Forex Books - page 126

[John Seekers]

Using only the 200 large-cap securities that make up the NYSE 100 and NASDAQ 100, this study investigates 130 randomly selected, 3-day formation periods from January 2000 through December 2012 (3,269 trading days). During these formation periods, the three worst and three best performing stocks (based on excess return) are flagged. Once flagged, the subsequent 10-day holding period excess returns are calculated. Results indicate that, on either exchange, investors can outperform the market by going long the stocks that have experienced an excessive 3-day loss. Beyond that, investors can also outperform the market by going long the stocks that have experienced and excessive 3-day gain, however, this result only holds true for NASDAQ securities. Results are robust to the number of best and worst stocks that are flagged. Results are also robust to other combinations of formation and holding period lengths.

[Afro-head]

techmac:
I find her overestimated

Then you could try "Forex Start Up for You !" by  Millie Atkinson, it is also amazing.

[John Seekers]

Motivated by range-related trading practices, this paper investigates the return-predictive role of relative price level. As the price of a stock moves to an unusually high or low level with respect to a long-term trading range, concern about mean-reversion in the price becomes important. I test this hypothesis using a mean-reversion-based measure to proxy for the relative price level. Tests show that the measure is a significant and robust predictor of cross-sectional variation in stock returns. The results suggest that in the presence of uncertainty about duration of firm-specific shocks, deviation from a perceived range makes investors conservative, which creates abnormal performance of a "buy high and sell low'' portfolio strategy. The relative price level effect is not driven by small-cap stocks and it is not a manifestation of momentum, reversal, the 52-week high, and volatility effects.

[Afro-head]

There are lots of books available online, but instead I like to learn it manually, that you could do through Demo Trading, According to me it is the best way to learn Forex Trading.

[John Seekers]

This article provides insight into the two major methods of analysis used to forecast the behavior of the Forex market. Technical analysis and fundamental analysis differ greatly, but both can be useful forecast tools for the Forex trader. They have the same goal - to predict a price or movement. The technician studies the effect while the fundamentalist studies the cause of market movement. Many successful traders combine a mixture of both approaches for superior results.

[John Seekers]

This paper investigates how the two technical drivers, volatility and correlation, influence the algorithm of the investment strategy pairs trading. We model and empirically prove the connection between the rule-based pair selection, the trading algorithm, and the total return. Our insights explain why pairs trading profitability varies across markets, industries, macroeconomic circumstances, and firm characteristics. Furthermore, we critically evaluate the power of the traditionally applied pair selection procedure. In the US market, we find risk-adjusted monthly returns of up to 76bp for portfolios, which are double sorted on volatility and correlation between 1990 and 2014. Our findings are robust to liquidity issues, bid-ask spread, and limits of arbitrage.

[Afro-head]

Thanks for sharing such a nice information, it would be quite useful for the traders to trade in Forex.

[John Seekers]

This note generalizes the results in Li et al. (2012) to threshold moving‐average (TMA) models with more than two regimes. Under some mild conditions, it is shown that multiple‐regime TMA models are always strictly stationary and ergodic without any restriction on the coefficients. This is very different from threshold AR models. An explicit/closed form of the solution to the multiple‐regime TMA model is derived as well. A three‐regime TMA model is illustrated with an application to monthly data of the exchange rate of the Japanese yen against the USA dollar from January 1971 to December 2000.

[techmac]

Anybody has the " threshold moving‐average"?

[John Seekers]

We show how bad and good volatility propagate through forex markets, i.e., we provide evidence for asymmetric volatility connectedness on forex markets. Using high-frequency, intra-day data of the most actively traded currencies over 2007 -- 2015 we document the dominating asymmetries in spillovers that are due to bad rather than good volatility. We also show that negative spillovers are chiefly tied to the dragging sovereign debt crisis in Europe while positive spillovers are correlated with the subprime crisis, different monetary policies among key world central banks, and developments on commodities markets. It seems that a combination of monetary and real-economy events is behind the net positive asymmetries in volatility spillovers, while fiscal factors are linked with net negative spillovers.