results and Analysis General Observations Prediction Accuracy Empirical Analysis Conclusions on Analysis Results Implementation and Test Requirements Gathering Database Architecture Development Environment Webpage Implementation Testing Validation Summary and Conclusions APPENDIX A System Manual APPENDIX B Gantt Chart APPENDIX C Data Pre-Processing Query APPENDIX D Empirical Results APPENDIX E Code ListS BIBLIOGRAPHY

7 List of Figures Figure 2.1 Investment Procedures... 7 Figure 4.1 Google Trends Figure 4.2 Price-Earning Ratio and Trade Volume: Libya Figure 4.3 Price-Earning Ratio and Trade Price: Libya Figure 4.4 Supply and Demand Distribution: Libya Figure 4.5 Moving 1-Week Historical Volatility: Libya Figure 4.6 Price-Earning Ratio and Trade Volume: Spain Figure 4.7 Price-Earning Ratio and Trade Price: Spain Figure 4.8 Supply and Demand Distribution: Spain Figure 4.9 Moving 1-Week Historical Volatility: Spain Figure 4.10 Price-Earning Ratio and Trade Volume: Japan Figure 4.11 Price-Earning Ratio and Trade Price: Japan Figure 4.12 Supply and Demand Distribution: Japan Figure 4.13 Moving 1-Week Historical Volatility: Japan Figure 4.14 Price-Earning Ratio and Trade Volume: Korea North Figure 4.15 Price-Earning Ratio and Trade Price: Korean North... 42

8 Figure 4.17 Price-Earning Ratio and Trade Volume: United States Figure 4.18 Price-Earning Ratio and Trade Price: United States Figure 4.19 Supply and Demand Distribution: United States Figure 4.20 Moving 1-Week Historical Volatility: United States Figure 5.1 Entity Relationship Diagrams Figure 5.2 Transaction History Page Figure 5.3 Transaction History Page Figure 5.4 Entity Data Figure 5.5 Transaction History Page Figure 5.6 Transaction History Page Figure 5.7 Countries Volatility Figure 5.8 Countries Market Index Figure 5.9 Estimated call and put option price of Japan (30 days to expiration) Figure 5.10 Estimated call and put option price of Japan (180 days to expiration) Figure 5.10 Transaction History Page (playerid= and Days=500) Figure 5.11 Query Result (Transaction History) Figure 5.12 Historical Volatility Chart in Excel... 62

9 List of Tables Table 4.1 Data Classification Table 4.2 General Observations on ExtZy Table 4.3 Prediction Accuracy of Trade Price... 30

10 CHAPTER INTRODUCTION This section gives an overview of this project, which derives from the proposed research project (ExtZy) supported by a financial risk management organization (Z/Yen Group). Aims and objectives of the project will also be described, and a brief overview of the organization of the report will be given. 1.1 Motivation Forecasting is the most difficult part in any science fields, the greatest challenge to human wisdom, but also what we are most in need of. Many people devoted their whole life to "predict" one thing, such as if the financial crisis will occur in Asia, if the swine flu will spread in the world, if the earthquake will occur at a specific time or place, etc. All these things may bring about a very far-reaching effect on human development and even threaten the basic human right for survival. Prediction Markets provides us an unprecedented, powerful and immediate mechanism to make it easier for us to master the future world. Traditionally, the so-called "prediction" is always based on the opinions or judgements of some experts and involves a lengthy process, but not necessarily with the accurate judgments or better results. In particular, in the era of information explosion, how do we select the useful comments or judgments is a more difficult problem. In other words, we need a more effective mechanism to exchange the whole variety of useful information and concentrate the wisdom of collectivity to predict future events [1]. 1

11 Till now, we often research public opinion and forecast specific events (i.e. election, sports game) results through traditional polls. However, the survey results are not necessarily accurate and continuous, but always expensive. Prediction markets can provide a low-cost and efficient mechanism to explore public opinion, which is far more reliable and accurate than the traditional methods. Economically, it is nowadays a general notion that large amounts of capital are traded through stock market. The common characteristic of all stock markets is their uncertainty [1], which is relevant to their future states. This uncertainty is not desirable for investors but also inevitable whenever the stock market is chosen as the investment tool. The best that can be done is to try to minimize this uncertainty. As stated previously, Prediction Markets can be one of the instruments to play a key role in this process. 2

