Do Individual Currency Traders Time the Market. Abstract

Do Individual Currency Traders Time the Market Abstract This paper tests whether individual Forex investors can predict future returns, are able to time the market, and can generate alpha after transaction costs. Using a sample of 1,231 Forex trading accounts and 72,072 trades, the results show, individual Forex investors can predict future returns up to eight days after trade execution even after controlling for volatility. The results of return predictability is significant because not only it supports the idea that linear independence is rejected ( Brock 1991 and Taylor 1986) but also provide empirical evidence that there is private information in the FX market which supports the arguments of (Ito et.al, (1998), Evans and Lyons (2004)). The results also show that individual Forex investors are able to time the market, and generate alpha after transaction costs. Keywords: Foreign exchange, Short sale, Return predictability, Transaction data, Alpha, Market timing, Individual investors performance, Market efficiency

1. Introduction Over the past decade there has been a revolution in individual investing and a rapid increase in short-selling. Prior to the advent of the internet and online trading many financial instruments and trading strategies were simply not available to individual investors. In modern times, with the increased growth of online brokerages, transaction costs have decreased and investment products that were once available only to professional and institutional investors are now accessible to individuals. The role of short-selling, and online Forex (here after, FX ) trading has witnessed significant growth (Luke 2005), yet there are no studies that have investigated this state of the art area. Previous studies investigated currency managers performance against benchmarks factors (Melvin and Shand (2010); Pojarliev and Levich (2008)), and style performance of currency fund managers (Pojarliev and Levich (2010)). This study investigates whether individual FX short sales trades can predict future returns, which in turn shows individual FX investors ability to predict future returns, and the study also investigates individual FX investors performance. We obtained proprietary transactional data for 1,231 individual investors FX accounts that contain short sale transactions, when the trade is open, when the trade is closed, open price and close price for the trade. We verified the data using Bloomberg Terminals and Thompson Reuter s database. We believe that online trading is ongoing future trend and individual investors will play a key role in the FX market. We also know that online trading platform offers many valuable information and that the markets are inefficient, so the idea of the need of sophisticated currency manager to trade FX is appealing and still needed, however, we feel that the individual FX 2

investing is an area that is not yet explored through research and the investigation of individual FX investors will be highly valuable. The major contribution of this study is its focus on individual FX investors, short-selling transactions, predicting future returns and investigating individual FX investors performance. We believe that the inquiry of this paper is different than previous studies, which have only investigated currency fund managers, and did not investigate short-selling transactions nor the ability of individual FX investors to predict future returns or their individual performance. The results show that the mean percent of winning trades by individual FX investors is 52.975 while the mean percent of losing trades is 47.03. The same investors executing on average 29.2 trades per month and 350.39 trades per year. In addition the percent of long winning trades is 56.7 and the percent of winning short trades is 56.26. Moreover, the results reveal that individual FX investors can predict returns for up to eight days even after controlling for volatility. The above discovery supports what previous studies such as Ito, Lyons, and Melvin (1998), and Evans and Lyons (2004) had argued, and that was, the individual customer trades contain pieces of new information about the underlying macroeconomic fundamentals driving the exchange rate. Moreover, our results support both Brock 1991 and Taylor 1986 evidence that the linear independence of FX prices is rejected. Therefore our discovery of return predictability is significant because not only it supports the idea that linear independence is rejected but also provides empirical evidence that there is private information in the FX market, and provides empirical evidence that it can be used to predict future movements in the FX market. The paper also shows that some individual FX investors can time the market, produce positive alpha after transaction costs, in addition, these investors do not have style persistence and their future alpha are not related to previous year s alpha, which is an additional evidence of 3

their market timing ability. Consequently, the ability of these investors to time the market cast doubts to the proponents of market efficiency The outline of this paper is as follows. Section 2 provides a literature review, description of data sources and reiterate the paper objective. Section 3 provides a detailed description of the data. Section 4 develops the methodology and provides the results. Section 5 concludes. 2. Literature review Short-selling stocks has been examined extensively in financial literature. To date all prior empirical research and theoretical models that have addressed short-selling have dealt solely with equities leaving financial instruments like foreign exchange contracts unexplored, therefore that presents an opportunity for empirical exploration. Short-selling theoretical models addressing equities have been built upon the theory that mispricing arises due to the divergence of price to fundamentals due to constraints on selling stocks short (Miller 1977). Diamond and Verrechia (1977) hypothesize that short sellers are informed about the true value of stocks and are thus able to exploit divergences from fundamental value. Empirical studies that have investigated the informativeness of short-selling remains mixed although the majority of the literature does support Diamond and Verrechia s hypothesis that short sellers are informed. Aitken, Frino, and McCorry (1998) analyzing Australian securities find that short trades near information events are associated with larger price reactions. Christophe, Ferri, and Angel, (2004) investigate short sale transactions in the five days prior to earnings announcements of 913 Nasdaq-listed firms and find evidence of informed trading in pre-announcement short-selling. Asquith, Pathak, and Ritter (2005) show high-short interest predicts negative abnormal returns and this relationship is strongest in stocks with low 4

institutional ownership. Boehmer, Jones, and Zhang (2007) using proprietary NYSE order data find heavily shorted stocks underperform lightly shorted stocks. Diether, Lee, and Werner (2007) find short sellers increase their trading following positive returns and correctly predict future negative abnormal returns. Conversely, Daske, Richardson, and Tuna (2005) examine short sale transactions around significant news events and discover no evidence that short sale transactions are concentrated prior to bad news events. A literature review of empirical studies addressing foreign exchange ( FX ) reveals shortselling has yet to be addressed. The majority of currency research conducted to date has focused on trader characteristics and performance of currency fund managers. In literature pertaining to trader characteristics, Silber (1984) and Kuserk and Locke (1993) examine the trading characteristics of scalpers for futures floor investors and market makers, respectively. Manaster and Mann (1996) study market-maker inventory positions and trading activity. Manaster and Mann (1999) analyze the trading profits of futures market makers due to liquidity trades and price movements. Locke and Mann (2000) find that professional future investors exhibit the Disposition Effect in that investors hold losing trades longer than winning trades and that position sizes for losing trades are larger than for winning trades. Regarding performance, Pojarliev and Levich (2008) examine the returns of professionally managed currency funds and a subset of returns from thirty-four individual fund managers finding that currency fund managers earned excess returns averaging twenty-five basis points per month. Their study also investigated the relationship of fund returns and four trading characteristic factors: Carry, Trend, Value, and Volatility and discover the four factors that explain the variability in returns. We focus on whether individual FX investors can predict future return, time the market, and produce alpha. 5

2.1 The retail FX market The retail FX market (RFM) is less than a decade old and thus warrants a review. The RFM has evolved from the foreign exchange market that arose from developments that occurred in the early 1970 s when the world switched to a floating exchange rate from the fixed exchange rate system. Today, the FX exchange (FX) market is one of the largest and most liquid markets in the world. Historically, the majority of currency trading occurred between central banks, governments, corporations and other large institutions. Individual investors were excluded due to the complexity of the instruments and large capital requirements needed to trade currency instruments. In the 1990 s a revolution began in the FX markets and retail trading was eventually introduced which opened the FX market to individual investors. The stock market crash of 2000, along with an increase of online FX brokers, attracted a multitude of investors that were looking for new and innovative instruments to trade which has resulted in the explosive growth rates of individual investors trading FX instruments (Luke 2005). It is estimated that retail FX trading market is to be approximately $50 60 billion in daily trading turnover and it continues to grow. 2.2 Retail FX trade systems The retail market has evolved so fast that many professional and individual investors have developed trading systems. Trading systems permit individual investors and professional investors to create computer based programs that transmit their trading signals online to other investors. One recent development is the evolution of paid-for subscription systems where individual investors pay trade system developers ( Developers ) for their services. Developers can be individuals or institutions that manually manage their trades and/or develop trading 6

