Author: Hamed Shafiee HasanabadiPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
An Application of Artificial Neural Networks in Forecasting Future Oil Price Return Volatilities
Author: Hamed Shafiee HasanabadiPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Artificial Neural Network Models for Forecasting Global Oil Market Volatility
Author: Saud Al-FattahPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Energy market volatility affects macroeconomic conditions and can unduly affect the economies of energy-producing countries. Large price swings can be detrimental to both producers and consumers. Market volatility can cause infrastructure and capacity investments to be delayed, employment losses, and inefficient investments. In sum, the growth potential for energy-producing countries is adversely affected. Undoubtedly, greater stability of oil prices can reduce uncertainty in energy markets, for the benefit of consumers and producers alike. Therefore, modeling and forecasting crude oil price volatility is critical in many financial and investment applications. The purpose of this paper to develop new predictive models for describing and forecasting the global oil price volatility using artificial intelligence with artificial neural network (ANN) modeling technology. Applying the novel approach of ANN, two models were successfully developed: one for WTI futures price volatility and the other for WTI spot prices volatility. These models were successfully designed, trained, verified, and tested using historical oil market data. The estimations and predictions from the ANN models closely match the historical data of WTI from January 1994 to April 2012. They appear to capture very well the dynamics and the direction of the oil price volatility. These ANN models developed in this study can be used: as short-term as well as long-term predictive tools for the direction of oil price volatility, to quantitatively examine the effects of various physical and economic factors on future oil market volatility, to understand the effects of different mechanisms for reducing market volatility, and to recommend policy options and programs incorporating mechanisms that can potentially reduce the market volatility. With this improved method for modeling oil price volatility, experts and market analysts will be able to empirically test new approaches to mitigating market volatility. The outcome of this work provides a roadmap for research to improve predictability and accuracy of energy and crude models.
Artificial Neural Networks
Author: Ali RoghaniPublisher: Createspace Independent Publishing Platform
ISBN: 9781536976830
Category :
Languages : en
Pages : 108
Book Description
Neural networks are state-of-the-art, trainable algorithms that emulate certain major aspects in the functioning of the human brain. This gives them a unique, self-training ability, the ability to formalize unclassified information and, most importantly, the ability to make forecasts based on the historical information they have at their disposal. Neural networks have been used increasingly in a variety of business applications, including forecasting and marketing research solutions. In some areas, such as fraud detection or risk assessment, they are the indisputable leaders. The major fields in which neural networks have found application are financial operations, enterprise planning, trading, business analytics and product maintenance. Neural networks can be applied gainfully by all kinds of traders, so if you're a trader and you haven't yet been introduced to neural networks, we'll take you through this method of technical analysis and show you how to apply it to your trading style. Neural networks have been touted as all-powerful tools in stock-market prediction. Companies such as MJ Futures claim amazing 199.2% returns over a 2-year period using their neural network prediction methods. They also claim great ease of use; as technical editor John Sweeney said in a 1995 issue of "Technical Analysis of Stocks and Commodities," "you can skip developing complex rules (and redeveloping them as their effectiveness fades) . . . just define the price series and indicators you want to use, and the neural network does the rest."
Non-Linear Time Series Models in Empirical Finance
Author: Philip Hans FransesPublisher: Cambridge University Press
ISBN: 0521770416
Category : Business & Economics
Languages : en
Pages : 299
Book Description
This 2000 volume reviews non-linear time series models, and their applications to financial markets.
Oil Price Volatility and the Role of Speculation
Author: Samya Beidas-StromPublisher: International Monetary Fund
ISBN: 1498333486
Category : Business & Economics
Languages : en
Pages : 34
Book Description
How much does speculation contribute to oil price volatility? We revisit this contentious question by estimating a sign-restricted structural vector autoregression (SVAR). First, using a simple storage model, we show that revisions to expectations regarding oil market fundamentals and the effect of mispricing in oil derivative markets can be observationally equivalent in a SVAR model of the world oil market à la Kilian and Murphy (2013), since both imply a positive co-movement of oil prices and inventories. Second, we impose additional restrictions on the set of admissible models embodying the assumption that the impact from noise trading shocks in oil derivative markets is temporary. Our additional restrictions effectively put a bound on the contribution of speculation to short-term oil price volatility (lying between 3 and 22 percent). This estimated short-run impact is smaller than that of flow demand shocks but possibly larger than that of flow supply shocks.
Artificial Neural Networks
Author: Ali RoghaniPublisher: CreateSpace
ISBN: 9781511712330
Category :
Languages : en
Pages : 108
Book Description
Neural networks are state-of-the-art, trainable algorithms that emulate certain major aspects in the functioning of the human brain. This gives them a unique, self-training ability, the ability to formalize unclassified information and, most importantly, the ability to make forecasts based on the historical information they have at their disposal. Neural networks have been used increasingly in a variety of business applications, including forecasting and marketing research solutions. In some areas, such as fraud detection or risk assessment, they are the indisputable leaders. The major fields in which neural networks have found application are financial operations, enterprise planning, trading, business analytics and product maintenance. Neural networks can be applied gainfully by all kinds of traders, so if you're a trader and you haven't yet been introduced to neural networks, we'll take you through this method of technical analysis and show you how to apply it to your trading style. Neural networks have been touted as all-powerful tools in stock-market prediction. Companies such as MJ Futures claim amazing 199.2% returns over a 2-year period using their neural network prediction methods. They also claim great ease of use; as technical editor John Sweeney said in a 1995 issue of "Technical Analysis of Stocks and Commodities," "you can skip developing complex rules (and redeveloping them as their effectiveness fades) . . . just define the price series and indicators you want to use, and the neural network does the rest."
Financial Forecasting Using Artificial Neural Networks
Author: Jayan Ganesh PrasadPublisher:
ISBN:
Category : Economic forecasting
Languages : en
Pages : 266
Book Description
Despite the extent of a theoretical framework in financial market studies, a vast majority of the traders, investors and computer scientists have relied only on technical and timeseries data for predicting future prices. So far, the forecasting models have rarely incorporated macro-economic and market fundamentals successfully, especially with short-term predictions ranging less than a month. In this investigation on the predictability of certain financial markets, an attempt has been made to incorporate a un-exampled and encompassing set of parameters into an Artificial Neural Network prediction system. Experiments were carried out on three market instruments - namely currency exchange rates, share prices and oil prices. The choice of parameters for inclusion or exclusion, and the time frame adopted for the experimental sets were derived from the market literature. Good directional prediction accuracies were achieved for currency exchange rates and share prices with certain parameters as inputs, which consisted of predicting short-term movements based on past movements. These predictions were better than the results produced by a traditional least square prediction method. The trading strategy developed based on the predictions also achieved a higher percentage of winning trades. No significant predictions were observed for oil prices. These results open up questions in the microstructure of the markets and provide an insight into the inputs required for market forecasting in the corresponding time frame, for future investigation. The study concludes by advocating the use of trend based input parameters and suggests ways to improve neural network forecasting models.
Using Artificial Neural Networks to Forecast the Futures Prices of Crude Oil
Author: Hassan A. KhazemPublisher:
ISBN:
Category : Commodity futures
Languages : en
Pages : 306
Book Description
AI in Business and Economics
Author: Isabel Lausberg, Michael VogelsangPublisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110790432
Category :
Languages : en
Pages : 236
Book Description
An Application of Artificial Neural Networks to Forecast Winning Price
Author: Yingchih LinPublisher:
ISBN: 9781109655759
Category : Back propagation (Artificial intelligence)
Languages : en
Pages : 160
Book Description