Home   |  Publications   |   Conferences    |  Join   |   Contact   | Sitemap  
                                 
 

Journal of Business & Financial Affairs


Open Access
 
 
 
ISSN: 2167-0234
 
 
 
home » journals » stock-picking-2167-0234 Rss Feed Rss Feed
 
Make the best use of Scientific Research and information from our 700+ peer reviewed, Open Access Journals that operates with the help of 50,000+ Editorial Board Members and esteemed reviewers and 1000+ Scientific associations in Medical, Clinical, Pharmaceutical, Engineering, Technology and Management Fields.
 
Meet Inspiring Speakers and Experts at our 3000+ Global Conferenceseries Events with over 600+ Conferences, 1200+ Symposiums and 1200+ Workshops on Medical, Pharma, Engineering, Science, Technology and Business
 
 
Editorial Open Access
 
Stock Picking
George Athanassakos*
Department of Finance, Ivey Business School, Western University, Canada
Corresponding Author : George Athanassakos
Department of Finance, Richard Ivey School of Business
University of Western Ontario, Canada
Tel: 519-661-3485
E-mail: gathanassakos@ivey.uwo.ca
 
Received November 12, 2013; Accepted November 13, 2013; Published November 15, 2013
 
Citation: Athanassakos G (2013) Stock Picking. J Bus & Fin Aff 2:e137. doi: 10.4172/2167-0234.1000e137
 
Copyright: © 2013 Athanassakos G. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
 
Related article at
DownloadPubmed DownloadScholar Google
 
Visit for more related articles at
DownloadJournal of Business & Financial Affairs
 
 
It is a commonly held misconception that all value investors do is sort stocks by P/E (or P/B) and invest in those with low P/E (P/B). But considering low P/E (P/B) stocks is only part of the value investing process. This is because, on average, about 39% of all low P/E (P/B) stocks have a negative return for the 12 months following their selection [1]. How do value investors separate the good low P/E (P/B) stocks from the bad ones? They do so by valuing each low P/E (P/B) stock to determine its intrinsic value and only invest in the stocks that afford them a satisfactory “margin of safety” - these are the good low P/E (P/B) stocks.
 
Athanassakos [2] followed this process in the first academic paper to do so. He examined whether value investors (following the right process) add value over and above a simple rule that dictates they invest only in stocks with low P/E (P/B) ratios. Using Canadian data, he found that value investors do add value, in the sense that their process of selecting truly undervalued stocks produced significantly positive excess returns over and above the naive approach of simply selecting stocks with low P/E and P/B ratios. The average annual outperformance between 1985 and 1998 was 1.10 per cent, while between 1999 and 2007 was 13.20 per cent.
 
But this is not easy to do for the average investor and, even for professionals, it is a very time consuming exercise. Is it possible to identify the good low P/E (P/B) stocks (i.e., the truly undervalued or quality stocks) without having to go through the time consuming estimation of each stock’s intrinsic value? Is there an additional screening, after the low P/E (P/B) stocks have been chosen, which will enable an investor to identify the low P/E (P/B) stocks worth investing in without having to go through the time consuming estimation of each stock’s intrinsic value?
 
Here are a few approaches that have been developed to deal with this question.
 
Athanassakos [1] has researched this question in the US markets using COMPUSTAT data for the period May 1, 1969 to April 30, 2011. He removed the time-consuming step of valuing each stock individually by assigning a SCORE to each stock that is based on publicly available financial ratios from historical company information.
 
He excluded from his data AMEX companies, high business risk companies, such as Software & Services, Semiconductors & Semiconductor Equipment, Transportation, Automobiles & Components, Real Estate/Construction Materials and Pharmaceuticals, Biotechnology & Life Sciences Capital Goods, and companies that had reported extraordinary items the year before. Those stocks were excluded as he found that they tended to have low returns over his sample period. He also excluded negative P/E stocks, stocks with P/E over 500 and stocks that traded for less than $1. His final sample included 78,752 annual observations belonging to 7,353 unique companies.
 
Each year at the end of April, Athanassakos [1] sorted the stocks in his sample by trailing P/E ratios from low to high and formed quartiles. Value stocks are those that fall in the lowest quartile. A SCORE for each value stock was then assigned based on six historical variables: market cap, stock liquidity (i.e., annual trading volume/shares), asset turnover (i.e., assets/revenues), total debt to equity, cash to assets and yearover- year EBIT annual growth rate, one variable at a time. The overall SCORE was derived by assigning a value of 1 (for good ranking) or the value of zero (for bad ranking) to each of the 6 firm-specific variables and summing up the zero or one values for each firm. He finally formed seven portfolios of firms with SCOREs from low (portfolio 0) to high (portfolio 6) and calculated each SCORE portfolio’s mean and median annual returns from May 1, 1969 to April 30, 2011. He found that value firms with the highest SCORE had a mean annual return of 54.38%. The lowest SCORE value firms had a mean annual return of 13.32%. Median annual returns were consistent with the mean values. For comparison, the mean annual return for all value stocks in his sample was 22.36%.
 