systems and offer access to their trades/systems for a fee. Developers promote their systems by offering them on the internet and in trade publications. Developers sell access to their accounts so other investors can earn a profit. Individual investors can subscribe to these automated trade systems and receive trade signals via E-mail, instant message, or have the signals routed directly to their own broker through computer programs that link the trade system developer s account directly to an individual investor s personal online brokerage account. The unique sample analyzed in this paper consists of proprietary data obtained from an online trade system hosting company ( Trade System Host ). For a monthly fee, Developers can join the Trade System Host and offer their trading system to the public. The Trade System Host records all trades entered by Developers, provides summary statistics for each system, and ranks all systems based on profitability. Individual investors visit the Trade System Host s website which contains thousands of trade systems. Individual investors can search through the various systems and subscribe to systems that suit their investment needs. Once an individual investor subscribes to a system, s/he have complete, real-time access to all trades executed by the Developer. For example, individual investors receive trade signals every time a trade is executed. These signals are transmitted via instant message, E-mail or are directly routed to the individual s home computer and then to the individual s own broker through software add-ons. In effect, the individual investor s personal brokerage account is being managed through the internet by the Developer. The trading system has a compelling advantage and that is individual investors can subscribe to many trading systems and develop their own trading strategy. The Trade System Host used in this sample currently contains 6,735 trade systems from developers from all over the globe. The systems trade a variety of financial instruments including stocks, futures, options and spot FX. 7

Since the primary objective of this paper is to investigate the retail spot FX market, the sample of this paper consists of 1,231 accounts that have at least one FX trade. The short-selling stream of research has shown that short sellers, who are generally inferred to be sophisticated investors, have the ability to identify mispricing and exploit market inefficiencies (Boehmer, Jones, and Zhang, 2008; Desai, et al., 2002; Asquith, Pathak, and Ritter, 2005). Empirical studies investigating the trading patterns transactions of individual investors have found support that they are unable to beat the market Black (1986), De Long et al (1990) and Lee et al (1991)) although some studies have discovered that a small percentage of investors has the ability to earn significant positive abnormal returns (Coval, Hirshleifer, and Shumway 2005; Weisbenner (2005) ). Diamond and Verrechia s (1977) contention that short sellers are capable of exploiting drifts from fundamental value arises from the assumption that short sellers are informed and are able to discover unique non-public information regarding firms. Currencies are not financial instruments that possess unique informational advantages like corporations. Thus there should be little, if any, private information for FX short sellers to exploit. The sample used in this paper is unique because we obtained proprietary transactional data for 1,231individual investor FX accounts that contain short sale transactions, when the trade is open, when the trade is closed, open price and close price for the trade. This paper investigates whether individual FX investors can predict future returns, have market timing ability, and can produce alpha after transactions costs. 3. Data The primary data set consists of a proprietary database of individual FX investing accounts obtained from an American internet based data FX trade System Host from April 2005 to March 8

2008. The secondary data sets used for the return analysis and benchmarking is daily FX spot price and return data obtained from MLDownloader which is a program that downloads FX, future, and stock data from multiple online resources including Yahoo! Finance. Benchmark data is also obtained from the Deutsche Bank web based index portal which provides Deutsche Bank's proprietary Investible and Benchmark indices. The sample consists of 1,231 individual FX investors accounts, 72,077 trades, and includes all active accounts during the sample time period with at least one transaction recorded. The 72,077 trades constitute all trades that have been opened and subsequently closed by the trader. Trades that are currently open are not available because only paid subscribers to these systems have access to this information. The number of trades used in the analysis is 72,072 because five transactions contained corrupted data. We verified the data using Bloomberg Terminals and Thompson Reuter s database. The sample is very detailed and includes the investors name, the number of trades, the type of FX instruments traded, and transaction specific data. Transaction specific data includes a unique trade identification number for each trade, the date, time (in seconds) of when the trade was opened and closed, the type of trade (short or long), the open and close price of the trade in U.S. dollars, the quantity of contracts traded, and the FX symbol. Furthermore, the data consists of whether stops or limits were used, a description of the individual s account which provides some insight on what type of trading strategies the individual investors uses. Tables 1 and 2 provide descriptive statistics of these investors. [Insert Table 1 about here] [Insert Table 2 about here] 9

Table 1 shows that the average account age is 0.27 years. Age is defined as the length an account is held open measured in calendar days. An age of an account of 0.27 reflects that individual investors are very short term investors so their trading account remains open for approximately 3 or 4 months of the whole year. The aggregate data also provides return and win/loss data for all accounts. The total dollar mean gross gain (loss) for each account is $55,261.91 (-$55,071.61) respectively. Overall, the dollar mean net gain for each account over its life span is $190.30. Furthermore, the total gross maximum dollar gain (loss) is $1,824,780 (-$1,557,940) and the total net maximum dollar gain (loss) is $937,220.00 (-$99,964.10) which reveals that some investors are winning, and losing, significant amount of money. The average account executes 350.39 (29.20) trades per year (month) showing that these investors are trading frequently. Table 2 provides summary data for the 72,072 individual transactions in the transaction portion of the database. Panel A shows that out of 72,072 trades, 34,982 (48.54%) of all trades are short sales and 37,090 trades (51.46%) are long positions. The magnitude amount of shortselling in the FX market seems to be quite large in comparison to the studies that have analyzed short-selling of equities. For instance, Boehmer, Jones, and Zhang (2007) examined daily panel of NYSE short sales during 2000 2004 and show shorting comprises 12.9 percent of NYSE volume. Diether, Lee, and Werner (2007) found slightly greater amounts of short sales of 24 percent for the NYSE and 31 percent for the Nasdaq for the period between January 2, 2005 to December 30, 2005. Furthermore, Table 2 Panel B shows that 56.73 percent of long trades are profitable, 41.66 percent are losing, and 1.6 percent break-even (zero gain/loss) on a pretransaction cost basis. Out of the 34,982 short sales, 56.26 percent of shorts are profitable, 42.16 percent are losing, and 1.58 percent break-even on a pre-transaction cost basis. 10

Table 2 Panel C provides a frequency distribution table of all forty-one FX traded by this sample. The top five contracts traded comprise nearly 70 percent of all contracts traded. The top five are the GBP/USD (21.6%), EUR/USD (21.27%), USD/JPY (11.4%), USD/CHF (10.22%) and the GBP/JPY (7.93%). In summation, and according to our calculations this sample shows that these accounts are short-lived, trade actively, and are relatively successful investors based upon their win/lose percentages 4. Methodology 4.1 Predictability of returns We test of whether individual FX short sales cannot predict future returns. This test is performed by regressing a series of windows of returns on the individual investors trade activity. While previous studies analyzing equities have focused on a five-day event window (Deither et al. 2007), but since we are analyzing FX and we have detailed transaction data in this study, that gave us the opportunity to conduct a more detailed analysis of the predictability of returns by focusing on a series of alternative windows To investigate whether individual FX short sales predict future returns we use Model (1) described in equation (1) which regresses a dummy variable, trade, that takes the value of one if the transaction is a short and zero if the trade is a long, on the cumulative raw returns after transaction costs r (window: x1, x2) over the event window. The event windows used in the analysis consist of cumulative raw returns after transaction costs of the all FX contracts from (0,1) to (0,10), where zero that signifies the execution day and the beginning of the return window calculations and one or ten signifies the end of the return windows calculations days after the execution of the trade. In accordance of our hypothesis that FX investors cannot predict future returns the coefficient of trade which is a binary variable and regressed against the 11

