Best forex trading books

Best forex trading booksBest currency trading books on Amazon. 2015 highest rated, top recommended best sellers Forex trading books to read for beginner to advanced traders.

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Best Forex trading book

Written by Wayne McDonell, the Chief Currency Coach at FX Bootcamp, this book shows readers how to successfully trade the Forex market on their own. FX Bootcamp's Guide to Strategic and Tactical Forex Trading skillfully explains how to combine popular technical indicators to formulate a comprehensive market strategy. Readers will then learn how to focus on using this information to create a tactical trading plan--one that will help them pull the trigger to get in and out of a trade. Along the way, McDonell takes the time to discuss the various challenges a Forex trader faces, such as greed, fear, loss, and isolation. As a Forex trader and educator of traders, Wayne McDonell knows what it takes to make it in the competitive world of Forex. And with FX Bootcamp's Guide to Strategic and Tactical Forex Trading he shows readers how.

Wayne really hits the mark with this book. It's a basic summary of the method he teaches at fxbootcamp. Not only does he cover the technical and fundamental aspects that a successful Forex trader needs to know, but he covers the other all important topics too, like risk analysis, trade plans & journals, trade reviews and having the right mindset to be a successful trader. All with no Fluff!

I found Wayne's book really helped me trade according to MY rules, not the markets rules. This meant that when I trade, it's become a lot of fun again. Now I am trading in one night, profit wise, what I used to trade in one month.

If you want to get a clear sense of what is in this book, have a look at his videos on FXStreet blogs called "2 Hour Video: Strategic & Tactical FOREX Trading". blogs. fxstreet/fxbootcamp/2008/03/12/2-hour-video-strategic-tactical-forex-trading

I recommend this book to anyone who feels there is room to improve in their Forex trading, or is looking for a reliable, simple system to follow. The method outline in Wayne's book won't give you every pip in a market's movements, but it will provide you with a large portion of them, in a conservative manner.

I also recommend this book to new FX Bootcampers, as it will help them come up to speed quickly, with the method Wayne teachers at FX Bootcamp.

In response to Jeff Marsick's review (RE - Good But Poorly Edited), I agree that this book could have done with more editting. In regards to Wayne's MACD settings of 21, 55, 8, this is in fact correct. One of the things with Wayne's method is that he uses a slowed down MACD oscillator to guage MARKET speed (medium term price action) and a sped up Stochastics oscillator to measure PRICE momentum (short term price action). This is covered in the above mentioned webinar video at around 18 minutes into it.

Tag currency trader magazines

Tag currency trader magazinesTag: currency trader magazines

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Community Information

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2highly profitable forex news trading strategies that matter

2highly profitable forex news trading strategies that matter2 Highly Profitable Forex News Trading Strategies that Matter

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In my previous post, we identified the Forex news reports that are trade worthy. Knowing what the tradable news items are is one thing, but knowing how to trade these news releases is another.

With this at the back of our minds, I would like to share with you two highly profitable Forex news strategies that I have used with great success over several years. Both are highly profitable and can be mastered in a short time.

Forex News Trading Strategy 1 – John Wayne

Let’s say you want to trade the next Retail Sales reports and plan to use EURUSD for the trade. Forecast is for a 0.2% increase in retail sales. It is the usual practice to use triggers for the trade. Triggers have to have enough deviation from the consensus numbers to match the required volatility that would make the news item worth trading. For the retails sales, a trigger number of 0.1% is sufficient.

It is essential to know your trigger numbers before the actual numbers are released. This helps you decide faster if the news release is tradable or not.

Step by step:

Get the forecast numbers and the trigger numbers on the buy and sell side. If you don’t know where to get them, download FxPulse. It’s free and displays the forecast all needed numbers directly on your chart.

Write both numbers down.

When the news comes out, place your trade immediately if your trigger numbers allow you to do so. Personally I use the 20% rule.

Shortly before the news comes out (30-40 seconds) open the order window and set max deviation to five Pips. Sure, slippage is an unwanted side effect from news trading but as the market moves fast during news releases, it is something we need to live with.

If you are doing it right, your news trades will look like on the screenshot below:

Although it sounds very easy (Like taking a candy from a little child. BTWthat’s something you should not do) it will take some practice to master the “John Wayne” news trading strategy.

When you just start with it, use a demo account, and once you feel ready for “lice action” use small lot size for the first 10-15 news trades.

Forex News Trading Strategy 2 – Straddle Trading

The straddle trading strategy is just as the name suggests; you are placing two pending orders (Sell Stop and Buy Stop) on both sides of the divide, effectively straddling the market price. The buy order should be placed 20-25 pips above the actual price, the sell order 20-25 pips below. If your broker supports one cancels the other (OCO) orders, please use this type of order for the straddle trade.

When the actual number from the news comes out, one order will be executed while the other order will be canceled. The straddle strategy works well in most cases.