12 1.2 Aims and Objectives In the past decades, there is an increasing interest in prediction markets among economists, policymakers, academics and market makers. This project derives from the proposed research project (ExtZy) supported by a financial risk management organization (Z/Yen Group). The overall aim of this study is to attempt to evaluate ExtZy, as a prediction market, whether or not can help to predict future country interest Technical Objectives This chapter discusses some of the objectives that the system should achieve. Most technical objectives are implemented with Python. The system must allow the user to access a list of historical transactions. The system must have output of charts and tables for fundamental data of each entity. The system must provide an instantaneous visualization of the market index. The system must allow the user to calculate the estimated option price of counties entity based on the historical volatility Experimental Objectives On the basis of analyzing and utilizing the existent ExtZy system, the first objective of the study is to examine the feasibility of the prediction task and provide evidence that the ExtZy are not fluctuating randomly. The second objective is, by querying the data, building dynamic WebPages and analyzing most typical cases to generalize its prediction accuracy. 3

13 1.3 Research Questions The following research questions allow the research to meet the objectives mentioned before: Q1: What is the relationship between share prices, dividend, P/E ratio and Google hits rate in ExtZy? Q2: How have players done in gauging what goes on in real world to make money in ExtZy? Q3: How to estimate the historical volatility of the whole ExtZy countries market? Q4: As a prediction market, can ExtZy accurately forecast the future country interest? 1.4 Project Scope and Limitation This research is only for the countries market of ExtZy. The goal is to evaluate the accuracy of the market outputs (or derived data from technical data) against the reality (data from Google trends) and gain insights in the players behavior. The limitation of this study is the availability of data. Z/Yen launched its prediction market ExtZy on Apr 2009 and the newest dividends calculation was added to the raw dataset in Mar 2010, therefore the availability of data is significantly decreased. In this regards, it limits the analysis and evaluation of ExtZy to only one year of data. However, this should not affect the effectiveness of prediction market. 4

14 1.5 Methodology The methodology used while conducting the research is as follows: Conduct a literature review through books, papers and online resources on the topic of prediction markets Determine the data (sample countries) to be analysis on and what to forecast o o o o Trade Price and Volume P/E Ratio Supply and Demand Structure Volatility Examine the trading behaviour against the reality with technical analysis o o Assemble the historical input data and pre-process it using Microsoft Excel Statistically compare market outputs vs. reality data Modelling Volatility on ExtZy o o o Look at the past trading behaviour and calculate historical volatility Build a dynamic webpage in Python language to present the market volatility Statistically compare webpage outputs vs. reality Run prediction markets on the results of ExtZy o o Identify the mathematical theory behind the Black-Scholes Model Build a webpage for calculating the option price for each ExtZy countries entity Evaluate the forecasting accuracy of ExtZy with above four indictors and draw a conclusion on whether or not ExtZy can predict future country interest 5

15 1.6 Report Structure The complementation of the aims and objectives of this project as described earlier take place throughout six chapters. Here we present a brief of the content of each chapter: Chapter2 will give detail background information on stock market and prediction market. Chapter3 will contain a description of the prediction market design in order to obtain data for further analysis. Chapter4 will explain the selection of data for the evaluation of prediction accuracy and the mathematical theory behind these data. This chapter will present the empirical analysis and tests the hypotheses regarding the influence of information on the market behaviour and prediction accuracy. Chapter 5 will present the further implementation and testing on system for exploring previously presented analysis. Chapter 6 will draw a critically conclusion on the project objectives of this study, point out the limitations and provides some plans for further developments. 6

16 CHAPTER LITERATURE REVIEW This chapter gives detailed background information from literatures to stock markets and prediction markets. The first section will give detailed explanations on two major investment theories in stock markets. And the second section will explain the need to manage intraday day liquidity; liquidity requirements for banks. 3.1 Stock Markets There is no doubt that most people are trying to achieve profits whenever they involved in the stock markets. Profit may come by investing in stocks that have a good future (short or long term future). Thus what they are trying to accomplish is to predict the future of the market. But what determines this future exactly? The way that people invest their money is the answer and people invest money based on the information they hold [1]. Therefore we have the following schema: Information Investors Market Level Figure 2.1 Investment Procedure According to this schema (Figure 2.1), the factors that need to be take into consideration are: The content of the Information component The reaction of the Investor when having this information. 7

17 Traditionally, the investment community accepts two major investment theories: the Firm Foundation and the Castles in the Air. With reference to these theories, it allows us to understand how the market is shaped, or in other words, how the investors think and react. It is this sequence of thought and reaction by the investors that defines the capital allocation and thus the level of the market [1]. According to the Firm Foundation theory, it postulates that the reaction of the investors is triggered by information that is related to the intrinsic value of firms [2]. And this value is determined by a comprehensive analysis of current situations and future prospects of a company. As for the Castles in the Air theory, the investors are influenced by information that is related with other investors behaviour [3]. The only concern for this theory is that the investor should have is to buy today with a lower price and sell tomorrow with a higher one, no matter what the intrinsic value is. Therefore, the former theory prefers the view that the stock markets are defined mostly by logic, while the latter one supports that the markets are depended mostly on psychology. 8