return window (0, 10) should be positive ( because it is a short sales variable) and statistically insignificant. Model (1) is as follows r (window: x1,x2) = α t + β 1 trade t + ε t (1) [Insert Table 3 about here] Table 3 provides the results. Overall, the results do not support the hypothesis that FX short sales trades or short sellers cannot predict future returns. The coefficients of the trade variable are negative and statistically significant (p-value ranges from 0.01 to 0.0062) for the windows of (0, 2) to (0, 8) suggesting that these investors can predict returns up to eight days in the future. Furthermore, the results show that their ability to predict future returns drops off nine days (0, 9) after the date the trade was executed. Although the coefficients are still negative for trades nine (0, 9) and ten (0, 10) days after the date of execution, the statistical significance is no longer present in the ten days window. It is notable that the model does have a relatively small coefficient of determination yet this significantly increases once additional control variables are used in Model (2) below that is described in equation (2). Our multivariate regression takes into consideration the effect of daily Volatility as proxy for Volume. There is a need to control for volume because recent data shows that for example the FX market amount to $1.20 trillion per day and since there is a fee in executing the exchange transactions then that will ultimately affect profits, but furthermore, retail FX investors and large institutions trade spot contracts on different markets and the contracts for individual currencies often trade at different prices due to the characteristics and sizes of lots purchased and sold. That means we cannot identify a single fee structure for individual currency investors. Additionally, even if a clearing house were available to provide the data, the effect of institutional volume may not be a good measurement for the retail market because the retail market comprises only 2 12

percent of the market. 1 To address this issue, Volatility is used because this data is available and it is recognized in the currency literature to be positively associated with volume and it has been used as a control in previous FX studies (Chaboud and LeBaron 2000). 2 [Insert Table 4 about here] The proxy for FX Volatility in this study is the intraday (high low), where each day t high and each day t low for the return windows (0, to 10) which is denoted as v (avg window: x1,x2) and has been used in previous studies (Chaboud and LeBaron 2000), then this variable averaged over the period window to measure the average FX Volatility. r (window: x1,x2) = α t + β 1 trade t + v (avg window: x1,x2) + ε t (2) The regression results, reported in Table 4, remain similar to the univariate regression results rejecting the hypothesis that individual short sellers FX investors cannot predict future returns. The variable trade retains its negative sign and its statistical significance for windows (0, 2) to (0, 7). Similar to Model (1) the statistical significance falls after the 8 th trading day at window (0, 8) (p-value=0.05) and is not statistically significant at windows (0, 9) (p-value=0.14) and (0, 10) (p-value=0.55). In summary, both models show that individual investors FX short sellers have the ability to predict future returns up to eight days after they execute their trade. Previous studies such as Ito, Lyons, and Melvin (1998), and Evans and Lyons (2004) argue that individual customer trades contain pieces of new information about the underlying macroeconomic fundamentals driving the exchange rate. Recent studies have shown while there is little linear dependence between past and future returns, however, there is a strong evidence that the linear independence is rejected 1 This argument supporting volatility as a proxy for volume. Volume data is not available for foreign exchange. 2 An alternative to using spot volume data would be to use future volume data from the CME. Free data is available but only for the previous six months. Additional time periods are available for a fee. Furthermore, volume data for currency futures is available from Tick Data Inc. and costs $150 per instrument. 13

(Brock 1991; Taylor 1986). Therefore our discovery of return predictability is significant because not only it supports the idea that linear independence is rejected but also provide empirical evidence that there is private information in the FX market, and provides empirical evidence that it can be used to predict future movements in the FX market. Also this discovery provides an alternative way to predicting FX rates than using the Artificial Neural Networks (ANN) and the Recurring Neural Networks (RNN) that were used in some studies (Logar et.al 1993, Fan et.al 1994 and Taylor,.S.J 1994) 4.2 Performance of individual FX investors The results thus far show that individual FX investors, on average, have more winning trades than losing trades and are able to predict future returns eight days after they execute a trade. Next, an analysis of the performance of these investors is warranted because if these investors are able to predict future returns, then they should also be earning abnormal returns. The summary aggregate data (Table 1), which provided information for all closed and open positions for the 1,231 accounts, shows that investors on average earned $190.30 post-transaction costs on their accounts. However, according to table 1 the total gross maximum dollar gain (loss) is $1,824,780 (-$1,557,940) and the total net maximum dollar gain (loss) is $937,220.00 (- $99,964.10) which reveals that some investors are winning, and losing, significant amount of money. This section analyzes the monthly returns of accounts to determine whether individual investors are able to earn positive and statistically significant abnormal returns. Table 5 provides the summary aggregate data for these accounts of 60 days and older and with ten more trades. The reason we used the accounts that are 60 days and older and with ten more trades to see if there were consistencies in earing abnormal returns among individual FX investors. Table 5 shows that 14

of all the accounts there were 153 trades made of which 56.29 percent were winning trades. It also shows 305 (25.49) trades made per year (per month). Table 5 also shows that the mean total dollar gain (loss) is $132,676.07 ($-131,751.97) respectively, and the mean net gain is $924.11. [Insert Table 5 about here] The results shown above indicates that individual FX investors earn and lose large sums of money but on average they earn $924.11 per trade after transaction costs. In analyzing the performance of individual FX investors we followed the methodology developed by Pojarliev and Levich (2008) who used a four-factor model that explains returns based on four distinct styles of currency trading. 4 r j,t = α j,t + i=1 β i,j F i,t + ε t (3) Where r j,t = the excess monthly return generated by the individual FX investors at time t α j,t = is the individual FX investor sskill β i,j = the coefficient that measures the sensitivity of the individual FX investors returns to the factor F i,t = the beta factor that requires a systematic risk premium in the market ε t = i.i.d. random error term Where excess returns are the daily returns for individual FX investors after transaction costs on day t less the daily returns on the one-month London Interbank Offered Rate. We used the four factors used by Pojarliev and Levich (2008) and those are (1) Carry factor measured as the Citibank Beta1 G10 Carry Index; (2) value factor measured by the Citibank Beta1 G10 Purchasing Power Index. (3) Trend-following factor measured by the AFX Currency Management Index which is consistent with Pojarliev and Levich (2008) who uses the AFX 15

Currency Management Index for the Trend-following factor;( 4) and Volatility factor proxied by the average of the one-month implied Volatility for the EUR/USD exchange rate and for the USD/JPY exchange rate. Carry trades consists of borrowing a low interest-rate currency and investing in a high interest-rate currency. Trend-following consists on following patterns or reversals. The value factor is used when investors have a long-term view and need an underlying benchmark to identify over- and undervalued currencies. Volatility is used because currency investors are recognized to trade on currency Volatility. Since, the frequency distribution of FX instruments traded in this sample reveals that only 32% of all trades are EUR/USD and USD/JPY contracts. Thus, the Volatility proxy used in this paper is the Deutsche Bank FX Volatility Index which consists of a basket of nine currencies which is more representative of the currencies traded by the individual FX investors in this sample. 3 For this paper to examine individual FX investors performance, we used the four factors as explained (Carry, Value or PPP, Volatility, and Trend) in equation (3) above, then we calculated the Information Ratio and an alternative measure of Information Ratio that depends on alpha. Information Ratio is used to gauge the skill of individual FX investors, because it takes the active returns achieved by individual investors and divide it by the risk they took. The higher the Information Ratio is, the better the skills of the individual FX investors. Information Ratio in equation (4) is defined as the ratio of annual excess returns to their standard deviation 3 The Deutsche Bank FX Volatility Index consists of the following currencies: EURUSD (35.9%); USDJPY (21.79%); GBPUSD (17.95%); USDCHF (5.13%); CADUSD (5.13%); AUDUSD (6.41%); EURJPY (3.85%); EURGBP (2.56%); EURCHF (1.28%). 16