The problem usually arises with the timing of the placement of the orders. There have been cases when extreme volatility caused choppy price swings to trigger both trades. As such, the Forex straddle strategy is not recommended for high impact volatile news releases like the NFP.

Step by step:

1) Shortly before the news is released, place a Buy Stop and Sell Stop orders (OCO if supported by your broker) 20-25 pips away from the market price. SL for the buy order should be the price from the sell order and visa verse.

2) When the news is released and the market starts to moves in one direction you have not much to do. Just close the other pending order manually and enjoy the ride and make sure you get the most profit out from the trade.

Tip: There are several expert advisors available what places trades automatically before the news comes out. Commercial and free versions. If you are interested in a straddle Expert Advisor, I recommend to stay away from the commercial versions. They are basically nothing else then copies from free versions you can find in several Forex forums.

If it really this easy to trade the news, why do many traders fail at it?

The main issue is that currency traders need to understand that different news items respond in different ways, and as a trader, you will have to understand how and why this happens, then practice and do your homework.

Trade the news only with a broker what offers STP or DMA, be disciplined and don’t expect to become a professional news trader within a few days. Don’t be greedy and start with small lot sizes (0.01 or 0.10). You can go a step further by getting your news in real time using FX Pulse, which is just perfect for news trading and 100% free. You can download FX Pulse here .

Happy News Trading!

P. S.: Some comments please!

Using moving averages in asystematic trading strategy

Using moving averages in asystematic trading strategyUsing Moving Averages in a Systematic Trading Strategy

by Wayne A. Thorp, CFA

Wayne Thorp recently spoke at the 2015 AAII Investor Conference. For information on how to subscribe to recordings of the presentations, go to aaii/conferenceaudio for more details.

In the August issue of the AAII Journal, I covered one of the more basic technical indicators: moving averages, including how you could construct simple, weighted, and exponential moving averages, as well as some practical applications.

This article moves a step forward and examines how you can use moving averages as part of a systematic trading strategy. First, it will look at one - and two-line moving average systems and how you can use them to generate buy and sell signals, and then, it will touch upon system optimization and how traders use it to improve the profitability of a trading system.

one-line moving average systems

Although this technique is not as popular as using multiple moving averages, it will allow you to understand the concepts behind such systems without being confused by several moving averages.

When you view a single moving average in unison with a price chart, buy signals are generated when the price rises above the moving average. In a broad context, the movement of the price above a moving average is considered a bullish signal. Similarly, when the price falls below the moving average line, a sell or sell-short signal is generated. In this case, the price falling below the moving average is viewed as being bearish. Keep in mind too that the longer the time period you use to construct the moving average, the more significant the signal is.

Figure 1 illustrates a one-line moving average trading system. Here you see the price behavior of AFLAC between October 1998 and June 1999. The up and down arrows on the chart indicate where the price broke above or below the 50-day simple moving average. The up arrows indicate buy signals while the down arrows denote sell signals. Using strictly long positions—buying when the price rises above the 50-day moving average and selling when the price falls below the moving average—the three round-trip trades (buy and sell) would have yielded a total gain of over 57%. Be aware that this figure excludes commissions and does not take into account slippage. Also, it is important to point out that all of these examples assume that trades are entered and exited at the point when the moving average is “violated.” This does not mean that a trade could have been placed at that price—an issue that can have a significant impact on the return of a trade and the system as a whole.

While the AFLAC example shows that a single moving average system can be profitable, using a single moving average does have its pitfalls—whipsaws. A whipsaw occurs when a signal is generated (typically a buy), only to have the price make a sudden reversal (to the downside). By the time a sell signal is generated, the transaction results in a loss. Figure 2 illustrates a whipsaw. Here we have a price chart for Indiana Energy from April until May 1999 as well as a 50-day simple moving average. On April 12, the price broke above the moving average at $19.968 and closed at $20.75. Over a week later, on April 20, the price fell below the moving average at $19.826 and closed at $19.687. If you had followed the buy signal when the price rose above the moving average and sold when the price fell below the moving average, this trade would have resulted in a slight loss (excluding commissions, slippage, etc.). If commissions were included, the percentage loss would have been even more.

If you trade over a longer time period, whipsaws could have a noticeable impact on your profits. There are ways to safeguard yourself—such as lengthening the time period over which you calculate the moving average. In doing so, the average becomes less responsive to sudden price movements and will help eliminate whipsaws. While such tactics tend to lower the occurrence of “bad” trades, you also tend to lower the profits gleaned from “good” trades. If you lengthen the time period of the moving average, the average will react more slowly to price changes. As a result, you will be entering trades at a later time, thus perhaps missing some of the upward movement. Furthermore, you will tend to exit trades later and may sacrifice some of your gains. These are some of the trade-offs one faces when optimizing any trading system.

two-line systems

By adding additional moving averages to a trading system, you lower the risk of whipsaws or other false trading signals. When using multiple moving averages, buy and sell signals are generated at points called “crossovers”—points where two averages cross one another. The concept is similar to that used with one-line moving average systems, but with multiple averages you are watching for the lines to cross one another rather than the actual price.

