Academy of Business Research Journal
Volume I 2017
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Journal Statistics for 2012-2013
Articles Submitted
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43
Acceptances without Revision
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Future Conference Dates
Academy of Business Research Fall 2017
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Las Vegas, NV October
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Table of Contents IS IT WISE TO FOLLOW THE MARKET? EMPIRICAL EVIDENCE FROM THE US MARKET
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VICTOR BAHHOUTH, University of North Carolina – Pembroke EDWIN MENSAH, University of North Carolina – Pembroke WILLIAM THOMAS, University of North Carolina – Pembroke
SEASONALITY AND THE NEWSPAPER DISTRIBUTION PROBLEM: USING DATA VISUALIZATION TO IMPROVE TREND LINE FORECASTS
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ANA MARIA GARCIA, Saint Peter's University GUEN PAK, Saint Peter's University FRANCIS ODURO, Saint Peter's University DEONDRE THOMPSON, Saint Peter's University KARLA ERAZO, Saint Peter's University JOSEPH GILKEY JR., Saint Peter's University
DOES EARNING A GRADUATE DEGREE IMPACT CPA EXAM PERFORMANCE?
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K. BRYAN MENK, Duquesne University BRIAN M. NAGLE, Duquesne University STEPHEN E. RAU, Duquesne University WORKPLACE BULLYING: A REVIEW OF RESEARCHERS’ FINDINGS AND A FORWARD-THINKING APPROACH FOR PRACTITIONERS
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SEAN WALKER, University of Tennessee at Martin
MANAGEMENT OF INTEGRATIVE HEALTHCARE BRIT PEEK, Texas A&M University-San Antonio JOSEPHINE SOSA-FEY, Texas A&M University- San Antonio
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AN APPLICATION OF BENFORD’S LAW TO DETECT DATA MISREPRESENTATION IN MUTUAL FUND REPORTING
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YAHUI ZHENG, Bryant University RICHARD GLASS, Bryant University ALAN OLINSKY, Bryant University
ACCOUNT AGGREGATION TOOLS: HISTORY AND USE FOR THE FUTURE JAMES R. GREEN, University of the Incarnate Word ANNETTE E. CRAVEN, University of the Incarnate Word
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Is It Wise to Follow the Market? Empirical Evidence from the US Market Victor Bahhouth University of North Carolina – Pembroke Edwin Mensah University of North Carolina – Pembroke William Thomas University of North Carolina – Pembroke
ABSTRACT Investors around the world are interested in the stock markets and they need to know more about the financial markets. There are many documented studies about predicting the earnings and the price movements of financial assets. Most of these studies rely on the basic assumption that the market is right and the stock price movement at any time reflects firm’s future earnings. The purpose of this study is to examine the efficiency of the US stock market across economic sectors and to test if the market signals were right during the 20-year period ended by December 31, 2012. The results were robust. The stock price movements of most economic sectors showed marginal positive correlation with the firm future earnings. In addition, it was quite interesting that the financial and technology sectors showed significant mixed signals. Keywords: Market measures, Expected earnings, Stock price, Earnings per share (EPS), and Economic sectors.
Introduction Analysts, traders, and investors assume that the market is efficient and the price of stock reflects firm’s future earnings. In their assessments, they use many tools among which is the price earnings ratio as a key indicator to predict firm future earnings. The purpose of this study is to test the efficiency of the US stock price movement during a 20-year time period by examining the market signals i.e. at a past point in time, examining the correlation between of the stock price movement and the relative growth in future earnings. In testing the efficiency of the stock market, many studies tested the significance of using both the fundamental and market measures in predicting stock price movements. In a study, Allouche et al. (2008) used the return on assets (ROA), return on equity (ROE), return on invested capital (ROIC) along with financial leverage ratios to predict the performance of 1,271 Japanese companies. Cooper, Gulen, and Schill (2008) showed evidence that asset performance strongly forecasts firm stock returns. They found a definite correlation between firm growth potential and return on assets. 8
Ofek and Richardson (2003) pointed out during year 2002 market crash period; the very high volume of trade in Internet stocks indicated a wide gap between the prices and their fundamental values. Along with other financial measures, Gompers et al. (2003) tested the relationship between return on equity and the firm’s value using financial measures. They concluded that corporate governance is positively correlated with equity return and firm value. This study is complementing other studies by testing the efficiency of the market. It examines the relationship between the stock price movement (market signal) and the change in the future earnings. The study includes the following sections: 1- Literature review section, it highlights the most relevant research in the field and concludes with the lay out of the testing hypotheses - defines research problem. 2- Research methodology section, it describes the research tools, data collection, data analysis, limitation and implication of the study. 5- Conclusions and recommendations section, it summarizes the research output.