IR j,t = R j,t σ(r j,t ) (4) We also used an alternative measure of Information Ratio which captures alpha directly as shown in equation (5) IR j,t = α j,t σ(α j,t ) (5) [Insert Table 6 about here] Table 6 shows information about Excess Annual Returns, Standard Deviation, and Information Ratio using equation (4), the Rank of each individual FX investors based on Information Ratio (IR) using equation (4), Annual Alpha, Tracking Error, Information Ratio using equation (5), and the Rank of individual FX investors based on IR * ( Information Ratio based on equation 5) From examining table 6 we identify that not all individual FX investors produced positive alpha and the mean of IR is -0.01633 while the mean of IR * is 0.009588. Moreover, table 6 shows that the ranking of individual FX trader changes with how the Information Ratio is calculated. For example, individual FX investor M60 produced annual alpha of 0.102 with IR 0.555 and ranked number 1, while the same individual M60 IR * was 0.622 and ranked number 3. On the other hand, individual investor M162 produced annual alpha of 0.075 with IR 0.401 was ranked number 2, while the same individual M162 with IR * 0.776 was ranked number 1. 4.3 Market Timing of Individual FX investors The above results prompted us to investigate if individual FX investors produce pure alpha through market timing or it was just an exposure to factor betas. To investigate that proposition we ran the cross sectional regressions on each of the four market factors in the FX markets and we used the following equation developed by Pojarliev, and Levich (2010). The idea behind this 17

analysis is to see whether there are significant or a non-significant Beta coefficients on the four factors. β k j,t = γ 0 + γ t 1 β k j,t + j,t (6) [Insert Table 7 about here] Table 7 Panel A to Panel D shows that Beta coefficients on all the four factors are not significant with t-stat from 0.011 to 1.478, therefore we can conclude that individual FX investor don t have significant exposure to the four FX market factors Betas, which leads to the idea that individual FX investors produced alpha without relying on passive exposure to market factors Betas. To further investigate the exposure of individual FX on the four FX market factors Betas, we investigated what percentage of individual FX investors are passively exposed to the four factor Betas. That investigation provides more robustness to the analyses above because a low percentage of individual FX traders who are passively exposed to the four factor Betas lends more evidence that FX investors produce pure alpha. [Insert Table 8 about here] Table 8 shows the fraction of individual FX investors with significant exposure to each individual factor through multiple year periods. Table 8 reveals that only 3.85% of individual FX investors had significant exposure to Trend factor in year 2005 2006, and approximately 4%-6% of individual FX investors had significant exposure to all four factors in year 2006 2007, 11.7% of individual FX investors had significant exposure to Carry factor while 8.51% had significant exposure to Volatility factor and 9.57% had significant exposure to both Value and Trend factor in year 2007 2008. While in year 2008 2009 13.13 % had significant exposure on Volatility factor, 16.16% on Trend factor, 4.04% on Value factor and 7.07% on Carry factor. The analysis 18

shown in table 8 reveal that small portion of the individual FX investors had significant passive exposure on the four-factor which means that some individual FX investors performance depended on that passive exposure, while a larger percentage of individual FX investors performance did not depend on the passive exposure to the four factors. All that leads to a conclusion that there is a reason other than the exposure to the factor Betas that enabled individual FX investors to produce alpha and that reason is their ability to time the market. Then we proceeded to investigate whether individual FX investors have timing ability. We followed the procedure used by Pojarliev and Levich (2008) in which they describe that timing ability is explored by decomposing the style factors into positive and negative returns and then exploring whether individual FX investors have the ability to time the changing returns. Therefore, we ran a regressions of the following form for each individual FX trader 4 4 r j,t = α j,t + i=1 β i,j [F i,t F i,t > 0] + i=1 γ i,j [F i,t F i,t < 0], (7) [Insert Table 9 about here] Then we calculated alpha for each individual FX trader, and followed the same procedure used in table 6 in calculating the Information Ratio. Table 9 shows that individual FX investors produced an average alpha of 0.0366. In addition, individual FX trader M60 produced annual alpha of 0.188 with IR 0.555 and IR * 0.932 and ranked # 1 in both IR calculations methods and that support the idea that some of these individual investors produced alpha through their timing abilities. Then we proceeded to investigate in details of how each individual FX investor timed the market. Put it in other words, we are investigating if the individual FX investors produced alpha by having certain active exposure on each of the four factors and that exposure is a reflection of their ability to time the market. Therefore, we used the same model we used above: Monthly data 19

on individual FX returns on Carry, PPP or value, Momentum or Trend and Volatility. Each factor is decomposed into observations of positive and negative returns and separate coefficients are estimated on each as a test of whether individual FX investors have skill in loading positively (negatively) on factors when factor returns are positive (negative). Statistically significant t- statistics are reported in bold. As you have noticed it is the same model used in table 9 but this time we analyzed it in more details [Insert Table 10 about here] Table 10 shows for example that 3 individual FX investors timed positive Carry or had positive (long position) exposure on positive factor Carry, 4 timed negative Carry or had negative (short position) exposure on negative Carry, 2 timed positive PPP or had positive (long position) exposure on positive PPP, 3 timed negative PPP or had negative (short position) exposure on negative PPP, 3 timed positive Volatility or had positive (long position) exposure on positive Volatility, 3 timed negative Volatility or had negative (short position) exposure on negative Volatility, 2 timed positive Trend or had positive (long position) exposure on positive, and 2 timed negative Trend or had negative (short position) exposure on negative Trend. The above analysis lead to another question of whether there is a style persistence for individual FX investors. Put it in other words, if we find that individual FX investors do not follow the same trading pattern then they do not have style persistence and that brings more evidence on their timing ability. For example if individual FX investors who are exposed to one of the factor in period t-1, and if they are not likely to maintain the same significant exposure in period t then they are timing the market. To perform the analysis we first run a regression on alpha where α j,t is the Excess Return for individual FX investor j that is not explained by the four factors. 20

α j,t 4 = R j,t i=1 β i,j F i,t (8) Then we run a second regression to investigate whether individual FX investors who have been performing well in the past continue to perform well in the future. The reason we investigate that is to see if future alpha is not related to past alpha because if the alphas are not related then that shows an additional evidence of individual FX investors market timing ability. So we use the model developed by Aggarwal and Jorion (2008) and use the following regression equation: α j,t = φ 0 + φ 1 α j,t 1 + μ j,t, (9) [Insert Table 11 about here] Table 11 shows that the regression doesn t yield a significant coefficient on the previous year s alpha which means that past performance measured by alpha is not related to future performance and that supports the idea that individual FX investors vary their exposure from year to year which is an additional evidence of their timing ability. 5. Conclusion This paper tests whether individual Forex investors can predict future returns, are able to time the market, and can generate alpha after transaction costs. Using a sample of 1,231 FX trading accounts and 72,072 trades, the results show that contrary to existing theoretical literature this group of individual investors can predict future returns up to eight days after trade execution. The above discovery supports what previous studies such as Ito, Lyons, and Melvin (1998), and Evans and Lyons (2004) had argued, and that was, the individual customer trades contain pieces of new information about the underlying macroeconomic fundamentals driving the exchange rate. Moreover, our results support both Brock 1991 and Taylor 1986 evidence that the linear independence of FX prices is rejected. Therefore our discovery of return predictability is significant because not only it supports the idea that linear independence is rejected but also 21

provides empirical evidence that there is private information in the FX market, and provides empirical evidence that it can be used to predict future movements in the FX market. The paper also shows that some individual FX investors can time the market, produce positive alpha after transaction costs, in addition, these investors do not have style persistence and their future alpha are not related to previous year s alpha, which is an additional evidence of their market timing ability. Finally, this study is limited in the sense that it offers no explanation as to why these investors can time the FX market which suggests that that some individual FX investors seem to act as informed FX managers. Unlike equities, currencies should have no information that can be exploited by individual investors. A review of the data shows that the overwhelming majority of investors use technical analyses. Consequently, the ability of these investors to time the market cast doubts to the proponents of market efficiency. This issue is left to future research which could be accomplished by conducting surveys and interviews with individual FX investors to bring forth a richer understanding on how this phenomenon has occurred. 22

