figure 3 teachers’ ratings of students’ social skills figure 4 teachers’ ratings of...

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Figure 3 Teachers’ Ratings of Students’ Social Skills Figure 4 Teachers’ Ratings of Students’ Health Concerns Mean Rating Market Mythbusters: The Counter-Cyclical Nature of Gold Prices & January as a Predictor of Market Performance for the Year So Goes January, So Goes the Year? Abstract There are many different theories and strategies concerning the stock market. We have compiled data to test two of these theories: The first theory we will be looking at is one that states that market performance in the month of January can be used as a predictor for the performance of the entire year. The second theory is that gold is counter-cyclical to the broad market and is a good place to invest in a bear market. The Chippewa Valley Center for Economic Research and Development (CVCERD) collects market data on four different investment strategies and also provides an overview of overall market conditions through its Stock Market Project. In addition to testing these market hypotheses we also present an overview of the broad stock market and the Eau Claire Basket (an index of locally represented publically traded companies). UW-Eau Claire Economics Department and The Chippewa Valley Center for Economic Research and Development Students: Zach Hines, Eric Nohelty, Matt Porwoll, Lauren Buxton Faculty Mentor: Dr. Eric Jamelske We gratefully acknowledge generous funding support from the UWEC Office of Research and Sponsored Programs, Differential Tuition, Xcel Energy-Eau Claire, and Northwestern Bank-Chippewa Falls 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 -15 -10 -5 0 5 10 15 20 25 30 35 S&P Percent Change 1980-1989 January % chg. Year-over-year % chg. 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 -20 -10 0 10 20 30 40 S&P Percent Change 1990-1999 January % chg. Year-over-year % chg. -15.00 -10.00 -5.00 0.00 5.00 10.00 15.00 -50.00 -40.00 -30.00 -20.00 -10.00 0.00 10.00 20.00 30.00 40.00 f(x) = 1.25000901986811 x + 8.47830136128339 R² = 0.15414900106169 Series1 Logarithmic (Series1) Logarithmic (Series1) Linear (Series1) Linear Returns JR (+) YR(+,-) JR (+) YR (+) JR (+) YR (-) Mean (y-bar) 4.17 16.14 4.07 18.74 4.96 -5.93 Standard Deviation (s) 3.24 12.68 3.39 10.50 2.10 6.51 Returns JR (-) YR (+,-) JR (-) YR (-) JR (-) YR (+) Mean (y-bar) -4.42 -0.93 -4.91 -17.79 -4.07 11.11 Standard Deviation (s) 3.15 17.79 2.31 12.76 3.78 8.09 Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Conclusion- Our data showed that annual returns followed January returns in 22 (71%) of the 31 years we tested. Although it seems that this relationship weakens when January returns are negative, our results confirm this market hypothesis and imply a relationship between January returns and annual returns. The Market Hypothesis As the name implies, “So Goes January, So Goes the Year” is a market hypothesis that states that the stock return in the month of January can be used as a predictor for the stock performance of the entire year. In order to test this theory we will compare returns from Januaries against full year returns from 1980 to 2010. We will also attempt to predict 2011 stock returns based on the stock market performance of January 2011. Figures 1-5 attempt to explain whether or not January stock returns correctly predict returns for the year. We use S&P 500 index as our stock market measure for the years 1980-2010. Figures 1-3 show the percentage change in stock performance for January graphed against the corresponding annual return. Figure 1 displays the years 1980-1989, Figure 2 shows 1990- 1999, and Figure 3 displays 2000-2010. We see that for the 31 years in our data set, 19 show positive growth in January and 12 show negative growth. Of the 19 years showing positive growth in January, 17 (89.4%) show positive growth for the year. In contrast, of the 12 years showing negative growth in January, only five (41.6%) show negative growth for the year. This