Stock exchange

About: Stock exchange is a research topic. Over the lifetime, 39566 publications have been published within this topic receiving 612044 citations.

Energy Shocks and Financial Markets: Nonlinear Linkages

Abstract: This paper examines the dynamic linkages between oil prices and the stock market. Prior work argues that daily oil futures price changes and the S&P 500 stock index movements are not related. This conclusion could be due to the fact that only linear linkages have been examined. Relying on nonlinear causality tests, this study provides evidence that oil shocks affect stock index returns, which is consistent with the documented influence of oil on economic output. Moreover, the study finds that the linkage between oil prices and the stock market was stronger in the 1990s.

On-line portfolio selection using multiplicative updates

Abstract: We present an on-line investment algorithm that achieves almost the same wealth as the best constant-rebalanced portfolio determined in hindsight from the actual market outcomes. The algorithm employs a multiplicative update rule derived using a framework introduced by Kivinen and Warmuth. Our algorithm is very simple to implement and requires only constant storage and computing time per stock in each trading period. We tested the performance of our algorithm on real stock data from the New York Stock Exchange accumulated during a 22-year period. On these data, our algorithm clearly outperforms the best single stock as well as Cover's universal portfolio selection algorithm. We also present results for the situation in which the investor has access to additional “side information.”

An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange

Abstract: Stock market prediction is important and of great interest because successful prediction of stock prices may promise attractive benefits. These tasks are highly complicated and very difficult. In this paper, we investigate the predictability of stock market return with Adaptive Network-Based Fuzzy Inference System (ANFIS). The objective of this study is to determine whether an ANFIS algorithm is capable of accurately predicting stock market return. We attempt to model and predict the return on stock price index of the Istanbul Stock Exchange (ISE) with ANFIS. We use six macroeconomic variables and three indices as input variables. The experimental results reveal that the model successfully forecasts the monthly return of ISE National 100 Index with an accuracy rate of 98.3%. ANFIS provides a promising alternative for stock market prediction. ANFIS can be a useful tool for economists and practitioners dealing with the forecasting of the stock price index return.

Business Cycles, Financial Crises, and Stock Volatility

Abstract: This paper shows that stock volatility increases during recessions and financial crises from 1834-1987. The evidence reinforces the notion that stock prices are an important business cycle indicator. Using two different statistical models for stock volatility, I show that volatility increases after major financial crises. Moreover. stock volatility decreases and stock prices rise before the Fed increases margin requirements. Thus, there is little reason to believe that public policies can control stock volatility. The evidence supports the observation by Black [1976] that stock volatility increases after stock prices fall.