18 3.1 Prediction Markets This chapter presents detailed background information to prediction markets and summarizes related apocalypse based on the former research literatures. 2.1 How Prediction Markets Work? Prediction markets works similar to stock exchanges. In prediction markets, a stock is often established to reflect an interesting topic to the company such as the product sales, the feasibility of new services or products, and other in-depth understandings of employees. In this way, employees participate as traders based on their perceptions of future trends. Traders are inspired to obtain relevant information and contribute their best assessments. Traders buy or sell shares of the stock in accordance with their confidence on future occurrences and wish to accumulate their portfolio value. When a trader, for instance, notes that the share price is higher (or lower) than his expected value associate with the issue, he will sell (or buy) the stock, and hence the decreased (increased) price. As the result of this volatile, the stock price in prediction market can be considered as a continuous real-time indication of future results relevant to the issue being raised. It also constantly reflects collective evaluation of the future occurrences from employees in same way that the trading of a firm s stock on a stock exchange (Figure 2.1) constantly indicates the aggregated assessment of the market level of the firm from traders. 9

19 2.2 Accuracy of Prediction Markets Since the late 1980s, firstly at the Iowa Electronic Marketplace, prediction markets have been started to investigate the accuracy of predictions of election outcomes derived from the trading activity within the markets [5]. Existing evidence from Iowa Electronic Marketplace shows that the political stock markets outperformed regular polls in most cases. As the success of prediction markets in aggregating information about events results, some other prediction markets have emerged to trade on events with either virtual or real money [6]. Over the past several years, the Hollywood Stock Exchange almost perfectly predicted the box office performance of Hollywood movies and the Oscar award winners, which has beaten the average forecasts of experts and individuals [7]. Furthermore, research indicates that prediction markets for business forecasting can also perform better than traditional methods. There is evidence that prediction markets on sales forecasting were significantly better than official company forecasts [8]. Companies such as Google, Yahoo, Microsoft and Hewlett-Packard are all running internal prediction markets to tap the collective intelligence of people within their organizations. 10

20 2.3 Benefits of Prediction Markets The basic idea of prediction markets is an electronic trading platform for virtual stocks whose payoffs are determined by the outcomes of some uncertain future events [9]. Prediction markets are also known as information markets, idea markets or decision markets [10]. As the names suggest, prediction markets offer a unique means to unify various information and market predictability within the enterprise. A prediction market is often established within an organization to produce predictions on the issues the company is interest in and settles the problem of internal communication constraints directly. By this kind of approach, even for a geographically distributed organization, prediction market can generate a virtual management environment by raising a smart forum in order to improve the final analysis of intelligence and influence in its interested fields [11]. In addition, prediction markets could lead to increased employee involvement and resources levels through its collaborative feature and provide staffs a unified platform that their intelligence to be taken seriously, while leveraging the possibility of receiving information from all minds within the company for leaders [12]. 11

21 2.4 Empirical Findings of Prediction Markets The prediction of markets has been given to attention over the past few decades with its wide deployment. Although the relevant evidence is still increasing in different areas, based on our preliminary information from literatures, we can draw some conclusions as following: i. The market price in prediction markets is an instant indicator of new information. Efficient market hypothesis indicates that all relevant and available information about an asset in a financial market is reflected in its market price [13]. ii. The prices time series seem correspond to the random walk model and the operations based only on public information are not profitable [13]. iii. The forecasting accuracy of prediction market is generally higher than the other forecasting methods, such as opinion polls or experts prediction. iv. The behaviour such as market manipulation whose aims to change the stock price deliberately by some technologies might lead to failure at last [14]. 12

22 CHAPTER MARKET ANALYSIS This chapter includes a analysis on the ExtZy markets and further experimental design of ExtZy, which will be used to collect more data for empirical analysis for this research. 3.1 Market Description A web-based trading platform with a set of continuous double auction prediction markets, called ExtZy was studies for the aim of this research in order to analyse historic trading data and test idea on prediction accuracy of individual traders and whole markets. Future interest of countries (on politics, wars, sports ) was determined as the event to be predicted. There are 245 different shares issued for trading in all and each share represents a country entity. Participants are able to access to news on international affairs easily through all kinds of means and media will give these affairs a lot of media coverage immediately. Therefore, it may lead to a high number of participants and thus have obvious preferences for particular country they bet on at a specific time so that indications of biases can be detected easily. This is the reason why future country interest was chosen. 13