Literature Review Analysts believe that prices move based on market signals; today’s market value is driven by news, which creates uncertainty. The price of a stock increases if the market signal about the firm is positive and decreases if the market signal is negative (Nettles & Matthew 2003). In the same direction, Lei, Noussair, and Plott (2001) stated that noise trader move the market. Lo and Lio (2005) claimed that the noise traders influence the price in the market. They added that this would drive the prices away from the fundamentals causing an extreme deviation from the means of the price of the asset. There are many documented studies about the financial markets with a focus on the pricing and returns. Demers and Lev (2001) gave two broad reasons for how Internet stocks reached unjustifiably high prices in the late 1990s and early 2000. The first focuses on the fundamental values that highlight the elements of capital gains and losses. Investors change their opinion often based on indicators rather than on fundamental values. The second suggests that fundamentals were indeed responsible for market prices but investors’ interpretations of fundamentals were irrationally optimistic in making their assessments. Other studies that continue to advocate the use of market measures in assessing firm returns have regularly promoted dividend yields, market to book ratios, and price earnings ratios. In a study, Shiller (2005) claimed the price-earnings ratio is a good indicator of future inflation-adjusted stock market returns. Fama and French (1992) discussed the relationship between return and size, priceto-book ratio, they showed evidence of the incremental return and risk not explained in the assets pricing model. Lamont (1998) argued that price-earnings ratio has independent predictive power for excess return. Frankel and Lee (1998) studied firm’s future firm performance and concluded that firm’s performance may be over-estimated when the forecasted return on equity is greater than the current one. They added, the inflated ROE affects the price of the stock. Hitt, Hoskisson, and Kim (1997) considered return on assets, return on sales, and return on equity to study firm performance and concluded that return on assets is highly correlated with firm performance. While 9
in a study about firm performance, McKee, Varadarajan, and Pride (1989) used return on assets and return on equity measures to control the difference in capital structure among firms. In a study, Hodrick (1992) argued that high dividend yields lead to high returns. Chen, et al (2005) incorporated the use of dividend yields to test firm performance and value. Their sample included 412 Hong Kong firms. They concluded that dividend policies are more correlated to firm performance in larger firms. Chan, Hamao, and Lakonishok (1991) looked at market-to-book ratio, earnings yield and cash flow yields to assess stock returns. Their study concluded that bookto-market is the most significant predictor of expected returns. Clubb and Naffi (2007) argued that return on equity along with book-to-market values could be used to predict stock returns. In their study, they found that ROE served as an additional explanatory variable to predict future stock returns. In a study about emerging markets, Aras and Yilmaz (2008) used price-earnings, dividend yield and market-to-book ratio to predict returns. Ang and Bekaert (2007) talked about the reliability of using price-earnings ratio to predict future dividend growth. Other studies used non-financial ratios. Peng (2004) studied the relationship between external directors and their impact on sales and return on equity as indicators of firm performance. Core, Holthausen, and Larcker (1999) studied ownership structure and its impact on firm operations and stock returns. They concluded that weak governance creates bigger principal-agent problems that adversely affect firm performance. Bhagat and Bolton (2008) studied how corporate governance might affect performance. They found that better governance often leads to better firm performance. Similarly, in a study Maher and Anderson (2000) addressed the corporate governance effect on firm performance. They concluded that different types of ownership influence management supervision, which in turn affect firm performance. In a study, Lewellen (2002) addressed the predictive power of financial ratios in determining firm returns. Dastgir and Velashani (2008) found evidence that comprehensive income is a good measure of a firm’s performance. They reported that the earnings per share (EPS) is positively correlated with the firm’s performance and argued