Data-Driven Portfolio Management

Allocate assets in your portfolio based on available data and track record

Benjamin Obi Tayo Ph.D.
May 12 · 5 min read
Image by Benjamin O. Tayo

Disclaimer: This article is meant to provide some basic analysis of portfolio management, and in no way should be considered as investment advice.

The purpose of this tutorial is to illustrate how to manage assets in a portfolio in a data-driven way. The portfolio of interest could be a personal investment account (standard brokerage account or non-retirement account) or a retirement account such as 401k, IRA, etc.

Financial Services Corporations such as Fidelity, TD Ameritrade, Vanguard, and TIAA offer wealth management services that would allow an individual to setup a personal investment account. Also, most employers provide retirement benefits through retirement accounts such as 401k.

Whether it is a personal investment account or a retirement account, Financial Services Companies often provide a wide variety of funds to choose from. Having some knowledge about these funds would enable you to leverage your asset allocation so as to increase return at a bearable level of risk.

Suppose you want to invest in a mutual fund either in an individual investment account or to add a new fund in your 401k portfolio. There are 2 main types of mutual funds, namely active and passive funds.

Passive funds are index funds that track a particular index of stocks. For example, the Fidelity 500 index fund (FXAIX) tracks the S & P 500 index (99% correlation coefficient). Because these funds are passively managed, they have lower expense ratios.

Active funds are typically blended funds that have diverse assets such as domestic stocks, foreign stocks, bonds, etc. These funds are actively managed, and therefore have higher expense ratios. Examples of actively managed funds are VTIVX (Vanguard Target Retirement 2045 Fund) and TTFRX (TIAA-CREF Lifecycle 2045 Retirement).

In this article, we will compare 3 popular mutual funds with excellent track records: one that is passive (FXAIX) and the other 2 that are active funds (VTIVX) and (TTFRX).

Suppose I wanted to choose one of these funds to add in my portfolio, which one should I pick?

To answer this question, we will compare the performances of these funds using 10 years of data. We would be interested in analyzing 4 important metrics: average annual return, risk (volatility or standard deviation), management fees (expense ratio), and diversification.

FXAIX Composition

  • Domestic Stock = 99.99%
  • Cash and Other Assets = 0.01%

VTIVX Composition

  • Domestic Stock = 53.27%
  • Foreign Stock = 36.10%
  • Domestic Bond = 6.86%
  • Foreign Bond = 3.55%
  • Others = 0.22%

VTIVX Composition

  • Domestic Stocks = 59.86%
  • Foreign Stock = 28.22%
  • Domestic Bond = 2.96%
  • Others = 8.96%

Table 1 shows the 10-year performance data (total annual return) for the 3 different funds.

Table 1. Total annual return (%) for the 3 types of mutual funds.

Figure 1 shows a barplot for the 3 funds over a 10-year period.

Figure 1. Ten years annual return (%) for the 3 funds. Image by Benjamin O. Tayo

Figures 2 and 3 show the performance of VTIVX and TTFRX, benchmarked against FXIAX (S & P 500).

Figure 2. Comparison of FXAIX (S & P 500) with VTIVX. Source: Fidelity
Figure 3. Comparison of FXAIX (S & P 500) with TTFRX. Source: Yahoo Finance

From Table 1, Figure 1, Figure 2, and Figure 3, we observe that for a period 10 years, FXAIX has outperformed VTIVX and TTFRX. To further quantify this, we performed more statistical analysis on the performance data.

Table 2 shows the comparison of the 3 different funds.

Table 2. Total annual return (%) for the 3 types of mutual funds.

From Table 2, we observe that the 10-year average annual return for FXAIX is better (14.48%) than those for VTIVX (10.67%) and TTFRX (10.75%). The standard deviations (measure of volatility) are about the same. Using the Central Limit Theorem, we can estimate the standard deviation of the mean using

where N = 10 is the sample size (10 years observation data). The standard deviation of the mean are 3.90% for FXAIX, 3.72% for VTIVX, and 4.09% for TTFRX. Using these values, the estimated 95% confidence intervals (CI) of the10-year average annual returns are:

  • FXAIX (6.68% to 22.27)
  • VTIVX (3.22% to 18.11%)
  • TTFRX (2.57% to 18.93%)

An expense ratio (ER), also sometimes known as the management expense ratio (MER), measures how much of a fund’s assets are used for administrative and other operating expenses. From Table 2, the expense ratios for the 3 funds are:

  • FXAIX (0.015%)
  • VTIVX (0.15%)
  • TTFRX (0.45%)

The low ER for FXAIX is because it is a passively managed fund, while VTIVX and TTFRX have higher ERs because they are actively managed funds.

In summary, we’ve compared 10 years of annual return data for 3 different types of mutual funds. Our analysis shows that in terms of 10-year average annual return data, FXAIX outperforms VTIVX and TTFRX. Also, the expense ratio for managing FXAIX is pretty low (0.015%) compared to VTIVX (0.15%) and TTFRX (0.45%). In terms of composition, FXAIX is almost 100% domestic stocks, while VTIVX and TTFRX are more blended (mix of domestic and foreign stocks and bonds).

For an investor interested in higher returns and low expense ratio, FXAIX is the best choice. For someone looking for a more diversified portfolio (at the expense of higher returns), either VTIVX or TTFRX would be good choices.

Personal Finance Analytics

Providing tips to help people learn about money, avoid debt, and invest using data-driven strategies

Benjamin Obi Tayo Ph.D.

Written by

Physicist, Data Science Educator, Writer. Interests: Data Science, Machine Learning, AI, Python & R, Personal Finance Analytics, Materials Sciences, Biophysics

Personal Finance Analytics

Providing useful tips to help individuals and families to be smart about money, avoid debt, and invest using data-driven strategies

Benjamin Obi Tayo Ph.D.

Written by

Physicist, Data Science Educator, Writer. Interests: Data Science, Machine Learning, AI, Python & R, Personal Finance Analytics, Materials Sciences, Biophysics

Personal Finance Analytics

Providing useful tips to help individuals and families to be smart about money, avoid debt, and invest using data-driven strategies

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