scholarly journals RESEARCH ON FUTURES TREND TRADING STRATEGY BASED ON SHORT TERM CHART PATTERN

2012 ◽  
Vol 13 (5) ◽  
pp. 915-930 ◽  
Author(s):  
Saulius Masteika ◽  
Aleksandras Vytautas Rutkauskas
Keyword(s):  
Historical Data ◽  
Decision Support Tool ◽  
Technical Analysis ◽  
Futures Market ◽  
Trading Strategy ◽  
Futures Contracts ◽  
Main Task ◽  
Short Term ◽  
Support Tool ◽  
Market Participants

The main task of this paper is to examine a short term trend trading strategy in futures market based on chart pattern recognition, time series and computational analysis. Specifications of historical data for technical analysis and equations for futures profitability calculations together with position size measurement are also discussed in the paper. A contribution of this paper lies in a novel chart pattern related to fractal formation and chaos theory and its application to short term up-trend trading. Trading strategy was tested with historical data of the most active futures contracts. The results have given significantly better and stable returns compared to the change of market benchmark (CRB index). The results of experimental research related to the size of trading portfolio and trade execution slippage are also discussed in the paper. The proposed strategy can be attractive for futures market participants and be applied as a decision support tool in technical analysis.

Testing Technical Trading Rules: Evidence from SAARC Countries

2018 ◽  
Vol 1 ◽  
pp. 1-36
Author(s):  
Faisal Anees ◽  
Shujahat Haider Hashmi ◽  
Muhammad Asad
Keyword(s):  
Historical Data ◽  
Moving Average ◽  
Technical Analysis ◽  
Moving Averages ◽  
Short Term ◽  
Technical Trading Rules ◽  
Average Rule ◽  
Data Representative ◽  
Simple Average

Technical analysis is widely accepted tool in professional place which is frequently used for investment decisions. Technical analysis beliefs that there exist patterns and trends and by capturing trends and patterns one can bless with above average profits. We test two technical strategies: Moving averages and Trading Range to question, either these techniques can yield profitable returns with the help of historical data. Representative daily indices of Four countries namely Pakistan, India, Srilanka, Bangladesh ranging from 1997 to 2011 have been examined. In case of Moving Average Rule, both simple and exponential averages have been examined to test eleven different short term and long term rules with and without band condition. Our results delivered that buy signals generate consistent above average returns for the all sub periods and sell signals generate lower returns than the normal returns. Intriguing observation is that Exponential average generates higher returns than the Simple Average. The results of Trading Range Break strategy are parallel with Moving average Method. However, Trading Range Strategy found not to give higher average higher return when compared with Moving Averages Rules and degree of volatility in returns is higher when compared with moving Average rule. In attempt to conclude, there exist patterns and trends that yield above average and below average returns which justify the validity of technical analysis.

Development of a decision support tool for optimizing the short-term logistics of forest-based biomass

2018 ◽  
Vol 216 ◽  
pp. 662-677 ◽  
Author(s):  
Krishna Teja Malladi ◽  
Olivier Quirion-Blais ◽  
Taraneh Sowlati
Keyword(s):  
Decision Support ◽  
Decision Support Tool ◽  
Short Term ◽  
Support Tool

A decision-support tool to assist muscle-invasive bladder cancer patients choosing between radical cystectomy and chemoradiotherapy.

2018 ◽  
Vol 36 (6_suppl) ◽  
pp. 505-505
Author(s):  
Brian Christopher Baumann ◽  
Wei-Ting Hwang ◽  
Sharadha Srinivasan ◽  
Xingmei Wang ◽  
Ronac Mamtani ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Decision Support ◽  
Decision Support Tool ◽  
Short Term ◽  
Muscle Invasive Bladder Cancer ◽  
Support Tool ◽  
Trade Offs ◽  
Muscle Invasive ◽  
Break Even Point

505 Background: Patients with high-risk muscle-invasive bladder cancer (MIBC) who are borderline medically operable for radical cystectomy (RC) face a difficult decision between RC which has higher short-term treatment-related morbidity/mortality & chemoradiotherapy (CRT) which is better tolerated in the short-term but may have worse long-term cancer control outcomes. There are no existing decision support tools to assist patients & providers in understanding these trade-offs. Herein, we developed a visualization tool to inform patients & providers how the relative risks & benefits of RC & CRT vary over time with respect to overall survival (OS). Methods: We identified cT2-3 N0 M0 urothelial bladder cancer patients ≥65 y/o treated with RC +/- chemo (n = 5981) or definitive-dose CRT after TURBT (n = 793) in the National Cancer Database, 2003-2011. The database was split into a development & validation cohort. Multivariate Cox regression with time-varying hazard ratio was performed to assess pre-treatment factors associated with OS. The inverse probability of treatment weighting method using the propensity score was employed to reduce selection bias. External validation was performed. Visualization tool showing adjusted survival curves based on pre-op patient features was generated with input from patients & a multidisciplinary expert panel. Tool calculates median OS & the “break-even point,” where the short-term OS disadvantage of RC equals the long-term advantage of RC (i.e. the point where the restricted mean survival for RC & CRT are equal). Results: On MVA, significant predictors of OS were age, Charlson Deyo comorbidity index, & cT stage (p < 0.001 for all). Using these results, we iteratively developed a web application that utilizes clinical inputs to generate patient-specific survival curves that display estimated OS differences over time. Median OS, the break-even point, & percent alive at the break-even point are provided. Conclusions: This is the first decision-support tool developed to assist high-risk borderline operable MIBC patients & their providers in understanding the short-term & long-term trade-offs between RC & CRT. Additional testing is underway.