that EPS is also a measure of shareholder value. Shiller (2014) used a modified price-earnings ratio (CAPE) Cyclically Adjusted Price Earnings as a measure of how expensive the market is relative to an objective measure of the ability of corporations to earn profits. CAPE measure (Shiller P/E ratio) is the real (inflation-corrected) S&P Composite Index divided by the ten-year moving average - real earnings on the index. In an article published in The New York Times, Shiller (2014) argued even though the CAPE was never intended to indicate the timing of market crash, there were three periods only when the CAPE was above 25. These periods were years 1929, 1999, and 2007 and earmarked with major market drops. He added, currently (at that time) the CAPE is hovering around 25. Penman (1996) noted how the price-earnings ratio is often utilized as an indicator for earnings growth and a barometer for misaligned stock prices. He also states that given how price earnings ratios are indicative of earnings growth, they are thereby correlated to expected investor returns (as measured through ROE). Many studies highlighted the importance of the price earnings ratio as a key market indicator. Shen (2000) discussed the role of P/E ratios in predicting stock returns. She claimed that normally high 10
P/E ratios precede weak stock returns. In a study about the efficiency of the Indian stock market and portfolio returns, Bodhanwala (2014) identified price earnings ratio as a powerful market indicator. He concluded his study by showing significant evidence that portfolios with low price earnings ratio outperformed BSE Sensex index. On the other hand, Baldwin (2014) suggested that markets with overvalued price earnings ratios are at risk of correction. James Montier (in a statement quoted from the same article – Baldwin 2014) argued that as stocks were currently overvalued by more than 50%, they might deliver negative returns over the next seven years. De Bondt and Thaler (1985) in a study about the stock market, they claimed that price earnings ratio and expected return are related. They added, a low price earnings ratio tends to encounter larger adjusted returns and high price earnings ratio tends to reflect lower adjusted returns. When considering the impact of price earnings ratios in explaining stock market performance, is the market right (i.e. market makes the right signals)? Research Problem: Is the change in price earnings ratio a good predictor to the change in firm future earnings?
Methodology Research Instrument To test the market signals, the following steps are applied: 1- The stock market is subdivided into ten economic sectors, which are consumer discretionary, consumer staples, energy, financials, health care, industrials, information technology, materials, telecommunication services, and utilities. 2- The correlation coefficient of the change in market measure (P/E) and the future earnings (i.e. the average annual earnings of the next three years) is measured. 3- Stocks per sector are subdivided into two groups, which are stocks with i- positive correlation (i.e. correct market signal) or ii- negative correlation (i.e. incorrect market signal). 4- For both groups, i- the significance of the coefficient of correlations is tested using the following: t stat = SQRT[(r – ƿ) / [(1-r2) / (n – 2)]; degrees of freedom: n - 2
….
equation (1)
ii- the reliability of the market signal is tested using the coefficient of determination i.e. raising r [coefficient of correlation] to power 2 [ Coefficient of Determination = R2 = r2 . equation (2)]. r = sample correlation coefficient, ƿ = population coefficient correlation, n = sample size, degrees of freedom = n – 2. Alpha of 5% is used in the four parts of the study (Hair et. Al., 2012). Sample and Data Collection Data is taken from Compustat and includes all public firms that are listed on national and regional exchange US stock markets. The original number of firms was 9,503 and because of missing data, 765 only firms remained in the study. The sample data includes the P/E and the average annual earnings of three years over a twenty-year period starting January 01, 1993 to December 31, 2012.
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Measurement of Variables To examine the market signals, two measures are included in the study, which are 1- the change in P/E (a market measure) and 2- the firm’s average annual earnings of the next three years (i.e. future earnings). An example, the coefficient of correlation of the change in P/E at time (T0) with the average change in the annual earnings of the next three years (i.e. T1 T2 and T3) is measured.