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Long term trades-a low risk trading strategy

Long term trades-a low risk trading strategyLong Term Trades A Low Risk Trading Strategy

by on May 13, 2010

One method that offers profit opportunities at very low risk are long term trades. Most people think of trading as a short term activity, however, a back or lay bet can be open for as long as a market is active. Some markets may be active for a short period of time such as 3 minutes in an in-play horse race or over 6 months in a football league winner market. The definition of a Long term trade is an open position which usually lasts for over 6 months before you trade out. In this period of time, you are waiting for the price to come in or drift out so that you are able to trade out for a profit.

Low risk speculation

Very liquid markets

Predictable volatility

No need to screen watch for hours on end

Long Term Trading Opportunities in Football

There are many opportunities for long term trades in football. Along with numerous markets to explore, there are also different techniques you can employ to execute your trade. Here are some quick examples along with an analysis of the risks and how to manage them:

Top Goal Scorer Markets

The top goal scorer market is an example of a long term market that can yield some great results. This market is fairly straight forward market and is basically where you are able to bet on the top goal scorer of a football league. The great thing about this market is that you do not need to pick the top goal scorer in order to make a profit. As long as your selection is scores goals and is within around the top 5 goal scorers in the league at some point in the season, you should be able to trade out for a profit.

To illustrate how it is possible to use this market with our long term trading strategy, please see the screenshot below of the Wayne Rooney price / volume over time graph:

From the screenshot above, we can clearly see that had you placed a back or lay bet at any point near 12.5 when the market opened there would have been an opportunity to trade out for a significant profit (illustrated by the blue line). Notice also that 12.5 was available when the market opened and also a short time after.

As further evidence, I decided to include the Didier Drogba price / volume graph as well. We can clearly see that the same type of price movement is present and had you place a back or lay bet at anywhere between 9.5 to 12.5 in the first few days when the market opened you would have had plenty of time to trade out for a substantial profit.

Overview of Risks

This market can be tricky and there are some risks. One major risk is injury. If your selection is injured and cannot play for the season or a part of the season, its obvious that the chances of them becoming a top goal scorer will be seriously hampered. If this happens mid-season and you are already in a profitable position it is advisable to trade out to limit the risk and come out ahead.

Another risk is the transfer market. Although this is less likely to happen, it is always good to keep an eye out for the latest transfer news regarding your player. If he is unhappy at the club or if there is a change of management, a transfer may occur which will ruin the chances of them becoming top goal scorer.

TIP: Do Not Place Your Initial Bet Blindly

Although this method is low risk, it is important to choose your trade carefully as its obviously possible to lose money if you make the wrong choice (although this can be somewhat minimised by trading out early). My initial screenshots show Wayne Rooney and Drogba but why did I choose them? There is actually an easy way to choose the top goal scorer choose the striker from the team that the bookmakers think will win the league .

There are two reasons for this. Firstly, bookmakers spend a long time pricing the market according to their research and statistical data so why not make use of their hard work? And secondly, the team that will win the league has to score goals and most of these goals will come from that particular teams striker. This is not exactly rocket science but its amazing how many people do not know which striker to choose.

Domestic Cup Competitions and the Champions League

If you are thinking of trying this strategy on domestic cup competitions and the Champions League, my advice is: Dont Do It unless you are absolutely sure you are getting a good price. The reason for my aversion to cup competitions and the champions league is because of the unpredictable nature of them and the fact that your team can be knocked out of the competition which means you lose ALL of your stake. Here are some things you will have to worry about:

Injured players

Suspended players in an important match (red or yellow cards)

Tough draw

Managers resting players in a less important domestic cup match

These risks are less important in other markets such as the League Winner market (this will be explained later) because losing a match does not mean you will lose the league whereas in a cup competition losing a match virtually means getting knocked out of the entire competition.

The League Winner

If you want to test out this long term trading strategy, the league winner market may be the best place to start. This market is probably one of the least risky markets to use the long term trading strategy. The reason for this is because the market is open for a long time and there are usually lots of opportunities for your chosen team to reach the top of the football table (assuming you have picked a top team).

To illustrate this, take a look at the graph below which shows the price / volume over time of Rangers winning the league. As you can see, whether or not you placed a back or lay bet at the starting price of 1.8 there would have been plenty of opportunities to trade out for a substantial profit.

A look at other leagues shows a similiar story. Placing a back or lay bet on Man Utd at the starting price of 2.3 would have given you opportunities to trade out at a profit. If you had placed a back bet, you could have traded out at 2 and if you made a lay bet, you could have traded out at a larger profit at 9.

