Transcript AN AGENT BASED STOCK PRICE PREDICTION MODEL FOR …

Agent application in the stock
market
Mr. Perminous KAHOME, University of Nairobi, Nairobi, Kenya.
Dr. Elisha T.O. OPIYO, SCI, University of Nairobi, Nairobi, Kenya.
Prof. William OKELLO-ODONGO, SCI, University of Nairobi,
Nairobi, Kenya.
Introduction
 The stock market is a key market in any economy and financial
forecast such as stock price prediction is a field receiving much
attention both for research studies and commercial applications.
 This Research involves an agent-based model for predicting the
price movement of stocks in the NSE .
 It utilizes agent methodology and is developed in JADE platform.
 The agents have the ability to scan the environment and
consolidate the gathered information whether positive or negative
thus enabling it to predict the trend of the stock prices and
provide an output advice informing the traders whether to buy,
sell or hold on to a stock.
Problem Statement
 Financial analysts, stock brokers, individual and fund managers
who trade in the NSE lack accurate models to enable them predict
future stock price. According To CMA they rely on fundamental
analysis (position of company in market) and technical analysis
only (charts)
 Most traders and investors cannot afford expensive algorithms and
software's for prediction.
OBJECTIVES:
 Assess the impact of using agent in stock price prediction
 Assess the agent based stock price prediction system
 Use data generated from the system to evaluate stock price
prediction.
 Provide support for the traders at the NSE on whether stock price
is going up, volatile or down thus enable them decide to BUY,
SELL or HOLD
Significance of the study
 This proposed model will enable traders at the NSE without
much complexity have a signal guide of whether the stock
price will rise, fall, Volatile and thus if it’s a good to buy, sell
or hold on to the stock.
 The traders will thus be able to improve profitability as they
are able to optimize decisions of Buy, Sell or Hold.
Related work
 Use of charts and candle sticks to visualize and forecast.
These are not reliable.
 Prediction was done using time series forecasting. This
proved inefficient and failed massively e.g. weather forecast
 Artificial neural networks has also been used extensively in
recent past. This has a partial success with some challenges
including training the network, over fitting, black box
property.
 Currently use of multi agents has proved more accurate and
able to predict complex interactions much more realistically.
Why Agents?
 With agents you use a computer to simulate decisions of
heterogeneous individual agents
 Can ground with micro-data. Potentially allow rich
calibration and validation.
 Can model complexity of a real economy. Can include stock
market, real estate, capital market, liquidity.
 Is ground with behavioral knowledge. +ve effect vs –ve
effects
Conceptual model
methodology
 The agent methodology to be utilized is PASSI methodology.
Due to its rich development lifecycle that spans initial
requirement through deployment.
TESTING
 Single agent test was conducted to indicate agent are
working as expected and below is their communication
GUI
 The stock Trend Board displaying stock predictions
Results Discussion
 10 Experts in Trading were given the system to evaluate and
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give a feedback on the system in the following criteria's.
System Usability –Good
System Functionality – excellent
System Performance.- Above Average
From the tests conducted by random sample of Expert users,
the system can be incorporated into an everyday tool for
stock brokers and investors to use to predict the movement
of stocks rather than manually reading through all possible
news articles and trying to identify the trend on their own.
CONCLUSION
 Multi agents have been used in the stock and financial services
sector which is a key area in any economy. Most of Traders and
stock Brokers have to read through news articles about the various
stocks that are trading in order to identify factors that positively or
negatively affect the stock price then they can choose a trading
strategy in order to place orders or publish shares to sell.
 With the use of the stock price prediction, its evident that the
model is a useful tool for stockbrokers and agents and even novice
traders who would then need not rely on the traditional methods
to be able to identify the trend of prices at the NSE, but also have
instant quick update of current trend and with pointer to what
caused the trend which then makes it much easier to follow up
using their individual trading strategies.
Thank You