Economic factors and the stock market: a new perspective

Article Abstract:

Studies on the predictability of stock returns commonly use linear models. However, one particular study used the artificial neural network methodology in conjunction with the linear model in explaining the link between economic factors and stock returns. A significant advantage of using network networks is their capability of generating flexible mapping between inputs and outputs. It was concluded that both models can predict significant market timing ability although linear regression models showed higher percentage of predicting profitability based on the switching strategy.

Securities Exchanges, Forecasting, Usage, Computer networks, Stock-exchange, Stock exchanges, Exchanges, Neural networks, Return on investment, Stock price forecasting, Business forecasting, Rate of return, Linear models (Statistics)

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Assessing inefficiency in the S&P 500 futures market

Article Abstract:

Inefficiencies in the S&P 500 futures market can be more accurately predicted using a trading strategy that accounts for short-term volatility. Volatility is defined as the daily high, low, and closing price. A Box and Jenkins technique is used to account for the trend-countertrend component, and the resulting model is applied to a simulated range of figures comprising 250 market days. The S&P 500 futures market shows only slight inefficiencies, but a buy-and-hold trading strategy is not the best choice. Three ad hoc strategies are discussed.

Analysis, Case studies, Futures market, Futures markets, Efficient market theory, Standard and Poor's 500-Stock Price Index

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Forecasting volatility in commodity markets

Article Abstract:

Commodity prices have historically been the most volatile of all global asset prices. Therefore, price forecasts are reliable only as far as volatility is predictable. However, volatility in commodity exchanges is far from constant and must be predicted before price forecasts can be made. A model that incorporates both time series forecasts and investors' expectations is proposed. This model results in long-term volatility forecasts that are more accurate than those obtained from other methods.

Securities & Commodities Exchanges, Commodity exchanges, Forecasting

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