suggests that when January returns are positive, we can be confident that returns for the year will also be positive. On the other hand, this confidence decreases when January returns are negative. Although more research is necessary to come to complete conclusions, it is worth noting that six of the nine years that did not match the “So Goes January” hypothesis occurred in the 2000-2010 decade. Figure 4 displays the January and yearly percent changes for each year with the x-axis defining January and the y-axis defining the year. A linear trendline was used to help explain a relationship between these variables. The resulting equation is: y = 1.25*x + 8.48 with the slope indicating a positive relationship between January and annual returns. More specifically, this could be interpreted as follows: all else equal, when January’s percentage change increases by one, the annual percentage change increases by 1.25. However, the relationship is very weak with an R-squared of only 0.154. Finally, Figure 5 shows the mean and standard deviation of six different scenarios. For years in which we see positive returns for January, the average return for the year is 16.14%. The standard deviation is fairly large for this yearly return, but small enough to conclude there’s a good chance that a positive return for January will correspond with a positive return for the year. On the other hand, for years in which we have a negative return in January, the average yearly return is -0.93%. What is striking is the very large Data Sources All data and information was obtained from Yahoo! Finance and individual company websites The Market Hypothesis For years investors have thought of gold as a “safe haven” when stocks and bonds are performing poorly, thus it is commonly referred to as being counter-cyclical to the broader stock market. We will test this theory by comparing gold returns against the stock market from 1975-2010. We will try to explain the price movement in gold and attempt to uncover the cause of this price movement. Gold Counter Cyclical? 1/1 4/1975 11/ 1/ 1976 8/2 5/1978 6/2 5/1980 5/6 /1982 3/1 2/1984 1/2 1/1986 11/ 19/198 7 9/1 8/1989 7/16/ 1991 5/1 2/1993 2/2 7/1995 1/2 /1997 10/ 21/199 8 8/1 6/2000 6/1 4/ 2002 5/1 8/2004 3/1 7/2006 1/1 4/2008 11/ 3/2009 0.00 500.00 1000.00 1500.00 2000.00 2500.00 S&P 500 vs Gold 1/1/1975-3/31/2011 Gold S&P 500 Figure 10: Figure 10 shows the returns of gold graphed against the S&P 500 from 1975 through 2010. At first glance it is unclear if gold is counter cyclical based on this graph. Figure 10 has three distinct time periods that should be discussed separately. First, from 1975-1980 gold returned 219% while the S&P increased 54%. Next, from 1980-2000 gold returned -49% compared to a return of 1,119% by the S&P 500. Finally, from 2000-2010 gold returned 392% compared to -18% by the S&P. This shows that for the majority of this time period the return of gold was in fact counter cyclical to the return of the S&P 500. Figure 11 shows the yearly returns of gold and the S&P 500. This graph allows us to look at the behavior of gold on a year by year basis instead of the broader time periods used in Figure 10. Of the 36 years we tested, gold moved counter cyclically to the S&P in 18, or exactly 50%, of the years. (A counter cyclical year is one in which gold’s return was negative and the S&P’s was positive or vice versa). This graph suggests that gold may not be as counter cyclical as Figure 10 seemed to show. Figures 12 and 13 attempt to explain the price movement in gold based on market volatility rather than the “counter cyclical hypothesis.” VIX is an index which measures the volatility of the S&P 500 with an increase representing higher volatility in the stock market. Figure 12 shows the price change in gold and VIX from 2007- 2010. These four years represented a period of extremely high volatility and corresponded with a 118% return in gold. In contrast, Figure 13 examines the years 1992- 1995, a time of much lower market volatility. This period of lower volatility