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Robot that assists the research team: 4SIGN

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Meet our recommender engine - 4SIGN!

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It performs analysis and generates long-term, or micro-term valuation of a given financial asset.

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4SIGN relies heavily on ML while generating recommendations about the fundamental and behavioral values of the tickers.

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And of course, we do our best and aim this engine to work with no/minimal human intervention while performing for our clients.

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Keeping the core philosophy of our company in mind, we started to work on this module (among our product canvas) back in 2015. The MVP of the 4SIGN was completed in April 2016. It met with its first client in February 2018. 

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With 4SIGN, we as Verizekalı aim at catering for both the sell-side and the buy-side spectrum of the finance industry.

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​How do we do it

In a nutshell, 4SIGN:

• ​Uses 3rd party data feed integrations for the raw data 

• Cleans and normalizes the data

• Organizes the data

• Infers associations, relations and patterns from the data based on renown ML approaches

• Converts this whole flow of analytics into simplistic investment recommendations

• These simple recommendations might be in the form of simple trading signals such as "BUY/SELL", or detailed back-test results and recommendation reports just like the sell-side brokers produce and distribute to their clients.

• Depending on the design and the use case, 4SIGN feeds either a signalling module, or an OMS module, or an UI module, and conveys its findings either in the form of trading signals, or stock recommendations, or, personal finance advisory.​

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What challenges do we address

We are well aware of the challenges our client deal with while they do their best in the field. 

• Deterministic: 4SIGN is by its very computer nature highly deterministic,

• Configurable: 4SIGN is not human; it learns whatever our clients needs and it learns fast. So our clients can easily redefine its core responsibility area and reconfigure it while their strategic clients, markets and products evolve over time.

• Sustainable: 4SIGN generates recommendations via repeatable routines and frameworks. So its performance should be sustainable over time.

• No errors: It also help our clients eliminate fraud, misconduct, other humanly sins.

• Costs: It has cost advantages over human fund managers (depending on the revenue model).

• Leaner organizations: It might pave the way for leaner organizations (robots require leaner organizational structures).

• Leaner processes: It might pave the way for leaner processes (i.e. the whole of the investment process can be simpler with 4SIGN)

• Democratization: Robots mean lower costs, lower costs mean lower fees, lower fees mean ability for hedge fundsto attract larger investor segments

• Scalability: Humans find it difficult to transfer and transform the knowledge base into a new area. Machines don't. 4SIGN is scalable across tickers (i.e. it can learn about more tickers of a given Exchange). It is scalable across assets (i.e. it can transfer what it knows about one asset class easily to another asset class). And It is scalable across countries (i.e. it can make use of what it learned in one particular region in some other regions/countries).

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Use cases for 4SIGN

4SIGN can be positioned in numerous ways among our clients. Buy-side, Sell-side, the banks, the exchanges all may find good use cases to deploy our product within their processes.

 

To give some examples, 4SIGN might be considered:

• As the quantitative research analyst in the brokers and sell-side research providers

• As the quantitative fund manager in the fund and wealth managers

• As the day-trading assistant for the individuals,

• As the treasury officer for the banks and other corporations that centralize the risk management under Treasury departments,

• As the trade-facilitator (signal generator) in the new-gen brokers which facilitate and encourage higher volumes via push notifications through their UIs, alerts, or, pop-ups.