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年度投资人:如何从交易历史与季节趋势中获利 THE ALMANAC INVESTOR:

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作者: Jeffrey A. Hirsch著

出 版 社: 吉林长白山

出版时间: 2006-12-1字数:版次: 1页数: 523印刷时间: 2006/12/01开本:印次:纸张: 胶版纸I S B N : 9780471654056包装: 平装编辑推荐

作者简介:

JEFFREY A. HIRSCH is President of the Hirsch Organization. He's worked with founder Yale Hirsch for sixteen years, taking over as President and Editor in 2001. He appears frequently on CNBC, FOX News, and Bloomberg to discuss market cycles and seasonal trends, and edits the firm's monthly newsletter, also entitled Almanac Investor.

内容简介

The key to successful financial research is the ability to access and manipulate accurate data. This book, and its cutting edge, completely Internet-based trading system--that can organise and evaluate any market data, with all parameters set by the researcher--introduces a new way of doing just that. The Hirsch name is known for time tested and successful research and analysis, and The Almanac Investor will share valuable Hirsch theories and strategies with investors.

Trusted advice and techniques that can improve any reader's overall trading efficiency.

Contains data, indicators, and patterns needed to understand how and why the stock market fluctuates.

A companion Web site will host a revolutionary Web-based system to evaluate and improve trading practices.

Includes a step-by-step tutorial, which supplements every study in the book.

目录

Preface

Part 1: Indicators and Patterns

January Indicators and Patterns

January Almanac

Typical January

January’s First Five Days “Early Warning System”

The Incredible January Barometer

1933 “Lame Duck” Amendment — Why JB Works

New Congress Barometers

JB versus All

Down Januarys Followed by Further Declines

The So-Called “January Effect”

February Indicators and Patterns

February Almanac

Typical February

Market Negative Before & After Presidents’ Day

March Indicators and Patterns

March Almanac

Typical March

Luck of the Irish Hits Wall Street Day Before StPat’s

April Indicators and Patterns

April Almanac

Typical April

Good Friday Better in April

Down January + Down April = Bad Sign

May Indicators and Patterns

May Almanac

Typical May

Wall Street Rallies For Moms

Memorial Day and the Stock Market

June Indicators and Patterns

June Almanac

Typical June

NASDAQ’s Powerful 12-Day Mid-Year Rally

July Indicators and Patterns

July Almanac

Typical July

Hot July Markets Often Precede Fourth Quarter Buying Opportunities

August Indicators and Patterns

August Almanac

Typical August

September Indicators and Patterns

September Almanac

Typical September

Trading the Labor Day Market

October Indicators and Patterns

October Almanac

Buy! Buy! Buy!

Typical October

November Indicators and Patterns

November Almanac

Typical November

Trading the Thanksgiving Market

December Indicators and Patterns

December Almanac

Typical December

Most of the So-Called “January Effect” Starts in Mid-December

The Only “Free Lunch” on Wall Street

Santa Claus Rally

Half-Hourly Trading Patterns

Weekly Trading Patterns

Dow Gains Most First Two Days of the Week

Monday Now Most Profitable S&P 500 Day of the Week

S&P 500 Daily Performance Each Year Since 1952

NASDAQ Strongest Last Three Days of the Week

NASDAQ Daily Performance Each Year Since 1971

Monthly Trading Patterns

Typical S&P Month

Typical NASDAQ Month

Dow Gains More Eight Days a Month Than On

All 13 Remaining Days Combined

S&P 500 Market Probability Calendars

Dow Jones Industrials Market Probability Calendars

NASDAQ Market Probability Calendars

Russell 1000 Market Probability Calendars

Russell 2000 Market Probability Calendars

Annual Trading Patterns

Market Behavior Three Days Before and Three Days After Holidays

Seasonal Rallies and Corrections

Best Months of the Year: Seasonal Switching Strategies

Aura of the Triple Witch: Fourth Quarter Most Bullish, Down Weeks Trigger More Weakness Week fter

Annual Trading Volume Patterns

The December Low Indicator: A Use Prognosticating Tool

The Four-Year Presidential Election/ Stock Market Cycle

Part 2: Seasonal Sector Investing

Part 3: Databank

 
 
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