corresponded with a 10% return in gold, significantly lower than gold’s return during the time of high volatility. These two graphs show that much of gold’s price movement 197 5 197 8 1981 198 4 198 7 199 0 19 9 3 1996 1999 200 2 200 5 200 8 -50.00 0.00 50.00 100.00 150.00 200.00 Yearly Returns Gold Yearly Returns S&P Figure 11: 1/3/2 007 3/1 2/ 2007 5/1 8/ 2007 7/25/ 2007 10/1/2007 12/ 5/ 2007 2/1 5/ 2008 4/24/ 2008 7/1 /2 008 9/8/2008 11 / 11 /2008 1/23/2009 3/3 1/ 2009 6/9/2 009 8/13/ 2009 10/ 20/2009 12/ 29/2009 3/9/2010 5/17/ 2010 7/22/2010 9/2 8/2010 12/2/ 2010 50 150 250 350 450 550 650 750 VIX Gold Figure 12: 1/2 /1 992 3/9 / 1 992 5/1 8/1992 7/2 3/1992 9/2 9/1992 12/ 3/1992 2/16/1993 4/2 3/1993 6/3 0/1993 9/7 /1 993 11/ 10/1993 1/2 1/1994 3/2 9/1994 6/6 /1994 8/1 0/ 1994 10/ 14 /1994 12/ 20 /1994 3/1 /1995 5/9 /1995 7/1 4/1995 9/2 8/1995 12/ 6/1995 0 20 40 60 80 100 120 140 VIX Gold Figure 13: Conclusion- We conclude that gold is certainly somewhat counter cyclical in nature. However, we feel that a more accurate description of gold’s performance would be to say that it is a “safe haven” for investors when the markets are extremely volatile, not just when stocks are performing poorly. -2 0 2 4 6 8 10 7.25 4.60 6.66 0.82 CVCERD Investment Track 2011 Year-to-Date: 1/1 - 3/31 ECB DOGS RSP GLD Percent Change Quarter 1 Figure 6: -10 -5 0 5 10 15 20 -7.05 17.06 6.39 CVCERD Investment Track 2011 ECB Performance by Sector : 1/1 - 3/31 RETAIL MANUF OTHER Percent Change Quarter 1 Figure 7: Series1 0 20 40 60 80 100 120 140 160 180 200 220 198.11 32.51 26.33 25.54 24.13 ECB Quarter One Winners SGI ROK CZWI UNH BWLD Percent Change Quarter 1 Figure 8: Series1 -80 -70 -60 -50 -40 -30 -20 -10 0 10 -16.43 -18.08 -18.82 -23.72 -74.44 ECB Quarter One Losers TGT CAS.TO RSH HTCH BGP Percent Change Quarter 1 Figure 9: Figure 6 shows quarter one returns for the Eau Claire Basket (ECB), Dogs of the Dow (DOGS), Rydex S&P Index (RSP), and SPDR Gold Shares (GLD). ECB performed the best with a return of 7.25% and gold performed the worst with a return of 0.82%. ECB’s performance bodes well for the Chippewa Valley local economy as it outperformed our other indexes as well as the broad S&P 500. Figure 7 breaks down the ECB into three sectors: Retail, Manufacturing, and Other. It appears that the retail industry took a massive hit while manufacturing performed extremely well. However, it is important to first examine all of the stocks being held in the ECB before drawing conclusions. Borders Group (BGP) , a retail company, went bankrupt in February. Likewise, Silicon Graphics (SGI), a manufacturing company, had by far the best return in the ECB. In order to more accurately compare sectors, it is important to remove the outliers. With Borders Group included in our calculation of sector performance, retail came in at a dismal -7.08% change. However, upon removing BGP from our calculation, retail’s performance improved to -0.9%. Similarly, under our initial sector calculation, manufacturing was up 17.06% for the quarter. However, with the exclusion of SGI from our calculation, manufacturing was only up 7.2%; which is much more in line with the rest of the sectors in the ECB. Figure 8 shows the top performing companies in the ECB. The clear winner in the ECB in quarter one was SGI, whose value rose by nearly 200%. In researching what happened to the company during the past several months, one main reason attributed to the company’s success. On March 25th, SGI announced a contract win with the Korean Air Force, who has begun using SGI computing systems for weather forecasting. Although the details of the deal were not announced, the good news of the contract shot the company’s stock price up 12% in one day followed by continued success thereafter. Figure 9 displays the worst performing companies in the ECB. Losing almost 75% of their value, Borders poor ECB Track 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 -25 -20 -15 -10 -5 0 5 10 15 20 25 S&P Percent Change 2000-2011 January % chg. Year-over-year % chg.

