Historical stock price data is a massive amount of time-series data with little-to-no noise. From all this relatively clean data it should be possible to predict accurate estimates of future stock prices. A number of different supervised learning methods have been tried to predict future stock prices, both for possible monetary gain and because it is an interesting research question. Examples o...

STOCK market price behavior has been studied extensively. It is influenced by a myriad of factors, including political and economic events, among others, and is a complex nonlinear time-series problem. Traditionally, stock price forecasting is performed based on technical analysis, which focuses on price action, which is the process of finding patterns in price history. More recently, research ...

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...

Commodity price always related to the movement of stock market index. However real economic time series data always exhibit nonlinear properties such as structural change, jumps or break in the series through time. Therefore, linear time series models are no longer suitable and Markov Switching Vector Autoregressive models which able to study the asymmetry and regime switching behavior of the d...

We investigate stock option exercise decisions by over 50,000 employees at seven corporations. Controlling for economic factors, psychological factors inuence exercise. Consistent with psychological models of beliefs, employees exercise in response to stock price trends—exercise is positively related to stock returns during the preceding month and negatively related to returns over longer hori...

In many emerging stock markets, price limits are imposed on the magnitude of daily price movements. Price limit advocates claim that such limits serve as "circuit breakers" and decrease stock price volatility. Critics argue that the limits cause supply and demand imbalances in trading. Consequently, they prevent immediate corrections in price and increase the volatility of the opening return on...

Article terms can move stock prices. By analyzing verbs in financial news articles and coupling their usage with a discrete machine learning algorithm tied to stock price movement, we can build a model of price movement based upon the verbs used, to not only identify those terms that can move a stock price the most, but also whether they move the predicted price up or down.

1. ASSETS AND DERIVATIVES. Assets of all sorts are traded in financial markets: stocks and stock indices, foreign currencies, loan contracts with various interest rates, energy in many forms, agricultural products, precious metals, etc. The prices of these assets fluctuate, sometimes wildly. As an example, Figure 1 shows the price of IBM stock within a single day. The picture would look more or...