Basic forex strategies - page 2

The main purpose of this paper is to efficiently utilize the forecasts made by multivariate linear regression analysis. To this end, we build up a mathematical model for the trading decisions based on the regression results. Given that these results are uncertain, we seek to maximize the expected profits acquired through opening and closing trade positions. To find the best trading strategy, a dynamic programming approach has been employed. In this sense, we firstly find optimal take profit levels associated with opening buy and sell positions. Then we decide to buy, sell or wait, based on the maximum profit we are going to expect in each case. To show how this method works we apply our approach to finding the optimal strategy for trading GBPUSD rate based on a multivariate regression model fitted to the historical daily data on the FOREX Market.

This Article argues that the emergence of algorithmic trading raises a new challenge for the law and policy of insider trading. It shows that securities markets comprise a cohort of algorithmic “structural insiders” that – by virtue of speed and physical proximity to exchanges – systematically gain first access to information and play an outsize role in price formation. This Article makes three contributions. First, it introduces and develops the concept of structural insider trading. Securities markets increasingly rely on automated traders utilizing algorithms – or pre-programmed electronic instructions – for trading. Policy allows traders to enjoy important structural advantages: (i) to physically locate on or next to an exchange, shortening the time it takes for information to travel to and from the marketplace; and (ii) to receive feeds of richly detailed data directly to these co-located trading operations. With algorithms sophisticated enough to respond instantly and independently to new information, co-located automated traders can receive and trade on not-fully-public information ahead of other investors. Secondly, this Article shows that structural insider trading exhibits harms that are substantially similar to those regulated under conventional theories of corporate insider trading. Structural insiders place other investors at a persistent informational disadvantage. Through their first sight of market-moving data, structural insiders can capture the best trades and erode the profits of informed traders, reducing their incentives to participate in the marketplace. Despite the similarity in harms, however, this Article shows that doctrine does not apply to restrict structural insider trading. Rather, structural insiders thrive in full view and with regulatory permission. Thirdly, the Article explores the implications of structural insider trading for the theory and doctrine of insider trading. It shows them to be increasingly incoherent in their application. In protecting investors against one set of insiders but not another, law and policy appear under profound strain in the face of innovative markets.

An asymptotic expansion scheme in finance initiated by Kunitomo and Takahashi [15] and Yoshida [68] is a widely applicable methodology for analytic approximation of the expectation of a certain functional of diffusion processes. [46], [47] and [53] provide explicit formulas of conditional expectations necessary for the asymptotic expansion up to the third order. In general, the crucial step in practical applications of the expansion is calculation of conditional expectations for a certain kind of Wiener functionals. This paper presents two methods for computing the conditional expectations that are powerful especially for high order expansions: The first one, an extension of the method introduced by the preceding papers presents a general scheme for computation of the conditional expectations and show the formulas useful for expansions up to the fourth order explicitly. The second one develops a new calculation algorithm for computing the coefficients of the expansion through solving a system of ordinary differential equations that is equivalent to computing the conditional expectations. To demonstrate their effectiveness, the paper g

As ever, the world of finance is abuzz with news of misconduct. For example, the judgment in Plevin [2014] UKSC 61 may induce refunds for Payment Protection Insurance (PPI) mis-selling to the tune of £33.5 billion. Equally, recent events leave little room for doubt that the LIBOR scandal was just the tip of the iceberg because the rigging of the $5.3 trillion-a-day forex markets completely dwarfs the total $500-$800 trillion value of financial contracts underpinned by LIBOR. In the first LIBOR trial, Tom Hayes, an obscure yen derivatives trader in UBS and Citigroup became the world’s first individual to be tried and convicted for benchmark rigging. He got 14 years’ imprisonment for his crimes. Against this nightmare sentence, his trial has set a chilling precedent for the 12 others in his shoes who are awaiting trial (one of whom pleaded guilty after sentence was passed on Hayes). Hayes used “clever” tactics that enabled him to avoid extradition to the US and allowed his trial to be conducted in the UK. However, he was unanimously convicted by the jury.