There are many other football leagues that this can be applied to. Dont be afraid to take a look at the Spanish and Italian leagues as they are often more competitive and can throw up some nice opportunities (screenshots of Spanish and Italian league price / volume graphs have been included below in case you are thinking about trading them):

TIP: Finding Value

Previously, we looked at the graph of Manchester Utd winning the league. You will have noticed that if you placed a back bet at 2.3, you would have only made a small profit trading out at 2. However, lets think about this in real terms for a moment. Is 2.3 a good price for Manchester Utd to win the league? Most people would argue that 2.3 does not really represent good value for a number of reasons and this is reflected in the price trend over the following days. In fact, had you waited a few weeks, you could have backed Man Utd at a more attractive price of 5.5.

The fundamental principle you should remember in trading is getting value. If you have value (i. e. a better price than everyone else) then you will have more chance of trading out as your profit margin will be higher than other people therefore you are able to trade out faster and ahead of other people.

A Gold Mine Still Uncovered

Please remember that these are just a small selection of markets that you can use with the long term trade strategy. There are many other football markets you can use and some of them are even more lucrative than the ones shown in the examples here.

Betfair horse racing trading strategies

Betfair horse racing trading strategiesBetfair horse racing trading strategies

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Campbell rharvey sresearch papers

Campbell rharvey sresearch papersCampbell R. Harvey's Research Papers

Phone . +1 919 660 7768

PGP . E004 4F24 1FBC 6A4A CF31 D520 0F43 AE4D D2B8 4EF4

Recovering Expectations of Consumption Growth from an Equilibrium Model of the Term Structure of Interest Rates. University of Chicago, December 1986. Chair: Eugene F. Fama (Nobel 2013 ). Committee Members: Wayne E. Ferson, Robert Stambaugh, Merton H. Miller (Nobel 1990 ), Shmuel Kandel and Lars P. Hansen (Nobel 2013 ).

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Convocation Program.

Picture with dissertation chair, Eugene F. Fama, May 2014.

Publications

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Forecasts of Economic Growth from the Bond and Stock Markets, Financial Analysts Journal September/October, 1989): 38-45. (P2)

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Bayesian Inference in Asset Pricing Tests, with Guofu Zhou, Journal of Financial Economics 26, (1990): 221-254. (P4)

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Reprinted in Andrew Lo, (ed.) Financial Econometrics. Edward Elgar, 2006.

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The Term Structure and World Economic Growth, Journal of Fixed Income 1, (1991): 4-17. (P6)

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Sources of Predictability in Portfolio Returns, with Wayne Ferson, Financial Analysts Journal May/June, (1991): 49-56. (P7)

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Les Taux d'Intérêt et la Croissance Economique en France, Analyse Financière 86, (1991): 97-103. (P8)

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Interest Rate Based Forecasts of German Economic Growth, Weltwirtschaftliches Archiv 127, (1991): 701-718. (P12)

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Dividends and SP 100 Index Option Valuation, with Robert Whaley, Journal of Futures Markets 12, (1992): 123-137. (P13)

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Information and Volatility in the FX Market, with Roger Huang, Finanzmarkt und Portfolio Management 6, (1992): 14-22. (P14)

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Market Volatility Prediction and the Efficiency of the SP 100 Index Option Market, with Robert Whaley, Journal of Financial Economics 31, (1992): 43-73. (P16)

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Explaining the Predictability in Asset Returns, with Wayne Ferson, Research in Finance 11, (1993): 65-106. (P17)

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The Term Structure Forecasts Economic Growth, Financial Analysts Journal May/June, (1993): 6-8. (P18)

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Seasonality and Heteroskedasticity in Consumption-Based Asset Pricing: An Analysis of Linear Models, with Wayne Ferson, Research in Finance 11, (1993): 1-35. (P19)

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Updated not-seasonally adjusted consumption data available here.

International Asset Pricing with Alternative Distributional Specifications, with Guofu Zhou, Journal of Empirical Finance 1, (1993): 107-131. (P20)

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The Risk and Predictability of International Equity Returns, with Wayne Ferson, Review of Financial Studies 6, (1993): 527-566. (P21)

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Reprinted in G. Andrew Karolyi and Rene M. Stulz, (eds.) International Capital Markets. Edward Elgar, 2003.

Strategic Treasury Debt Management in Public Policy, Policy Studies Review 12, (1993): 76-89. (P22)

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National Risk and Global Fixed Income Allocation, with Claude Erb and Tadas Viskanta, Journal of Fixed Income September, (1994): 17--26. (P23)

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Sources of Risk and Expected Returns in Global Equity Markets, with Wayne Ferson, Journal of Banking and Finance. (1994): 775-803. (P24)

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Also published as NBER working paper 4622.