23 3.2 Market Design The basic idea of ExtZy markets is similar to that of some prediction markets mentioned before (in Chapter 2) such as the Yahoo! Tech Buzz Game, Iowa Electronic Markets, InTrade and Hollywood Stock Exchange, etc. Though these public prediction markets designed their markets in different ways and served for different functionalities, they all are attempting to settle the same problem: how to attract more participants into these markets, and generate significant predictions. Like these markets, participants trade in ExtZy in order to make a profit. In addition, ExtZy is also about to learn how markets work, the effect of popularity and perception on prices (or other derived data), and the inspiration of exploring hidden value [14]. ExtZy markets allow participants to trade in shares whose underlying value is associated with their popularity and earning dividends based on search volume at Google on a weekly basis. To encourage participation, participants can exchange their earnings into real prizes. In addition, different from InTrade and IEM, the payment in ExtZy is made by virtual money instead of real money. ExtZy Countries markets was launched on Apr 13 th, 2009 and remained open for trade 24 hours 7 days until now. For each country entity, ExtZy issued 1000 shares when it started. 14

24 3.3 Account Basics Each participant will be given a credit line of of Zoints (a virtual currency) after registration by which he/she can buy or sell shares later. Each trader is able to get instantaneous information about his account (with his assets value at current market prices), a list of current holdings, current offers, registered entities, dividend history and transaction history (Further Implementation will be shown in Chapter 5). The system permits traders to view a list of all country shares with their last transaction price and their current best available offer price. Traders are able to directly buy (sell) shares at current price or make an offer to buy or sell at your own valuation in the respective markets with other participants. 15

25 3.4 Contract Design As mentioned in Chapter 3.1, the mechanism that matches buyers to seller is a continuous double auction in ExtZy, which means that once reach a mutually agreeable price, a transaction will occur. In this regards, a large volume of buy or sell orders will be continuously submitted. In addition, the clarity of contract is on the basis of two things: knowing how the dividend is paid, and understanding the relation between searches and dividend. These concepts are extremely simple, and one can even observe the historical search hits that represent a country. Furthermore, ExtZy Countries will introduce option contracts. By applying Black-Scholes model to prediction markets, traders are able to estimate prices of option that is at-the money with different time to the expiration date. (Further Implementation will be discussed in Chapter 5). 16

26 3.5 Trading rules Traders are able to make an offer on an appropriate share by selecting the quantity of units and the offer price. The trade processing system checked only the best available price but ignored the following offers in the queue to make sure that the transaction is always be agreed on a common price and minor quantity of both sides. The remaining quantity will be made as a separate order then kept in the order queue. There are not any charges or transaction fees in ExtZy. However, traders are only able to place a buy order with a sufficient amount of free Zoints. With such rules, the system allows traders to interpret easily that whether or not their holding contracts is more valuable than some time before. 3.6 Market Information All trades in ExtZy are anonymous which means that traders are not able to recognize the identity of other marketers. Traders are provided continuously information and graphs about past transaction prices, the volume of each transaction and even historical search hits numbers of each share. Besides, information on a total of dividends paid, trade volume, average price for last certain days are accessible for traders. Furthermore, the daily updated market index and market volatility are demonstrated by charts, which imply market participation and movements (Further Implementation will be discussed in Chapter 5). The purpose of such charts is to increase market liquidity more or less by making traders more clear about the general situations. 17

27 CHAPTER DATA ANALYSIS & EMPIRICAL RESULTS This chapter presents the gathering, querying and analyzing of data in this study. The overall aim is to analyze the data (derived data) from the ExtZy and evaluate the accuracy of the market outputs against the reality in order to gain insights in the trader behaviour in real life. 4.1 Data Classification The investigation about markets derived from the study on relevant data. Here we attempt to describe and classify the data into three major groups that will be used in following study. The following Table 4.1 gives a detail of the three categories of data: Table 4.1 Data Classification 18

28 4.2 Methodology The study of the prediction market has been always given to attention long time. Reference to the previous literature there are a variety of methods have been applied to accomplish this task. In the following study, we will mainly work with technical analysis method. The basis of technical analysis is that the price movements of stocks are influenced by the constantly changing attitudes of investors in response to a variety of market forces [19]. The key belief of technical analyst is that the historical performance of stocks and markets are important indicators of their future trends. By using data such as historical price series, trading volume and other market data, future stock movements can be easily found. Although this approach is very popular, there are some shortcomings have been severely criticized. The main objection to this method is that the analysis of charts is highly subjective and thus different analysts may hold different views on the same charts. 19