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Appendix Table 1: Summary Statistics for Aggregate Data for all Systems This table presents aggregate summary statistics for all 1,231 accounts. This data were precompiled by the Trade System Host for each system and the mean, standard deviation, minimum and maximum were calculated from each individual account. The time period is from April 2005 to March 2008. The data used to compile this table consists of all trades, currently open and closed, and is unlike the transaction data contained in Table 2, which contains only closed positions. The reason for the difference is that only paid subscribers have access to open positions. Age of Account in Years measures the life of the account measured in years. Monthly Subscription Cost reflects the cost that Developers charge for access to their system. Average Holding Time for Trades (in Hours) shows the average time each trade is held. Opening Equity Value represents the amount of capital that each account started with on its inception date. Number of Trades reflects the total number of trades that are closed and currently open at the time of the extraction of data that occurred on April 2005. Number of Winning Trades, Number of Losing Trades, Percent of Losing Trades, Percent of Winning Trades, Trades per Month and Trade per Year are also compiled from the aggregate data provided by the Trade System Host and are based on all open and closed positions. Total Dollar Gain and Total Dollar Loss are the pre-transaction gross gains realized by each account and are measured in U.S. dollars. Total Gain/Loss is the net of the Total Dollar Loss/Gain. Variable Mean Std Dev Minimum Maximum Age of Account in Years 0.27 0.42 0.00 3.55 Monthly Subscription Cost $134.90 $205.34 $0.00 $2,000.00 Average Holding Time for Trades (in Hours) 1,508.48 4,541.57 0.00 41,282.77 Opening Equity Value $90,854.03 $28,944.96 $1,000.00 $400,000.00 Number of Trades 59.59 181.47 1 4002 Number of Winning Trades 33.52 102.06 0 1936 Number of Losing Trades 26.08 85.99 0 2066 Percent of Winning Trades 52.97% 29.97% 0.00% 100.00% Percent of Losing Trades 47.03% 29.97% 0.00% 100.00% Total Dollar Gain $55,261.91 $139,666.16 $0.00 $1,824,780.00 Total Dollar Loss -$55,071.61 $118,632.96 -$1,557,940.00 $0.00 Total Net Gain/Loss $190.30 $59,972.54 -$99,964.10 $937,220.00 Trades per Year 350.39 1176.87 - - Trades per Month 29.20 98.07 - - 26

Table 2: Summary Statistics for Transaction Data for all Systems This table presents summary statistics on the transaction portion of the data obtained from the Trade System Host. Unlike Table 1, it only contains closed positions because open position transaction data is only available to subscribers. Panel A and B divide the sample into Trade Positions which consist of Shorts and Longs, provides the total number of closed trades executed and the percentage of each for all trades. Panel B shows the percent of the longs and shorts that are winning trades, losing trades, and even trades (realized gain of zero). Panel C presents the frequency distribution and number of trades of all sport FX contracts that have been opened and closed by Individual FX investors. Table 2 Panel A Table 2 Panel B Trade Position Number of Trades Percent of All Trades Percent Winning Percent Losing Trades Percent Even Trades Trades Long 37,090 51.46 56.73 41.66 1.6 Short 34,982 48.54 56.26 42.16 1.58 Table 2 Panel C FX Symbol # of Trades Percent FX Symbol # of Trades Percent GBPUSD 13656 21.60 USDDKK 40 0.06 EURUSD 13446 21.27 AUDCHF 36 0.06 USDJPY 7203 11.40 USDZAR 35 0.06 USDCHF 6459 10.22 EURNOK 21 0.03 GBPJPY 5014 7.93 USDHKD 19 0.03 EURJPY 4256 6.73 GBPHKD 16 0.03 AUDUSD 3073 4.86 EURSEK 15 0.02 USDCAD 2597 4.11 GBPDKK 14 0.02 EURGBP 1119 1.77 GBPSEK 14 0.02 GBPCHF 1060 1.68 GBPNOK 12 0.02 CHFJPY 870 1.38 GBPSAR 12 0.02 NZDUSD 848 1.34 USDSEK 10 0.02 EURCHF 828 1.31 USDINR 7 0.01 AUDJPY 759 1.20 GBPEUR 5 0.01 EURAUD 628 0.99 USDTHB 5 0.01 EURCAD 473 0.75 GBPINR 4 0.01 CADJPY 277 0.44 USDMXN 3 0.00 GBPAUD 114 0.18 GBPSGD 2 0.00 GBPCAD 104 0.16 USDISK 2 0.00 USDSGD 55 0.09 BAREUR 1 0.00 GBPNZD 54 0.09 BARGBP 1 0.00 USDNOK 42 0.07 27

Table 3. Regression Results of Returns as a Function of Short/Long Trades This table presents the regression results of Model (1) which regresses a dummy variable trade that takes the number 1 if the transaction is a short and zero if the trade is a long on the CRR r (window: x1, x2) over the event window. The event windows used in the analysis is the CRR of the FX contract traded from one (0, 1) to ten (0, 10) days after the execution of the trade. In accordance with the main hypothesis that FX investors cannot predict future returns the coefficient for trade should be positive and statistically insignificant. r (window: x1,x2) = α t + β 1 trade t + ε t Window Variable Coefficient x 100 Std. Error x 100 t-statistic p-value R 2 (0,1) Constant 0.00036 0.00858 0.0418 0.9666 0.00024 TRADE -0.01699 0.00973-1.7465 0.0807 (0,2) Constant 0.00633 0.01256 0.5037 0.6145 0.00057 TRADE -0.03728 0.01361-2.7395 0.0062 (0,3) Constant 0.01289 0.01513 0.8521 0.3942 0.00043 TRADE -0.03936 0.01616-2.4350 0.0149 (0,4) Constant 0.01524 0.01842 0.8275 0.4080 0.00057 TRADE -0.05387 0.01948-2.7659 0.0057 (0,5) Constant 0.02944 0.01981 1.4858 0.1373 0.00090 TRADE -0.07479 0.02138-3.4971 0.0005 (0,6) Constant 0.01887 0.02193 0.8603 0.3896 0.00065 TRADE -0.06979 0.02334-2.9904 0.0028 (0,7) Constant 0.02026 0.02416 0.8384 0.4018 0.00064 TRADE -0.07543 0.02584-2.9190 0.0035 (0,8) Constant 0.01880 0.02530 0.7453 0.4561 0.00048 TRADE -0.06870 0.02680-2.5622 0.0104 (0,9) Constant 0.01884 0.02728 0.6905 0.4899 0.00031 TRADE -0.05931 0.02852-2.0799 0.0375 (0,10) Constant 0.00828 0.02911 0.2844 0.7761 0.00013 TRADE -0.03971 0.03030-1.3104 0.1901 28