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Economic Activity Measures in Nonlinear Asset Pricing, Advances in Financial Economics. (1995): 123-154. (P25)

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Country Credit Risk and Global Portfolio Selection, with Claude Erb and Tadas Viskanta, Journal of Portfolio Management Winter, (1995): 74-83. (P26)

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Forecasting International Equity Correlations, with Claude Erb and Tadas Viskanta, Financial Analysts Journal November/December, (1994): 32-45. (P27)

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Predictable Risk and Returns in Emerging Markets, Review of Financial Studies 8, (1995): 773-816. (P32)

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Also published as NBER working paper 4621.

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Time-Varying World Market Integration, with Geert Bekaert, Journal of Finance 50, (1995): 403-444. (P33) [Lead Article]

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Also published as NBER working paper 4843.

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Reprinted in G. Andrew Karolyi and Rene M. Stulz, (eds.) International Capital Markets. Edward Elgar, 2003.

Inflation and World Equity Selection, with Claude Erb and Tadas Viskanta, Financial Analysts Journal November-December, (1995): 28-42. (P34)

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Also in Japanese, Security Analysts Journal Part I, October, (1996): 45-61; Part II, November, (1996): 84-90. (P34J)

The Relation Between the Term Structure of Interest Rates and Canadian Economic Growth, Canadian Journal of Economics 30:1, (1997): 169-193. (P35)

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Expected Returns and Volatility in 135 Countries with Claude Erb and Tadas Viskanta, Journal of Portfolio Management Spring, (1996): 46-58. (P36)

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Market Timing Ability and Volatility Implied in Investment Newsletters' Asset Allocation Recommendations, with John Graham, Journal of Financial Economics 42, 3, (1996): 397-421. (P37)

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Also published as NBER working paper 4890.

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Political Risk, Financial Risk and Economic Risk, with Claude Erb and Tadas Viskanta, Financial Analysts Journal 52:6, (1996): 28-46. (P38) [prev. W23]

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Also in Japanese, Security Analysts Journal (1998):1 Part I, 109-119; (1998): Part II, 84-95 (1998). (P38J)

The Influence of Political, Economic and Financial Risk on Expected Fixed Income Returns, with Claude Erb and Tadas Viskanta, Journal of Fixed Income 6:1, (1996): 7-30. (P39) [prev. W24] [Lead Article]

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Emerging Equity Market Volatility, with Geert Bekaert, Journal of Financial Economics 43, 1, (1997): 29-77. (P40) [prev. W14]

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Also published as NBER working paper 5307.

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Demographics and International Investment, with Claude Erb and Tadas Viskanta, Financial Analysts Journal 53, 4, (1997): 14-28.(P41)[prev. W27]

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Fundamental Determinants of International Equity Returns: A Perspective on Conditional Asset Pricing, with Wayne Ferson, Journal of Banking and Finance 21, (1997): 1625-1665. (P42)[prev. W7]

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Also published as NBER working paper 5860.

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The Making of an Emerging Market, with Claude Erb and Tadas Viskanta, Emerging Markets Quarterly 1, 1, (1997) 14-19. (P43)

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Grading the Performance of Market Timing Newsletters, with John Graham, Financial Analysts Journal 53, 6, (1997): 54-66. (P44)[prev. W26]

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What Matters for Emerging Market Investment, with Geert Bekaert, Claude B. Erb and Tadas E. Viskanta, Emerging Markets Quarterly 1, 2, (1997): 17-46. (P45)

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Distributional Characteristics of Emerging Market Returns and Asset Allocation, with Geert Bekaert, Claude B. Erb and Tadas E. Viskanta, Journal of Portfolio Management Winter, (1998): 102-116. (P46)

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Measurement Error and Nonlinearity in the Earnings-Returns Relation, with Messod Beneish, Review of Quantitative Finance and Accounting 11, (1998): 219-247. (P47) [prev. W4] [Lead Article]

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Emerging/Developed Market Portfolio Mixes, with Stefano M. F. G. Cavaglia, Magnus Dahlquist, Peter L. Rathjens and Jarrod W. Wilcox. Emerging Markets Quarterly Winter, (1997): 47-62. (P48) [prev. W29]

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The Future of Investment in Emerging Markets NBER Reporter Summer, (1998): 5-8. (P49)

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Risk in Emerging Markets with Claude B. Erb and Tadas E. Viskanta, The Financial Survey July/August, (1998): 42-46. (P50) [prev. W41]

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Contagion and Risk with Claude Erb and Tadas Viskanta, in Emerging Markets Quarterly 2, Summer, (1998): 46-64. (P51) [prev. W42]

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A New Perspective on Emerging Market Bonds, with Claude Erb and Tadas Viskanta, Journal of Portfolio Management (1999): 83-92. (P52) [prev. W36]

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Stock Selection in Emerging Markets: Portfolio Strategies for Malaysia, Mexico and South Africa with Dana Achour, Greg Hopkins and Clive Lang, Emerging Markets Quarterly Winter, (1999): 38-91. (P53)

