Financial Data Forecaster

The tool you used to predict future stock market prices.

Supervisor: Dr Yip, Chi Lap
Student: Lo Chun Wing, Matsumoto Keisei

Introduction

The introduction gives an overview of the whole project including why this project is held and what this project is about. Predicting market trend is not a new stuff in this world yet this issue is kept being discussed by various parties. Being able to predict accurately the future financial outcome is equivalent to earning big money. This project aims at analyzing this problem in an academic way which provides a different way of prediction on the market trend.

 

Project Overview

Stock market prediction has always been regarded as a challenging task in the business field. There are financial models trying to describe the trends of stock market price also data-mining methods trying to find out non-random movements of stock price based on historical stock data. In contrast, some proposed that the stock market trend is non-predictable because the trend of stock is governed by random walk.
In this project, it is believed that market trend is a financial time-series prediction problem which historical data is able to give some hints on predicting future price of stock market. There are studies trying to solve this problem by means of artificial neural networks (ANNs), support vector machine (SVM) or other data-mining methods which attain a certain extend of success. However, there are also limitations in those studies like over-fitting problem.
This project aims at finding out an algorithm which can most fit Hong Kong’s stock market prices using machine learning and financial models. We hope that by hybridizing different algorithm constructed by past studies, this project can eliminate limitations from each method. Different sets of real stock data will be training sets and experiments will be carried out in order to verify the accuracy of the algorithm.

 

Project Deliverables

This project will create a financial data forecaster program based on the algorithm to be developed by this project. There will be backend database storing all historical stock prices available for supporting the forecaster and data will be treated as training sets for the program.
To predict the stock prices in Hong Kong, this project aims at developing a program which serve several functions including predicting trends, predicting tomorrow’s closing price and formulating the best solution for buying stock according to the predicting result.

Objectives

 

Scope of Project

In this project, 15-20 stocks from Hang Seng Index (HSI) components will be selected for prediction. Prediction will base on historical market data only hence news or company background will not be included in the prediction.

Algorithm

The prediction will not only base on a single financial indicator or just a few indicators. In contrast, the algorithm to be developed aims at combining multi financial indicators and different market strategies by machine learning methods such as artificial neural networks (ANNs) and finding out the complex correlations between indicators.
In addition to correlations between indicators, the algorithm aims to predict the closing price of the next day and also the rough market trend of that particular stock. After the prediction, the best action to be taken (buy or sell) will be generated for reference of client.

Project Organization

 

This project can be mainly divided into four major parts including collection and organization of data, review of related materials, development of new algorithm, experiment and refine of the algorithm.
For collection and organization of data, all information related to Hang Seng Index (HSI) components will be collected and stored in a format suitable for database use. Data will be collected every 15 minutes so that a more detailed trend can be obtained. This process will be done by all teammates to ensure no missing of data.
Besides this routine job, review of related materials will be done in the early stage of the project. Materials will be mainly related to two aspects which are financial modeling and data-mining. All teammates should be familiar with both aspects in order to carry out the best outcome for this project yet a small division of focus will be necessary for more in depth understanding. Justin will focus more on data-mining methods and usage while Louis will focus more on financial indicators and financial modeling.
Combining all knowledge after review of materials, new algorithm will be developed together. With the slightly difference in review of materials, algorithm maybe slightly varies for each teammate yet the backbone will be mainly the same. In the development stage, the implementation part of ANN will be mainly done by Justin while each calculation of financial indicators and the database management will be mainly done by Louis. After developing the algorithm, each teammate will be responsible for implementing it and testing it.
For experiment, past cases will be used for testing also continuously assessment on future prediction will be evaluated. As there should be two slightly different programs at the time of experiment, so refinement will be done based on evaluation of both programs.