You benefit from an investing process that combines decades of investing experience at major financial institutions with rigorous academic research.
Our investing strategy is transparent. You can see your portfolio any time by logging into your Charles Schwab account. You can also read our ongoing market updates and analysis.
Many investors lack access to the kinds of resources and strategies used by sophisticated financial institutions. We set out to change that. At QuantStreet, you’ll have access to sophisticated investment tools available to our clients.
We design portfolios with the highest expected return at each client’s level of risk. All our strategies are dynamic, and respond to changing market conditions. We don’t use short selling, speculative options trading, and other high risk strategies, unless you ask us to.
We believe that ethical behavior is the key to long-term success. We put client interests first in all we do. QuantStreet is also completely independent: We aren’t affiliated with any larger financial institution, insurance company, or other organization that incentivizes us to prioritize their products, so we never have a special interest in buying or recommending particular investments.
We share our investing strategies and research insights with clients. We aspire to be an open book rather than a black box.
Leveling the Playing Field
We give you access to the types of resources and strategies used by sophisticated financial institutions.
We charge a management fee that is lower than that of many investment advisers. We never charge any hidden costs.
We respect our clients, employees, vendors, and partners. Our goal is for every person who engages with us to feel heard and valued.
Our research-driven investment process applies machine learning and the latest analytical tools to to generate your portfolio. The world is always changing, and we continually update our models to keep pace.
Through years of industry experience, we’ve seen how real-world markets can behave outside the scope of any model. This is why we use human judgment to review suggestions from the model.