29 4.3 Initial Data Selection As specified in Chapter 1.4, a new dividend calculation was introduced to the raw datasets on Mar 5 th 2010, through traders were able to make offers and transactions since Apr 13 th 2009 in ExtZy Countries markets. For the effectiveness of information aggregation and market evaluation, the more valid data includes, the more accuracy the prediction is. Therefore, we will consider all the transactions and offers from May 1 st 2010 to Aug 1 st 2011 (15 months, 457 trading days) for analysis. The thing we are going to explore is the predictive capability of markets and traders Market Prices and Trade Volume In prediction markets, we often first choose the last transaction prices of shares as prediction measurement since these sets of figures should send the up-to-date information about future event. In spite of that, these figures may deviate from rational levels occasionally when take into account of the influence of market bubbles, market manipulation or non-rationally trader behaviours. In this regards, in the following study we calculated the average transaction prices of each share on a daily basis instead of last transaction prices. We consider the former ones implies more accurate information while the latter ones contain more actual movements about traders. In addition, we also computed a total of trade volume of each country entity on a daily basis to support the analysis. 20

30 4.3.2 Future Interest In ExtZy, the dividend of each share is link to the Google search traffic volume for predefined country terms. In the following study, Google Trends is as selected to represent the future interest in reality. With Google Trends, we can view and even compare the world s interest in different countries as shown in Figure 2.1. Figure 4.1 Google Trends Besides, we will distinguish between the normalized and non-normalized search data. All data from Google Trends are normalized. Suppose that we don t normalize the search numbers and display the absolute value of countries, the results would not make any sense, for example, China, a country of with dense population, definitely have a higher number of searches every day than other countries. 21

31 4.3.3 Market Volatility As mentioned before, the market prices of countries in ExtZy can be interpreted as a market forecast on future interest. However, in this way we have little knowledge of the stability or robustness of this estimation. By regarding the share price as a decisive factor, we are not able to capture the implications hid inside such as how likely the price may go above or below some specific points, etc. And beyond the average trading price of each country entity, we should consider further information reflected by the market price to fully understand the stability of the markets. The following step is to study the volatility of the market. Volatility is a measurement for price movement of a share in markets over time. In other words, volatility reflects the degree of risk or uncertainty about the changes on market value [20]. In the following study, we will firstly look at the historical volatility according to the historical behaviour, then to estimate the option price by using Black-Scholes formula. 22

32 4.3.4 Supply and Demand Distribution Like other prediction markets, traders in ExtZy make their offer prices individually based on their estimations. This information, together with the quantity of offers can be used to describe the relation between demand and supply. The imbalances between two sides could lead to an uncompetitive price or incorrect aggregation of market information. With a limited number of traders in ExtZy, the supply and demand curves could not demonstrate a continuous shape but a stepped shape Prediction Accuracy In this dissertation, the keywords like prediction accuracy and information aggregation efficiency are always repeated. They are defines as the capability of an individual or market to make the best assessment on a specific event with available resources or information. Analogously, it is sensible to make a comparison on prediction accuracy of the same event between traders. And also we can compare the predictive capability of the price series with the average of individual forecasts on that event. In the following study, we will adopt a quantitative method for the evaluation of the prediction accuracy of ExtZy, which is to compute the ratio of accurately predicted events within ExtZy Countries market 23

33 4.4 Data Preparation This chapter explains the mathematic theories of automatic data processing in ExtZy database and electronic data processing in excel after data query Dividend As explained in previous sections, the dividends in ExtZy are calculated on a weekly basis based on searches volume for each country entity. In case of a possible wide range of return values, we use a logarithmic scale to calculate the actual earning of traders. The formula for dividend calculation as following: Each trader can receive a corresponding part from total dividend for each country according to the shares they holds. The exchange rate of dividend and Zoints is 5:1. For example, if the total dividend for the Entity 'United Kingdom' in a week was 1,000, therefore the total payout in Zoint is 2,00. In this case, if you have 10 out of 1000 available shares. Your earning for 'United Kingdom' in this week should be 2 Zoints. 24

34 4.4.2 Price-Earning Ratio By relating share prices with dividends, the P/E ratio highlights the relation between the price and recent country popularity. If prices get lower and dividends get lower, the ratio will stay the same. In real life, the share price rises usually before its dividend is increased since this increase has already been discounted. In this case, the ratio will increase as price and dividend become disconnected. In this connection, when the ratio goes above or below the normal level, it implies that recent dividend levels are no longer the main basis in pricing. The reason might be that the change is ongoing and traders are expecting a much better or worse performance in the future. Normally, the price-earnings ratio is defined as the nearest whole number of the ratio of the latest price and the last 12 months earnings per share. In ExtZy, the price is the latest transaction price and the earnings per share represent the sum of the dividends per share calculated over the last rolling 52-week period. And the P/E Ration here is usually a decimal. The formula as follows: 25