Table 4. Regression Results of Returns as a Function of Short/Long Trades and Volatility This table presents the regression results of Model (2) which regresses a dummy variable trade that takes the number 1 if the transaction is a short and zero if the trade is a long on the CRR r (window: x1, x2) over the event window. The event windows used in the analysis is the CRR of the FX contract traded from one (0, 1) to ten (0, 10) days after the execution of the trade. Furthermore, the control variable of Volatility (v t) is added to the regression to control for Volatility. In accordance with the main hypothesis that FX investors cannot predict future returns the coefficient for trade should be positive and statistically insignificant. r (window: x1,x2) = α t + β 1 trade t + v (avg window: x1,x2) + ε t Window Variable Coefficient x 100 Std. Error x 100 t- Statistic p-value R 2 (0,1) Intercept 0.0065 0.0147 0.44 0.66 0.0003 TRADE -0.0169 0.0097-1.74 0.08 V(0,1) -0.9688 2.3915-0.41 0.69 (0,2) Intercept 0.0518 0.0235 2.20 0.03 0.0018 TRADE -0.0364 0.0136-2.67 0.01 V(0,2) -6.9957 4.1050-1.70 0.09 (0,3) Intercept 0.1612 0.0327 4.93 0.00 0.0071 TRADE -0.0357 0.0161-2.22 0.03 V(0,3) -22.4176 5.5260-4.06 0.00 (0,4) Intercept 0.2759 0.0422 6.54 0.00 0.0125 TRADE -0.0482 0.0194-2.49 0.01 V(0,4) -39.4394 7.3096-5.40 0.00 (0,5) Intercept 0.4175 0.0527 7.92 0.00 0.0204 TRADE -0.0664 0.0210-3.16 0.00 V(0,5) -58.8198 8.8345-6.66 0.00 (0,6) Intercept 0.4917 0.0640 7.69 0.00 0.0241 TRADE -0.0586 0.0228-2.57 0.01 V(0,6) -72.2336 10.6245-6.80 0.00 (0,7) Intercept 0.6207 0.0737 8.42 0.00 0.0320 TRADE -0.0608 0.0251-2.42 0.02 V(0,7) -91.4966 12.2978-7.44 0.00 (0,8) Intercept 0.7139 0.0857 8.33 0.00 0.0376 TRADE -0.0518 0.0259-2.00 0.05 V(0,8) -105.2422 14.0045-7.51 0.00 (0,9) Intercept 0.8234 0.0912 9.03 0.00 0.0426 TRADE -0.0404 0.0275-1.47 0.14 V(0,9) -121.6535 14.8699-8.18 0.00 (0,10) Intercept 0.9329 0.0969 9.63 0.00 0.0498 TRADE -0.0174 0.0292-0.60 0.55 V(0,10) -139.8944 15.8239-8.84 0.00 29

Table 5: Summary Statistics for Aggregate Data for all Systems 60 days and older and with ten more trades This table provides aggregate summary statistics for all accounts that are 60 days or older and with ten more trades. This data were precompiled by the Trade System Host for each system and the mean, standard deviation, minimum and maximum were calculated from each individual account. The time period is from April 2005 to March 2008. The data used to compile this table consists of all trades, opened and closed, and is unlike the transaction data contained in Table 2, which contains only closed positions. The reason for the difference is that only paid subscribers have access to open positions. Age of Account in Years measures the life of the account measured in years. Monthly Subscription Cost reflects the cost that Developers charge for access to their system. Average Holding Time for Trades (in Hours) shows the average time each trade is held. Opening Equity Value represents the amount of capital that each account started with on its inception date. Number of Trades reflects the total number of trades that are closed and currently open at the time of the extraction of data that occurred on April 2005. Number of Winning Trades, Number of Losing Trades, Percent of Losing Trades, Percent of Winning Trades, Trades per Month and Trade per Year are also compiled from the aggregate data provided by the Trade System Host and are based on all open and closed positions. Total Dollar Gain and Total Dollar Loss are the pre-transaction gross gains realized by each account and are measured in U.S. dollars. Total Gain/Loss is the net of the Total Dollar Loss/Gain. Variable Mean Std Dev Minimum Maximum Age of Account in Years 0.59 0.47 0.16 3.55 Monthly Subscription Cost 130.14 181.20 0.00 2000.00 Holding Time for Trades (in Hours) 195.67 587.98 0.72 8140.00 Opening Equity Value 91,413.53 25,696.99 1000.00 10,0000.00 Number of Trades 153.53 286.30 11.00 4002.00 Number of Winning Trades 87.34 160.15 2.00 1,936.00 Percent of Winning Trades 56.29% 17.50% 16.36% 96.50% Number of Losing Trades 66.20 137.84 1.00 2066.00 Percent of Losing Trades 43.71% 17.50% 3.50% 83.64% Total Dollar Gain 132,676.07 211,741.89 317.38 182,4780.00 Total Dollar Loss -131,751.97-173,591.07 149.13 1,557,940.00 Total Dollar Net Gain/Loss 924.11 86,550.70-99,964.10 937,220.00 Trades per Year 305.84 439.31 - - Trades per Month 25.49 36.61 - - 30

Table 6: Individual FX investors Annual alpha and their Ranking based on Information Ratio. Table 6 uses the following specification to capture alpha for individual FX investors 4 r j,t = α j,t + i=1 β i,j F i,t + ε t Where r j,t = the excess monthly return generated by the individual FX investors at time t α j,t = is the individual FX investor sskill β i,j = the coefficient that measures the sensitivity of the individual FX investors returns to the factor F i,t = the beta factor that requires a systematic risk premium in the market ε t = i.i.d. random error term The four factors used are the (1) Carry factor measured as the Citibank Beta3 G10 Carry Index; (2) Trend-following factor measured by the AFX Currency Management Index; (3) value factor measured by the Citibank Beta3 G10 Purchasing Power Index; and (4) Volatility factor proxied by the Deutsche Bank FX Volatility Index.. Table 6 has 1,883 account-month observations and covers the time period of April 2005 to March 2008. Then we calculated the Information Ratio using the following two equations IR j,t = R j,t σ(r j,t ) ; IR j,t = α j,t σ(α j,t ) The table shows the Annual alpha, IR, IR * and Ranking for individual FX trader. We omitted from the table below the rest of the information about all the individual FX investors for space consideration Individual Id Excess Annual Return S.D. IR RANK Annual Alpha Tracking Error IR* RANK M4 0.008 0.204 0.038 23-0.034 0.163-0.21 41 M13 0 0.122 0.003 27-0.007 0.112-0.059 33 M17-0.027 0.246-0.111 37 0 0.218-0.001 29 M21 0.097 0.271 0.359 3 0.097 0.226 0.431 6 M47 0.013 0.074 0.177 9 0.015 0.066 0.231 9 M48-0.002 0.063-0.035 33-0.003 0.045-0.077 35 M52 0.001 0.305 0.003 28 0.014 0.283 0.051 21 M57 0.038 0.446 0.086 19 0.023 0.389 0.059 20 M60 0.093 0.167 0.555 1 0.102 0.164 0.622 3 M91 0.015 0.082 0.185 8 0.009 0.077 0.12 15 M92-0.108 0.23-0.468 49-0.071 0.215-0.33 43 M101-0.044 0.232-0.189 38-0.061 0.17-0.36 44 M102 0.002 0.094 0.023 25 0.002 0.09 0.018 26 M105-0.087 0.278-0.313 44-0.017 0.194-0.089 36 M123 0.014 0.086 0.167 11 0.023 0.078 0.29 8 M125 0.016 0.14 0.114 17 0.015 0.136 0.107 17 M133 0.034 0.392 0.087 18 0.051 0.292 0.174 11 M135 0.026 0.211 0.121 16 0.026 0.154 0.169 13 M139-0.087 0.25-0.35 47-0.038 0.151-0.254 42 M144 0.03 0.216 0.138 14 0.043 0.204 0.213 10 M159 0.016 0.212 0.077 21 0.021 0.2 0.104 18 M162 0.051 0.128 0.401 2 0.075 0.097 0.776 1 M168 0.006 0.039 0.141 13 0.018 0.029 0.632 2 M183-0.119 0.421-0.283 42-0.022 0.15-0.144 38 M191-0.015 0.301-0.051 35-0.009 0.233-0.04 30 M195-0.299 0.981-0.304 43-0.12 0.777-0.154 39 M216-0.098 0.203-0.484 50-0.059 0.145-0.411 45 M217 0.015 0.173 0.086 20 0.025 0.141 0.174 12 M222-0.114 0.465-0.246 41-0.008 0.165-0.048 31 M226 0.035 0.226 0.155 12 0.027 0.223 0.12 16 M228-0.001 0.113-0.005 30-0.005 0.085-0.065 34 M240-0.064 0.274-0.234 40 0.002 0.079 0.023 23 M245 0.002 0.089 0.025 24-0.007 0.076-0.098 37 M247-0.004 0.201-0.022 31 0.001 0.196 0.006 27 31