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Page 1: Figure 3 Teachers’ Ratings of Students’ Social Skills Figure 4 Teachers’ Ratings of Students’ Health Concerns Mean Rating Market Mythbusters: The Counter-Cyclical

Figure 3

Teachers’ Ratings of Students’ Social Skills

Figure 4

Teachers’ Ratings of Students’ Health Concerns

Mea

n R

atin

g

Market Mythbusters: The Counter-Cyclical Nature of Gold Prices & January as a Predictor of Market Performance for the Year

So Goes January, So Goes the Year? AbstractThere are many different theories and strategies concerning the stock market. We have compiled data to test two of these theories: The first theory we will be looking at is one that states that market performance in the month of January can be used as a predictor for the performance of the entire year. The second theory is that gold is counter-cyclical to the broad market and is a good place to invest in a bear market.

The Chippewa Valley Center for Economic Research and Development (CVCERD) collects market data on four different investment strategies and also provides an overview of overall market conditions through its Stock Market Project. In addition to testing these market hypotheses we also present an overview of the broad stock market and the Eau Claire Basket (an index of locally represented publically traded companies).

UW-Eau Claire Economics Department and The Chippewa Valley Center for Economic Research and DevelopmentStudents: Zach Hines, Eric Nohelty, Matt Porwoll, Lauren Buxton Faculty Mentor: Dr. Eric Jamelske

We gratefully acknowledge generous funding support from the UWEC Office of Research and Sponsored Programs, Differential Tuition, Xcel Energy-Eau Claire, and Northwestern Bank-Chippewa Falls

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989

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f(x) = 1.25000901986811 x + 8.4783013612834R² = 0.154149001061691

Series1

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Linear

Returns JR (+) YR(+,-) JR (+) YR (+) JR (+) YR (-)

Mean (y-bar) 4.17 16.14 4.07 18.74 4.96 -5.93

Standard Deviation (s) 3.24 12.68 3.39 10.50 2.10 6.51

Returns JR (-) YR (+,-) JR (-) YR (-) JR (-) YR (+)

Mean (y-bar) -4.42 -0.93 -4.91 -17.79 -4.07 11.11

Standard Deviation (s) 3.15 17.79 2.31 12.76 3.78 8.09

Figure 1:

Figure 2:

Figure 3:

Figure 4:

Figure 5:

Conclusion- Our data showed that annual returns followed January returns in 22 (71%) of the 31 years we tested. Although it seems that this relationship weakens when January returns are negative, our results confirm this market hypothesis and imply a relationship between January returns and annual returns.

The Market Hypothesis

As the name implies, “So Goes January, So Goes the Year” is a market hypothesis that states that the stock return in the month of January can be used as a predictor for the stock performance of the entire year. In order to test this theory we will compare returns from Januaries against full year returns from 1980 to 2010. We will also attempt to predict 2011 stock returns based on the stock market performance of January 2011.

Figures 1-5 attempt to explain whether or not January stock returns correctly predict returns for the year. We use S&P 500 index as our stock market measure for the years 1980-2010.

Figures 1-3 show the percentage change in stock performance for January graphed against the corresponding annual return. Figure 1 displays the years 1980-1989, Figure 2 shows 1990-1999, and Figure 3 displays 2000-2010. We see that for the 31 years in our data set, 19 show positive growth in January and 12 show negative growth. Of the 19 years showing positive growth in January, 17 (89.4%) show positive growth for the year. In contrast, of the 12 years showing negative growth in January, only five (41.6%) show negative growth for the year. This suggests that when January returns are positive, we can be confident that returns for the year will also be positive. On the other hand, this confidence decreases when January returns are negative. Although more research is necessary to come to complete conclusions, it is worth noting that six of the nine years that did not match the “So Goes January” hypothesis occurred in the 2000-2010 decade.

Figure 4 displays the January and yearly percent changes for each year with the x-axis defining January and the y-axis defining the year. A linear trendline was used to help explain a relationship between these variables. The resulting equation is: y = 1.25*x + 8.48 with the slope indicating a positive relationship between January and annual returns. More specifically, this could be interpreted as follows: all else equal, when January’s percentage change increases by one, the annual percentage change increases by 1.25. However, the relationship is very weak with an R-squared of only 0.154.

Finally, Figure 5 shows the mean and standard deviation of six different scenarios. For years in which we see positive returns for January, the average return for the year is 16.14%. The standard deviation is fairly large for this yearly return, but small enough to conclude there’s a good chance that a positive return for January will correspond with a positive return for the year. On the other hand, for years in which we have a negative return in January, the average yearly return is -0.93%. What is striking is the very large standard deviation of 17.79 suggesting that a negative return for January corresponds with a wide range of both positive and negative outcomes.

In January 2011 the S&P 500 had a return of 1.12%. Based on our data this positive return suggests that the mean expected return for the full year will be 16.14%.

Data Sources

All data and information was obtained from Yahoo! Finance and individual company websites

The Market HypothesisFor years investors have thought of gold as a “safe haven” when stocks and bonds are performing poorly, thus it is commonly referred to as being counter-cyclical to the broader stock market. We will test this theory by comparing gold returns against the stock market from 1975-2010. We will try to explain the price movement in gold and attempt to uncover the cause of this price movement.

