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BUSINESS FORECASTING AND PREDICTION MARKETS POTENTIAL ON BANKING INDUSTRY IN KENYA
KIMANI FLORENCE WANJIRUDR. JAMES M. NJIHIA
SWAZZ2000@GMAIL.COMDEPARTMENT OF MANAGEMENT SCIENCE
SCHOOL OF BUSINESS UNIVERSITY OF NAIROBI
OCTOBER 16TH 2014
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?
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.
Research Questions
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!
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 ?
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)
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.
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
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.
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.
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.
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.
TheoriesWhy PM is closer to final results as compared to traditional methods:Decision theory,Hayek and efficient market hypothesisCrowd sourcing & collective intelligence
Technology usedIS/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)
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
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.
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
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
Prediction Market awareness
Statistics on Prediction Market Awareness Prediction
markets are the same as
financial markets
I Know what a prediction market is
I have heard of
prediction markets
Prediction Market Awareness
(Average)
Valid 35 35 35 Missing 0 0 0 Mean 2.4286 3.2000 3.3714
3
satisfaction of current forecasting Tools
Statistics on satisfaction of current
forecasting Tools
Accurac
y CostTime
SpendAvailable Expertise
Average Mean
Valid35 35 35 35
Missing0 0 0 0
Mean3.5429 3.0286 3.1429 3.5714
3.32145
Readiness to Adopt Prediction Markets
Coefficients
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
95% Confidence Interval for
B
BStd. Error
Beta
Lower Bound
Upper
Bound
(Constant) 2.091 .635 3.29
3 .003 .794 3.388
Management Support
.359 .097 .569 3.690 .001 .161 .558
Time spend -.094 .647 -.102 -.14
6 .885 -1.416 1.227
Accuracy .168 .651 .182 .258 .798 -1.161 1.49
7Legal matter -.152 .125 -.180
-1.22
0.232 -.407 .103
a. Dependent Variable: Readiness to Adopt prediction Markets
It cuts a cross all Industries not only the Banking Sector.
Materials on Prediction Markets in relation to Africa is
rare.
Why you need to get interested?
Recommendation
Prediction Markets Awareness-Creation of More Awareness.
Satisfaction of current forecasting tools-Adopt Prediction Markets
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