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Some investors use a technique called the "Dogs of the Dow" to invest. They pick several stocks that are performing poorly from the Dow Jones group (which is a composite of 30 wellknown stocks) and invest in these. Explain why these stocks will probably do better than they have done before.

Short Answer

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The 'Dogs of the Dow' strategy relies on mean reversion theory, which suggests that stocks currently underperforming are likely to increase in value in the future. The high dividend yields provide additional investment incentives. The portfolio is rebalanced yearly to continually target so-called 'dogs' or underperforming stocks.

Step by step solution

01

Understand 'Dogs of the Dow' Strategy

The Dog of the Dow strategy is amongst the simplest stock picking strategies. In this, at the beginning of each year, an investor selects the ten highest dividend-yielding stocks in the Dow Jones Industrial Average (DJIA). These are often companies that are currently out of favor in the market but are still fundamentally strong.
02

Explain Mean Reversion Theory

This strategy is based on a concept in finance called 'mean reversion', which is the assumption that the price of a stock will move towards its average over time. Hence, if a stock is performing poorly compared to its historical average, it is expected to increase in value in the future.
03

Discuss High Dividend Yield

The Dogs of the Dow are the highest dividend-yielding stocks, which means they distribute a large portion of profits back to investors. High dividends can indicate a undervalued stock, suggesting it may appreciate in price. Also, these dividends provide a steady income stream, regardless of stock performance.
04

Mention Portfolio Rebalancing

At the end of each year, the investor re-evaluates their portfolio, selling stocks that are no longer 'dogs' and buying those that have become 'dogs'. This ensures the method focuses on underperforming stocks, continuing the cycle of buying low and selling high.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Mean Reversion Theory
Mean reversion theory is an essential concept in understanding the valuation and long-term trajectory of stock prices. It is predicated on the premise that asset prices and historical returns eventually revert back to their long-term mean or average. This implies that stocks that have underperformed the market over a certain period have a tendency to increase in value and converge back to their mean performance.

In the context of the 'Dogs of the Dow' strategy, investors rely on this theory, asserting that the selected companies with poor recent performance are likely to see a reversal towards their historic norms. Despite the short-term downturns, these companies are anticipated to be fundamentally strong with potential for recovery. This belief in mean reversion supports the rationale that current 'Dogs' could be undervalued, presenting a prime opportunity for appreciation.
High Dividend Yield
A high dividend yield often signals that a stock is undervalued. It is calculated by dividing the annual dividends per share by the stock's price per share. When yields are high, it means investors are getting a significant return on their investment through dividends, relative to the stock's price.

Investors favor high-yielding stocks for the Dogs of the Dow strategy because they not only provide a steady income stream through dividends but may also depict potential for capital appreciation. It's important to mention, however, that while a high dividend yield can be attractive, it shouldn't be the sole factor in investment decisions. Other metrics and company fundamentals should also be reviewed to avoid chasing yields in companies with unsustainable dividends or deteriorating business models.
Portfolio Rebalancing
Portfolio rebalancing is a significant part of investment management, ensuring that an investor's holdings align with their risk tolerance and investment goals. This involves periodically buying or selling assets in a portfolio to maintain a desired level of asset allocation or exposure.

With the Dogs of the Dow strategy, rebalancing happens annually. Investors sell stocks that are no longer part of the highest dividend yielders and purchase new ones that have entered the category. This methodical portfolio rebalancing aids in maintaining a disciplined approach, encourages the practice of buying low and selling high, and minimizes the risks associated with holding onto stocks that no longer fit the initial investment criteria.
Dow Jones Industrial Average
The Dow Jones Industrial Average (DJIA) is one of the oldest and most widely followed stock market indices in the world. It consists of 30 prominent companies from the United States, representing various sectors of the economy except for transportation and utilities.

The stocks within the DJIA are typically seen as industry leaders and are influential in their respective sectors. Because of the Dow's long-standing reputation and its composition of established, reliable companies, the index is often used as a barometer for the overall health of the U.S. stock market and economy. The Dogs of the Dow strategy specifically utilizes the DJIA because of the index's proven blue-chip stocks that have an extensive track record of performance and paying dividends.

