Advanced trading strategies - page 4

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This paper extends the literature on the profitability of technical analysis in three directions. First, we investigate the performance of complex trading rules based on moving averages computed over longer periods than those usually considered. Different trading rules are simulated on daily prices of the Standard & Poor’s 500 index and we find that trading rules are more profitable when signals are generated over long horizons. Second, we analyse whether financial leverage can improve the profitability of different strategies, which appears to be the case when leverage is achieved with debt. Third, we propose a new market timing test that assesses whether a trading strategy can generate signals corresponding to bull and bear markets. The results of this test show that complex rules produce high proportions of accurate signals.
 
The evolution of financial data shows a high degree of volatility of the series, coupled with increasing difficulties of forecasting the shorter is the time horizon, when using standard (i.e., based on linear models) forecasting methods. Some alternative forecasting methods for non-linear time series, based on the literature on complex dynamic systems, have been recently developed, which can be particularly useful in the analysis of financial time series. In this paper we present a summary of some of these new techniques, and then show some applications to the analysis of several financial series (i.e., exchange rates, stock prices, and interest rates), which illustrate the usefulness of the approach. Since non-linear forecasting methods require the usage of very long time series, the availability of high-frequency data for these variables make them the best candidates among economic time series for the application of this methodology.
 
The U.S. equity market changed dramatically in recent years. Increasing automation and the entry of new trading platforms has resulted in intense competition among trading platforms.

Despite these changes, traders still face the same challenges as before. They seek to minimize the total cost of trading including commissions, bid/ask spreads, and market impact. New technologies allow traders to implement traditional strategies more effectively. For example, dark pools and indications of interest are just an updated form of tactics that NYSE floor traders used search for counterparties while minimizing the exposure of their clients’ trading interest to prevent front running.

Virtually every measurable dimension of U.S. equity market quality has improved. Execution speeds and retail commission have fallen. Bid-ask spreads have fallen and remain low, although they spiked upward along with volatility during the recent financial crisis. Market depth has increased. Studies of institutional transactions costs find U.S. costs among the lowest in the world. Unlike during the Crash of 1987, the U.S. equity market mechanism handled the increase in trading volume and volatility without disruption. However, our markets lack a market-wide risk management system that would deal with computer generated chaos in real time, and our regulators should address this.

“Make or take” pricing, the charging of access fees to market orders that “take” liquidity and paying rebates to limit orders that “make” liquidity, causes distortions that should be corrected. Such charges are not reflected in the quotations used for the measurement of best execution. Direct access by non-brokers to trading platforms requires appropriate risk management. Front running orders in correlated securities should be banned.
 
We use realized volatilities based on after hours high frequency returns to predict next day volatility. We extend GARCH and long-memory forecasting models to include additional information: the whole night, the preopen, the postclose realized variance, and the overnight squared return. For four NASDAQ stocks (MSFT, AMGN, CSCO, and YHOO) we find that the inclusion of the preopen variance can improve the out-of-sample forecastability of the next day conditional day volatility. Additionally, we find that the postclose variance and the overnight squared return do not provide any predictive power for the next day conditional volatility. Our findings support the results of prior studies that traders trade for non-information reasons in the postclose period and trade for information reasons in the preopen period.
 
The literature on modeling and forecasting time-varying volatility is ripe with acronyms and abbreviations used to describe the many different parametric models that have been put forth since the original linear ARCH model introduced in the seminal Nobel Prize winning paper by Engle (1982). The present paper provides an easy-to-use encyclopedic reference guide to this long list of ARCH acronyms. In addition to the acronyms associated with specific parametric models, I have also included descriptions of various abbreviations associated with more general statistical procedures and ideas that figure especially prominently in the ARCH literature.
 
In this paper, the late part of a room response is modeled in the frequency domain as a complex Gaussian random process. The autocovariance function (ACVF) and power spectral density (PSD) are theoretically defined from the exponential decay of the late reverberation power. Furthermore we show that the ACVF and PSD are accurately parametrized by an autoregressive moving average (ARMA) model. This leads to a new generative model of late reverberation in the frequency domain. The ARMA parameters are easily estimated from the theoretical ACVF. The statistical characterization is consistent with empirical results on simulated and real data. This model could be used to incorporate priors in audio source separation and dereverberation.
 
The forecasting of financial markets is one of the most challenging tasks in predictive analytics. The non-stationarity and the noisy nature of financial time series have driven the debate about whether it is really possible to predict market
movements with sufficient con fidence. The "Eficient Market Hypothesis" provides theoretical grounds for the belief that the best strategy is the "buyand-hold" passive investment strategy, since no excess return can be obtained consistently by predicting and timing the market
 

Technical trading rules are extensively used by foreign exchange (forex) traders. Despite the essential need to the forex diversification, it is not addressed by academic researches to generate forex portfolio trading systems based on technical indices. This paper aims to develop an interpretable and accurate Takagi-Sugeno-Kang (TSK) system for forex portfolio trading. The system uses technical indices of the forex rates and delivers the preferred portfolio composition among multiple foreign currencies. The proposed model considers the transaction cost and trading risk, which are the two important factors in the high frequency trading strategies. The proposed model was implemented to develop a trading system for portfolio trading among the five of the most traded currencies in the Tehran forex market. Four experiments were designed to examine the performance of the proposed model in different market trends, in terms of the portfolio return and risk adjusted return.According to the experimental results, the proposed model is able to extract profitable portfolio trading systems in this market, especially when the market is in the downward trend.



 

Most technical analysis tools focus traditionally on the simple and exponential moving average technique. This study looks at the performance of an optimized fractal adaptive moving average strategy over different frequency intervals, where the Euro/US Dollar currency pair is analyzed due to the increased correlation between the Euro Index and EUR/USD, and the Dollar Index and EUR/USD over the last year compared to the last 15 years. The optimized strategy is evaluated against a buy-and-hold strategy over the 2000- 2015 period, using annualized returns, annualized risk and Sharpe performance measure. Due to the existence of different number of long and short trades in every trading scenario, this paper proposes the use of a new measure called the Sharpe/Total trades ratio which takes into account the number of trades when evaluating the different trading strategies. Findings strongly support the use of the adaptive fractal moving average model over the naïve buy-and-hold strategy where the former yielded higher annualized returns, lower annualized risk, a higher Sharpe value, although it was subject to more trades than the buy-and-hold strategy. The best market timing strategy occurred when using 131 daily fractal data with a Sharpe/Total trades ratio of 0.31%.


 
In this paper we provide MATLAB routines for two major used trading rules, the moving average indicator and MACD oscillator as also the GARCH univariate regression with Monte Carlo simulations and wavelets decomposition, which is an update of an older algorithm
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