Data Analysis The first part of the study starts by highlighting the characteristics of the firms’ correlations per sector. Table 1 represents data output of the measures of correlations and it shows that during the span of 20 years, the market correctly signaled the price movement of 525 firms out of the total 765 (i.e. 69%) and incorrectly signaled the price movement of 239 firms (i.e. 31%). The highest proportion of correct market signals per sector was for the industrial sector (82%) followed by the health care sector (73%). The highest proportion of incorrect market signals was for the financials sector (19%), followed by information technology sector (18%). Table 1 Economic Sector Consumer Discretionary Consumer Staples Energy Financials Health Care Industrials Information Technology Materials Telecommunication Services Utilities Market
Firms’ Characteristics Positive Count % Count 84 64% 48 36 62% 22 16 62% 10 97 66% 49 35 73% 13 111 82% 25 102 68% 48 26 63% 15 5 56% 4 13 72% 5 525 69% 239
Sector % 36% 38% 38% 33% 27% 18% 32% 37% 44% 28% 31%
Count 132 58 26 147 48 136 150 41 9 18 765
% 17% 8% 3% 19% 6% 18% 20% 5% 1% 2% 100%
The second stage includes measuring the coefficient of determination and testing the significance of the coefficient of correlation of the firms per sector that include 1- correct market signal, 2incorrect market signal, 3- both correct and incorrect signals (i.e. sector). Table 2 is the summary output of firms that included correct market signal; it shows eight economic sectors had a significant correct market signal at an alpha of 5%. The sector with the highest significance was financials with a t stat = 5.25 (t crit = 1.99) and R2 = 22% followed by information technology with a t stat = 5.04 (t crit = 1.98) and R2 = 20%. There were two sectors with insignificant correct market signals, which are telecommunication services and utilities. The correct market signals in general showed significant results with a t stat = 11.08 (t crit = 1.96) and R2 = 19%. 12
Table 2 Economic Sector Consumer Discretionary Consumer Staples Energy Financials Health Care Industrials Information Technology Materials Telecom Services Utilities Market
Correct Market Signal Positive Correlation Count 84 36 16 97 35 111 102 26 5 13 525
% Correl 64% 42% 62% 35% 62% 52% 66% 47% 73% 52% 82% 40% 68% 45% 63% 41% 56% 44% 72% 40% 69% 44%
R2 18% 12% 27% 22% 27% 16% 20% 17% 20% 16% 19%
t stat 4.18 2.17 2.26 5.25 3.48 4.61 5.04 2.18 0.86 1.44 11.08
t crit 1.99 2.03 2.14 1.99 2.03 1.98 1.98 2.06 3.18 2.20 1.96
Evidence Significant Significant Significant Significant Significant Significant Significant Significant Significant
Table 3 is the summary output of firms that included incorrect market signal; it shows two economic sectors with significant incorrect market signal. These sectors are information technology with a t stat = 2.32 (t crit = 2.01) and R2 = 10% followed by financials with a t stat = 2.06 (t crit = 2.01) and R2 = 8%. Even though there were two economic only out of ten with significant incorrect market signals, the overall incorrect market signals showed significant results with a t stat = 3.98 (t crit = 1.97) and R2 = 6%. Table 3 Economic Sector Consumer Discretionary Consumer Staples Energy Financials Health Care Industrials Information Technology Materials Telecomm Services Utilities Market
Incorrect Market Signal Negative Correlation Count 48 22 10 49 13 25 48 15 4 5 239
% Corre 36% -23% 38% -24% 38% -15% 33% -29% 27% -19% 18% -21% 32% -32% 37% -23% 44% -12% 28% -14% 31% -25%
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R2 5% 6% 2% 8% 4% 4% 10% 5% 1% 2% 6%
t stat -1.63 -1.08 -0.43 -2.06 -0.65 -1.03 -2.32 -0.85 -0.17 -0.25 -3.98
t crit 2.01 2.09 2.31 2.01 2.20 2.07 2.01 2.16 4.30 3.18 1.97
Evidence Significant Significant Significant
Table 4 is the summary output of the signals per economic sector i.e. included both firms with correct and incorrect market signal; it shows that five economic sectors had a significant correct market signal at an alpha of 5%. The sector with the highest significance was industrial with a t stat = 3.52 (t crit = 1.98) and R2 = 8% followed by financials with a t stat = 2.76 (t crit = 1.98) and R2 = 5%. There are five economic sectors with insignificant correct market signals, which are consumer staples, energy, materials, telecommunication services and utilities. The market in general has an overall significant correct signals with a t stat = 6.29 (t crit = 1.96) and R2 = 5%. Table 4 Economic Sector Consumer Discretionary Consumer Staples Energy Financials Health Care Industrials Information Technology Materials Telecomm Services Utilities Market
Market Signal per Sector Economic Sector R2 Count % Corre t stat 132 17% 18% 3% 2.11 58 8% 13% 2% 0.96 26 3% 26% 7% 1.33 147 19% 22% 5% 2.76 48 6% 33% 11% 2.34 136 18% 29% 8% 3.52 150 20% 20% 4% 2.52 41 5% 17% 3% 1.11 9 1% 20% 4% 0.53 18 2% 25% 6% 1.03 765 100% 22% 5% 6.29
t crit 1.98 2.00 2.06 1.98 2.01 1.98 1.98 2.02 2.36 2.12 1.96
Evidence Significant Significant Significant Significant Significant Significant
Limitations of the Study There were four limitations in the study and they are 1- Missing variables: due to missing data, 765 firms remained in the study out of 9503 firms i.e. 8% of the stock market. 2- Time span: it was limited to 20 years based on the set of the available data. 3- Market: The study included the US market only. 4- Market signal: The study included the P/E measure as a proxy to the market signal.