The Optimal Wheat Futures Hedge at the Euronext Paris from a Farmer’s Perspective

2020 ◽  
Vol 69 (1) ◽  
pp. 49-63
Author(s):  
Teresa Vollmer
Keyword(s):  
Commodity Prices ◽  
Futures Market ◽  
Futures Contracts ◽  
Risk Minimization ◽  
Price Shocks ◽  
Market Participants ◽  
Hedge Ratios ◽  
Optimal Dynamic ◽  
One Year ◽  
The Cost

Futures contracts are extensively used by commer-cial market participants to hedge commodities against the risk of adverse price fluctuations. But although farmers have faced increased volatility in commodity prices in recent years, only very few of them actively use hedging as a risk management instrument. In this article we analyze the hedging potential of the Euronext milling wheat futures market for German farmers based on the estimation of optimal static as well as optimal dynamic hedge ratios. We find that both hedging approximately one year and half a year before harvesting leads to a reduction in the variance of returns compared with unhedged portfolios. But this risk minimization is achieved at the cost of lower returns on average. In addition we find that margin calls might be one of the reasons why so few farmers hedge since they cause liquidity problems especially in marketing years with unanticipated price shocks.

scholarly journals Are bitcoin futures contracts for hedging or speculation?

2020 ◽  
Vol 17 (3) ◽  
pp. 1-9
Author(s):  
Ramzi Nekhili
Keyword(s):  
Trading Volume ◽  
Futures Market ◽  
Futures Contracts ◽  
Risk Minimization ◽  
Full Sample ◽  
Market Participants ◽  
Hedging Effectiveness ◽  
Informed Traders ◽  
Increasing Demand ◽  
Robustness Check

The emerging interest in Bitcoin futures market has led to questions on its trading form and contribution to risk minimization. These questions are important for market participants, including hedgers and speculators. This paper addresses the possible trading motive in Bitcoin futures market in being speculation or hedging. The author first tests a model relating Bitcoin futures returns with trading volume and conditional volatility, estimated with a GJR-GARCH specification, on a full sample of daily futures prices. A robustness check is then conducted by investigating the hedging effectiveness of Bitcoin futures and the speculation-hedging ratios on individual Bitcoin futures contracts. The estimation results on Bitcoin futures contracts, spanning from December 2017 to February 2020, show a significant positive relationship between futures returns and lagged volume. The speculation-hedging measures used for Bitcoin futures contracts maturing in March, June, September, and December reveal an increasing demand for speculation. Also, the Bitcoin spot’s full-hedge and OLS-hedge strategies with Bitcoin futures provide no gain over a no-hedge strategy. The results reveal strong evidence that traders in the Bitcoin futures market are motivated by speculation rather than hedging. This further puts in evidence the existence of asymmetric information within informed traders in Bitcoin futures market, and therefore market participants would not insure their positions against Bitcoin price movements.

Theory and Evidence of Arbitrage Trading of Equity Futures

2010 ◽  
Vol 18 (4) ◽  
pp. 69-108
Author(s):  
Jin Yoo ◽  
Geun Beom Kim
Keyword(s):  
Stock Market ◽  
Futures Market ◽  
Futures Contracts ◽  
Spot Price ◽  
Market Participants ◽  
Arbitrage Opportunities ◽  
Time Period ◽  
Arbitrage Trading ◽  
Arbitrage Opportunity ◽  
First Time

The equity futures market was opened in May 6th, 2008 for the first time in Korea but nonetheless it has rarely been researched since. In this paper, we examine whether the market, combined with the stock market, its underlying market, has been offering any arbitrage opportunities to market participants for the period of May 6th, 2008 to March 11, 2010, focusing on the two futures contracts of Samsung Electronics and Hyundai Motors, the two most actively traded ones. Our findings are as follows. First, there have been arbitrage opportunities for the two futures in either direction. Second, the average time period for an arbitrage opportunity was two seconds so arbitrage transactions were feasible indeed. Third, nevertheless, some arbitrage transactions ended up with a loss because the estimated spot price at maturity to carry out an arbitrage trading turned out to be significantly different from the realized one. The discrepancy in these two prices causes a seemingly very safe arbitrage trading a risky one. This risky feature of an arbitrage trading has never been addressed in depth in a paper or a book before, and is a major contribution of this paper.

scholarly journals The Application of Stock Index Price Prediction with Neural Network

2020 ◽  
Vol 25 (3) ◽  
pp. 53
Author(s):  
Penglei Gao ◽  
Rui Zhang ◽  
Xi Yang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Financial Markets ◽  
Short Term Memory ◽  
Historical Data ◽  
Stock Index ◽  
Main Task ◽  
Short Term ◽  
Price Prediction ◽  
Long Short Term Memory

Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we introduce four different methods in machine learning including three typical machine learning models: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and one attention-based neural network. The main task is to predict the next day’s index price according to the historical data. The dataset consists of the SP500 index, CSI300 index and Nikkei225 index from three different financial markets representing the most developed market, the less developed market and the developing market respectively. Seven variables are chosen as the inputs containing the daily trading data, technical indicators and macroeconomic variables. The results show that the attention-based model has the best performance among the alternative models. Furthermore, all the introduced models have better accuracy in the developed financial market than developing ones.

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