# Articles by Franklin Parker

### Five Factors Across the Business Cycle

October 13, 2020 |

Probably the most popular models in modern investment management are factor models. Growing out of the Capital Asset Pricing Model (CAPM), factor models were first theorized in Arbitrage Portfolio Theory and the concept was expanded and applied to risk premiums by Nobel-laureate Eugene Fama and Kenneth French (French, surprisingly, did ...

### Recession Forecasting with a Neural Net in R

September 4, 2020 |

I spend quite a bit of time at work trying to understand where we are in the business cycle. That analysis informs our capital market expectations, and, by extension, our asset allocation and portfolio risk controls. For years now I have used a trusty old linear regression model, held together ...

### How to Optimize a Goal-Based Portfolio

August 21, 2020 |

Traditional portfolio optimization (often called modern portfolio theory, or mean-variance optimization) balances expected portfolio return with expected portfolio variance. You input how opposed you are to portfolio variance (your risk tolerance), then you build a portfolio that gives you the best return given your risk tolerance. Goals-based investing, by contrast, ...

### How Much Can You Lose Before You’ve Lost Too Much?

August 17, 2020 |

I started my career in finance in 2007. For about a year I thought “this is great!” Then 2008 hit and I thought “this is terrible!” After 2008, I had one fundamental question that I wanted answered: how much can you lose in an investment portfolio before you’ve lost too much? After ...

### Labor Force Growth by Decade – R Code

August 16, 2020 |

I just posted an interesting look at the growth of the labor force by decade. Given that I used R to produce it, I thought it interesting to share the R code and method. Just for reference, here is the chart: First, we will need the following libraries The quantmod ...

### Optimizing a portfolio in R – Monte Carlo method

August 15, 2020 |

I regularly use monte carlo engines to answer questions. First, they are really flexible in their ability to model non-normal distributions and assumptions. Second, you can incorporate any constraints you want which may be outside the scope of a non-linear optimization function. At any rate, this is how to use ...