Portfolio managers have to deal with tax lots—records of stock transactions and their tax implications, including purchase date and numbers of shares, particularly for those actively trading in exchange-traded funds (ETFs). As management of ETFs continues to grow, tax rebalancing has become a more time- and resources-heavy process.
JP Morgan Asset Management (JP Morgan AM) snags the best data management initiative from the previous winner, Deutsche Bank, thanks to the tax lot harvester application JP Morgan AM developed to help portfolio managers be more efficient with this process. Previously, the tax team for JP Morgan’s portfolio managers spent up to seven hours per portfolio performing manual analytics on tax-lot data to devise optimal trades to execute during quarterly portfolio rebalancing.
Tax-lot harvesting is hugely data-intensive—each tax lot of a security needs to be analyzed before it is sold. For each tax lot, the firm needs to maintain several data points such as the date, cost basis, gain/loss, as well as in-kind indicator, among others. Also, there are factors that determine whether a tax lot should be relieved in kind or by using market trades. If not managed effectively, taxes can erode portfolio performance, and possibly create unnecessary tax liability for investors.
“It became clear that unless we did something, we would not be able to scale our ETF launches,” says David Lin, head of beta technology, asset management, and head of global research technology for asset and wealth management at JP Morgan AM. “The availability of tax-lot information in the desk tools allows JP Morgan to consider and plan for tax impacts as part of the portfolio managers’ daily process rather than rely on scheduled check-ins with the team.”
The tax lot harvester was designed to handle large volumes of data, in this case, billions of tax-lot records, and apply various tax strategies to harvest gains or losses efficiently. Using the tax-lot harvesting tool, portfolio managers are able to consistently approach tax-lot management and generate reports in seconds, with greater accuracy.
JP Morgan AM created the technology to systematically optimize various rebalancing scenarios. Some of the steps require interacting with market participants and, based on the liquidity of the ETF baskets, the underlying transaction cost can impact some of the parameters used in the firm’s optimization. “We have some ideas on how to solve this, and look forward to doing so in 2019,” Lin adds.
This year, the asset manager plans to incorporate transaction cost into the optimization process, as well as add functionality to perform more “what-if” type scenarios to help in non-rebalance-related opportunities.
Bryan Cross, who heads UBS Asset Management's QED group, joins to discuss alternative data and AI.Subscribe to Weekly Wrap emails