Value & Growth Stock



BA 453 - Global Asset Allocation & Stock Selection

Assignment 1: Growth vs. Value Trading Strategies

Consistent performance Asset Management

John O’Reilly

Sebastian Otero Barba

Nikolay Pavlov

Franck Violette

Table of Content

1. Introduction 4

2. Methodology 5

General 5

Predictive Model Variables 6

Multivariate Linear Regression 7

3. Value & Growth Indexes Trends 7

4. Wilshire All Cap Indexes 11

Best Value and Growth Variables 11

Best Value Regression 11

Best Growth Regression 13

5. Wilshire Large Cap Indexes 16

Large Cap Value and Growth analysis: 16

Wilshire Large-Cap Growth historical 17

Wilshire Large-Cap Value historical 17

Growth-Value historical returns: 17

Large cap value predictions histogram: 20

Large cap growth predictions histogram 20

Out-of-sample predicted returns vs. actual returns for the growth stocks: 21

Out-of-sample predicted returns vs. actual returns for the value stocks: 21

6. Wilshire Mid Cap Indexes 21

Wilshire Mid Cap Value Index Predictive Model: 23

Out of Sample Forecast of Wilshire Mid Cap Value Index: 23

In-Sample Forecast of Wilshire Mid Cap Value Index: 24

Wilshire Mid Cap Growth Index Predictive Model: 25

Out of Sample Forecast of Wilshire Mid Cap Growth Index: 26

In-Sample Forecast of Wilshire Mid Cap Growth Index: 27

7. Wilshire Small Cap Indexes 27

Wilshire Small Cap Value Index Predictive Model: 29

Out of Sample Forecast of Wilshire Small Cap Value Index: 30

In-Sample Forecast of Wilshire Small Cap Value Index: 31

Wilshire Small Cap Growth Index Predictive Model: 31

Out of Sample Forecast of Wilshire Small Cap Growth Index: 32

In-Sample Forecast of Wilshire Small Cap Growth Index: 33

8. Summary of Directionality Performance 33

9. Simple Trading Strategy 34

10. A More Complex Trading Strategy 35

11. Summary & Conclusions 37

12. Appendix 37

Adjusted R^2 for all Predictive Models 37

Introduction

Growth and Value are two fundamental investment approaches that have been the subject of significant research by Sharpe, Fama & French, Harvey to name a few. In brief, growth and value are defined as follows

Growth stocks represent companies that have demonstrated better than average gains in earnings in recent years and are expected to continue delivering high levels of profit growth.

Value Stocks represent companies that are currently out of favor in the marketplace and are considered bargain priced. Value stocks are typically priced much lower than stocks of similar companies in the same industry and may include stocks of newer companies with unproven track records.

By combining the two styles, one can help to reduce portfolio volatility because each has outperformed the other at different phases of the business cycle. The characteristics that affect the valuation of a stock as a member of the growth or value asset class are as follows:

|Valuation Measure |Value |Growth |

|Dividend Yield |Higher |Lower |

|Price/Earnings |Lower |Higher |

|Price/Book |Lower |Higher |

|Price/Net Tangible Assets |Lower |Higher |

|Price/Cash Flow |Lower |Higher |

Historical trends are shown in the following figure and illustrate periods of growth/value dominance associated with the different phases of the business cycle.

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In being able to forecast the switch between growth and value, one may expect a significant increase in returns. As an indicator of potential strategy performance, we investigated a strategy with no short selling and whereby under negative returns, the allocation is transferred into TBills. All value and growth indexes were considered for this strategy and it was assumed that if the predictive model were perfect then one could place 100% in the best performing asset class every month or if the return were negative then place it into TBills. From January 78 to November 82, the table below gives the percentage over which a certain asset class is selected e.g. 13.71% large cap growth, 20.74% Small Cap Growth, 7.02% Tbill. This trading strategy would yield 6.27% annualized returns and a volatility of 16.27%. The effective returns of each asset class are then shown for only the periods where they were selected. Obviously, it is observed a significant gain in returns and reduced volatility compared with the actual value that represents a 100% allocation in each separate asset class over the whole of the period. While this example may be hypothetical, it sets the scene for the potential tremendous benefits that could be gained from reliable predictive models for this family of value and growth indexes. The figure below also illustrates the allocation over the sample period for this given hypothetical trading strategy.

[pic]

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Methodology

General

To assess the performance of various trading strategies involving allocation between value and growth, multivariate predictive models of the value and growth indexes expected returns have been derived.

The selected set of variables for the predictive models represent variables which are expected to have an effect on the market as a whole as well as variables that are expected to influence the index directly. We also considered economic indicators such as the monthly consumer confidence index in our analysis.

The following Growth and Value indexes - independent variables - considered in this study are as follows:

|Wilshire All Cap Growth |Wilshire All Cap Value |

|Wilshire Large Cap Growth |Wilshire Large Cap Value |

|Wilshire Mid Cap Growth |Wilshire Mid Cap Value |

|Wilshire Small Cap Growth |Wilshire Small Cap Value |

The indexes’ data series commence in January 1978 and end in November 02. The data sample was divided into an in-sample dataset from January 1979 to November 00 and an out-of-sample dataset from December 00 to November 02.

The indexes’ predictive models were tested bout in- and out-of-sample, and the expected returns for December 02 were predicted as well as the volatility using an ARCH(1) model.

The predictive models were used to back test and define proposed asset allocations for each index capitalization class using the following trading strategies:

• Growth Long/Hold

• Value Long/Hold

• Value/Growth Swap

Predictive Model Variables

The following variables were considered in developing the predictive models.

|Independent Variable |Lag |Relationship to |Format |

|- Change in - | | | |

|CPI |1 |Interest rates |Positive, Negative |

| | |Economic activity | |

|Aaa minus T-Bill |1 |Market risk |Positive, Negative |

|Aaa minus Baa |1 |Market risk |Positive, Negative |

|Dividend Yield |1 |Market return |Positive, Negative |

|Treasury Bill rate |1 |Economic activity |Positive, Negative, squared |

| | |Risk free rate | |

|U Michigan Consumer Index |1 |Economic activity |Positive, Negative, squared |

| | |Expectation | |

|IT Govt. Treasury |1 |Expectation | |

| | |Economic activity | |

|10yrs-3months Government Bond |1 |Expectation |Positive, Negative, squared |

| | |Economic activity | |

|Aaa Corporate Bond Yield |1 |Economic activity |Positive, Negative |

|Disposable Personal Income |1 |Economic activity |Positive, Negative |

|New Private Housing started |1 |Economic activity |Positive, Negative |

| | |Expectation | |

|Initial Claims of Unemployment |1 |Economic activity |Positive, Negative |

| | |Expectation | |

|Cons Credit Out |1 |Economic activity |Positive, Negative |

| | |Expectation | |

|Index Total Return |1,2 |Momentum |Positive, Negative |

A total of 15 raw independent variables were considered for this analysis. The raw data was obtained from DataStream and transformed into suitable dependent variables using transformations such as:

• Separation into positive and negative change

• Square of the change

• Separation according Terms Structure sign

• Stochastic detrending

It is worth to note that other variables such as P/E, B/P would have provided enhanced regression models for the value-based indexes. However, we were unable to locate the data due to the short timescale, but consider that despite this, the validity of the model is still confirmed by their high R^2.

Multivariate Linear Regression

The Excel regression data analysis add-in has been used to determine the regression parameters. In performing the analysis, the correlation between the variables has been examined and closely correlated variables discarded. Significance tests using the t-statistics and p-value were applied to define the significant variables with threshold levels of >1.0~1.5 and ................
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