35 4.4.3 Historical Volatility In stock markets, the share price is normally observed at fixed intervals of time (i.e. days, weeks, months) for the estimation of volatility. The historical volatility mentioned in this study is on a daily basis and represents the volatility of historical path of past trading prices for a particular country or the whole market. The calculation of historical volatility in ExtZy is given by following steps: Firstly, the log relative returns u i are defined as. where S represents daily closing value for ExtZy Countries Markets Then, the estimated volatility σ can be calculated by and Where n represent the number of daily values includes in moving samples Last, to normalize the estimation with the number of intervals per annum T In this case, the daily data is used and thus we use T =

36 4.4.4 Pricing Options and the Black-Scholes Formula The theoretical value for a standard European option can be calculated by the formula of B-S Model. This formula is consist of six parameters, includes stock price, strike price, volatility, risk-free interest rates, time to expiration and option price. And if any five of them are given, we can solve the value of the sixth parameter based on this formula. In the following study, we will firstly use the historical volatility before to arrive at the theoretical value for each country option by following steps: Call option Put option Where, According to the actual situation in ExtZy Countries Market, the stock price is the same as the strike price (i.e. S=X) and the risk-free interest rate is equal to 0 (assume as 5% later). 27

37 4.5 Empirical Results and Analysis The following study is conducted with the methods described in previous chapters by using the data or derived data collected from ExtZy within the scope General Observations There are a total number of 245 country entities in ExtZy and 8 out of them haven t got any transactions and market price. Up to Sep 1 st, 2011, 672 participants are registered on ExtZy. The results showed in Table 4.2 revealed the overall activity of ExtZy Countries Markets since the launched date, Apr 13 th, ExtZy Average [Range] Sum Transactions per entity [2-3328] 153,933 Offers per entity [1-1485] 64,788 Transactions per day [1-16,374] 153,933 Offers per day [1-6839] 64,788 Traders per entity 24.6 [12-95] 557 Table 4.2 General Observations on ExtZy From the table above, it can be seen that the average number (24.6) of activity traders on each entity is relatively lower than other prediction markets. However, 557 of registered users participated into the trades on ExtZy Countries Market. Each entity has attracted at least 12 traders while the most popular one have absorbed 95 traders during the past more than two years. Therefore, we can rely on the market results from ExtZy in the following study. 28

38 4.5.2 Prediction Accuracy In this section, the main premise of this study is that the market price reflects all known information and is the best predictor of the future interest of a country. To focus in depth in the predictive accuracy of ExtZy, we have examed the market behavior of 50 country entities selected at random and compared with their search indexes in reality. These entities were randomly divided into 25 groups to examine that whether a higher trading price in ExtZy could imply a higher future interest, respectively. In this case, the price was the last trading price by Dec 31 st, 2010 and the search index was the normalized average search index in the following week reported on Aug 7 th from Google. As shown in Table 4.3, (68%) of the cases predicted the more popular country correctly. Though this figure could not represent the predictive accuracy of ExtZy at anytime, we could find that the market price in ExtZy performed quite well in predicting country interest by the collective wisdom of traders in this investigation. 29

39 Countries Price Search Index United Kingdom, Chile 4.85/ /19.0 Turkey, Spain 3.99/ /1.13 Japan, Korea 7.37/ /0.25 Pakistan, Emirates 4.86/ /0.34 Germany, Australia 3.82/ /4.80 India, China 13.99/ /0.34 South Africa, United States 9.99/ /0.68 Indonesia, Sweden 3.01/ /0.16 Canada, Singapore 2.40/ /1.02 Nigeria, Georgia 6.95/ /2.54 Uganda, Slovakia 1.56/ /0.45 Denmark, Iceland 2.08/ /0.84 Poland, Italy 1.85/ /4.22 Viet Nam, Ireland 1.25/ /1.92 Haiti, Brazil 3.21/ /2.72 Slovenia, Mexico 1.70/ /74.0 Romania, Portugal 1.95/ /2.26 Afghanistan, Iraq 2.00/ /0.69 France, Libya 2.97/ /0.02 Yemen, Finland 3.02/ /2.44 Greece, Nepal 2.19/ /0.47 Norway, Egypt 1.99/ /1.60 Austria, Iran 1.73/ /0.90 Netherlands, Afghanistan 1.99/ /1.80 Thailand, Argentina 1.50/ /3.64 Table 4.3 Prediction Accuracy of Trade Price 30

