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This paper formulates and examines a new type of bivariate time series trading strategy based on signals generated from cross-country quantiles of return distributions. We conduct rolling quantile trading strategies separately in the U.S. and Chinese futures markets for soybeans, wheat, corn and sugar over very short (daily, intraday and overnight) holding periods. Overall, we find that these practical strategies outperform various benchmarks and there is a large profit potential when trades follow quantile-based signals rather than focusing on the median only. The results highlight the value of cross-country trading strategies and the harnessing of information from different parts of the return distributions which have so far been neglected. [ABSTRACT FROM AUTHOR]

This study explores how high frequency data can be turned to be an opportunity to generate possible trading strategies by examining the information flow direction between Indian and US Crude oil Future Markets. The profit opportunities in a market is special indicator of the market efficiency. Thus this paper simulates the trading strategies to check the efficiency of the Indian & US Crude Oil future Market. First, the informational relationship is examined on short term as well as long term basis. The results indicate that there is bidirectional information flow in long term, but in short term only US market is information provider. Next, VECM suggests that this long term and short term relationship between these two markets can be useful for formulating some trading strategies. And trading simulation confirm that very little profit opportunities exists on regular basis for hedgers. Both U.S. and Indian Future market are found to be efficient on daily basis, because there exist very little profit opportunity. But US Future market is found to be more efficient than the Indian Future market. [ABSTRACT FROM AUTHOR]

This research proposes a new non-parametric approach to pairs trading based on renko and kagi constructions which originated from Japanese charting indicators and were introduced to academic studies by Pastukhov. The method exploits statistical information about the variability of the tradable process. The approach does not find a long-run mean of the process and trade towards it like other methods of pairs trading. The only assumption we need is that the statistical properties of the spread process volatility remain reasonably constant. The theoretical profitability of the method has been demonstrated for the Ornstein–Uhlenbeck process. Tests on the daily market data of American and Australian stock exchanges show statistically significant average excess returns ranging from 1.4 to 3.6% per month and annualized Sharpe ratio from 1.5 to 3.4. [ABSTRACT FROM AUTHOR]

We propose a novel rule-based mechanism that identifies Horizontal Support And Resistance (HSAR) levels. The novelty of this system resides in the manner it encloses principles, found in well known technical manuals, used for the identification via visual assessment. The drawing of these levels derives from historical locals, rather than denoting support (resistance) levels from the lowest (highest) price levels of precedent constant time intervals. We further proceed in evaluating whether these levels are efficient trend-reversal predictors, and if they can generate systematic abnormal returns. The dataset used includes adjusted for dividends and splits, daily closing prices of stocks listed on National Association of Securities Dealers Automated Quotation (NASDAQ) and New York Stock Exchange (NYSE) for the last 2 decades. Our results are aligned with the efficient market hypothesis. More concretely, support levels outperform resistance ones in predicting trend interruptions but they fail to generate excess returns when they are compared with simple buy-and-hold strategies. [ABSTRACT FROM PUBLISHER]