Proper data evaluation and use of artificial intelligence in stock market are critical to asset management. Whether it’s the stock market, forex, or cryptocurrency, plenty of analytical work, AI capabilities, and in-depth research is required. Artificial intelligence stock trading software comes to the fore today.
Lewis Sanders, CEO of Alliance Capital Management, says that understanding human behavior is also crucial for investors.
Capital markets themselves are derivative of the biases and preferences people bring to decision-making. Everybody has the information. But are people reacting to it rationally?
Who and what influences the market at any given moment? How do people feel about a new product from a company in the Dow Jones Industrial Average? Artificial intelligence (AI) and deep learning can help us answer these questions and predict market movements to earn – or save – millions of dollars.
Trading firms, hedge funds, banks, and brokers analyze volumes of data to make investment decisions. Sifting through so-called alternative data that refers to data assets used to get insight into the investment process costs firms a lot of money and enormous effort. Alternative data itself has created a separate market where information is scraped, filtered, and sold to trading companies. This makes optimizing data analysis (and as a result, financial predictions) an essential task for the investment community and the use of AI in finance and AI for trading a real remedy for artificial intelligence predictions of market trends. Artificial intelligence stock trading software, obviously, will have a huge impact.
AI in stock trading and financial markets: key applications
Traditional software is helpless at making financial predictions since it has predefined rules while predicting financial markets requires constantly changing algorithms. Hedge funds have been using computer algorithms in trading for years, but these algorithms were developed based on static models that don’t account for market volatilities.
Machine learning for trading and deep learning have brought innovative solutions and approaches to the financial market for implementation of AI in stock trading, FinTech, and other fields. Neural networks trained by deep learning algorithms create their own rules, connections, and patterns while analyzing data, including the digital layer. What’s more, neural networks adapt as they receive more information and can, therefore, make better predictions in future based on insights from previously analyzed data. The volume of information processed with the help of deep learning and the level of detail at which it’s analyzed would be impossible for humans to manage but AI in stock market makes finance assets management a real case.
When making decisions, FinTech firms, funds, and brokers use both structured historical information about markets and a massive layer of unstructured data from various outside sources. Today, using AI trading software solutions in financial markets helps to analyze both time series and alternative data that makes possible even the use of AI with cryptocurrency. Some big names among hedge funds rely on AI stock trading software, including Renaissance Technologies, Man Group, Aidyia, Binatix, Sentient Technologies, and Bridgewater Associates. These hedge funds use AI and machine learning for stock trading to:
- analyze exposure gaps, asset classes, volatility, and trading costs
- find the fastest ways to execute trades and make bets
- look for investment opportunities
- identify market patterns
- evaluate trading strategies
- automate work processes
When making decisions, FinTech firms, funds, and brokers use both structured historical information about markets and a massive layer of unstructured data from various outside sources. Today, AI helps to analyze both time series and alternative data.
Combining AI, neural networks, and alternative data to predict financial markets
The most important thing that a neural network needs to work efficiently is information. We’re talking about hundreds of thousands or even millions of images, pages, cases, graphs, examples, and so on that are needed for proper training and accurate predictions.
The investment industry is a perfect match for deep learning since it can provide enough data for financial prediction software and can unite AI and FinTech into interrelated fields. Here are some cases when trading companies and funds use deep learning algorithms:
- Portfolio management. Neural networks can effectively deal with development and risk strategies, compiling portfolios and predicting long-term price movements.
- Social media analysis. Deep learning algorithms can find financial market influencers, monitor trends, track people’s reactions to events and products, provide demographic data, and more.
- News and event sentiment analysis. By browsing thousands of news events, press releases, reports, customer reviews, regulatory announcements, and economic and political headlines, neural networks can evaluate the polarity of events – positive, negative, or neutral – and provide market predictions. Using sentiment analysis, AI can serve as a hype detector to determine when markets are overreacting and forecast later corrections.
- Inspecting various sources. Thanks to image recognition and natural language processing, sophisticated AI-based financial prediction software can reliably analyze job posts, satellite and drone images, GPS tracking data, credit card history, information from mobile devices, and more to create a comprehensive picture of market trends.
How companies use alternative data and AI in FinTech market
If you still doubt the efficiency of financial prediction algorithms, here’s some solid evidence of their worth.
- Chipotle. Based on foot traffic, Foursquare correctly forecasted the Chipotle restaurant chain’s drop in earnings before Chipotle published its quarterly report.
- GoPro. The case with GoPro is even more interesting. While different analysts were giving bullish forecasts, Quandl, a platform for financial, economic, and alternative data, analyzed GoPro’s email receipts and predicted a drop in share prices compared to the previous quarter. In the end, Quandl turned out to be right.
- JCPenney. The JCPenney department store chain had a different situation. Some investors predicted the rise of its stock thanks to information provided by RS Metrics – satellite images of JCPenney parking lots across America, which showed an increasing number of visitors.
Using neural networks for financial predictions could make an even bigger difference. Neural networks can automatically collect and analyze textual and visual information such as satellite photos, emails, and foot traffic data to make precise predictions without human input and without paying third parties for alternative data.
Using AI models for time series predictions
Market time series is a broad field to which deep learning models and algorithms can be applied. Banks, brokers, funds, and FinTech firms are now experimenting with deploying them for analyzing and predicting indexes, exchange rates, futures, cryptocurrency prices, public equities, and more.
Artificial neural networks find predictable patterns by inspecting the structures and trends of markets and provide a second opinion to traders. These networks can also help in detecting anomalies such as unexpected spikes, drops, trend changes, and level shifts.
Many AI models can be used for financial predictions:
- Hidden Markov
- Gaussian Processes
- Logistic Regression
- Naive Bayes
- Support Vector Machine
- Linear Regression Models
Although AI in stock trading is useful for analyzing past market behaviors and understanding the critical features of such behaviors, however, there are still a lot of challenges for AI in financial time series forecasting.
Challenges of developing AI-based prediction solutions
The role of AI in FinTech as well as of machine learning applications in finance is huge, still training a neural network is challenging and exhausting since there are a lot of parameters involved. Engineers need to know precisely which algorithms and optimization methods work best for financial predictions in general and for time series or alternative data prediction in particular.
Selecting the proper input parameters, deploying the network, adjusting it to ever-changing conditions, using several networks at once, combining networks with the general classical trading approach – these are all tasks for deep learning professionals.
Sometimes, markets move in mysterious ways under the influence of the economy, politics, news events, and human judgment. The ground is always shifting, and any tool that offers an edge on competitors is priceless. Big players in the FinTech market are investing significant funds in AI technologies, which have become the next-generation tools for detecting subtle patterns that are invisible to other technical analysis methods.
The extensive use of AI for financial market predictions will require asset managers to catch up; otherwise, they won’t be able to compete with the united power of human and artificial intelligence.
Contact AI experts at Intellias if you have any questions about or troubles with financial prediction software.