Prediction of Stock Market Using Artificial Intelligence Assignment

Assignment Task

Aim

This dissertation will thoroughly examine the potential for accurately predicting stock market trends using artificial intelligence systems which will be powered by an FPGA board.

As stated before, the aim of this project is to find a way to accurately predict the stock market by using an FPGA board. The use of an FPGA board will show how that Al can be run on limited hardware and provide a low-cost way for people to utilise Al within the stock market.
In order to do this multiple algorithms will be developed and tested on an FPGA board using the strategies described in the literature review. Once they have been developed, the results will be compared to real life prices.

Upon thoroughly looking into which programming language to use to develop these algorithms, the final decision is that Python will be used. This is because it is not only a commonly used language within the software industry, but also easy to learn especially with the fact that it has prebuilt libraries for many mathematical applications.

At the moment, there is no decision as to what FPGA board will be used to run the Al however this will be decided after further research.

Once the algorithms have been developed and tested, the results will be compared with real life stock prices in order to see how accurate the algorithm is as well as how stressful the algorithms are on the hardware.

The stock market is an intense and complex financial environment which is affected by various components: ranging from human behaviour to government policies, making it harder to predict stock prices. Although there is already a large amount of artificial intelligence present within the stock market, the licenses towards these software's tend to be expensive and stressful on computer hardware. This means it is harder for people with a lower start up capital to find their way into the stock market

The project aims to develop an accurate artificial intelligence module based off extensive research, development and comparisons. As a result, investors would be better equipped to navigate the complex world of finance and make decisions with even more confidence. By dissecting several artificial intelligence methods and their stock market prediction applications, the study seeks to offer important new insights into the fields of finance and artificial intelligence. This will provide a deeper understanding of how cutting-edge technologies and traditional financial techniques interact.

The Plan

• Conduct thorough research into software development techniques and hardware used for Al
• Examine the most common methods investment bankers utilise whilst making trades.
• Find the bare minimum hardware requirements for an artificial intelligence system of this magnitude for the software to run smoothly.
• Develop the program then test it on an FPGA board and compare the accuracy to any other artificial intelligence available on the market.

Although Al is very useful within the stock market for prediction, this does not mean that there are no issues concerning the use of it.

Ethical issues

Al models don't tend to be the most transparent things making it hard for the user to understand what the models are doing. To combat this, developers must ensure that the models do provide a clear understanding of any output that the model provides.

There is always a possibility for Al to be bias towards ethnic groups due to the large amounts of data available on ethnic groups that the Al can be fed. To mitigate this, it is best not to use stock market data that links with ethnic groups, regularly audit any data that the Al to remove any biases.

Professional issues

A major issue is that finance professionals require training to use the algorithms however there are simple ways around this. The main one is to develop algorithms that are easy to use as well providing the sufficient training and knowledge on how to use these algorithms.

Security Risks

Financial data is very sensitive therefore it is imperative that any financial data is thoroughly encrypted as hackers are always looking for this data.

Not only should the data be thoroughly encrypted, but companies must also utilise cyber security protocols to prevent hackers from being able to gain access to any data.

Environmental issues

As stated previously Al requires powerful hardware to run effectively. Due to this they use more energy, which also increases the carbon footprint.

These issues can be resolved by developing Al that does not require powerful resources and find ways to make the Al run as efficiently as possible.

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