Basic forex strategies - page 4
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One of the issues. Reported it to developers - waiting for a response
Seems that pdf updates are working now (at least for me)
Under the same assumptions, we show the existence of an optimal duration which is equal to the Kalman filter duration if the parameters are well-specified.
For the cross moving average strategy, we also provide the asymptotic logarithmic return of this strategy as a function of the modelparameters.
Moreover, the simulations show that, with a model based on an unobserved mean-reverting diffusion, and even with a stochastic volatility, technical analysis investment is more robust than the optimal trading strategy. The empirical tests on real data confirm this conclusion.Trading Systems
Stock recommendations based on technical analysis have been evaluated by researchers in terms of the abnormal excess returns generated compared to some benchmark return. Here, instead of looking at the magnitude of excess returns, we study the liquidity of trading strategies based on analyst recommendations as an indicator of their efficacy. We use an event study methodology for 403 technical calls published over a period of four years from 2011 to 2015 on an on-line finance portal. Parametric survival models were built to understand the factors that might affect the time taken for a stock to reach the targeted sell/buy price. Lower targeted returns, a bullish market trend, and greater volumes of trading in the pre-recommendation period lead to smaller times to fulfillment for technical calls. However, consistent with other studies, we find that analysts using technical analysis have not been able to provide recommendations that consistently yield high returns in a short period of time.Trading Systems
This book contains lecture notes from the course "Risk Management" given at the University of Paris-Saclay/Evry. These lecture notes are divided into three parts. After an introductory chapter presenting the main concepts of risk management and an overview of the financial regulation (Basel I-IV, Solvency I-II, Dodd-Frank, UCTIS, etc.), the first part is dedicated to the risk management in the banking sector and consists of six chapters: market risk, credit risk, counter party credit risk and collateral risk, operational risk, liquidity risk and asset/liability management risk. We begin with the market risk, because it permits to introduce naturally the concepts of risk factor and risk measure and to define the risk allocation approach. For each chapter, we present the corresponding regulation framework and the risk management tools. The second part is dedicated to non-banking financial sectors with four chapters dedicated to insurance, asset management, investors and market infrastructure (including central counter parties). This second part ends with a fifth chapter on systemic risk and shadow banking system. The third part of these lecture notes develops the mathematical and statistical tools used in risk management. It contains seven chapters: risk model and derivatives hedging, statistical inference and model estimation, copula functions, extreme value theory, Monte Carlo simulation, stress testing methods and scoring models. Each chapter of these lectures notes is extensively illustrated by numerical examples and contains also tutorial exercises. Finally, a technical appendix completes the lecture notes and contains some important elements on numerical analysis.Trading Systems
In this study we use machine learning algorithm to test Amareos sentiment indicator's predictive power of market reversals. We then build and test a viable trading strategy.As input for the algorithm, we used eight market sentiment indicators (Anger, Anticipation, Disgust, Fear, Gloom, Joy, Optimism and Sentiment) on 20 major equity indices from January 1, 2005 to April 15, 2016.
As the target output, we use a classification of the performance of the indices on the following 182 days - approximately six months - split between bottom, top and neutral days.
Our learning algorithm is of the type called random forest. Through calibration on a training set composed of 64% of the data, we obtain a final set of decision trees, or forest. We then examine the out of sample accuracy of this forest on the remaining 36% of the data.
As the accuracy on the test set is relatively high - a result that cannot be explained just by luck - we simulate a trading strategy based on the forest output. The resulting trading strategy produces strong performance, certainly much better than a simple buy and hold, even when adjusted for risk.
Trading Systems
This paper describes design and back-testing of an automated delta-hedging strategy applied to short-dated fx options (specifically – weekly and monthly at-the-money EURUSD straddles).The results indicate that systematic sale of options that are delta-hedged according to the suggested algorithm generates financial gain for the seller with an attractive Sharpe ratio exceeding 3.0 on after-cost basis (back-testing accounts for volatility bid-offer as well as spot market bid-offer).
For weekly options Sharpe ratio significantly depends on the day of week on which the algorithm systematically sells options: delta-hedging of options sold on Thursdays results in highest Sharpe ratio; delta-hedging of options sold on Fridays results in second-best Sharpe ratio.
The performance of the algorithmic strategy is not correlated with linear changes in spot price which is in line with Black-Scholes theory.
The proposed algorithmic strategy has just a few parameters which serves as a natural protection against over-fitting bias. Further fine-tuning of the algorithm requires access to historical data over longer period and/or access to live trading environment.