Take a brief adaptive risk preference survey to determine what level of volatility you are comfortable with in your portfolio. We will then analyze your current investments to see if you are allocated in the way you should be based on the results of that survey.
We will design a portfolio for you that matches up to the characteristics agreed upon during the risk discovery process. Our assessment software uses historical volatility and correlation figures to determine a statistically likely range of potential returns.
We will build and manage your portfolio based on a foundation of mathematical risk assessment. With this program, you will know why your portfolio is allocated the way that it is; can you say that about your current advisor?
An already bad situation in Venezuela is spiraling out of control. Humanitarian aid is being blocked at the border by the regime of socialist dictator Nicolas Maduro and the dire economic situation in the country is leading to calls in some circles for US military intervention. This month we examine the economic side of this story. Hyperinflation is perhaps the most severe economic sickness that a country can contract. It makes day to day life […]
Amid the longest government shutdown in U.S. history, this month we will examine the historical context and some of the potential economic impacts it may have for the rest of the year. Unless you have somehow managed to avoid any news or social media for the last month, you are likely aware that approximately 25% of the federal government is shut down. You are still receiving your mail, the police are responding to calls, and […]
The 2018 midterm elections are two weeks away. This month we look at some of the market and financial implications of the results and some historical context on stock market performance in mid-term years. People like to forecast. Attempting to make sense of the world and predict how interacting factors will ultimately play out is in our human nature. Sometimes these trends and forecasts are accurate, other times; not so much (looking at you 2016). […]