D I G I TA L F I N A N C E
Data: an investment manager’s best friend
THE FINTECH TIMES
Applying machine learning to domain knowledge can sharpen investment decision strategies
Genevieve Goh, CEO, Smart Solutions
T
he year 2020 is definitely one that no business playbook could have told you what and how to navigate against. Organisations that have made transitions to embrace the new economy have definitely flourished. This new world is not new; leveraging a digital economy through technology has aid many companies achieve better competitiveness and differentiation for many years. The world’s most valuable commodity is no longer just oil, but data. In our digital economy where interactions with consumers, institutions and businesses are generating a large footprint of complex data transaction, we could be harnessing the insights from data patterns to drive actionable outcomes. Flourishing from an era of data abundance with organisations embarking on company-wide digital transformation requires balancing an ecosystem of valuable data with domain insights on its value and how to interpret it for creation of knowledge. The financial industry has been forefront in digitalisation of its business model, process and approach. One area of focus innovation is in leveraging artificial
intelligence in algorithmic trading strategies that harness all the data that they collect for knowledge-based predictions. Machine learning-backed Quant platforms that aid data driven assistance to models created and prediction tested are widely deployed to harness these data patterns. Modelling financial data is not new and has been done for years to build an abstract representation of a real-world financial decision-making situation. It has however been lacklustre in accurate predictions especially in black swan events in the financial world. Why so? A balanced ecosystem of the right type of data, knowledge of a domain expert on training of the model and suitable application of real-world market drivers on the data is key. There is never existence of that one machine learning model that aces 100 per cent of its predictions made. However, machine learning helps us provide leverage on the abundance of data in existence. Technical trading theories can complement the prediction and a machine learning-based modelling that encompasses logic from data patterns in the theory could greatly assist. Machine learning algorithms can identify patterns which we cannot represent in a finite set of equations and we can associate patterns with features of the theoretical approach that led to that outcome. We simply let the data speak and
Organisation must embrace the need of data stewards and data scientist to first catalogue the value of data collected against the knowledge outcome that the company would like to leverage it for
track leading indicators of our theory and formulate a decision-making strategy that would harness the learning for a more robust prediction. Coupled with an expert view of the type of data used by the model in its conclusion; this new knowledge would be how we could probably flourish in the new digital economy. How do we ensure that data authenticity and integrity are harnessed in our world of fake news, noisy data and unstructured data? Organisation must embrace the need of data stewards and data scientists to first catalogue the value of data collected against the knowledge outcome that the company would like to leverage it for.
About Smarts At Smarts.sg, our team of data science and finance trained consultants have built a AI Quant platform that allows investment managers to incorporate their domain expertise and crunching of large scale of technical and fundamental datasets that are translated practically into relevant features and factors at play to allow insights into a theory of decisions. RoboInvestor was built with the unique proposition of harnessing patterns from crunching large scale of data that are chosen as relevant indicators by the users of the platform. The users can formulate trading strategy using technical indicators, macro indicators and fundamental indicators and select features based on thresholds and observations optimised on our platform. These strategies are back-tested using a suite of machine learning algorithms to incorporate them in making the forward price predictions of their portfolio
Digital transformation projects that simply embrace the introduction of software to its users, products or processes would see less success. This is so as the first step in leverage of these abundance of assets; data is in placing relevancy metrics against importance, impact and purpose in the entire lifecycle of data being created. Companies that leverage feature engineering, a process of using domain knowledge to extract features from raw data via data mining techniques have flourished. Coupled with the right data security and regulation, transitioning to a knowledge-backed digital economy provides any financial institution that critical ‘secret sauce’ for alpha growth. watchlist. Effective back-testing of the machine learning empowered models allows transparency in expected returns and metrics to be viewed by the user and backing up a non-bias recommendation to augment their investment decisions. In our application of robo advisory, we envision to provide a simple and convenient method of applying distinct machine learning solutions on both widely available and unique data sets of clients on a no-code required platform geared towards identifying alpha opportunities and leverage of the new oil – data to fuel investment strategies. Website: www.smarts.sg LinkedIn: www.linkedin.com/company/ smartsolutions-sg
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