Introduction:

1. BUSINESS FORECASTING AND PREDICTION MARKETS POTENTIAL ON BANKING INDUSTRY IN KENYA Kimani Florence WanjiruDR. James M. NjihiaSwazz2000@gmail.comDepartment of Management ScienceSchool of Business University of NairobiOctober 16th 2014

2. Introduction: • Presently in the Banking Sector they are dependent on ERPs which have shown dissatisfaction i.e. 88% of corporations are not satisfied especially on cash flow forecasting. • Lack of proper planning & control of cash resources e.g. Economic recession as a result of poor Predictive tools. • Experts are expected to know a variety of forecasting tools depending on the present situation. • According to studies done businesses cannot rely on only one method; thus a better forecasting tool is required that cuts across all areas without been limited by the complexity of the problem or a number of variables. • How can Kenyan Banks take Advantage of Prediction Markets?

3. Research Questions In relation to Kenyan banks the research objectives was: • To find out the satisfaction of the present forecasting systems in use. • To determine awareness of prediction markets. • Develop a prediction model for organization readiness to adopt prediction Market.

4. Motivation for ResearchPrediction Markets in Financial Sector • In 2007 there was a serious Economic recession where the Banking industry & corporate companies got seriously affected; this continued to 2008-2009; A prove that existing traditional forecasting tools did not predict correctly as expected. • This explains further the need for better forecasting tools that capture relevant information on ESG( Environmental, Social& Corporate governance ); this can only be done better by prediction Markets. • If you are ignorant of information as an Entrepreneur you cannot thrive in the Competitive Environment. • INFORMATION is POWER!

5. Business Forecasting(Literature Review) • Business Forecasting :is a planning tool that helps management in its attempts to cope with the uncertainty of the future, relying mainly on data from the past and present and analysis of trends e.g. Time series method, Dephi method, surveys, polls e.t.c. • How Well Do they Aggregate Information in the short term & long run term ?

6. Prediction Market • A place where information is aggregated via market (or other) mechanisms for the primary purpose of forecasting events, or the probability that an event will occur. Types of Prediction Markets • Enterprise Prediction Markets are markets internal to an organization that support business forecasts on sales, new product development, project management, market and economic indicators .e.g. Siemens, Microsoft, Google, Nokia, HP(Main memory-Constitutes 7-10% Computer Cost)this Has greatly improved the memory price forecast by 1-2%. • Public Prediction Markets are created in the interest of the public for the purpose of attracting enough traders. • Focus is on interesting topics that are of concern to the public. E.g : sports events, box office, elections or any other people related news. Companies that use: Iowa Electronic Markets, Hollywood Stock Exchange, Tradesport, Intrade, Foresight Exchange-IEM (Presidential elections)

7. What makes prediction Markets Interesting to Use • Aggregation of information in real time. • Change to the latest information that can help Users gather useful insights. • Freedom, Flexibility,Moltivation & Efficiency. • Help Participants to Proactive. • Diverse/Dynamic information collected that gives traders insights on new ideas.

8. How prediction Markets work • Prediction markets are said to work the same way as the stock exchange or financial markets. • Traders can be employees of the organization or individuals from the public. • Traders participate based on their perceived understanding concerning the future events with protection of anonymity and well defined incentive structure • The market price reveals the probability of an issue occurring • Type of money used-Real money/Play money

9. Benefits of Prediction Markets • Work as continuous dynamic markets that run over a relatively short or extended period of time. • Give traders instant feedback, giving them chances to reconsider their own information and act in response to that feedback. • Traders who are more certain in their ideas participate actively in the market thereby influencing the market prices. • They are cheaper to use because information is gathered from different participants thereby reducing bias.

10. Benefits of Prediction Markets • Participation of employees gives them an opportunity to speak up their mind. • It creates incentives for information discovery. • It does not require all traders to be informed and rational. • Markets do not require many traders for it to be efficient.

11. Challenges of Prediction Markets • Lack of access to all relevant information:-due to lack of experts and business leaders knowledgeable in the area of interest. • Fast technology requires a great design with value proposition in mind because prediction markets are required to be easy to use, smarter, valuable and more popular. • A company must be well established in order to motivate employees and other participants using incentives and other fun events. • Top managers are threatened by the hierarchical control of prediction markets.

12. Challenges of Prediction Markets • Lack of access to all relevant information:-due to lack of experts and business leaders knowledgeable in the area of interest. • Fast technology requires a great design with value proposition in mind because prediction markets are required to be easy to use, smarter, valuable and more popular. • A company must be well established in order to motivate employees and other participants using incentives and other fun events. • Top managers are threatened by the hierarchical control of prediction markets.

13. Theories • Why PM is closer to final results as compared to traditional methods: • Decision theory, • Hayek and efficient market hypothesis • Crowd sourcing & collective intelligence • Technology used • IS/IT theories e.g. Technology Acceptance Model(TAM), Factors that describe the theory: Perceived ease-of use (PEOU) &Perceived usefulness • Innovation decision process theory: stages of diffusion process are (Knowledge, persuasion, decision, implementation, confirmation) • Individual innovativeness theory(Early Adopters/Risk takers)

14. Adoption of Prediction Markets • Factors necessary for Adoption of Prediction markets • Time-Research reveals that PM run over short run/long run is more Accurate than the traditional methods • Incentives-Play money(May yield efficient information Aggregation).Real money(may better motivation discovery of information) • Participants • Legal matters • Management Policy

15. Knowledge Gap • Based on the various theories, benefits, challenges and factors that influence adoption of prediction markets different scholars show that there is need for more research concerning prediction market • Currently, in Africa there are hardly materials that discuss how receptive people would be on prediction market as a forecasting tool thus the area needs to be fully exploited especially by Kenyan firms; since an opportunity is available for them to further cut down on cost. • This therefore, represents a research gap which this study seeks to address and also provides a basis for future studies of further exploration on prediction markets.

16. Summary and Knowledge Gap(cont’d) • This will be the basis of change to large Kenyan firms that have been dependent on surveys and polls that have proved to be very costly to implement

17. Conceptual Framework

18. Research Methodology • Research Design (Cross-sectional survey design)-provided data,knowledge&beliefs on the entire population • Study population: Banking Industry • Population Size – 50 • Data collection method-Questionnaires(Semi structured) • Target group-Managers/Executive(in 50 banks only 35 positive responded(70%) • Data Analysis Technique- • Prediction Market Awareness –Descriptive Analysis • Satisfaction of current forecasting tools-Descriptive Analysis • Readiness to Adopt Prediction Markets-Multiples Regression Analysis

19. Prediction Market awareness

20. satisfaction of current forecasting Tools

21. Readiness to Adopt Prediction Markets

22. DATA ANALYSIS & DISCUSSIONS • Having Administered questionnaires to 50 respondents only 35 responded i.e. response rate-70% • Prediction Market Awareness –Average • Satisfaction of current forecasting tools-Average(This can be explained by the growth, complexity and trends observed in ICT , business intelligence, and risk management strategies ) • Readiness to Adopt Prediction Markets(Used to create the PM model) • Management Support(very Significant) • Perception of accuracy(Not significant) • Legal matters(Not Significant) • Time spend(Not significant)

23. Why you need to get interested? • It cuts a cross all Industries not only the Banking Sector. • Materials on Prediction Markets in relation to Africa is rare.

24. Recommendation • Prediction Markets Awareness-Creation of More Awareness. • Satisfaction of current forecasting tools-Adopt Prediction Markets

25. Thank YouQuestions?