Hayes contended that he was operating in a “grey area” where there were “no rules” and that he had no compliance training, but this did nothing to help him. The archetypical Foucauldian fiend, he stood accused of using corruption and accepted making “concerted efforts to influence LIBOR” but argued he “was operating within a system”. Discussing the dilemma’s associated with punishment, in Discipline and Punish, Foucault concludes that the offender is “worse than an enemy” and that transgressing the boundaries set by society makes him “nothing less than a traitor, a ‘monster’.” For Foucault this is so because “it is from within society that he [the offender] delivers his blows.” In discussing concept of punishment, Foucault explains that the philosophy underpinning the evolution of the penal system, from one involving public spectacles (displays) such as public torture and execution, to new modern and discreet technologies is to “punish exactly enough to prevent repetition.” However, the system had to make an example of Hayes to create a deterrent effect. From that perspective, his trial was a show trial. I argue that a lighter sentence would have equally prevented Hayes from engaging in financial misconduct again.

Jonathan Karpoff has advanced a telling analysis about whether reputation works to discipline corporate misconduct when companies “lie, cheat, steal, mislead, disguise, obfuscate, feign, distort, and confuse” in order to maximise profits. For Karpoff, “few matters of economic policy are as contentious as the extent and consequences of misconduct.” In Seven Deadly Sins, Roger McCormick codifies culpability for corporate wrongdoing under seven heads. In the past (see http://ssrn.com/abstract=2600407, 28 April 2015), I have applied these to Navinder Singh Sarao and here I argue the counter-point in relation to the “flash crash trader” as it intersects with (applies to) Hayes the LIBOR rigger. I use Karpoff and McCormick’s theories to analyse the comparative financial misconduct evinced in these cases.

In Flash Boys, the impressive Michael Lewis argues that the flash crash occurred “for no obvious reason”. For Lewis, the report of the Securities and Exchange Commission – linking the episode on a large sell order of futures contracts by “an obscure Kansas City mutual fund” – was “only true by accident” because “the regulators did not possess the information they needed to understand the stock market.” So applying the theory proposed by Michael Lewis, it may well be that Sarao has a case to answer in America. And I argue this counter-point here. Sarao’s extradition appeal is due to be heard next month and attempts to postpone the hearing scheduled on 25 September 2015 have been unsuccessful. Sarao was his own master. Whereas Hayes, who was a mere puppet, was just trying to please his masters by making money for them – enough for them to roll in.

In light of the ongoing witch-hunt to bring market manipulators to justice at any costs, bankers are being excoriated for seeing misconduct as no worse than driving over the speed limit. Yet this sentence amply abnegates such arguments. Using Foucault, Karpoff and McCormick as my analytical toolkit, I argue that because of the fact that the average jail sentence for terrorists (jihadis) is a mere two years’ imprisonment, the sentence inflicted upon Hayes can quite easily be described as disproportionate. The sentencing judge was aware that “everyone” was rigging LIBOR. Yet, unmoved by this disturbing fact, he went for overkill in passing sentence on Hayes. We must therefore ask: where are the rest of the people who, in concert, participated in the dishonesty of rigging LIBOR? I conclude that Hayes has clearly borne the brunt for others of his ilk and has been made to pay for their wrongdoing. Ultimately, sacrificing a dozen people over wholesale dishonesty only makes a mockery of justice.

Hayes is appealing his conviction and his sentence, the longest ever imposed in the UK for financial fraud. If permission is granted the Court of Appeal will hear his case.

The underlying concept behind the technical trading indicators based on moving averages of prices has remained unaltered for more than half of a century. The development in this field has consisted in proposing new ad-hoc rules and using more elaborate types of moving averages in the existing rules, without any deeper analysis of commonalities and differences between miscellaneous choices for trading rules and moving averages. In this paper we uncover the anatomy of market timing rules with moving averages. Our analysis offers a new and very insightful reinterpretation of the existing rules and demonstrates that the computation of every trading indicator can equivalently be interpreted as the computation of the weighted moving average of price changes. This knowledge enables a trader to clearly understand the response characteristics of trading indicators and simplify dramatically the search for the best trading rule. As a straightforward application of the useful knowledge revealed by our analysis, in this paper we also entertain a method of finding the most robust moving average weighting scheme. The method is illustrated using the long-run historical data on the Standard and Poor's Composite stock price index. We find the most robust moving average weighting scheme and demonstrates its advantages.