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Most popular questions from this chapter

The following table shows the weights and prices of some turkeys at different supermarkets. a. Make a scatterplot with weight on the \(x\) -axis and cost on the \(y\) -axis. Include the regression line on your scatterplot. b. Find the numerical value for the correlation between weight and price. Explain what the sign of the correlation shows. c. Report the equation of the best-fit straight line, using weight as the predictor \((x)\) and cost as the response \((y)\). d. Report the slope and intercept of the regression line, and explain what they show. If the intercept is not appropriate to report, explain why. e. Add a new point to your data: a 30 -pound turkey that is free. Give the new value for \(r\) and the new regression equation. Explain what the negative correlation implies. What happened? f. Find and interpret the coefficient of determination using the original data. $$ \begin{array}{|c|c|} \hline \text { Weight (pounds) } & \text { Price } \\ \hline 12.3 & \$ 17.10 \\ \hline 18.5 & \$ 23.87 \\ \hline 20.1 & \$ 26.73 \\ \hline 16.7 & \$ 19.87 \\ \hline 15.6 & \$ 23.24 \\ \hline 10.2 & \$ 9.08 \end{array} $$

In Exercise \(4.1\) there is a graph of the relationship between SAT score and college GPA. SAT score was the predictor and college GPA was the response variable. If you reverse the variables so that college GPA was the predictor and SAT score was the response variable, what effect would this have on the numerical value of the correlation coefficient?

The data shows the number of calories, carbohydrates (in grams) and sugar (in grams) found in a selection of menu items at McDonald's. Scatterplots suggest the relationship between calories and both carbs and sugars is linear. The data are also available on this text's website. (Source: shapefit.com) $$ \begin{array}{|c|c|c|} \hline \text { Calories } & \text { Carbs (in grams) } & \text { Sugars (in grams) } \\ \hline 530 & 47 & 9 \\ \hline 520 & 42 & 10 \\ \hline 720 & 52 & 14 \\ \hline 610 & 47 & 10 \\ \hline 600 & 48 & 12 \\ \hline 540 & 45 & 9 \\ \hline 740 & 43 & 10 \\ \hline 240 & 32 & 6 \\ \hline 290 & 33 & 7 \\ \hline 340 & 37 & 7 \\ \hline 300 & 32 & 6 \\ \hline 430 & 35 & 7 \\ \hline 380 & 34 & 7 \\ \hline 430 & 35 & 6 \\ \hline 440 & 35 & 7 \\ \hline 430 & 34 & 7 \\ \hline 750 & 65 & 16 \\ \hline 590 & 51 & 14 \\ \hline 510 & 55 & 10 \\ \hline 350 & 42 & 8 \\ \hline \end{array} $$ $$ \begin{array}{|l|l|} \hline \text { Calories } & \text { Carbs (in grams) } & \text { Sugars (in grams) } \\ \hline 670 & 58 & 11 \\ \hline 510 & 44 & 9 \\ \hline 610 & 57 & 11 \\ \hline 450 & 43 & 9 \\ \hline 360 & 40 & 5 \\ \hline 360 & 40 & 5 \\ \hline 430 & 41 & 6 \\ \hline 480 & 43 & 6 \\ \hline 430 & 43 & 7 \\ \hline 390 & 39 & 5 \\ \hline 500 & 44 & 11 \\ \hline 670 & 68 & 12 \\ \hline 510 & 54 & 10 \\ \hline 630 & 56 & 7 \\ \hline 480 & 42 & 6 \\ \hline 610 & 56 & 8 \\ \hline 450 & 42 & 6 \\ \hline 540 & 61 & 14 \\ \hline 380 & 47 & 12 \\ \hline 340 & 37 & 8 \\ \hline 260 & 30 & 7 \\ \hline 340 & 34 & 5 \\ \hline 260 & 27 & 4 \\ \hline 360 & 32 & 3 \\ \hline 280 & 25 & 2 \\ \hline 330 & 26 & 3 \\ \hline 190 & 12 & 0 \\ \hline 750 & 65 & 16 \\ \hline \end{array} $$ a. Calculate the correlation coefficient and report the equation of the regression line using carbs as the predictor and calories as the response variable. Report the slope and interpret it in the context of this problem. Then use your regression equation to predict the number of calories in a menu item containing 55 grams of carbohydrates. b. Calculate the correlation coefficient and report the equation of the regression line using sugar as the predictor and calories as the response variable. Report the slope and interpret it in the context of this problem. Then use your regression equation to predict the number of calories in a menu item containing 10 grams of sugars. c. Based on your answers to parts (a) and (b), which is a better predictor of calories for these data: carbs or sugars? Explain your choice using appropriate statistics.

a. The first scatterplot shows the college tuition and percentage acceptance at some colleges in Massachusetts. Would it make sense to find the correlation using this data set? Why or why not? b. The second scatterplot shows the composite grade on the ACT (American College Testing) exam and the English grade on the same exam. Would it make sense to find the correlation using this data set? Why or why not?

If the correlation between height and weight of a large group of people is \(0.67\), find the \(\mathrm{co}\) efficient of determination (as a percentage) and explain what it means. Assume that height is the predictor and weight is the response, and assume that the association between height and weight is linear.

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