This paper surveys recent literature in the area of stock market forecasting using advanced engineering based methods like Neural Network, fractal theory, Data Mining, Hidden Markov Model and Neuro-Fuzzy system. Neural Networks and Neuro-Fuzzy systems are emerging as an effective tool to be used in the forecasting of stock market especially in machine learning techniques. Due to chaotic behavio...

Statistical arbitrage strategies have always been popular since the advent of algorithmic trading. In particular, Exchange traded fund (E.T.F.) arbitrage has attracted much attention. Trading houses have tried to replicate ETF arbitrage to other stocks. Thus, the objective is to be able to develop a long term pricing relationship between stocks and profit from their divergence from this relatio...

Forecasting volatility is important to financial asset pricing because a more accurate forecast will allow for a more accurate model to price financial assets. Currently the VIX is used as a measure of volatility in the market as a whole, but a major issue with this is that it is calculated based on manually traded options on the S&P 500. Another method of forecasting volatility is that of solv...

The time evolution of aggregate economic variables, such as stock prices, is affected by market expectations of individual investors. Neo-classical economic theory assumes that individuals form expectations rationally, thus enforcing prices to track economic fundamentals and leading to an efficient allocation of resources. However, laboratory experiments with human subjects have shown that indi...

There is an old Wall Street adage goes, ‘‘It takes volume to make price move”. The contemporaneous relation between trading volume and stock returns has been studied since stock markets were first opened. Recent researchers such as Wang and Chin [Wang, C. Y., & Chin S. T. (2004). Profitability of return and volume-based investment strategies in China’s stock market. Pacific-Basin Finace Journal...

Numerous academic studies examine equity risk premium predictability based on various macroeconomic variables and price and volume based variables from stock market. In this article, we extend the frontier of the set of predictors from macroeconomic variables and stock market variables to foreign exchange market variables due to various reasons. Firstly, foreign exchange market reflects various...

T his study examines how oil price shocks interact with the stock market index within a nonlinear autoregressive distributed lag model in Iran. Based on quarterly data for the period from 1991 to 2017, the findings revealed statistically significant evidence of short-run and long-run asymmetric behavior of stock market index in response to the positive a...

Foreseeing assumes an indispensable part in setting exchanging methodology or deciding the ideal opportunity to purchase sell stock. We propose element combination long transient memory-convolutional neural organization (LSTM-CNN) model, which joins highlights gained from various presentations of similar information, i.e., stock timetable and outline pictures, anticipate costs. The proposed mod...