Title

FORECASTING EXCHANGE RATES WITH TWO DIMENSIONAL PATTERNS USING SUPPORT VECTOR MACHINES AND TECHNICAL INDICATORS

Abstract

Abstract

The value of a country’s currency is expressed in terms of other countries’ currencies. That value is called an exchange rate. Many currencies are freely floating and do not have a fixed value that is pegged by the central bank of a country. The value of currencies are determined in the foreign exchange market (Forex). Forex market is an extensive trading ground for traders across the world. It is available for trade 24 hours a day, 5 days a week. The trade volume per day is in excess of 4 trillion USD. Many different bilateral currency pairs are traded in the Forex market. In the Forex market a trader can profit from predicting the direction and magnitude of price fluctuations of a currency pair. Using a leverage value, it is possible to multiply wins and losses.
Technical indicators are statistical metrics whose values are calculated from price history of financial instrument. Technical indicators are generated to represent the behavior of the price and they are used to determine the future trend of the price of the financial instrument.
Chart patterns are two-dimensional formations that appear on a financial instrument’s price-action chart. Chartists and traders use these patterns to identify the cur-rent trends of the instrument to trigger buy and sell signals.
This thesis presents a method to predict the direction and magnitude of movement of currency pairs in the foreign exchange market. The method uses clustering and classification methods with a combination of two dimensional chart patterns, processed price data and technical indicator data. The input data is adapted to each trading day with a moving time-frame. The accuracy of the prediction models are tested across several different currency pairs. The experimental results suggest that using two dimensional chart patterns mixed with processed price data and the Zigzag technical indicator improves overall performance and adapting the input data to each trading period results in increased accuracy and profits. The predictions should be applicable in real world, since trading concepts such as spreads, swap commissions and leverages are taken into account.

Biography:

Supervisor(s)

Supervisor(s)

MUSTAFA ONUR OZORHAN

Date and Location

Date and Location

2017-05-24;10:30:00-A105

Category

Category

PhD_Thesis