Back To Top

Trading Exchange Forecasting

Background

As early as the 1990s, AI researchers had speculated that AI will change the forex market. A groundbreaking article called Managing Foreign Exchange for Competitive Advantage published in the MIT Sloan Management Review underlined the importance of computerized models, how they’d become indispensable in the foreign exchange market and by an extension, trading exchanges. Traders nowadays, are increasingly relying on predictive analytics and big-data as sophisticated AI algorithms are capable of real-time data collection and eventually making on-point forecasts.

The algorithmic trading market was valued at $11.1 billion in 2019 and is projected to grow by over 10% annually, reaching $18 billion by 2024. This highlights that the forex and trading market has transformed over time, giving rise to predictive analytics models and machine learning that  have given a significant advantage while making forecasts that were unavailable earlier.

Traditionally, three methods were used to analyse and predict the stock market: financial, technical and sentiment. Financial analysis involves evaluating past statements, reports and balance sheets, and comparing it to prospects, the market and changes in government policy. Technical analysis relies on the idea that all factors which can influence the price are included in the current price of the stock and therefore no fundamental information analysis is required. This comes with the precondition that prices move in trends and have the same historic patterns. Sentiment analysis relied on taking advice from experts and going through newspapers to monitor the stocks prospective as well as existing investors would like to invest in.  Now, all of these methods can be handled easily as  AI-driven systems are able to effectively process millions of data points in real time.

Challenges/Problems

Although AI systems have proved worthy for scalping trading, their veracity is yet to be established over sustained periods of time. As the number of variables — or probability of events which can affect stocks — increases, it becomes more difficult to study, analyze and forecast pricing changes.

This becomes potent while considering the ‘chaos’ prevalent in the trading markets that influence stock fluctuations. These include ‘self-fulfilling’ prophecies, unquantifiable factors that constitute human emotions and sentiment and are almost impossible to predict. Chaos can be prediction-influenced and unaffected by predictions. Political turmoil and events, public protests, social unrest, state of the stock markets (or market sentiment) are the usual ‘chaos’ that influences pricing fluctuations and predictions from investors, traders and advisors. On the other hand, weather is a ‘chaos’ that usually doesn’t not impact the stock market. In this context, it is important for investors, traders and advisors to recognise these meticulously and understand them before making predictions as these are generally overlooked.

Since humans are prone to strong cognitive biases, an AI system seems the best-fit solution to predict market movements based on the easy availability of vast amounts of data and algorithm-based trading. But, even though data and huge amounts of it are now more readily available, analytics and models created off this data have been overwhelmed with irrelevant data. This can lead forecasts astray rather than helping different engagements become fruitful.

Mere historical data is inadequate to forecast outperforming investment strategies. In fact, investors who try to predict the market and rely on naive AI approaches end up incurring financial losses. This is primarily due to the lack of enough data to meaningfully train algorithms.

Solution

xpresso.ai is a Auto-ML AI Ops platform that excels in data collection to inference generation. The Auto-ML framework made available by xpresso.ai leverages the latest ML and Deep Learning tools while preparing models. The platform can potentially be fed with input data in the form of historical trading performance of securities in a fund, and data on risk factors to predict future performance under different economic test conditions.

Data Collection and Analysis

The data transformation journey while creating models involves setting up the required infrastructure and collecting raw, continuous data (unformatted, unparsed) from a huge number of sources. These can be latest announcements about an organization on various social media channels and traditional sources such as newspapers, their annual and quarterly revenue results, and other ‘chaotic’ elements that are likely to influence stock prices.

xpresso.ai’s Auto-ML AI Ops framework can be leveraged to collect and analyse this extensive  data repository and analyse details. Details can be collected, analysed and added as exploratory variables by using xpresso.ai libraries.

Data Preparation

xpresso.ai can read factors from a varied recommendation text connection and generate an output. This can be supplanted with additional data collected (with the aid of xpresso.ai libraries) while preparing a predictive model.

Model Processing/Evaluation

From all these variables obtained, models can be created and versioned — enabled by xpresso.ai Auto ML-AI Ops framework.

Inference Generation

The inference gathered from this analysis and training models can provide investment managers in financial institutions with better risk analysis and portfolio management skills. Individual investors and asset managers can assess the levels of risk or returns in a particular portfolio of investments by easily monitoring numerous risk-related factors each day (like interest rates or currencies rates) and test portfolio performance under different economic conditions.

By giving investment managers the capability to predict the performance of portfolios (much faster than if done manually) in real-time, it is possible to augment the capabilities of human investment managers.

 

Speak to AI Expert