Algorithmic trading ... - page 5

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Using daily data for the Swiss franc/US dollar exchange rate, this paper studies the trading profitability of the technical indicator Relative Strength Index (RSI). The authors find that for the past decade or so, using the standard configuration of RSI < = 30 and RSI > = 70 as buy or sell threshold, RSI offers no trading profit, but a small loss instead. However, when the buy/sell threshold parameters are altered, to deviate from the combination most commonly used, using RSI as the trading signal still yields profits. The authors also provide an explanation of this phenomenon. One implication of our findings is that consistent profit opportunities should no longer exist in what is already commonly and widely known, but taking a path less travelled could still lead to profit opportunities not yet discovered and utilized.
 
There are many studies dealing with the analysis of similarity among currencies in foreign exchange market by using network analysis approach. In those studies, each currency is represented by a univariate time series of exchange rate return. This is the standard practice to analyze the underlying information in the foreign exchange market. In this paper, Escoufier's RV coefficient is applied to measure the similarity among currencies where each of them is represented by bivariate time series. Based on that coefficient, we analyze the topological structure of the currencies. An example of FOREX analysis will be presented and discussed to illustrate the advantages of RV coefficient.
 

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K -means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.


 
Forecasting economic and financial variables is highly important for economic policy-makers in any country. In this study, using Brock, Dechert and Scheinnkman (BDS) test, firstly linearity or non-linearity and then, chaoticity of the Returns in Tehran Stock Exchange Price Index (TEPIX) were examined within the period starting from March 25th, 2009 and ending on October 15th, 2011 (625 cases). Afterwards, linear and non-linear forecasting models such as ARIMA, GARCH, ANN and ANFIS were estimated using different forecasting techniques. Measures of forecast accuracy including RMSE, MAE, U-Thiel and MAPE were used to evaluate and compare the models. ANFIS model had the best performance in forecasting daily returns of Stock Index. Then, Morgan-Granger-Newbold Test was utilized to examine the significance of difference of forecast accuracy between linear and non-linear models. The results showed a significant difference between forecasting linear and non-linear methods.
 
Researchers gathered abundant evidence on foreign exchange market inefficiency by regressing excess returns on lagged forward premia but they rarely investigated coefficient instability and its consequences for market efficiency testing. We allow for endogenous changes in the parameters when estimating by using rolling regressions and a Kalman Filter algorithm. Time variation in the regression coefficients is found to be statistically significant. If the regression parameters have changed over time, estimation methods that assume constant parameters may be inappropriate. We argue that the observed time variation in the forward premium slope is so large that a negative OLS slope for the post-Bretton Woods sample size is not improbable.
 
Recent years have witnessed the advancement of automated algorithmic trading systems as institutional solutions in the form of autobots, black box or expert advisors. However, little research has been done in this area with sufficient evidence to show the efficiency of these systems. This paper builds an automated trading system which implements an optimized genetic-algorithm neural-network (GANN) model with cybernetic concepts and evaluates the success using a modified value-at-risk (MVaR) framework. The cybernetic engine includes a circular causal feedback control feature and a developed golden-ratio estimator, which can be applied to any form of market data in the development of risk-pricing models. The paper applies the Euro and Yen forex rates as data inputs. It is shown that the technique is useful as a trading and volatility control system for institutions including central bank monetary policy as a risk-minimizing strategy. Furthermore, the results are achieved within a 30-second timeframe for an intra-week trading strategy, offering relatively low latency performance. The results show that risk exposures are reduced by four to five times with a maximum possible success rate of 96%, providing evidence for further research and development in this area.
 
I don't believe there is a certain algorithm in trading. Forex needs to be be flexible and know how to react on different situations.
 

Emissions Trading Systems (ETSs) with fixed caps lack provisions to address systematic imbalances in the supply and demand of permits due to changes in the state of the regulated economy. We propose a mechanism which adjusts the allocation of permits based on the current bank of permits. The mechanism spans the spectrum between a pure quantity instrument and a pure price instrument. We solve the firms' emissions control problem and obtain an explicit dependency between the key policy stringency parameter – the adjustment rate – and the firms' abatement and trading strategies. We present an analytical tool for selecting the optimal adjustment rate under both risk-neutrality and risk-aversion, which provides an analytical basis for the regulator's choice of a responsive ETS policy.


 
NYSE and Nasdaq trades increasingly cluster on multiples of 500, 1,000, and 5,000 shares. Such clustering varies over time and across stocks, and tends to increase with the level of trading activity. Furthermore, rounded trades tend to have more persistence both in occurrence and in trade initiation. Finally, medium-sized rounded trades tend to have greater relative price impact than large rounded trades. From these observations we surmise that trade-size clustering is consistent, at least in part, with the actions of stealth traders who tend to use medium-sized rounded transactions in an attempt to disguise their trades.
 
This chapter talks about buying cheap stocks which is a popular and well&;#x02010;known investment strategy, applied by many investors all over the world. Although there are numerous different ways to value invest, the common denominator is to invest in stocks that are temporarily on sale. Value investors hunt for those stocks that are often legitimately, not in favor with other investors. Their stock prices are often cheap when compared to the company&;#x00027;s intrinsic value based on its fundamentals. Income is an elegant value indicator as it favors companies that at least make profits while their shares are trading on the cheap. Some stocks have a low price and offer a high income yield but are cheap for a reason. Momentum and income are a remarkably powerful combination, as they tell us how to buy the right stock at the right moment.
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