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Autoregressive Conditional Skewness, with Akhtar Siddique, Journal of Financial and Quantitative Analysis 34, 4, (1999): 465-487. (P54) [prev. W22]

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Stock Selection in Malaysia with Dana Achour, Greg Hopkins and Clive Lang, Emerging Markets Quarterly Spring, (1999): 54-91 (P55)

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Conditional Skewness in Asset Pricing Tests, with Akhtar Siddique, Journal of Finance 55, (2000): 1263-1295. (P56) [prev. W17]

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Conditioning Variables and the Cross-Section of Stock Returns, with Wayne Ferson, Journal of Finance 54, (1999): 1325-1360. (P57) [prev. W32]

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Also published as NBER working paper 7009.

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Reprinted in Robert R. Grauer, (ed.) Asset Pricing Theory and Tests. Edward Elgar, 2003.

Capital Markets: An Engine for Economic Growth, with Geert Bekaert, Brown Journal of World Affairs 5, 1, Winter/Spring, (1998): 33-53. (P58)[prev. W21]

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Brazil in Crisis with Chris Lundblad and Diego Valderrama, Emerging Markets Quarterly Spring, (1999): 4-9. (P59)

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Efficient Online Non-Parametric Density Estimation, with Christophe G. Lambert, Scott E. Harrington, Nathan D. Bronson and Arman Glodjo. Algorithmica 25, (1999): 37-57.(P60)[prev. W28]

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Forecasting emerging market returns using neural networks: A comparative study of nine emerging markets, with Kirsten E. Travers and Michael J. Costa, Emerging Markets Quarterly 4, 2, (2000): 43-54. (P61) [prev. W44]

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Economic, Financial and Fundamental Global Risk In and Out of the EMU, with Wayne Ferson, Swedish Economic Policy Review 6, (1999): 123-184. (P62) [prev. W40]

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Also published as NBER working paper 6967 .

Stock Selection in Mexico with Dana Achour, Greg Hopkins and Clive Lang, Emerging Markets Quarterly 3, Fall, (1999): 38-75. (P63) [prev. W46]

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Firm Characteristics and Investment Strategies in Africa: The Case of South Africa with Dana Achour, Greg Hopkins and Clive Lang, African Finance Journal 1, (1999): 1-68. (P65) [prev. W47]

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Understanding Emerging Market Bonds with Claude Erb and Tadas Viskanta, in Emerging Markets Quarterly 4, 1, (2000): 7-23, (P66) [prev. W48]

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Time-Varying Conditional Skewness and the Market Risk Premium, with Akhtar Siddique, Research in Banking and Finance 1, (2000): 27-60. (P68) [prev. W53]

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Emerging Equity Markets and Economic Development, with Geert Bekaert and Chris Lundblad, (W49) Journal of Development Economics 66, (2001): 465-504. (P70) [prev. W49].

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Also published as NBER working paper 7763.

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Global Tactical Asset Allocation, with Magnus Dahlquist, Emerging Markets Quarterly (2001): 6-14. (P71) [prev. W57].

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The Impact of Federal Reserve Bank's Open Market Operations, with Roger Huang, Journal of Financial Markets 5, 2, (2002): 223-257. (P73) [prev. W3].

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Also published as NBER working paper 4663 .

The Specification of Conditional Expectations, (previous title: Is the Expected Compensation for Market Volatility Constant?) Journal of Empirical Finance 8, 5, (2001): 573-637. (P74) [prev W6]

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How do CFOs make capital budgeting and capital structure decisions?, with John Graham, Journal of Applied Corporate Finance 15, 1, (2002): 8-23. (P76) [prev. W62]

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Research in Emerging Markets Finance: Looking to the Future, with Geert Bekaert, (P78) [prev. W69], Emerging Markets Review 2002, 429-448.

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What Determines Expected International Asset Returns? with Bruno Solnik and Guofu Zhou. (P79) [prev. W8] Annals of Economics and Finance 3, (2002): 249-298.

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Also published as NBER working paper 4660 .

Are Correlations of Stock Returns Justified by Subsequent Changes in National Outputs, with Bernard Dumas and Pierre Ruiz (P80) [prev. W34] Journal of International Money and Finance 22, (2003): 777-811.

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A Brief Note from the Editors, with Javier Estrada and Robert Bruner, (P81), Emerging Markets Review. (2002): 329.

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Implications for Asset Allocation, Portfolio Management, and Future Research, AIMR Equity Premium Forum, October, (2002): 92-96. (P82)

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Foreward by the Editors, with Geert Bekaert, (P84) Journal of Empirical Finance 10, (2003): 1.

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Equity Market Liberalization in Emerging Markets, with Geert Bekaert and Christian Lundblad, (P85a) The Federal Reserve Bank of St. Louis Review 85:4, (2003): 53-74.