Recommendations Based on the above, it is recommended to 1- conduct a study about the financial and technology sectors that exhibited significant correct and incorrect market signals. 2- Investigate the sources of variations that affect in general the stock market as the correct market signals were reliable 5% only. 3- In addition, it is recommended to apply the model in different markets such as in Europe and or developing nations.
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Conclusions In general, during the 20-year span period, the market signals were marginally correct as it explained 5% only of the firm future earning variations. At a sector level, the market signals of consumer staples, energy, material, telecommunication services, and utilities sectors were insignificant, which means the market signals were irrelevant. In checking the market signals, two sectors showed significant incorrect market signals and these were the financials and technology sectors, which were the fastest growing sectors during that period. As for the correct market signals, all sectors (except telecommunication and utilities) showed significant correct signals. Even though the correct market signals were reliable 19% only (R2 = 19%), they validated the efficient market theory. However, it is quite interesting to notice that the two sectors that exhibited significant both correct and incorrect signals and these were the financials and technology sectors that were the fastest growing sectors during that period. Financial sector encompasses the group of low-risk investors (retirement / pension funds) and Technology sector encompasses the group of high-risk investors. The practical implication of the study is significant. It showed clearly that the market correctly correlated stock price movement to future earnings during a 20-year period 5% of the times only. Investors are making risky investment decisions without regard to firm performance. It will consequently lead to market adjustments and probably market crash. While this information is of a secondary importance for short-term investors and daily traders as they don’t hold stocks for long periods, it is of critical importance for long-term investors who are entering the market at different times where stocks are mispriced and they ultimately suffer of the consequences.
References Allouche, J., Amann, B., Jaussaud, J. & Kurashina,T. (2008). The Impact of Family Control on the Performance and Financial Characteristics of Family Versus Nonfamily Businesses in Japan: A Matched-Pair Investigation. Family Business Review, 21, 315-329. Ang, A., & Bekaert, G. (2007). Stock Return Predictability: Is it There? The Review of Financial Studies, 20, 651-707. Aras, G., & Yilmaz, M.K. (2008). Price-earnings Ratio, Dividend Yield, and Market-to-book Ratio to Predict Return on Stock Market: Evidence from the Emerging Markets. Journal of Global Business and Technology, 4 (1), 18-30. Baldwin, William (2014). Irrational Exuberance: The Sequel. Forbes. May 5, 2014. Vol. 193 Issue 6, p9898. Bhagat, S., & Bolton, B. (2008). Corporate Governance and Firm Performance, Journal of Corporate Finance, 14, 257-273. Bodhanwala, Ruzbeh J (2014). Testing the Efficiency of Price-Earnings Ratio in Constructing Portfolio. IUP Journal of Applied Finance. July 2014, Vol. 20 Issue 3, 111-118.
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Chen, Z., Cheung, Y.L., Stouraitis, A., & Wong, A.W.S. (2005). Ownership Concentration, Firm Performance, and Dividend Policy in Hong Kong, Pacific-Basin Finance Journal, 13, 431-449. Chan, L.K.C., Hamao, Y., & Lakonishok, J. (1991). Fundamentals and Stock Returns in Japan, The Journal of Finance, 46, 1739-1764. Clubb, C., & Naffi, M. (2007). The Usefulness of Book-to-Market and ROE Expectations for Explaining UK Stock Returns, Journal of Business Finance & Accounting, 34, 1-32. Cooper, M.J., Gulen, H., & Schill, M.J. (2008). Asset Growth and the Cross-Section of Stock Returns, The Journal of Finance, 63, 1609-1651. Core, J.E., Holthausen, R.W., & Larcker, D.F. (1999). Corporate Governance, Chief Executive Officer Compensation, and Firm Performance, Journal of Financial Economics, 51, 371-406. Dastgir, M. & Velashani, A.S. (2008).Comprehensive Income and Net Income as Measures of Firm Performance: Some Evidence for Scale Effect. European Journal of Economics, Finance and Administrative Sciences, 12,123-133. De Bondt, W.F.M., & Thaler, R. (1985). Does the Stock Market Overreact?, The Journal of Finance, 40, 793-805. Demers, E. and B. Lev (2001). A rude awakening: Internet shakeout in 2000, Review of Accounting Studies 6: 331-359. Fama, E.F., & French K.R. (1992). The Cross-Section of Expected Stock Returns, Journal of Finance, 47, 427-465. Frankel, R. & Lee, C.M.C. (1998). Accounting Valuation, Market Expectation, and CrossSectional Stock Returns, Journal of