Stock Market Intelligence Gamification Model

Project Abstract

In today’s world, anyone can enter the stock market with just a few clicks on a mobile application. Many people who do not have any background or knowledge of investment enter the market because their friends or family advised them. With the advent of technology, it has become relatively easy for scammers to spread false information and lure investors into Ponzi schemes where they might face financial risks.

We can understand the magnitude of the problem by looking at previous scams in the Thai market:

1) The LM Investment Management (LMIM) scam involved 12,000 investors with a total loss of 2 billion USD.

2) The UFUN scam conned 120,000 Asians losing around 620 million USD.

In this research project, we explore the use of gamification elements and Artificial Intelligence to make investors thoroughly aware of the concepts and common pitfalls in investing. We will adopt various Machine Learning techniques to segment investors and provide them personalized learning experience based on their knowledge of the stock market. The objective of the project is to help the Securities and Exchange Commission (SEC), Thailand, towards their goal of having confident and informed investors.

Phishing websites are developing as communication technology improves and are a cornerstone of internet criminal activities. 

The spread of malicious Uniform Resource Locators (URLs) is one of the main methods used to conduct phishing attacks. 

In addition to traditional defenses, such as blacklist filtering, educating users about characteristics of potentially malicious URLs has proved to be one of the most effective ways to prevent damage caused by phishing attacks. Inspired by the Generative Adversarial Network (GAN) method, this research aims to learn the patterns of phishing URLs and generate synthetic URLs, which could be applied for phishing attack education or training purposes. Our system includes two parts: a phishing URL generator and a detector. 

During the training process, the generator learns to produce seemingly benign URLs according to feedback provided by the detector. These URLs are actually fake and could be used by phishers in phishing attacks. The detector learns to distinguish benign URLs from real phishing ones or synthetic ones. By applying the framework of GAN, these two parts co-evolved, improving through mutual feedback.

Read Technical Report 2020_Prabh Simran Singh Baweja

Prabh Simran Singh Baweja
CMKL University
Akkarit Sangpetch
Assistant Professor
CMKL University
Orathai Sangpetch
Assistant Professor
CMKL University
Chaya Hiruncharoenvate Securities and Exchange Commission, Thailand