Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda

The stock market is characterized by extreme fluctuations, non-linearity, and shifts in internal and external environmental variables. Artificial intelligence (AI) techniques can detect such non-linearity, resulting in much-improved forecast results. This paper reviews 148 studies utilizing neural and hybrid-neuro techniques to predict stock markets, categorized based on 43 auto-coded themes obtained using NVivo 12 software. We group the surveyed articles based on two major categories, namely, study characteristics and model characteristics, where ‘study characteristics’ are further categorized as the stock market covered, input data, and nature of the study; and ‘model characteristics’ are classified as data pre-processing, artificial intelligence technique, training algorithm, and performance measure. Our findings highlight that AI techniques can be used successfully to study and analyze stock market activity. We conclude by establishing a research agenda for potential financial market analysts, artificial intelligence, and soft computing scholarship.

Improving Weather Forecasting Using De-Noising with Maximal Overlap Discrete Wavelet Transform and GA Based Neuro-Fuzzy Controller

Adaptive Neuro-Fuzzy Inference Systems (ANFIS) is one of the most important neuro-fuzzy systems. ANFIS can be successfully applied to different real-world problems. However, it is difficult to create the ANFIS structure whose parameters directly influence the solutions. Therefore, hybrid ANFIS methods are generally used to increase efficiency and adaptability. This paper used an integrated neuro-fuzzy controller that is also known as PATSOS. The main purpose of this study is to improve the performance of the PATSOS method for weather forecasting. Our proposed PATSOS method is different from the previous ones since it embeds Genetic Algorithm (GA) into the PATSOS and also de-noising with Maximal Overlap Discrete Wavelet Transform (MODWT) is used to improve the data quality. GA is employed to optimize the moving average type, moving average degree, and de-noising degree. Furthermore, epoch number, membership function type, and membership function number for the PATSOS are optimized by GA. The results obtained by the hybrid PATSOS method are presented and compared with different cities and different models. It is concluded that proposed hybrid method forecasts daily mean temperature accurately. Proposed GA based PATSOS method can also provide remarkable advantages for determining parameter values in other complex, dynamic and non-linear forecasting problems.

Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach

Financial volatility refers to the intensity of the fluctuations in the expected return on an investment or the pricing of a financial asset due to market uncertainties. Hence, volatility modeling and forecasting is imperative to financial market investors, as such projections allow the investors to adjust their trading strategies in anticipation of the impending financial market movements. Following this, financial volatility trading is the capitalization of the uncertainties of the financial markets to realize investment profits in times of rising, falling and side-way market conditions. In this paper, an intelligent straddle trading system (framework) that consists of a volatility projection module (VPM) and a trade decision module (TDM) is proposed for financial volatility trading via the buying and selling of option straddles to help a human trader capitalizes on the underlying uncertainties of the Hong Kong stock market. Three different measures, namely: (1) the historical volatility (HV), (2) implied volatility (IV) and (3) model-based volatility (MV) of the Hang Seng Index (HSI) are employed to quantify the implicit volatility of the Hong Kong stock market. The TDM of the proposed straddle trading system combines the respective volatility measures with the well-established moving-averages convergence/divergence (MACD) principle to recommend trading actions to a human trader dealing in HSI straddles. However, the inherent limitation of the MACD trading rule is that it generates time-delayed trading signals due to the use of moving averages, which are essentially lagging trend indicators. This drawback is intuitively addressed in the proposed straddle trading system by applying the VPM to compute future projections of the volatility measures of the HSI prior to the activation of the TDM. The VPM is realized by a self-organising neural-fuzzy semantic network named the evolving fuzzy semantic memory (eFSM) model. As compared to existing statistical and computational intelligence based modeling techniques currently employed for financial volatility modeling and forecasting, eFSM possesses several desirable attributes such as: (1) an evolvable knowledge base to continuously address the non-stationary characteristics of the Hong Kong stock market; (2) highly formalized human-like information computations; and (3) a transparent structure that can be interpreted via a set of linguistic IF-THEN semantic fuzzy rules. These qualities provide added credence to the computed HSI volatility projections. The volatility modeling and forecasting performances of the eFSM, when benchmarked to several established modeling techniques, as well as the observed trading returns of the proposed straddle trading system, are encouraging.