Introduction.
We are living in an era where we Humans are making Computers to learn by itself and identify patterns. Artificial Intelligence is a present-day concept that everyone is heard of and maybe everyone knows the idea about it. From Images, Videos to text data. AI is taking over in every field.
Gone are those days where we had to call a broker to place or sell order. Now, we are making use of a Computer that uses a computer program that follows a defined set of instructions (an algorithm) to place a trade.
The impact of human emotions on trading decisions is often the greatest shortcomings to outperformance. Algorithms and computers make decisions and execute trades faster than any human can, and are completely free from the influence of different emotions. The trade, when you see in theory which generally can generate profits at a speed and frequency that is impossible for a human trader to even think of.
Machine Learning
We have talked about Artificial Intelligence, now let’s see about Machine Learning, Machine Learning is the subset of Artificial Intelligence which works in finding the pattern in the data to form informed decisions, it takes into account intelligent computer programs to calculate, reason, learn from experience, adapt to new situations and solve complex problems. Artificial Intelligence is shaping the future of stock trading and it is going to continue making the trading industry profitable in the coming time.
The advent of machine learning, deep learning and reinforcement learning enabled us to come up with various algorithms that could solve complex tasks like image recognition completely automatically. This motivated researchers and financial institutions to try to come up with a machine/deep learning framework for trading.
AI has been widely used in the finance and technology sector. Predictive models are what we call as Machine Learning Models were the first AI application in finance, which brought its benefits to the AI application in finance. Now, the Fintech sector is heavily investing in developing AI application-driven Algorithmic Trading.
The main problem with the stock markets, that is being the most dynamic and tough predictable area. The trading algorithms which are programmed have to be changed and adapted according to the situation and time. Needless to say, it was really hard for humans to follow and adapt in a timely manner. That’s when Machine Learning became used to it when algorithms can be changed automatically and their performance can be checked automatically as well with learning from the pattern of the data.
Artificial Intelligence or even Machine learning is a powerful technology which helps to analyse numerous data points within seconds. In this way,it can identify those trading patterns rapidly which are rapidly changing according to the time and in historical and replicating conditions for smart trading.
Figure -1, This figure describes the explanation and correlation of Artificial Intelligence and Machine Learning and Deep Learning.
In model-based strategy building, we start with a model of market inefficiency, construct a mathematical representation (eg price, volume, returns) and test the validity in the long term. This model is usually a simplified representation of the true complex model and it’s long term significance and stability need to be verified. Common trend-following, mean reversion, arbitrage strategies fall in this category.
The other approach is analyzing the price patterns and trying to fit an algorithm to it in the Machine Learning approach. Predictive models correctly forecast the future trends are crucial for investment management and algorithmic trading. The use of technical indicators for financial forecasting is quite common among the traders. Input window length is a time frame parameter required to be set when calculating many technical indicators.
Since it is the era of fast-paced technology-oriented functioning, AI helps as it facilitates trading every millisecond. Also, AI leads to such fast-paced automated trading which needs no human intervention.
Artificial Intelligence and Machine Learning have the real potential to solve all the problems in large scales in the trading sector. These situations or problems that are in the trading domain are usually with regard to optimization, analysis, and forecasting. With this power, AI and ML have impacted trading in the following ways.
Why ML?
Compared to the Traditional Rule-Based Methodology used, Machine learning adds a lot of potential in solving problems on a large scale. It learns the pattern from the data and makes informed Decisions for the output. Artificial Intelligence and Machine Learning use neural networks and different algorithmic methods for identifying and analysing factors that lead to particular stock prices. These factors are particularly also known to be predictors or features. Based on these factors, the future prices and the movement of the prices is forecasted.
Predicting the movement of the Price from the Historical Prices.
Machine Learning algorithm works with the defined steps that imply feeding the historical price data to the algorithm for it to create a base for its decision on them in the future. Hence, for predicting the stock prices which are called target variables, Machine Learning algorithm uses historical data which is called predictor variables. For doing so, the algorithm in ML learns to apply predictor variables for forecasting the target variables.
What is the future now?
Deep learning for finance is the art of using neural network methods in various parts of the finance sector such as customer service, forecasting the price, algorithmic trading, and high-performance computing.
The financial industry which used to be dominated by the business professions and the finance professionals is now shifting its focus towards the technology sector with programming knowledge, cloud computing and deep learning.
Deep learning is taking over the traditional machine learning methods, one needs computational power to run the trading algorithms to come up with informed decisions.
Engineers play a very pivotal role in setting up and managing the computing power devices to meet new challenges. In the end, everyone needs to work together to make the decision driven growth.
Let’s see how the AI-Driven Financial Industry is taking over in the future.
