New AI based model for prediction of financial markets’ behaviour will be developed at ECON MUNI. It is expected to be more precise than current econometric and machine learning models. Shafqat Iqbal, who recently received funding for his research from Programme Johannes Amos Comenius, hopes to create a more reliable way to predict risks at financial markets.
I chose this faculty because of its research excellence and supportive research environment. Also, I seek mentorship from experienced professors in my field of research. Moreover, the faculty already has experience with hosting international fellows.
I focused on developing new hybrid prediction models based on statistics, machine learning and artificial intelligence to study and predict behaviour of markets. Simply put, imagine a hybrid prediction model as a group of scientists. Each scientist alone has their own strengths and weaknesses and can do the research by themselves. However, when they team up with other experts, they as a group can provide more accurate and reliable results. Likewise, if different types of models are combined, they become stronger and their predictions more accurate and reliable.
My aim is to explore new, more precise ways to predict behaviour of financial markets. To predict the market’s behaviour accurately, I will combine the latest forecasting models and artificial intelligence. Since investors make decisions on the market to gain profit, it is not surprising that assets valued billions of dollars are traded on stock exchanges every day. If the investors can predict the market’s behaviour in a correct way, they may avoid the risks related to their investments and be one step ahead of the market each time. This could significantly increase their profit. Therefore, the model I intend to develop will help investors to better manage their wealth, retirement plans and future investments in general.
Current econometric and machine learning models are powerful tools, but they have certain limitations. For example, the existing models often assume that financial markets behave in a linear fashion. However, it is not the case because the markets tend to exhibit non-linear behaviour and their properties tend to change. That may lead to inadequate representation of markets dynamics, inaccuracy, and unreliable results. AI based approach should resolve this.
The future of artificial intelligence in financial risk modelling is promising. It allows us to apply the so-called fuzzy logic to large volume of data to improve forecasting accuracy. Fuzzy logic refers to things that are ambiguous or unclear and it uses linguistic variables which are words or sentences instead of numbers. When we encounter circumstances in the real world in which we cannot determine whether a condition is true or false, fuzzy logic offers extremely helpful reasoning flexibility. Let’s say that we are trying to determine temperature of something. Rather than simply saying it is true or false, assigning the answer variables of either 1 or 0, we can employ linguistic variables. For example, we can say that the thing is very hot, slightly hot, warm, slightly warm, cold, or very cold. In other words, the answer does not take the value of 1 or 0 but can be located anywhere between 1 and 0. That is what helps us to produce a more accurate answer or a prediction.
The biggest challenge I expect to face in terms of research is the dynamic behaviour of financial markets. However, I believe that challenges related to research can be addressed in collaborations with the expert in AI and finance at Masaryk University. Otherwise, when moving to a new country like the Czech Republic, it can be challenging to adopt the culture including different customs, traditions, and social norms. Additionally, I may encounter the language barrier and I will have to learn to navigate the healthcare system. Moreover, I am used to living in places with short winter, so in the Czech Republic I will have to face the cold temperatures and snow.
Shafqat Iqbal focuses on development of hybrid prediction models with implications to financial markets data. He completed his doctoral degree studies in statistics at Guangzhou University, China. He is especially interested in data science, fuzzy time series, machine learning, and financial markets forecasting. During his time at ECON MUNI, he will conduct his research in cooperation with Stefan Lyocsa from the Department of Finance.
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