# Equity Return Measures

**Holding Period Return (HPR)**

r = ((Dividend + Share Price Change)/Share Price Paid)-1

**Expected vs. Realized Holding Period Return**

Expected HPR is based on investor expectations for share price appreciation and dividend payments; different investors may have different expectations for the same stock and would therefore have different expected HPRs.

Realized HPR is based on historical fact, as share price change and dividend payment are known values.

**Required Return**

This value is the minimum return that an investor demands for a specific asset based on its riskiness.

Required return reflects the opportunity cost of forgoing the next best investment.

Expected Alpha = expected return – required return

Realized Alpha = actual HPR – comparable asset return

**Discount Rate**

The discount rate is used to calculate the present value of future cash flows.

Generally, a single discount rate is used for all future period cash flows.

When calculating the intrinsic value of a stock, the investor will apply a discount rate that is based on the risk free rate of return plus some equity risk premium.

**Internal Rate of Return (IRR)**

IRR is the case of a discount rate that equalizes the present value of cash inflows with present value of cash outflows.

Within the context of a net present value analysis, when the cash inflows and outflows are known, IRR will be the rate that causes the NPV to equal zero.

**Convergence to Intrinsic Value**

When calculating the intrinsic value of a stock, an analyst may discover that his/her calculated value differs from the current market value.

If the investor’s calculated intrinsic value is more accurate than the current market price, then a special investment opportunity may be present.

When the market value shifts to the calculated intrinsic value over time, then convergence is taking place.

Despite a perceived mispricing, convergence may not occur over the investor’s time horizon. Quoting the legendary economist John Maynard Keynes “the market can stay irrational longer than you can stay solvent.”

## Data Science in Finance: 9-Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

### What's Included:

- Getting Started with R
- R Programming for Data Science
- Data Visualization with R
- Financial Time Series Analysis with R
- Quantitative Trading Strategies with R
- Derivatives with R
- Credit Risk Modelling With R
- Python for Data Science
- Machine Learning in Finance using Python

Each book includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $39 (Regular $57)## Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.