Chaikin Analytics#Chaikin Money Flow

The Chaikin Power Gauge Rating is a stock rating system developed by Marc Chaikin that assigns a bullish (green), neutral (yellow), or bearish (red) rating to a particular stock.{{cite web|url=http://online.barrons.com/article/SB50001424052970204582404576214663897591494.html?mod=googlebarrons|title=OptionsXpress Gives Schwab a Leg Up|date=March 26, 2011|publisher=Barron's|author=THERESA W. CAREY|access-date=March 28, 2012}}

The rating system accounts for over 20 metrics and organizes these into four categories:

• Financial metrics
• Earnings performance
• Price-volume activity
• Expert opinions

The system has been back-tested on 10 years of data.{{cite web|url=https://www.chaikinanalytics.com/wp-content/uploads/2016/12/PowerGaugeWhitePaper2.0.pdf|title=Evaluation of the Chaikin Power Gauge Stock Rating System|first=Marc|date=February 2, 2013|website=Chaikin Analytics|access-date=May 26, 2017|author=Gerstein|archive-url=https://web.archive.org/web/20170331075749/https://www.chaikinanalytics.com/wp-content/uploads/2016/12/PowerGaugeWhitePaper2.0.pdf|archive-date=March 31, 2017|url-status=dead}}

In April 2014, Chaikin Analytics collaborated with Nasdaq to overlay the Chaikin Power Gauge stock rating on three popular Nasdaq stock indexes: Large Cap, Small Cap, and Dividend Achievers.{{Cite web|url=http://ir.nasdaq.com/releasedetail.cfm?releaseid=837733|title=NASDAQ OMX and Chaikin Analytics Introduce Enhanced Alpha Indexes (NASDAQ:NDAQ)|website=ir.nasdaq.com|language=en|access-date=2017-05-26}}

In 2017, Chaikin Analytics in collaboration with Nasdaq and IndexIQ brought the Chaikin Power Gauge stock rating approach to the ETF marketplace for the first time.{{Cite news|url=https://www.etfstrategy.co.uk/indexiq-launches-multifactor-us-small-cap-etf-65248/|title=IndexIQ launches multifactor US small cap ETF|date=2017-05-17|work=ETF Strategy|access-date=2017-05-26|language=en-US}}

The Chaikin Oscillator was developed in the 1970s.{{cite news |url=https://www.forbes.com/sites/johndobosz/2012/01/18/separating-chumps-from-champs-in-retail-banks-and-tech/ |publisher=Forbes: Market Blaster |date=January 18, 2012 |title=Separating Chumps From Champs In Retail, Banks And Tech |author=John Dobosz |access-date=March 28, 2012}} The indicator is based on the momentum of the Accumulation/Distribution (AD).{{cite book|url=https://books.google.com/books?id=kDbOOGSNMW8C&q=marc+chaikin&pg=PT2|title=CMF--Chaikin Money Flow: Changes Anticipating Price Reversal|author=Michael C. Thomsett|publisher=The Financial Times Press|year=2010|isbn=9780132492065}} AD calculates the position of a stock's daily closing price as a fraction of the daily price range of the stock. This fraction is multiplied by the daily volume to quantify the net accumulation or distribution of a stock. AD is expressed mathematically as:

$AD\; =\; cum\backslash frac\{(C\; -\; L)\; -\; (H\; -\; C)\}\{(H\; -\; L)\}\backslash times\; V\backslash !\backslash ,$

or

$AD\; =\; cum\backslash frac\{2C\; -\; (H\; +\; L)\}\{(H\; -\; L)\}\backslash times\; V\backslash !\backslash ,$

where "AD" represents the Accumulation/Distribution cumulative total running line, "cum" is an instructive abbreviation meaning "calculate a cumulative total running line", "C" is the daily closing price, "H" is the daily high price, "L" is the daily low price, and "V" is the daily total volume.

The indicator is measured as the difference between the 3-day exponential moving average (EMA) of the AD to the 10-day EMA of the AD.{{cite book |url=https://archive.org/details/encyclopediaofte00colb |url-access=registration |quote=marc chaikin. |title=The Encyclopedia of Technical Market Indicators |author=Robert W. Colby |page=[https://archive.org/details/encyclopediaofte00colb/page/51 51] |publisher=McGraw-Hill Professional |year=2003}} It signals when crossing above or below the zero line or when bullish/bearish departures take place. These signals anticipate the change in the direction of the AD. Stock analysts observe a Chaikin Oscillator graph to look for the signal to buy or sell a stock.{{cite news |url=https://www.bloomberg.com/apps/news?pid=newsarchive&sid=aJlJMnDM9zdE |title=London Sumatra May Fall on Chaikin Turn: Technical Analysis |date=December 21, 2009 |author=Berni Moestafa |publisher=Bloomberg}}

Chaikin Money Flow (also referred to as CMF) is one of the metrics taken into account by the Chaikin Power Gauging System. CMF tracks cash flow volumes over a fixed period, usually around 20 days.{{cite book |author= Charles D. Kirkpatrick, Julie R. Dahlquist |title=Technical Analysis: The Complete Resource for Financial Market Technicians |pages=421–422 |publisher=Financial Times Press |year=2012}} The indicator oscillates above and below the zero line, indicating a bullish or bearish trend.{{cite book |title=Investing with Volume Analysis: Identify, Follow, and Profit from Trends |author=Buff Dormeier |publisher=Financial Times Press |pages=136–137 |year=2011}} The indicator also calculates Chaikin’s Accumulation/Distribution (AD).{{cite book|title=Bloomberg Visual Guide to Candlestick Charting|author=Michael C. Thomsett|publisher=John Wiley & Sons|year=2012|page=265}}

Chaikin Money Flow is derived from foundational trading principles; gauging buying support and/or selling pressure. Buying support is typically indicated by increased trade volume and repeated closes in the top half of the daily range while selling pressure is indicated by increased trade volume and recurrent closes in the lower half of the daily range. Rising prices often accompany buying support and decreasing prices usually characterize selling pressure. The result provides insight into cash flow into or out of a stock.{{cite book |title=Momentum Explained, Volume 2 |author=Martin J Pring |year=2002 |publisher=McGraw-Hill Professional |pages=60–68}}{{cite book |title=Modeling Uncertainty with Fuzzy Logic: With Recent Theory and Applications |author=Asli Celikyilmaz and I. Burhan Türkşen |year=2009 |publisher=Springer |page=368}}

To determine the CMF one must first determine the Close Location Value (CLV) as follows:

$CLV\; =\; \backslash frac\{(close\_\{1\}\; -\; low\_\{1\})\; -\; (high\_\{1\}\; -\; close\_\{1\})\}\{(high\_\{1\}\; -\; low\_\{1\})\}\backslash !\backslash ,$

or

$CLV\; =\; \backslash frac\{2close\_\{1\}\; -\; (high\_\{1\}\; +\; low\_\{1\})\}\{(high\_\{1\}\; -\; low\_\{1\})\}\backslash !\backslash ,$

The next step is to take the CLV and determine the CMF, as follows:

$CMF\; =\; \backslash frac\{\backslash sum\_\{t=20\}^tCLV\_\{t\}\; \backslash times\; volume\_\{t\}\}\{\backslash sum\_\{t=20\}^t(vol\_\{t\})\}\backslash !\backslash ,$

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