40 4.5.3 Empirical Results In this section, I reselected 48 sample countries, which may have big events during the chosen period (May 1st 2010 to Aug 1st 2011, 15 months, 457 trading days) to explore whether the search spikes can indeed be predicted in ExtZy. Then the study will make further analysis on some typical country entities to verify the accuracy of ExtZy outputs against the reality and thus gain insights in the trader behavior in real life. Case One: Libya Since February 17, 2011, the unrest in Libya was growing and Libyan current government was experiencing a full-scale revolt. After one month of evolution (March 21, 2011), it turned to be a war against international military forces. The following Figure 4.2 and 4.3 compares ExtZy outputs with reality. Figure 4.2 Price-Earning Ratio and Trade Volume: Libya 31

41 Figure 4.3 Price-Earning Ratio and Trade Price: Libya As the above chart shows, these events do have a great impact on market behaviour. The searches spiked during that period on Google. In ExtZy, there was a large volume of transactions (Mar 19, 2011) before the war really broke out, which indicated that traders expect an increasing trend of this country. In fact, the price before the war can be seen as a fairly low level with the highest value of 0.08 Zoints per share, but after the war, the market value of Libyan leapt to 1.60 Zoints per share. Besides, the P/E ratio had a long period below the average level before February After the war, the P/E ratio increased sharply to 9.06 (Mar 24), which reflected an increasing confidence in the growth potential of the entity. Several weeks later, it steadily decreased to and the price is unchanged, which implies for every points the share cost, it paid one points in dividends. That is to say the entity has been more profitable for its holders. 32

42 In addition, the distribution of supply and demand corresponded to the offers prices of Libya are demonstrated in the following Figure 4.4 Figure 4.4 Supply and Demand Distribution: Libya As shown in above chart, several offers emerged together during the period of Libya war. Due to the imbalance between supply and demand, the market was lack of liquidity and may lead to an uncompetitive price. In prediction markets, these offer prices should logically associated with the trader s assessment on the underlying future country interest together with the offered quantities. In this case, the total demand quantity (Feb, Apr, 2011) reached to 350 with an average bid price at Zoints per share while the supply quantity was 95 at a price of 3.25 Zoints per share. Before the Libya war, the market price of this entity showed in Figure 4.3 was as low as 0.08 Zoints per share. 33

43 Actually, it s difficult to take all the market factors into consideration, such as the difference in the number of participants in markets, the liquidity of the markets, how much the information actually affects the traders behaviour, etc. In this regards, we can assess the accuracy by whether the new additional information will change the original trend of the entity. As shown in above charts, since plenty of media reports have covered on the civil unrest in Libya, the market presented a period of unusually active behaviour, which lead to a higher value of share price than its average level before. Figure 4.5 Moving 1-Week Historical Volatility: Libya Furthermore, the moving historical volatility of Libya showed the price fluctuation since May, 2011, which proved that the market price of Libya entity indeed change dramatically during that time of period. The similar variations also appeared when a series of protests occurring in Egypt, Sudan and Jordan. All the relevant results can be seen in Appendix C. 34

44 Case Two: Spain During the summer in 2010 (Jun 11- Jul 12), Spain put all to rout to get into the world cup finals. On 12 July 2010, Spain defeated Netherlands to win the 19th FIFA World Cup held in South Africa. Football is the most favorable sports event in Europe and even in the world. Traders can have access to any event information easily through a variety of ways. In this way, they were able to attempt a judgment before the game. Figure 4.6 and Figure 4.7 presents the market behaviour on this event. Figure 4.6 Price-Earning Ratio and Trade Volume: Spain 35

45 Figure 4.7 Price-Earning Ratio and Trade Price: Spain As shown in charts, there was a large volume of transactions at an average price of 1.74 Zoints per share before the finals on 8 Jul, 2010, which was almost twice of the original trade price. On 13 Jul, 2010, the day after the finals, the market price returned to 1.03 Zoints per share immediately which indicated that traders expect a decreasing trend on Spain after World Cup finals. In real life, as the grey shadow demonstrated in charts, the search index has been increased from 0.63 to 1.52 during World Cup and decreased sharply to 0.54 in the following week. Besides, the distribution of supply and demand corresponded of Spain are demonstrated in the following Figure

46 Figure 4.8 Supply and Demand Distribution: Spain It can be seen that on Jul 11, 2010, there was a heavy demand for Spain suddenly in the markets. And the volatility in following chart also illustrated an extraordinary variation on price before the games. Figure 4.9 Moving 1-Week Historical Volatility: Spain 37