Accounting and Economics, 25, 283-319. Gompers, P. A., Ishii, J. L., &Metrick, A. (2003).Corporate Governance and Equity Prices. The Quarterly Journal of Economics, 118, 107-155. Hair J., Anderson R., Tatham R, & Black W. (2012). Multivariate Data Analysis, 7th edition, Prentice Hall. Hitt, M.A., Hoskisson, R.E., and Kim, H. (1997). International Diversification: Effects on Innovation and Firm Performance in Product-Diversified Firms, The Academy of Management Journal, 40, 767-798. Hodrick, R.J. (1992). Dividend Yields and Expected Stock Returns: Alternative Procedures for Inference and Measurement, The Review of Financial Studies, 5, 357-386. Lamont, O. (1998). Earnings and Expected Returns, Journal of Finance, 53, 1563-1587. Lei, V., Noussair, C., & Plott, C. (2001). Non-speculative Bubbles in Experimental Asset Markets: Lack of Common Knowledge of Rationality vs. Actual irrationality. Econometrica,69, (4), 831-859. Lewellen, J. (2002). Predicting Returns with Financial Ratios. Journal of Financial Economics,74, 1-38.
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Lo, W.C., , & Lio, K.J.. (2005). A Review of the Effects of Investor Sentiment on Financial Markets: Implications for Investors. International Journal of Management. 22(4), 708-715. Maher, M., & Anderson, T. (2000). Corporate Governance: Effects on Firm Performance and Economic Growth. Manuscript in preparation. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=218490 McKee, D.O., Varadarajan, P.R., & Pride, W.M. (1989). Strategic Adaptability and Firm Performance: A Market-Contingent Perspective, Journal of Marketing, 53, 21-35. Nettles, M., & Mathew, S. (2003). Investing During Turbulent Times. Journal of Black Enterprise,33, (11), 41. Ofek, E., & Richardson, M. (2003). Dot.Com mania: the rise and fall of internet prices. Journal of Finance, 58, 1113-1138. Peng, M.W. (2004). Outside Directors and Firm Performance During Institutional Transitions, Strategic Management Journal, 25, 453-471. Penman, S.H. (1996). The Articulation of Price-Earnings Ratios and Market-to-Book Ratios and the Evaluation of Growth, Journal of Accounting Research, 34, 235-259. Shen, P. (2000). The P/E Ratio and Stock Market Performance, Federal Reserve Bank of Kansas City, Economic Review, Fourth Quarter, 23-36. Shiller, R.J. (2005). Irrational Exuberance – 2nd edition. Princeton University Press, 2005. ISBN: 9780691123356. Shiller, R.J. (2014). The Mystery of Lofty Stock Market Elevations. The New York Times, Economic View - August 12,2014.
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Seasonality and The Newspaper Distribution Problem: Using Data Visualization to Improve Trend Line Forecasts Ana Maria Garcia Saint Peter's University Guen Pak Saint Peter's University Francis Oduro Saint Peter's University Deondre Thompson Saint Peter's University Karla Erazo Saint Peter's University Joseph Gilkey Jr. Saint Peter's University
ABSTRACT Given the persistent poor, uncertain economic performance in the print media industry, newspaper distributors are challenged to visualize and leverage data from their various distribution routes and business store points to identify the routes and distribution points along those routes that hold the potential for profitability. This paper analyzes the geographic overview and corresponding data from the prime distribution areas of a newspaper distribution company operating in the major urban corridors of southwestern Pennsylvania and north-central New Jersey regions. Trend line forecasts are then generated to predict sales performances in each area for specific newspaper products. A waterfall model further pinpoints the type of newspaper and product and the best distribution points in the company’s areas of responsibility. Index Terms—data visualization, newspaper distribution, sales trend line forecasting, seasonality
Introduction Liberty News Distributors, Inc., founded in 2006, encompasses more than 5,000 national accounts and distributes more than 1,500 titles including domestic newspapers, periodicals and international magazines. Major distribution points include convenience stores, shopping plazas and airports along with more than 300 select independent and chain stores accounts via the FedEx Corp. Recognizing the need to identify sales opportunities and ongoing positive communication with retailers, distributors and publishers, the company tasked a research team of university students, 18
led by a faculty member, to use data visualization tools (e.g., Tableau and Microsoft Excel) to finetune daily distribution operations that reflect consumer buying behavior of newspaper products and the distribution points at stores they patronize. This study focuses on distribution of four newspapers in the Pennsylvania-New Jersey market covered by the company: Delco Times, The New York Times, New York Daily Post and New York Daily News.