In a recent empirical study by Glabadanidis ("Market Timing With Moving Averages" (2015), International Review of Finance, Volume 15, Number 13, Pages 387-425; the paper is also available on the SSRN and has been downloaded more than 7,500 times) the author reports striking evidence of extraordinary good performance of the moving average trading strategy. In this paper we demonstrate that "too good to be true" reported performance of the moving average strategy is due to simulating the trading with look-ahead bias. We perform the simulations without look-ahead bias and report the true performance of the moving average strategy. We find that at best the performance of the moving average strategy is only marginally better than that of the corresponding buy-and-hold strategy. In statistical terms, the performance of the moving average strategy is indistinguishable from the performance of the buy-and-hold strategy. This paper is supplied with R code that allows every interested reader to reproduce the reported results.

The objective of this document is to find out if the Firefly Algorithm (FA), a nature-inspired metaheuristic optimization algorithm, can be used to find effective optimized trading rules and compare the results to a simple Buy and Hold strategy. FA is one of the most recent evolutionary computing models and was studied by several researchers who concluded that it is an optimal and powerful algorithm to resolve complex problems. Despite the fact that some algorithms like Genetic Algorithm or Particle Swarm Optimization proved in the last decades to be optimal metaheuristics to find the fittest parameter for a trading rule, it was found out with limits and gaps such as the frequent transaction costs. This paper is to show if FA can be used to find optimal technical trading rules and see if it can resolve the limits of the other algorithms. We use the FA to find out effective optimized trading rules and use them firstly for the testing phase with daily prices of the S&P500 index during the last recession period and compare it with an upward period for our training phase to be able to prove the effectiveness of the algorithm. After that, we compare it with a benchmark used in most litteratures, the Buy and Hold (B&H) strategy. After transaction costs, FA produced higher results compared to the B&H strategy during the training period. However, during the out-of-sample tests, it showed contrary results. On one side, FA gained a simulated out-of- sample profit of over 4% for the chosen periods when daily transaction costs were taken into account. On the other side, FA could not outperform the benchmark when monthly transactions were taken into account. This is probably due to the selected and predefined trading system. However, globally FA demonstrated to be a more efficient metaheuristic algorithm than the PSO.

Stock prices are subject to both systematic risk and unsystematic risk. While unsystematic risk can be reduced to some extent by portfolio diversification, systematic risk is external to a firm and in a sense reflects market sentiment and the existing macroeconomic condition. In this paper we ask the question, is there some way in which savers/traders could control this latter risk and still expect reasonable returns from the market? The answer lies in Pairs Trading which involves trading the ratio of prices of two stocks which belong to the same sector and whose prices are highly correlated. Ideally, the ratio of the price of the two such stocks should be steady. However, given the inherent randomness of stock price movements, this ratio tends to fluctuate. The fundamental basis of Pairs Trading is that although there would be fluctuations in the ratio of the prices, this ratio would be mean reverting. Thus if the ratio rises the trading strategy would be to short the faster moving stock and long the slower moving stock. This paper is written from the trading point of view. It differs from the existing literature as it provides a framework for a trader to profit in the short run by using technical analysis. The three technical indicators that we use in our study are Momentum, Bollinger Band and Moving Average Convergence Divergence (MACD).

This paper provides some new empirical evidence on the weekend effect, one of the most recognized anomalies in financial markets. Two different methods are used: (i) a trading robot approach to examine whether or not there is such an anomaly giving rise to exploitable profit opportunities by replicating the actions of traders; (ii) a fractional integration technique for the estimation of the (fractional) integration parameter d. The results suggest that trading strategies aimed at exploiting the weekend effect can generate extra profits but only in a minority of cases in the gold and stock markets, whist they appear to be profitable in most cases in the FOREX. Further, the lowest orders of integration are generally found on Mondays, which can be seen as additional evidence for a weekend effect.