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Equity Market Liberalization in Emerging Markets, with Geert Bekaert and Christian Lundblad, (P85b) Journal of Financial Research 26, (2003): 275-299.

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Does Financial Liberalization Spur Growth, with Geert Bekaert and Chris Lundblad, Journal of Financial Economics 77, (2005): 3-55. (P87) [Prev. W56] [Lead Article]

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NBER working paper 8245.

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Reprinted in Stijn Claessens and Luc Laeven, eds. A Reader in International Corporate Finance, Volume 2, World Bank, 2006, pp. 37-90.

The Long-Run Equity Risk Premium, with John Graham, Finance Research Letters. 2, (2005): 185-194. (P91) [Prev W79] [Lead Article]

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The Strategic and Tactical Value of Commodity Futures with Claude Erb, Financial Analysts Journal. 62:2, March/April, 69-97. (P91) (Prev W77)

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Also published as NBER working paper 11222.

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Graham and Dodd Award (Best paper in 2006 Financial Analysts Journal, Sponsored by the CFA Institute).

First prize, Roger F. Murray Prize Competition (Award for Excellence in Quantitative Research in Finance, Sponsored by the Q-group.

Value Destruction and Financial Reporting Decisions with John Graham and Shiva Rajgopal, Financial Analysts Journal. Nov/Dec 2006, 27-39. (P94) [Prev W79]

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Graham and Dodd Scroll (Runner-up best paper in 2006 Financial Analysts Journal, Sponsored by the CFA Institute).

The Equity Risk Premium in January 2007: Evidence from the Global CFO Surveyt with John Graham (P97) [Prev W78], The ICFAI Journal of Financial Risk Management. IV:2, June 2007, 46-61.

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Darden Conference Issue: Capital Raising in Emerging Economies, with Marc Lipson and Frank Warnock, Journal of Financial Economics. (2008) Volume 88:3, 425-429. (P99)

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The Effect of the May 2003 Dividend Tax Cut on Corporate Dividend Policy: Empirical and Survey Evidence with Alon Brav, John Graham, and Roni Michaely, National Tax Journal. Winter, 611-624. (P101) [Prev W87],

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Access to Liquidity and Corporate Investment in Europe During the Financial Crisis with Murillo Campello, Erasmo Giambona, and John Graham, Review of Finance . 2012, 16(2) 323-346. [Lead Article]

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Managerial Attitudes and Corporate Actions with John Graham and Manju Puri, Journal of Financial Economics . 2013, 109:1, 103-121. [P109] (Prev. W84).

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2014 Jensen Prize for Best Corporate Finance Paper in the Journal of Financial Economics

The Truth about Gold: Why It Should (or Should Not) Be Part of Your Asset Allocation Strategy, CFA Institute Conference Proceedings Quarterly . 30:1, March 2013, 9-17. [P112].

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The Golden Dilemma with Claude Erb, Financial Analysts Journal. 2013:69:4, July-August, 10-42. [P113] (Prev. W110) [Lead Article]

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Also published as NBER working paper 18706.

Graham and Dodd Scroll, 2014.

Financial Analysts Journal Readers’ Choice Award, 2014.

Earnings Quality: Evidence from the Field with Ilia Dichev, John Graham, and Shiva Rajgopal. Journal of Accounting and Economics. 2013:56, 1-33. [P114] (Prev. W108) [Lead Article]

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Political Risk Spreads with Geert Bekaert, Christian Lundblad and Stephen Siegel, Journal of International Business Studies 45:4, (2014): 471-493. [P115] (Prev. W116).

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Evaluating Trading Strategies. with Yan Liu, Journal of Portfolio Management . 2014, 40:5, 108-118. [P116] (Prev. W126)

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Capital Allocation and Delegation of Decision-Making Authority Within Firms with John Graham and Manju Puri, Journal of Financial Economics . 2015, 115:3 (March): 449-470. [P117] (Prev. W103)

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Also published as NBER working paper 17370.

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. and the Cross-Section of Expected Returns with Yan Liu and Heqing Zhu, Review of Financial Studies. 2015, forthcoming. [P118] (Prev. W114)

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Also published as NBER working paper 20592.

NASDAQ OMX Award, 2014, for the best paper in asset pricing at the Western Finance Association Meetings.

Best Paper Award, 2014, INQUIRE-Europe-UK.

