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Real-time ETF Enterprise Analytics
Real-time ETF Enterprise Analytics
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Written by APEXE3HQ
Updated over a week ago

ETF spread analysis is crucial for large institutions to monitor bid/ask prices in real-time. It offers several benefits, including: detecting trading opportunities, enhancing trade execution, monitoring liquidity, evaluating risk, and recognising trends.

APEX's tech stack enables rapid data processing and low latency response times for high volume market data. This capability allows for hosting real-time ETF prices for over 60 equities, including crucial and liquid information such as individual stock prices and Net Asset Values (NAV).

The net asset value (NAV) of an exchange-traded fund (ETF) is a measure of the value of the fund's underlying assets. Knowing the NAV of an ETF can be beneficial for several reasons:

1. Price Discovery: NAV is used to calculate the price of ETF shares, so by knowing the NAV, an investor can determine the fair market value of the ETF shares. This can help investors to make more informed investment decisions by allowing them to compare the ETF's price to its NAV.

2. Tracking Error: NAV can be used to measure the performance of an ETF compared to its underlying benchmark index. If the NAV of an ETF deviates significantly from its benchmark index, it may indicate that the ETF is not tracking its benchmark effectively.

3. Portfolio Management: NAV can also be used by portfolio managers to monitor the composition and value of the ETF's underlying assets. By knowing the NAV, portfolio managers can ensure that the ETF's holdings are consistent with the fund's investment objective and can make adjustments as necessary.

4. Taxation: NAV is also used in calculating capital gains and losses for ETFs, which are important for tax purposes. Knowing the NAV can help investors to determine the tax implications of their ETF investments.

When approached by a leading asset management company, APEX had capabilities to simulate and develop fully operational and real-time ETF spread analytics across around 60+ ETFs, with immense data ingestion. Furthermore, developing web and desktop applications of the model enabled further analysis and user-friendly utilisation of the model which APEX generated.

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