AI in Finance: Stock Market Investing with Machine Learning Algorithms
It is well known that machine learning has been revolutionizing a wide range of industries on an incredibly large scale. This has left many wondering about AI in finance and if the stock market can be another industry in which artificial intelligence can take over.
Many methods of machine learning have been tested to see if they can be valuable for investing. Read on to learn about how to apply AI to investing yourself:
How is AI being applied to the stock market?
Sentiment Analysis
In the stock market, investors are now using sentiment analysis to interpret the opinions of certain companies, which can contribute greatly to the buyers discretion on how to trade, considering companies rely on investor confidence in order for their stock to succeed. If one learns how to extrapolate keywords from a large data set such as Twitter, then it can be much less challenging to predict from the general public how a company will perform that day.
Supervised Learning
Another way AI in finance can be utilized is with deep learning algorithms through supervised learning, a training model that can predict a desired output. By processing stock market data beforehand and using prebuilt models in a deep learning system, the model can be trained to predict price trends.
Techniques include using a vector machine model (SVM) and a multi-layer perceptron (MLP). Typically, vector models have been more effective for predictions, but the two results recommend different trading methods.
Vectors are a part of the machine’s input that can be used to extract data and classify it accordingly. This can help predict pricing of stocks with multi-dimensional layering that can predict at a multidimensional data structure. However, this has not proven to be effective in predicting data sets, such as the S&P 500, and still needs more work in order to be used in the future.
Convolutional Neural Networks
Furthermore, a convolutional neural network model (CNN) can serve for a stock selection adviser, as it is known to be very effective in extracting quantitative data and putting it into long short-term memory, a neural network structure. This is a neural network that has feedback connections, giving it the ability to process entire sequences of data simultaneously. A CNN also serves as a classification algorithm that uses computer vision to analyze multiple channels of data at once, adding much needed bias to an algorithm. This bias will prove helpful when predicting the stock market due to its volatility.
How Heavily Should I Rely on AI for Investing?
Although these machines can be a useful tool in aiding investors on day-to-day decisions, artificial intelligence should be used as a tool instead of a sole source of advice. Vectors and sentiment analysis can be a good way to predict reactionary information of a population, but these methods cannot be used as the sole means of trading advisory.
Simply relying on an algorithm for investing will inevitably fail. This is because machines are used to predict technical variables of an asset, such as past pricing. Although more complex neural networks can add bias into this, the landscape in which the market changes may not be able to predict as fast. The market is a competitive environment that aims to trade on information based on the reaction of investors, therefore, it can vary greatly in a short period of time.
The Future of Investing with AI
In conclusion, there are many ways that artificial intelligence can revolutionize the methods of investing and will continue to as models improve over time. However, AI in finance will not replace the high demand for humans to make predictions, as the machine will not be able to confidently predict market trends with just empirical data.
