A novel recurrent neural network-based prediction system for option trading and hedging

Abstract

In order to reduce their exposure to the erratic fluctuations of the financial markets, traders are nowadays increasingly dealing with options and other derivative securities instead of directly trading in the underlying assets. This paradigm shift has attracted the attention of many researchers, and there has been a tremendous increase in the awareness and activities of derivative securities. In particular there is a need to devise new techniques to address the limitations of traditional parametric pricing methods, which rely on assumptions and approximations to capture the complex dynamics of pricing processes. This paper proposes a novel non-parametric method using an ad-hoc recurrent neural network for estimating the future prices of war commodities such as gold and crude oil as well as currencies, which are increasingly gaining importance in the financial markets. The price predictions from the network, shown to be accurate and computationally efficient, are used in a hedging system to avoid unnecessary risks. Experiments with actual gold and currency trading data show that our system using the proposed network and strategy can construct portfolios yielding a return on investment of about 4.76%.

Role of soft computing techniques in predicting stock market direction

“…All research study [4][5][6][7][8] described in section II are deals with complex problem where raw data have highly noise due to buying and selling security with different time and different volume. All research study discussed in this paper is used soft computing techniques to solve two different market, stock market and commodity market.…”

“…All research study discussed in this paper is used soft computing techniques to solve two different market, stock market and commodity market. These research study [4][5][6][7] take the problem example of stock market while research study [8] takes the problem example of commodities and oil. Research study [8] may not provide accuracy on the stock market without modifying its input parameter due to both the stock market and commodity input parameter are affected different way.…”

“…So that the model represented in study [4][5][6][7] are not fully represent the whole market input parameter. Even input parameter table represented in research study [8] where other research models represented also miss the derivative segment knowledge representation in input parameter consideration is not available. The performance and prediction accuracy of soft computing techniques are highly relies on selection on input parameter.…”

“…The performance and prediction accuracy of soft computing techniques are highly relies on selection on input parameter. If some commodity don't have derivative segment then this model will not work [8] . The prediction methodology in research study [8] has developed CPM model.…”

“…This paper is structured as follows: the second section deals with major work done on each of five research papers [4][5][6][7][8] . The third section draws commonalities and differences between these papers to establish the technical gap.…”