47 All the data above showed a higher valuation and interest on Spain before the World Cup final. That is to say, traders expected a better performance of Spain on the future performance of the underlying event. In this regards, this expectation indeed agree with the actual results in reality. The similar situations can also be found on the trade performance of some other competing nations, such as Germany, Argentina and Netherlands. All the relevant results can be seen in Appendix C. 38

48 However, traders in ExtZy were not capable of responding to all kinds of situations in advance. The following study will explain the other two cases in detail. Case Three: Japan On March 11, 2011, an 8.9 Richter scale earthquake shook the northeast of Japan. The quake set off a massive tsunami, explosions and fires. Further, the Fukushima Nuclear Power Plant burned due to the terrible quake-tsunami. All sides in world attached high attention to this disaster in Japan as the grey shadow displayed in the accompanying charts (Figure 4.10 and Figure 4.11), the search index turned extremely high Figure 4.10 Price-Earning Ratio and Trade Volume: Japan 39

49 Figure 4.11 Price-Earning Ratio and Trade Price: Japan In the ExtZy markets, there were also some frequent trades once the news spread out. It can be seen that the price of Japan jumped to 4.07 and then reached 8.72 Zoints per share on Mar 17 and the P/E ratio increased to over 5, which was at a significantly higher level than before. Figure 4.12 Supply and Demand Distribution: Japan 40

50 Figure 4.13 Moving 1-Week Historical Volatility: Japan As shown in above charts, traders respond swiftly to the market changes after the spikes from Google. Therefore, it is easy to detect that people usually use the present (adding their experience judgment) to shape their view of the future. For issues like wars (case one), sports games (case two), etc., people could estimate their trends in advance and thus traders behaviour could lead the reality. But in this case, when users come up against unexpected events, such as natural disaster or contingency, their behaviour would definitely lag behind the reality. The similar trends can also be seen after the earthquake happened in Haiti and the Volcano eruption occurred in Iceland. All the relevant results can be seen in Appendix C. 41

51 Case Four: Korea North Figure 4.14 Price-Earning Ratio and Trade Volume: Korea North Figure 4.15 Price-Earning Ratio and Trade Price: Korean North 42

52 As illustrated in above charts, though there were two sudden spikes in search volume on July and September 2010, the marketplace maintained a steady state. The markets did not respond to the events in advance, not even after that. Besides, there was no additional buy or sell offers and variations on market price. In this case, the market price cannot reflect all the information related to the events completely which may cause by the insufficient knowledge of North Korea among traders. Such flat price trends also appeared when the search spikes came out in Nepal, Switzerland and Somalia. All the relevant results can be seen in Appendix C. 43

53 Furthermore, what might happen in ExtZy if there were no such big events as previous cases during the observed 15 months? Keeping at a low market price Case Five: United States United States has global impact on economic, political and intellectual climate and has been the focus of attention and discussion all along. There are always much information and news on this country every day. From May 1, 2010 to Aug 1, 2011, there were no obvious spikes on Google Trends as demonstrated in following graphs. Figure 4.17 Price-Earning Ratio and Trade Volume: United States 44

54 Figure 4.18 Price-Earning Ratio and Trade Price: United States However, there have always had some transactions in the markers with a relatively high trade price, which reflected a continuous high expectation on this entity. Figure 4.19 Supply and Demand Distribution: United States 45

55 Additionally, the demand for this entity remained high, despite its high price. Therefore, not only the search spikes can be predicted by the aggregated information of traders, but also relative trends can be predicted, which have been discussed earlier in Chapter Figure 4.20 Moving 1-Week Historical Volatility: United States The similar situations can be found on the trade performance of United Kingdom and China. All the relevant results can be seen in Appendix C. 46

56 4.6 Conclusions on Analysis Result Actually, it s difficult to draw a conclusion on the prediction accuracy of ExtZy on different events. In this study, But we have indeed found some necessary relations between the market behavior and future country interest (searches). As the previous evidence shown, the ExtZy are not fluctuating randomly. Generally, the market price in ExtZy performed quite well in predicting country interest by the collective wisdom of traders in this investigation. ExtZy indeed can be used to predict search trends and spikes in some cases. People usually use the historical performance with their experience judgment to shape their view of the future. For issues like wars, sports games, elections and even government policy, people could estimate their trends in advance and thus traders behaviour could lead the reality. But when traders come up against unexpected events, such as natural disaster or contingency, market behaviour would definitely lag behind the reality. As prediction markets, the enthusiasm for ExtZy is on the basis of the efficient markets hypothesis. The market price should be the best indication of the underlying event in a truly efficient prediction market. 47