This paper develops a pairs trading framework based on a mean-reverting jump–diffusion model and applies it to minute-by-minute data of the S&P 500 oil companies from 1998 to 2015. The established ...

https://www.econstor.eu/bitstream/10419/157807/1/885569830.pdf

Pairs trading with a mean-reverting jump–diffusion model on high-frequency data

Technical analysis of stocks mainly focuses on the study of irregularities, which is a non-trivial task. Because one time scale alone cannot be applied to all analytical processes, the identification of typical patterns on a stock requires considerable knowledge and experience of the stock market. It is also important for predicting stock market trends and turns. The last two decades has seen attempts to solve such non-linear financial forecasting problems using AI technologies such as neural networks, fuzzy logic, genetic algorithms and expert systems but these, although promising, lack explanatory power or are dependent on domain experts. This paper presents an algorithm, PXtract to automate the recognition process of possible irregularities underlying the time series of stock data. It makes dynamic use of different time windows, and exploits the potential of wavelet multi-resolution analysis and radial basis function neural networks for the matching and identification of these irregularities. The study provides rooms for case establishment and interpretation, which are both important in investment decision making.

Automatic extraction and identification of chart patterns towards financial forecast

Financial forecasting is an area of research which has been attracting a lot of attention recently from practitioners in the field of artificial intelligence. Apart from the economic benefits of accurate financial prediction, the inherent nonlinearities in financial data make the task of analyzing and forecasting an extremely challenging task. This paper presents a survey of more than 100 articles published over two centuries from 1933 up to 2013 in an attempt to identify the developments and trends in the field of financial forecasting with focus on application of artificial intelligence for the purpose. The findings from the survey indicate that artificial intelligence and signal processing based techniques are more efficient when compared to traditional financial forecasting techniques and these techniques appear well suited for the task of financial forecasting. Some of the issues that need addressing are discussed in brief. A novel technique for selection of the input dataset size for ensuring best possible forecast accuracy is also presented. The results confirm the effectiveness of the proposed technique in improving the accuracy of forecasts.

Artificial intelligence applications in financial forecasting --a survey and some empirical results

Technical analysis mainly focuses on analyzing the chart patterns, which is a non-trivial task. Because one time scale alone cannot be applied to all analytical processes, the identification of typical patterns on a stock price chart requires considerable knowledge and experience. The last two decades has seen attempts to solve such non-linear financial forecasting problems using AI technologies such as neural networks, fuzzy logic, genetic algorithms and expert systems but these, although accurate, lack explanatory power or are dependent on domain experts. This paper introduces a case based reasoning (CBR) system that provides an explainable method of financial forecasting [4] that is not dependent on the inputs of domain experts. This study proposes an algorithm, PXtract, which identifies and analyses possible chart patterns, makes dynamic use of different time windows, and introduces a wavelet multi-resolution analysis incorporated within a radial basis function neural network (RBFNN) matching method that can be used to automate the chart pattern matching process.

Chart Patterns Recognition and Forecast Using Wavelet and Radial Basis Function Network

To determine the buy and sell time is one of the most important issues for investors in stock market. In this paper, a fuzzy approach to stock market timing is investigated. A fuzzy decision system is constructed based on experiences and techniques of stock and future opportunist. The fuzzy rules are optimized by taking exchange volume into account for better fitting the stock market of mainland China. Simulations are conducted to some well-known stocks in China's A-share market, and the simulation result reveals that the proposed method has desirable performance in a rapid change market.

A fuzzy approach to stock market timing

Technical indicators are normally used to monitor the stock prices and assist investors to set up trading rules to make the buy-sell-hold decision. Although some trading rules are clear, most of them are vague and fuzzy. Therefore, an investor cannot be the winner all the time with the same set of trading rules. The weight of trading rules should be varied with time. A Genetic Fuzzy Expert Trading System (GFETS) was designed to simulate the vague and fuzzy trading rules and give the buy-sell signal. Fuzzy trading rules are optimized and selected using genetic algorithm in GFETS. Experimental evaluations showed that trading with the optimized fuzzy trading rules obtains a good profitable return. To maintain the quality of the fuzzy trading rules being in-used, GFETS must be re-trained from time-to-time. In this paper, an incremental training approach was studied and evaluated with all Hang Seng China Enterprises Index (HSCEI) stocks. The risk and the profit return compared with other trading strategies were reported.

https://ieeexplore.ieee.org/iel5/8512/26901/01196291.pdf

Incremental genetic fuzzy expert trading system for derivatives market timing

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Incremental genetic fuzzy expert trading system for derivatives market timing