How analytics makes wealth management unbiased?

Investment managers of today are out chasing the alpha. They are expected now, more than ever, to justify their management fees by giving a superior return on investment. This is especially the case when there are so many free and cheap passive investment avenues out there for investors to invest in. Only a market-beating return will satisfy customers of today and investors are looking at analytics aided improvements to achieve these performance improvements especially through the debiasing of investments.

Through advanced analytics, investment managers are able to rely less on instinct and more on data, thereby reducing the chances of making sub-optimal trading decisions. The ‘debiasing’ effect that analytics is playing can help bring about a lot of positive change, in the highly competitive and sometimes opaque industry.

Analytics increasingly becomes popular as wealth managers are recognizing the risk of relying purely on instinct for their decision making. It has come to the fore that human beings are incapable of being purely rational. Behavioral economics has proved that biases and irrational considerations based on our lived experience affect all of our decisions including those such as investments. Hence it is essential for us to rely on ‘Artificial intelligence’ that is sophisticated and data-driven as a way to counteract bias.

How does bias affect decision making?

In a study conducted by McKinsey, it was observed that traders were overcommitted to the positions they held and tended to hold on to them even when contrary evidence was presented. This was due to endowment effects and confirmation bias. The ‘endowment effect’ referred to the cases in which owners of a certain asset held on to it, despite any change in conditions and ‘confirmation bias’ refers to how our stereotypes lead us to discount beliefs that go contrary to our own.

How can Analytics help?

Using a digitized wealth management platform with Robo advisory and actionable analytics, that uses pure data to drive conclusions, can help temper the bias in investment management. Machine learning algorithms that learn from swathes of historical data and investment patterns help decision-makers get better and more scientific. The consistent biases and the emotions that made investment managers hold on to bad investments and stay away from certain industries due to ‘lived experiences’ could be neutralized by the power of Analytics and Robo/AI based investing.

What gives investment managers using analytics an edge?

When investment managers are using a digitized wealth management platform, they benefit from the huge data sets and patterns that the AI has been able to analyze over time. Big data is compiled about the investment performances and patterns of millions of users over millions of trades. This factors into the recommendations that the AI-driven Robo advisor makes.

It also factors in a number of variables such as the investment horizon, risk tolerance, preferred investment types and more to help arrive at the perfect investment strategy for you. Biases such as overconfidence, loss aversion, endowment effect are all erased as the AI relies purely on performance data collected from thousands of variables.

Looking ahead

Analytics will definitely impact the decision making of wealth managers for the better. This will help wealth managers make more profitable decisions for their clients and increase the value of their portfolios. A study conducted by Mckinsey revealed that debiasing using analytics helped fund managers gain potential improvements between 100 and 300 basis points.

Wealth management firms that leverage this cutting-edge technology will consistently outperform others in the market. They will create a lot of value for their investors and will fare much better than traditional firms without the analytics edge. It has become evident that only the firms that are willing to change with the times and adopt technology will capture the major share of the market, especially the tech-savvy and millennial one.

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Portfolio analytics is not just for Wall Street Bankers

Things are moving fast in the financial world as newer technologies surface and disrupt the investment ecosystem every few years. Blockchain and cryptocurrencies, although highly volatile, are having a considerable impact on the market and don’t seem to waver any time soon. This means that the demand for effective portfolio analytics will only become more acute as we move forward.

Analyzing an investment portfolio helps Fund managers understand the existing asset groups and help them in making decisions that provide the best returns. It also helps managers in understanding the risks and the required steps to minimize them.

So, what exactly is portfolio analytics?

In simple words, it’s the analysis of risks and returns in an investment portfolio. However, there’s a lot of complicated work involved, work that takes about a week (if not weeks) for a man to complete, for each client.

With comprehensive analytics, a fund manager can find both real-time and historical data saving time to focus on other equally important tasks like asset allocation. It gives you an advantage over your competitors and helps you close more clients in less time.

This need for comprehensive analytics demanded the creation of a technological solution that will ease some burden off the shoulders of Fund Managers. Valuefy’s team of engineers, prompted by this need, came with an effective solution in the form of ValueAT.

With Valuefy’s ValueAT, you can create better portfolios with ease and agility. It provides you with comprehensive analytics with a focus on attribution, performance benchmarking, and risk management. It also empowers you with style analysis, portfolio slice and dice, and limit monitoring. With ValueAT by your side, you would not only analyze the portfolios effectively but do so in seconds instead of weeks.

With it’s Zero Manual intervention, there are no more data hassles for you.

How does ValueAT work?

The data from the portfolio and market is fed into the ValueAT database via the ETL process with a maker checker in place to perform data quality checks. The portfolio data like Transactions, Holdings, NAV, and AUM are sourced from custodian feed files and internal data warehouse.

The market data like prices, Index constituents, Yield Curve Matrix, and Instrument masters are sourced from exchange files and data feed files. ValueAT can also source the market data from the external provider warehouse or Thomson Reuters market data system.

Once the data is fed into ValueAT, the Data Preparation engine manages the model portfolio while also creating synthetic and composite indices. It also creates carved-out/in portfolio as well.

Once all this heavy-lifting is done by ValueAT, you can study the visual interpretation of the data and point out the opportunities and risks to your clients. That’s why portfolio analytics is not just for the hotshot bankers on Wall Street, it’s important for all asset & fund managers, including you.