Table 7: Beta Regressions To investigate if individual FX investors produce pure alpha and not just an exposure to the four factor Betas. We ran the cross sectional regressions on each of the four-factor and we sued the following equation β k j,t = γ 0 + γ t 1 β k j,t + j,t Panel A to Panel D shows that Beta coefficients on all the four factors are not significant with t-stat from 0.011 to 1.478, therefore we can further conclude that individual FX investors produce pure alpha and not just an exposure to factor Betas. Panel A Number of Individual FX investors Intercept t-stat Coefficient, Beta Vol year t-1 t-stat R- Square April 05-March 06 13-0.705-0.752 0.220 1.478 0.166 April 06-March 07 27 0.032 0.009 0.302 0.568 0.013 April 07-March 08 37 1.794 1.154 0.555 1.182 0.038 Panel B Number of Individual FX investors Intercept t-stat Coefficient, Beta Val year t-1 t-stat R-Square April 05-March 06 13 4.290 0.901 0.799 1.787 0.225 April 06-March 07 27-0.868-0.581 0.255 1.214 0.040 April 07-March 08 37 0.949 0.673 0.024 0.465 0.009 Panel C Number of Individual FX investors Intercept t-stat Coefficient, Beta Trend year t-1 t-stat R- Square April 05-March 06 13 1.447 0.828 0.032 0.218 0.004 April 06-March 07 27-5.693-0.377-0.194-0.429 0.007 April 07-March 08 37 2.035 0.734-0.105-0.718 0.015 Panel D Number of Individual FX investors Intercept t-stat Coefficient, Beta Carry year t-1 t-stat R- Square April 05-March 06 13 3.016 0.560-1.360-1.783 0.224 April 06-March 07 27 1.726 0.868 0.001 0.011 0.000 April 07-March 08 37-0.094-0.084 0.111 0.604 0.010 32

Table 8: Fraction of Individual FX investors with Significant Betas The table 8 shows the fraction of individual FX investors with significant exposure to each individual factor through multiple year periods. Table 8 reveals that only 3.85% of individual FX investors had significant exposure to Trend factor in year 2005 2006, and approximately 4%-6% of individual FX investors had significant exposure to all four factors in year 2006 2007, 11.7% of individual FX investors had significant exposure to Carry factor while 8.51% had significant exposure to Volatility factor and 9.57% had significant exposure to both Value and Trent factor in year 2007 2008. Volatility Value Trend Carry April 05-March 06 0.00% 0.00% 3.85% 0.00% April 06-March 07 5.56% 4.17% 5.56% 5.56% April 07-March 08 8.51% 9.57% 9.57% 11.70% 33

Table 9: Individual FX investors Producing Alpha By Their Timing Ability Monthly data on individual FX returns on Carry, Purchasing Power Parity (PPP) or Value, Momentum or Trend and Volatility. Each factor is decomposed into observations of positive and negative returns and separate coefficients are estimated on each as a test of whether individual FX investors have skill in loading positively (negatively) on factors when factor returns are positive (negative) 4 r j,t = α j,t + i=1 β i,j [F i,t F i,t > 0] + i=1 γ i,j [F i,t F i,t < 0] Then we calculated the Information Ratio using the following two equations 4 IR j,t = R j,t σ(r j,t ) ; IR j,t = R j,t σ(r j,t ) The table shows the Annual alpha, IR, IR * and Ranking for individual FX trader. We omitted from the table below the rest of the information about all the individual FX investors for space consideration Individual Id Excess Annual Return S.D. IR Rank Annual Alpha Tracking Error IR* Rank M4 0.008 0.204 0.038 23-0.07 0.187-0.376 48 M13 0 0.122 0.003 27-0.03 0.175-0.17 41 M17-0.027 0.246-0.111 37 0.129 0.266 0.485 11 M21 0.097 0.271 0.359 3 0.026 0.339 0.078 29 M47 0.013 0.074 0.177 9 0.018 0.084 0.221 19 M48-0.002 0.063-0.035 33 0.006 0.084 0.074 30 M52 0.001 0.305 0.003 28 0.041 0.424 0.096 27 M57 0.038 0.446 0.086 19-0.001 0.53-0.001 33 M60 0.093 0.167 0.555 1 0.188 0.202 0.932 1 M91 0.015 0.082 0.185 8 0.027 0.15 0.179 21 M92-0.108 0.23-0.468 49-0.075 0.475-0.157 40 M101-0.044 0.232-0.189 38-0.143 0.384-0.372 47 M102 0.002 0.094 0.023 25-0.009 0.109-0.085 34 M105-0.087 0.278-0.313 44 0.061 0.221 0.274 15 M123 0.014 0.086 0.167 11 0.028 0.09 0.311 14 M125 0.016 0.14 0.114 17 0.03 0.246 0.122 26 M133 0.034 0.392 0.087 18 0.091 0.445 0.205 20 M135 0.026 0.211 0.121 16-0.117 0.449-0.26 42 M139-0.087 0.25-0.35 47 0.092 0.288 0.319 12 M144 0.03 0.216 0.138 14 0.081 0.257 0.318 13 M159 0.016 0.212 0.077 21 0.312 0.603 0.518 9 M162 0.051 0.128 0.401 2 0.206 0.269 0.766 4 M168 0.006 0.039 0.141 13 0.069 0.089 0.775 3 M183-0.119 0.421-0.283 42 0.135 0.267 0.505 10 M191-0.015 0.301-0.051 35-0.146 0.443-0.331 44 M195-0.299 0.981-0.304 43 0.071 1.521 0.046 31 M216-0.098 0.203-0.484 50 0.039 0.317 0.124 25 M217 0.015 0.173 0.086 20-0.034 0.23-0.147 39 M222-0.114 0.465-0.246 41 0.159 0.306 0.519 8 M226 0.035 0.226 0.155 12 0.002 0.285 0.008 32 M228-0.001 0.113-0.005 30-0.063 0.158-0.395 49 M240-0.064 0.274-0.234 40 0.047 0.083 0.566 7 M245 0.002 0.089 0.025 24-0.03 0.089-0.335 45 M247-0.004 0.201-0.022 31 0.034 0.214 0.157 22 M253 0 0.006 0 29-0.001 0.009-0.139 37 M263-0.035 0.174-0.203 39-0.026 0.187-0.139 38 M264 0.005 0.311 0.017 26 0.033 0.411 0.08 28 M282-0.224 0.368-0.61 51 0.07 0.524 0.133 24 M287 0.458 2.685 0.17 10-2.182 2.756-0.792 51 M316 0.595 2.908 0.205 7-2.26 2.942-0.768 50 M325-0.139 0.439-0.318 45 0.2 0.245 0.816 2 M338 0.013 0.051 0.263 5 0.046 0.067 0.68 6 M339 0.004 0.072 0.061 22 0.022 0.088 0.249 16

