August Pham, entrepreneur, investor and CEO of Win FX Technologies explores how next generation technologies can be leveraged by retail traders to level the playing field.
There was a time when the foreign exchange market was restricted to large banks, wealthy institutions and extraordinarily wealthy individuals. Not even 30 years ago, individual or retail investors faced exorbitant transaction fees since banks regarded their trading volumes as too insignificant to be of interest to them.
Retail trading has come a long way since then. The emergence of online trading platforms and tools such as MT4 has historically empowered retail investors to participate in the financial markets and levelled the playing field between them and institutional investors. However, the advent of big data and artificial intelligence (AI) is arguably changing the landscape once again, widening the gap between them. Individual investors are already at a disadvantage compared to big firms equipped with AI, top-tier research and legions of traders tuned to the markets, now intensified by firms who can harness advanced big data tools to further widen their competitive edge. Yet it is becoming increasingly evident that investing is crucial for long-term financial mobility, especially in era of job-threatening automation.
This will not always be the case of course, primarily because such technology and innovation will become increasingly commonplace and accessible as it develops over time. Although there are various trends that characterise today’s technology market, big data is perhaps the most prominent. There’s no denying that big data is already a seminal technology in the world of financial trading; with an estimated 2.5 quintillion bytes of data created each day and the big data analytics market expected to grow to $103 billion by 2023, especially when you consider its relationship with AI and machine learning.
What is Big Data?
Simply, big data is a field that explores ways to analyse and systematically extract information from data sets that are too large or complex to be processed by traditional software applications. These data sets can be used for a wide range of applications from business intelligence to financial trading. When it comes to existing financial models and analysis, it’s fair to say that these rely heavily on volume and accuracy of data collated. So, the more data that is produced, the more accurate the models become, allowing investors to make more informed (and ultimately profitable) decisions.
Due to the increase in data mining becoming available for the public – the process used to discover patterns and turn raw data into useful information, investors can gain information and insights in the marketplace that were traditionally exclusive to institutional investors. Retail investors are able to gain the same facts and financial forecasts as institutional investors, individuals may perform more of the work themselves rather than employing the firms’ services, posing a potential threat to the ways many investment firms make money.
The Power of AI and Machine Learning
The growing interdependence of big data, artificial intelligence (AI) and machine learning (ML) has created increasingly powerful tools for investors to utilise. Machine learning essentially enables computers and software to learn from historical trends and past mistakes, before making informed decisions based on brand new information received. This is the very essence of AI which when paired with incredibly high and structured data analysis, is capable of underpinning an optimal trading or investment strategy that can effectively remove human error, emotion and bias out of the equation. As a result, the symbiotic relationship between big data and machine learning has already started to revolutionise forex trading in particular, while improving the efficacy of algorithmic and high-frequency trading across the marketplace.
Some speculate that AI will take the place of investment analysts. Since analyst spend hours gathering and analysing data, and AI can execute these functions in minutes, a lot of time and money is saved by using machines. Humans also take time to adapt and learn from their mistakes, while AI can correct their errors in minutes. In addition, humans can be sluggish to respond to evolving market conditions, while machines adapt quickly and without human input. This is why some automated trading strategies are gaining popularity today.
Are AI strategies winning?
When the AI Powered Equity ETF (AIEQ) launched, it signified one of the biggest milestones for artificial intelligence in fintech. AIEQ is the brainchild of the investment company Equbot, it uses IBM Watson’s big data processing and machine learning capabilities to process millions of data points every day, helping it construct profitable stock portfolios rivalling those by larger, institutional investors. The first exchange-traded fund driven by artificial intelligence has gained about 13% year-to-date versus 9% gain in the S&P 500.
The quantitative model behind the fund selects 30 to 70 stocks by assessing more than 6,000 U.S. publicly traded companies each day. It processes millions of regulatory filings, market data, news stories, management profiles, sentiment gauges and financial models.
