A Comparative Study of SVM and LSTM Deep Learning Algorithms for Stock Market Prediction

The comparison between domestic focusing and domestic diversifying stock exchange mergers (Panel A1 and C Tables 5) seems to indicate that stock exchange’s diversification might be

strategies, keeping their investment proportion in the stock fixed at 20%. The rest of the investors we took as in Figure ID with memory span and preference chosen randomly.

Therefore, following this line of literature, we empirically examine whether the price impact asymmetry between block purchases and sales in the Saudi Stock Market, can be

Frino et al (2003) and Gregoriou (2008) find that the asymmetric price impact of block purchases and sales is diminished in the DJIA and the LSE respectively, when price impacts

However, if (as a result of categorisation) sector reclassification moves stocks into sectors that are subject to different levels of investor interest and demand, then

Keywords: Expectations, Financial Crisis, Stock-Market, Second-Order-Beliefs, False Consensus Effect, Bias Blind Spot, Investor Sentiment.. ∗ We are grateful to Barclays

In this research we evaluate the potential for automating structure learning and hyperparameter selection for neural networks for stock market trend prediction with a

Le messager économique et financier, a purely financial weekly, published a lot of qualitative and quantitative informations on listed stocks (balance sheets, coupons payments,