Gold Counter Cyclical?

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S&P 500 vs Gold 1/1/1975-3/31/2011

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Figure 10: Figure 10 shows the returns of gold graphed against the S&P 500 from 1975 through 2010. At first glance it is unclear if gold is counter cyclical based on this graph. Figure 10 has three distinct time periods that should be discussed separately. First, from 1975-1980 gold returned 219% while the S&P increased 54%. Next, from 1980-2000 gold returned -49% compared to a return of 1,119% by the S&P 500. Finally, from 2000-2010 gold returned 392% compared to -18% by the S&P. This shows that for the majority of this time period the return of gold was in fact counter cyclical to the return of the S&P 500.

Figure 11 shows the yearly returns of gold and the S&P 500. This graph allows us to look at the behavior of gold on a year by year basis instead of the broader time periods used in Figure 10. Of the 36 years we tested, gold moved counter cyclically to the S&P in 18, or exactly 50%, of the years. (A counter cyclical year is one in which gold’s return was negative and the S&P’s was positive or vice versa). This graph suggests that gold may not be as counter cyclical as Figure 10 seemed to show.

Figures 12 and 13 attempt to explain the price movement in gold based on market volatility rather than the “counter cyclical hypothesis.” VIX is an index which measures the volatility of the S&P 500 with an increase representing higher volatility in the stock market. Figure 12 shows the price change in gold and VIX from 2007-2010. These four years represented a period of extremely high volatility and corresponded with a 118% return in gold. In contrast, Figure 13 examines the years 1992-1995, a time of much lower market volatility. This period of lower volatility corresponded with a 10% return in gold, significantly lower than gold’s return during the time of high volatility. These two graphs show that much of gold’s price movement might be explained by volatility rather than a strict “counter cyclical hypothesis.”

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Conclusion- We conclude that gold is certainly somewhat counter cyclical in nature. However, we feel that a more accurate description of gold’s performance would be to say that it is a “safe haven” for investors when the markets are extremely volatile, not just when stocks are performing poorly.

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CVCERD Investment Track 2011 Year-to-Date: 1/1 - 3/31

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Figure 6 shows quarter one returns for the Eau ,Claire Basket (ECB), Dogs of the Dow (DOGS) Rydex S&P Index (RSP), and SPDR Gold Shares ECB performed the best with a return .(GLD) of 7.25% and gold performed the worst with a return of 0.82%. ECB’s performance bodes well for the Chippewa Valley local economy as it outperformed our other indexes as well as.the broad S&P 500

Figure 7 breaks down the ECB into three.sectors: Retail, Manufacturing, and Other It appears that the retail industry took a massive hit while manufacturing performed extremely well. However, it is important to first examine all of the stocks being held in the ECB before drawing conclusions. Borders Group (BGP) , a retail company, went bankrupt in February. Likewise, Silicon Graphics (SGI), a manufacturing company, had by far the best return in the ECB. In order to more accurately compare sectors, it is important to remove the outliers. With Borders Group included in our calculation of sector performance, retail came in at a dismal -7.08% change. However, upon removing BGP from our calculation, retail’s ,performance improved to -0.9%. Similarly ,under our initial sector calculation .manufacturing was up 17.06% for the quarter However, with the exclusion of SGI from our ;calculation, manufacturing was only up 7.2% which is much more in line with the rest of the .sectors in the ECB

Figure 8 shows the top performing companies in the ECB. The clear winner in the ECB in quarter one was SGI, whose value rose by nearly 200%. In researching what happened to ,the company during the past several months one main reason attributed to the company’s success. On March 25th, SGI announced a contract win with the Korean Air Force, who has begun using SGI computing systems for weather forecasting. Although the details of the deal were not announced, the good news of the contract shot the company’s stock price up 12% in one day followed by continued .success thereafter

Figure 9 displays the worst performing companies in the ECB. Losing almost 75% of their value, Borders poor performance in quarter 1 was due to the fact that the company declared bankruptcy during February of this year. The company stated that they were going to restructure and close one-third ,of their stores. Their future remains unclear but during the first quarter of 2011, they were .by far the worst performer in the ECB

ECB Track

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