Literature Review Distribution and Seasonality Newspapers have a short time-sensitive life. For national dailies, such as The New York Times and The Daily News, the respective value of each copy is zero the day following its publication. The lifecycle is rapid, as most readers prefer to receive the news before 9 a.m. or whatever time their workday begins, unlike with longer-form media products (e.g., novels, hardbacks, magazines, or other periodicals). These problems have been compounded by large-scale changes in commuting habits of consumers who must contend with heavy traffic volume, especially during the morning hours in major urban areas. The challenge existed long before technological advancements in information dissemination and communication began to affect print media’s profitability, as Fowler’s model of comparative readability of newspapers and novels has demonstrated (1904, 1933 and 1965). The newspaper distribution problem also has been compounded by the geography of distribution and allocation points, along with fleet routing problems. In this case, Liberty News Distribution has experienced routing problems, given the geographical distance of some locations that confound the economies of scale in distribution. In a 1996 study of a major U.S. metropolitan newspaper, cost savings were realized by reducing the number of distribution centers along with a corresponding decrease in truck fleets and drivers required to serve the distribution centers from the newspaper production facilities. Resolving the distribution component problem requires coordinating and overcoming the problems of uncertain demands to identify potentially profitable drop-off points for newspaper products in targeted areas that can be delivered within the shortest amount of time possible. The objective is to reduce undue costs and mitigate risks of sale losses that are aggravated by high levels of return and stocking costs and transportation expenses, especially on the least profitable segments of routes in the company’s areas of responsibility for distribution. Readership Newspaper circulation and readership decline continues a long-term trend. In 2015, the average weekday circulation fell seven percent, despite annual increase of 2 percent in digital subscriptions to newspapers. Sunday circulation during the same year declined by 4 percent, again despite a 4 percent increase in digital Sunday newspaper subscriptions (Barthel, 2016). The most recent declines occurred after a brief rebound in print subscriptions in 2013
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Despite the declines, print circulation still accounts for the largest share of readership (78 percent, weekdays; 86 percent, Sunday) and one survey indicates that 59 percent of consumers who read newspapers still do so in print-only formats (Barthel, 2016).
Data The company provided 12 months of aggregate data for the 2015-16 period so that the research team could prepare visualizations for analysis. The data include distribution locations for each route in all three areas designated for analysis, including street addresses. Sales data for each day of the week and the sales of individual newspaper editions are indicated for each location, along with prices, revenues and gross profit margins. The team accounted for aspects that might have hindered effective visualizations. One instance involved negative numbers in the data signifying a potential revenue loss due to not properly accounting for “Draws” from “Returns” to equaling “Sales.” Problems were resolved by segmenting data (i.e., querying identical records of data) into measurable pieces by establishing a coordinated hierarchy covering the four geographical areas of operations. For example, if a Philadelphia store sells papers every day of the week, but circulation data separate weekdays, Saturdays and Sundays (Fig. 1). To make the analysis more efficient and comprehensible, data records were formatted to chart weekly sales, as opposed to day-to-day sales (Fig. 2). A script was created to automate the process for the entire raw data set (https://github.com/gpak/SchoolprojectOR/blob/master/Combining%20Cells). This procedure facilitated ease and efficiency in data visualization and storage.
Fig. 1. The raw data as presented by the company. Fig. 2. The condensed data after file manipulation. The research team identified four areas to further organize the data set: Area 1 (greater Philadelphia and Delaware County, Pennsylvania), Area 2 (north and central New Jersey), Area 3 (central and coastal New Jersey) and Area 4 (newly acquired distribution outlets lacking in sufficient data timespan set aside temporarily to be considered in follow-up research).
Results One example of a linear trend model (0.0878696*Week of W/E+4379.1) was computed for The New York Times in Area 1, with Measure Values given W/E Week (R2=0.818676; F14, 406 = 140.622; p