Athanassakos [3] examined Canadian non-interlisted companies that traded on Canadian Stock Exchanges from May 1, 1985 to April 30, 2009 also employing COMPUSTAT data. He zeroed in on noninterlisted Canadian stocks as a test of the robustness of Athanassakos’ [1] approach since these stocks are very different from the stocks examined in the US study in terms of size, liquidity and visibility. He used the same methodology as the one followed for the US markets. The Canadian non-interlisted sample contained 7,145 annual observations belonging to 1,237 unique companies. He found that the highest SCORE portfolio had a mean annual return of 36.89%, whereas the lowest SCORE portfolio had a mean annual return of -11.35%. The median annual returns were consistent with the mean values. For comparison, the mean annual return for all value stocks in his sample was 16.86%.
 
Others have followed similar approaches to identify quality or outperforming low P/E (P/B) stocks. Greenblatt [4] develops a “magic formula” that uses return on capital (ROC) (namely, EBIT/Tangible Capital) as a key metric to select quality value stocks. He ranks value firms (i.e., low P/E firms) by ROC and buys only stocks with high return on capital.
 
Piotroski [5] uses a SCORE to separate the good from the bad value stocks. His SCORE consists of 9 variables that take the value 0 (bad signal) or 1 (good signal). His variables capture profitability (positive earnings, positive cash flows from operations, increasing return on assets and negative accruals), operating efficiency (increasing gross margins and asset turnover) and liquidity (decreasing debt, increasing current ratio, and no equity issuance). The SCORE for a stock is the sum of the 0 or 1 values for all firm-specific variables.
 
Graham [6] uses another SCORE related approach to identify quality value stocks. A good SCORE (i.e., value of 1) is assigned, if current ratio exceeds 2, or net current assets exceed long term debt, or ten year history of positive earnings, or ten year history of returning cash to shareholders or EPS that are at least a third higher than they were 10 years ago. Otherwise, the SCORE is zero. The SCORE assigned to a stock is then the sum of all 0 or 1 values.
 
Finally, Novy-Marx [7] uses a simple measure for quality, namely gross profits to assets (GPA), and focuses on those value stocks that have a high GPA.
 
For all markets examined, irrespective of the approach followed to identify truly undervalued stocks, it was possible to separate winning from losing value stocks when stock selection took place by focusing on high SCORE or quality value stocks. Consequently, an additional screening (based on a SCORE or quality indicator) to the first screening of the value investing process (i.e., only looking at low P/E (P/B) stocks) adds considerable value to an investment strategy and makes stock picking simpler, easy to standardize and, hence, faster.
 
References
 







 


 View 

 Download    pdf version of this article

Select your language of interest to view the total content in your interested language
 
Share This Article
   
 
   