Table 10: Individual FX investors Timing Ability Monthly data on individual FX returns on Carry, PPP or Value, Momentum or Trend and Volatility. Each factor is decomposed into observations of positive and negative returns and separate coefficients are estimated on each as a test of whether individual FX investors have skill in loading positively (negatively) on factors when factor returns are positive (negative). Statistically significant t-statistics are reported in bold. 4 4 r j,t = α j,t + i=1 β i,j [F i,t F i,t > 0] + i=1 γ i,j [F i,t F i,t < 0] Indidviual ID Constant Tstat Carrypos Tstat Carryneg Tstat PPPpos Tstat PPPneg Tstat VOLpos Tstat VOLneg Tstat TRENDpos Tstat TRENDneg Tstat Rsquare Nobs M48 0.0040 0.1000-0.5070-0.5100 0.6600 0.6600 0.4950 0.3900-0.7000-0.3200-0.0780-0.1600 0.2620 0.8400-0.0330-2.1000-0.0440-1.6400 0.5370 18.0000 M52 0.2140 1.5500-2.4850-0.3900 18.3990 2.85* 4.6890 0.6200-24.3290-2.66* -2.5390-0.9100 14.0940 2.59* -17.4290-1.3700 14.0790 0.7900 0.5600 25.0000 M57-0.1190-0.9300 7.3860 1.4600 2.8630 0.6200 2.5850 0.4900-1.9230-0.2900-0.9840-0.4700-4.5220-2.69* 0.0430 0.4100 0.2040 1.3000 0.2810 47.0000 M92 0.1430 1.1100 0.8050 0.1400-0.4720-0.0800 1.4950 0.2000-17.3350-2.0000-0.0800-0.0300 8.6960 1.8900 35.1540 2.51* 48.2760 2.85* 0.6020 20.0000 M125 0.0980 1.5500-3.4770-1.4500 1.0130 0.4500 0.1160 0.0400 1.2900 0.3500 1.6420 1.2500-3.8610-1.8400-7.3490-1.6700-10.697-2.41* 0.3330 30.0000 M135-0.1110-1.6800 5.5270 1.9200-22.5870-6.06*** -1.9020-0.4600 26.9960 2.0600 1.7930 1.1900-5.8610-2.0800 13.6420 0.6800 2.7360 0.3500 0.9000 18.0000 M139-0.0450-0.3200-1.5470-0.2500-4.3870-0.5500 17.0850 1.9200-4.3450-0.1500-4.6970-1.4500-3.0400-0.5000-38.6730-0.9000 5.6980 0.3400 0.6710 18.0000 M144 0.1680 1.6300-0.9940-0.2600-2.7060-0.9300-4.6040-0.9900 4.1100 0.7100-1.9270-1.1400 1.6360 1.0900 1.7390 0.3100 5.0120 0.7200 0.2180 31.0000 M159-0.0240-0.1900 3.7170 0.8700-5.1060-0.9900 3.0210 0.4400-25.0850-2.53* -3.0000-1.1300 6.5910 1.4700-26.6060-2.0300-3.1270-0.3100 0.3970 22.0000 M168 0.0060 0.3000-0.3180-0.5000-1.5530-2.1300 2.4970 2.0700-2.5520-1.8100 1.3480 3.58** 0.5290 0.8300-2.3930-1.2900-0.8280-0.5700 0.6710 20.0000 M183 0.0430 0.8400 4.8620 2.56* -4.1290-3.01** -10.0460-4.39*** -5.4440-1.8800-2.0170-1.8300 0.0560 0.0300-9.9660-3.22** 8.0280 2.42* 0.9600 26.0000 M191 0.2390 2.0800-3.4550-0.8300 7.6490 1.8800-13.3910-2.14* 8.5980 1.3400-0.2480-0.1000 2.1000 0.4900 15.8900 1.9400 2.1200 0.2800 0.6370 25.0000 M195-0.6370-1.1200 18.6750 0.7800-25.5180-1.2100 2.3170 0.0700 68.3990 1.7500 3.0340 0.2200-53.0170-2.22* -68.6450-0.8400 2.9750 0.0400 0.6790 17.0000 M216-0.1000-0.9400 3.9510 0.7700-6.0200-1.2800 12.0030 1.4900-5.4310-0.6800-2.7090-0.8100 1.4080 0.2900-19.8020-1.6100 6.5510 0.4300 0.7130 16.0000 M222 0.0560 1.0900 4.1410 2.2* -5.1170-3.62** -7.4630-3.16** -6.2750-2.0900-1.7910-1.6000 0.5840 0.3100-13.9020-4.29** 8.6230 2.56* 0.9710 23.0000 M240 0.0180 0.5000 4.9180 3.56** 0.0700 0.0700-6.7580-3.73** -2.1120-0.9700-0.8420-1.0600-0.7340-0.5500-4.7230-2.0000 1.3270 0.5400 0.9600 22.0000 M245-0.0020-0.0800-1.1040-1.0500 0.4040 0.4400 1.6380 1.4900 1.6510 0.9900 0.5920 1.3700-0.3270-0.9700-0.0260-1.2900 0.0370 1.2200 0.4480 32.0000 M247 0.1480 1.8300-3.6550-1.3400 1.3680 0.5700 0.6650 0.2300 0.9870 0.2300-2.7250-2.42* 1.2640 1.4400 0.0470 0.9100-0.0260-0.3400 0.2700 32.0000 M253 0.0040 0.9600-0.0550-0.3000 0.0000 0.0000-0.1480-0.8400 0.2820 0.9400-0.0100-0.1200 0.0580 1.3500 0.1140 0.5400-0.0090-0.9300 0.2400 17.0000 M263 0.0540 0.5600-1.4020-0.4500 3.8480 1.0700-11.4450-1.2500 0.9770 0.1500 2.0170 1.1100-1.7990-0.5500-2.8320-0.4000 1.6750 0.3200 0.6160 19.0000 M287-0.1780-0.4000-3.2340-0.2200 58.5320 5.17** 45.6390 2.34* -5.6320-0.2400 24.7720 2.44* 15.6460 0.9700 29.2730 1.0000-11.6720-0.4400 0.9620 18.0000 M316-0.2540-0.4700-6.7210-0.3600 55.5000 4.25** 57.1160 2.55* -11.1030-0.4200 20.6220 1.8100 23.7100 1.3100 34.3850 1.0200-24.8210-0.7000 0.9650 16.0000 M338 0.0100 0.5200-0.3870-0.6500-0.0370-0.0700-0.4830-0.8100-2.1710-2.25* -0.0920-0.3800-0.2960-1.5600 0.0010 0.1200 0.0300 1.7900 0.6190 23.0000 M426 0.3890 1.1000-17.2710-1.1700-12.1380-1.3000-27.5060-1.9800 7.8100 0.3300 29.5680 4.32** 9.7900 2.92* 92.5810 5.54** 1.6950 2.1400 0.9110 16.0000 M439 0.0390 0.4000 0.3380 0.1200-1.2190-0.4400-3.0290-0.9200 3.2800 0.5700 0.7020 0.5500 0.6270 0.7200 0.0760 0.9600-0.1820-3.99** 0.7320 19.0000 M443 0.1690 1.8300 1.1730 0.2100-4.7360-0.6300-4.8670-0.8300 4.0230 0.5800-2.4570-1.0900-8.553-2.24* -1.6790-0.2100-5.8580-0.5400 0.6720 19.0000

Table 11: Style Persistence and Alpha Regressions To perform the analysis we first run a regression on alpha where α j is the Excess Return for individual FX trader j that is not explained by the four factors. 4 = R j,t α j,t i=1 β i,j F i,t Then we run a second regression to investigate whether individual FX investors who have been performing well in the past continue to perform well in the future. The reason we investigate that is to see if future alpha is not related to future alpha because if the alphas are not related then that shows an additional evidence of individual FX investor s market timing ability. So we use the model developed by Aggarwal and Jorion (2008) and use the following regression equation: α j,t = φ 0 + φ 1 α j,t 1 + μ j,t, Table11 shows that the regression doesn t yields a significant coefficient on the previous year s alpha which means that past performance measured by alpha is not related to future performance and that supports the idea that individual FX investors vary their exposure from year to year which is an additional evidence of their timing ability. Number of Individual FX investors Intercept t-stat Coefficient, Alpha Year t-1 t-stat R-Square April 05-March 06 13 0.038 2.407* 0.067 0.516 0.024 April 06-March 07 27-0.022-0.716-0.064-0.343 0.005 April 07-March 08 37 0.009 0.252 0.059 0.175 0.001