Stock market volatility is the systematic risk faced by investors who hold a market portfolio (e.g., a stock market index fund). Schwert (1989b) has undertaken an extensive study of stock market volatility, using historical data back to the 19th century. Some of his major findings are illustrated in Figure 1, which plots quarterly stock market volatility for the post-World War II period.1 The f...

We use daily price indices obtained from the Morgan Stanley Capital International to construct realized volatility for 18 individual stock markets, including the US, and the world stock market. In contrast with the CAPM, we find that volatility by itself does not forecast excess returns in most countries; however, it becomes a significant predictor when combined with the US consumptionwealth ra...

Stock market volatility is the systematic risk faced by investors who hold a market portfolio (e.g., a stock market index fund). Schwert (1989b) has undertaken an extensive study of stock market volatility, using historical data back to the 19th century. Some of his major findings are illustrated in Figure 1, which plots quarterly stock market volatility for the post-World War II period.1 The f...

This paper discusses the application of support vector machine (SVM) in stock price change trend forecasting. By reviewing prior research, thirteen technical indicators are defined as the input attributes of SVM. By training this model, we can forecast if the stock price would rise the next day. In order to make best use of market information, analyst recommendations about upgrading stocks are ...

Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is ...

With the characteristics of nonlinearity and randomness, stock prices change with a strong feature of disorder, and its mathematical model is often complex which makes it difficult to accurately determine the price or contain chaos. One single forecast method can only describe the stock price information partially, but fails to reflect the overall picture. In this paper, a method of Radial Basi...

Stock trend forecasting is a one of the main factors in choosing the best investment, hence prediction and comparison of different firms’ stock trend is one method for improving investment process. Stockholders need information for forecasting firm’s stock trend in order to make decision about firms’ stock trading. In this study stock trend, forecasting performs by data mining algorithm. It sho...

With the development of global economic integration, stock market occupies an important position in economy. Accurately predict is social and value, has huge amounts data sources, such features to capture hidden rule market, associated accurately proposed new challenge, with vigorous mining technology sample unceasingly rich, The value more fully recognized widely concerned, business analysis b...

The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock pr...

Since analysis of time series is so hard to do, a support vector machine can be more proper for the purpose of forecasting in field of stock market. The support vector machine (SVM) can explore suitable knowledge from so vague data, which usually is necessary to interpret the financial data. But single SVM cannot achieve accurate results. Subsequently, in this paper a combinational intelligent ...