 
Relevant Topics
Disc Account
Disc Accountancy and Finance
Disc Accounting Information
Disc Accounting Review
Disc Accounting ethics
Disc Accounting information system
Disc Advertising
Disc Applied Economics
Disc Assessment Scales
Disc Audit
Disc Avenues of Investment
Disc Balance sheet
Disc Banking
Disc Banking Research
Disc Banking Research Studies
Disc Budgeting
Disc Bullion Market
Disc Business
Disc Business Cycle
Disc Business Development
Disc Business Ethics
Disc Business Management
Disc Business Theory
Disc Business and Management
Disc Business organization
Disc Capital Marketing
Disc Capital Markets
Disc Capital Movements
Disc Capital Structure
Disc Chief Marketing Officer
Disc Computable General Equilibrium Model
Disc Corporate Finance
Disc Corporate Governance
Disc Corporate Governance Structure
Disc Cost Accounting
Disc Credit
Disc Currency
Disc Customer Satisfaction
Disc Decision Analysis
Disc Decision Making Process
Disc Deflation
Disc Demand Theory
Disc E-Governance
Disc E-Retailing Market
Disc E-Tourism
Disc E-banking
Disc E-business
Disc Economic Cycle
Disc Economic Growth
Disc Economic Policies
Disc Economic Policy
Disc Economic Resources
Disc Economics Studies
Disc Economy Policy
Disc Electronic Commerce
Disc Emerging Markets Economy
Disc Empirical Analysis
Disc Entrepreneurial Management
Disc Entrepreneuship organization
Disc Exchange Traded Funds
Disc Fair Trade
Disc Finance and accounting
Disc Finance management
Disc Finance of Commodity Markets
Disc Financial Analysis
Disc Financial Crisis
Disc Financial Econometrics
Disc Financial Markets
Disc Financial Reporting
Disc Financial Reporting Standard
Disc Financial Risk
Disc Financial and Nonfinancial Information
Disc Financial plan
Disc Financial valuation
Disc Fiscal and tax policies
Disc Food Service
Disc Foreign Exchange
Disc Global Accounting
Disc Global Market
Disc Gross Domestic Product -GDP
Disc Hotel Management
Disc Human Capital
Disc Human Resource
Disc Income Smoothing
Disc Indexation
Disc Industrial Business
Disc Industrial Policy
Disc Inflation
Disc Information Technology Management
Disc Innovation Management
Disc Intellectual Capital Disclosures
Disc Intellectual property
Disc International Business
Disc International Relations
Disc International finance
Disc Internet role and telecommunications
Disc Investment
Disc Labour Economy
Disc Leadership
Disc Leadership and Organization Behaviour
Disc Macro Economics
Disc Management
Disc Management Accounting
Disc Management Development
Disc Management Information System
Disc Managerial Economics
Disc Manufacturing Operations
Disc Manufacturing and investment
Disc Manufacturing business
Disc Marginal Utility
Disc Market Analysis
Disc Market Equilibrium
Disc Marketing Analysis
Disc Marketing Performance
Disc Marketing management
Disc Marketing-Accounting-Finance Interface
Disc Micro Economics
Disc Monetary Policy
Disc Nasdaq
Disc New Trade Theory
Disc Organizational studies
Disc Panel Data
Disc Parameter Estimation
Disc Primary Market
Disc Production & Operations Management
Disc Profitability
Disc Project and Team Management
Disc Reporting Management
Disc Resource Management
Disc Secondary Market
Disc Small Business
Disc Small Firms
Disc Social Economics
Disc SocioEconomics Status
Disc Statistics
Disc Stock Exchange Business Studies
Disc Stock Market
Disc Stock Market Returns
Disc Stock Return Predictability
Disc Strategic Cost Analysis
Disc Strategic Information
Disc Strategy Management
Disc Talent Management
Disc Taxation
Disc Time Series
Disc Trading
Disc Trading forex
Disc Venture Capital
Disc Wealth Management
Disc Women Entrepreneur
Disc World banking
Disc spreadsheet design
 
Related Journals
Disc Accounting Journal
Disc Business Journals
Disc Economics Journal
Disc Entrepreneurship Journal
Disc Global Economics Journal
Disc Hotel Management Journal
Disc Internet Banking Journal
Disc Marketing Journal
Disc Stock & Forex Trading Journal
 
Related Conferences
Disc Social Media, SEO and Marketing Strategies
September 12-13, 2016 Phoenix, Arizona, USA
 
Article Tools
Disc Export citation
Disc Share/Blog this article

Post your comment

Name:
E-mail:
Your question:
Anti Spam Code:
  Reload  Can't read the image? click here to refresh

OMICS International Conferences 2016-17

Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings
Conferences By Country
  USA   Spain   Poland
  Australia   Canada   Austria
  UAE   Switzerland   Turkey
  Italy   France   Finland
  Germany   India   Ukraine
  UK   Malaysia   Denmark
  Japan   Singapore   Mexico
  Brazil   South Africa   Norway
  South Korea   New Zealand   China
  Netherlands   Philippines
 
Medical & Clinical Conferences
Microbiology Oncology & Cancer
Diabetes & Endocrinology Cardiology
Nursing Dentistry
Healthcare Management Physical Therapy Rehabilitation
Neuroscience Psychiatry
Immunology Infectious Diseases
Gastroenterology Medical Ethics & Health Policies
Genetics & Molecular Biology Palliativecare
Pathology Reproductive Medicine & Women Healthcare
Alternative Healthcare Surgery
Pediatrics Radiology
Ophthalmology  
 
Conferences by Subject
Pharmaceutical Sciences
Pharma Marketing & Industry
Nutrition
Environmental Science
Physics & Materials Science
Environmental
EEE & Engineering
Veterinary
Chemical Engineering
Business Management
Massmedia
Geology & Earth science
 
 
©2008-2016 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version