Institutional investors have been using machine learning and other AI elements for decades to make stock picks. Top hedge funds such as Bridgewater Associates and Renaissance Technologies have even become fully dependent on AI, relying on computer-generated analytics for day-to-day market and trading decisions. JP Morgan calculated that quant funds have 60% of all trades in the US stock market and only 10% is done by traditional humans. The major players on Wall Street have had the advantage of using automated trading strategies based on secretive algorithms and complex infrastructure that were once inaccessible.
Recently, however, retail investors have been looking into AI and big data analytics as signs of an ongoing disruption becomes evident. Many of these upcoming AI-powered platforms have the potential to transform the game, largely because they empower novice investors with the knowledge of strategy. Many of these platforms have a way of tracing back their decisions to some rational premise, which makes them understandable to humans – unlike the bigger, complex systems by institutional investors. Some of these retail investment manager and individual investors even go as far as integrating top-tier machine learning systems that give larger investors a run for their money.
Application of institutional grade toolsets for retail traders
While humans remain a large part of the trading equation, AI plays an increasingly significant role. Retails investors can save time and money by using AI to invest rather than paying personal advisors to research, select and manage their portfolios. In addition, the funds selected by the machine do not have the management costs normally charged by investment firms. Also, because AI can place trades in less than a second, computers can exploit small changes in stock prices or indexes to bring in more profit. Machines are also not subjected to greed or fear, meaning they are unlikely to sell when the market is down or buy when the market is up.
Here are examples of some technology and tools today that can help retail investors:
- Retail investors can use AI platforms for trading that uses speech recognition and natural language processing technology to save traders time searching through conversations, financial data and notes. With the platform, traders are using AI to sift through, and access, notes, market insights and trending companies in real-time.
- AI that develops quantitative trading and investment strategies. By combining evolutionary intelligence technologies with deep learning algorithms, the distributed AI system continuously processes and learns from vast amounts of data in order to develop new investment strategies.
- Platform that makes investing easy and simple. With algorithms that match your personality so you can invest and align the strategies with your values.
- You can build, test and run automated trading strategies in the could via an easy-to-use mobile app. Build upon the unique framework, create fully customised strategies or choose from proven trading models.
- Institutional-level Order Flow Data for retail traders. Enabling traders to identify and follow smart money transactions.
- Bloomberg Terminal alternative investment research tool. There are mobile apps and platforms that give you access to financial modelling, portfolio tracking, news analysis and benchmarking tools. Modernised investment research tools through an intuitive platform that’s easily accessible across all devices, while democratizing access to institutional-quality investing tools that were once only available to Wall Street professionals.
The pandemic has expedited innovation and experimentation of various trading tools for the financial market, most notable is the introduction and usage of augmented reality headset that allows traders to interact with data in a whole new way.
Investment banks have been struggling with how to get their workers back to the office safely at a time when coronavirus infection rates are soaring in some parts of the world. UBS and Citi have been experimenting with Microsoft’s HoloLens holographic headset that allow their staff to recreate the experience of a busy trading floor. Traders are able to build a virtual workstation and view data as 3D images without leaving their homes.
Traders wearing the HoloLens are presented a 3D virtual space with three-tiered system of dynamically updated and interactive information such as trade ticket, charting and order blotter. High-level market conditions are represented in holographic spheres hovering at the top of the workstation like cloudscape.
In the middle tier traders can use voice and hand gestures to drill further into market segments and filter financial instruments to trade. The lowest tier comprises the ‘Holographic Stage’ where the trader can view historical and real-time performance before executing a trade as well as remotely collaborating with others at room-scale. Many see great potential for this technology to enhance and humanise the next generation working environment for financial trading. As virtual reality (VR) and augmented reality (AR) technologies endeavour to find their place in the consumer space, we may one day see individual investors utilising these gadgets to fully immerse themselves in the financial markets with greater efficiency in the comfort of their own homes.
Big data and AI appear to be here to stay. Through continued use and improvement, the two are likely to continue improving how retail investors grow their money and shape the future of the financial sector. At the end of the day, AI and big data will undoubtedly disrupt traditional investment strategies in the retail niche. For retail investors and portfolio managers, it means better decisions and leaner business processes.