The Misrepresentation of Earnings, with Ilia Dichev, John Graham and Shiva Rajgopal, Financial Analysts Journal. 2015, forthcoming. [P119] (Prev. W124)

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Backtesting with Yan Liu Journal of Portfolio Management 2015, forthcoming. [P120] (Prev. W115)

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New Working Papers

Lucky Factors with Yan Liu (W128)

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Rebalancing Risk with Nicolas Granger, Douglas Greenig, Sandy Rattray and David Zou (W127)

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Multiple Testing in Economics with Yan Liu.(W122)

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Stock Market Valuations across U. S. States with Geert Bekaert, Christian Lundblad and Stephen Siegel.(W121)

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Emerging Equity Markets in a Globalizing World with Geert Bekaert (W119)

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Where are the World's Best Analysts? with Sam Radnor, Khalil Mohammed and William Ferreira (W118)

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A View Inside Corporate Risk Management with Gordon Bodnar, Erasmo Giambona, and John Graham (W117)

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Managing Risk Management with Gordon Bodnar, John Graham, and Richard Marston (W107)

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Crypto-related research (Blockchain and cryptocurrencies)

Bitcoin Myths and Facts. (W126)

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Teaching materials

A Guide to Earnings Quality with Ilia Dichev, John Graham, and Shiva Rajgopal.(W120)

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A Guide to Corporate Risk Management, with Gordon Bodnar, Erasmo Giambona, and John Graham (W129)

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How to use fibonacci retracements

How to use fibonacci retracementsHow to use Fibonacci Retracements

The Fibonacci retracements pattern can be useful for swing traders to identify reversals on a stock chart. On this page we will look at the Fibonacci sequence and show some examples of how you can identify this pattern.

Fibonacci numbers were developed by Leonardo Fibonacci and it is simply a series of numbers that when you add the previous two numbers you come up with the next number in the sequence. Here is an example:

1, 2, 3, 5, 8, 13, 21, 34, 55

See how when you add 1 and 2 you get 3? Now add 2 and 3 and you get 5, and so on. So how does this sequence help you as a swing trader?

Well, the relationship between these numbers is what gives us the common Fibonacci retracements pattern in technical analysis.

Fibonacci retracements pattern

Stocks will often pull back or retrace a percentage of the previous move before reversing. These Fibonacci retracements often occur at three levels: 38.2%, 50%, and 61.8%. Actually, the 50% level really does not have anything to do with Fibonacci, but traders use this level because of the tendency of stocks to reverse after retracing half of the previous move. Here is an example using a graphic explaining the retracement pattern:

This picture shows a graphical representation of the reversal points for stocks in an uptrend. The pattern is reversed for stocks that are in down trends.

After a stock makes a move to the upside (A), it can then retrace a part of that move (B), before moving on again in the desired direction (C). These retracements or pullbacks are what you as a swing trader want to watch for when initiating long or short positions.

Once the stock begins to pull back (retrace), then you can plot these retracement levels on a chart to look for signs of a reversal. You do not automatically buy the stock just because it is at a common retracement level! Wait, and look for candlestick patterns to develop at the 38.2% area. If you do not see any signs of a reversal, then it may go down to the 50% area. Look for a reversal there. You do not know if or when the stock will reverse at a Fibonacci level! You just mark these areas on a chart and wait for signal to go long or short.

How to draw a fib grid

So how do we identify Fibonacci patterns on a chart. Easy, we draw a Fibonacci grid (fib grid) using swing points. Here is an example:

Draw the fib grid from the swing point high and the swing point low of a swing. Your charting software should come with this feature. It is a standard option on most charting packages. If not, you can calculate it manually by using this formula:

Calculate the range from the swing point high to the swing point low.

Now multiply the range times a Fibonacci ratio: 38.2% (0.382), 50% (0.500), and 61.8% (0.618).

Finally, subtract that number from the swing point high. That will give you your Fibonacci levels.

This chart shows an actual trade that I made. HS pulled back into the TAZ and then formed a bullish engulfing candle right at the 50% level. That gave me the signal to go long. Nice trade!

Is it useful?

Well. maybe. sometimes.

Most of the time, when you draw a fib grid on a chart, you will notice that the grid lines up with support and resistance areas that you would see anyway without drawing the lines in! So you really do not need to draw the lines in. Instead, you can just look at a chart and estimate where the levels are.

Look again at the chart above of HS. If you didn't draw the Fibonacci retracement lines in, you can still tell just by looking at the chart that the stock has retraced 50% of the previous move.

If drawing the lines in helps you to better visualize the fib levels, then by all means use it! The choice is up to you.

Fibonacci eBook

Also, if you want to get into some really advanced Fibonacci analysis, then you may want to check out this Fibonacci eBook. It was written by Wayne Gorman who has 25 years experience in trading, forecasting, and portfolio management. He also worked for Citibank and Westpac Banking Corporation.

This 90 page e-book goes into detail on Fibonacci time relationships, retracements, extensions, clusters, etc. There is also a big emphasis on Elliott Wave theory in this eBook. Some of it can get complicated but you'll definitely be an expert on Fibonacci by the time you finish reading this!

So there you have it. Hopefully, this page gave you a good idea of how Fibonacci works. At least now you can start plotting fib grids and looking at retracement levels the next time you consider a trade.

Just remember.

Price is king. Wait for signs of a reversal before you initiate a trade!