How Does the Buffett Indicator Work in China?

This study investigates whether the Buffett indicator can be used to make investment decisions in China. The investigation has two approaches. First, this study determines the scaling relationship between the Buffett Indicator and the GDP in China. Previous research and findings in this research regarding the scaling relationship can help international investors when comparing China with a different country as potential investment opportunities. Second, this study also examines whether the Buffett Indicator, the P/E ratio and composite models including the Buffett Indicator can be used as tools for international investors in predicting the Shanghai Index and making investment decisions for the Chinese stock market. The analysis is based on Chinese data from the World Bank, the National Bureau of Statistics of China, the Federal Reserve and the Yahoo Finance. This study finds that there is a sublinear relationship between the Buffett indicator and GDP in China and that the composite models which include the Buffett Indicator perform better to forecast the stock market in China than other indicators

Charting Technical Trading Rules and the Lottery of Technical Analysis: Empirical Evidence from Foreign Exchange Market

We use trend-following, trend continuation and trend reversal pattern recognition techniques to apply technical charting rules to trading seven major currency pairs for the period of 1999 through early 2007. Our results suggest that the persistent popularity of technical analysis among practicing traders may be the result of a “lottery” wherein most of the participants end up with zero profits. However, the rest of the participants are much more likely to end up winning rather than losing. In this way, the popularity of technical trading rules may co-exist with the validity of market efficiency hypothesis.market efficiency; technical analysis; forecasting; foreign exchange markets

Charting Technical Trading Rules and the Lottery of Technical Analysis: Empirical Evidence from Foreign Exchange Market

We use trend-following, trend continuation and trend reversal pattern recognition techniques to apply technical charting rules to trading seven major currency pairs for the period of 1999 through early 2007. Our results suggest that the persistent popularity of technical analysis among practicing traders may be the result of a “lottery” wherein most of the participants end up with zero profits. However, the rest of the participants are much more likely to end up winning rather than losing. In this way, the popularity of technical trading rules may co-exist with the validity of market efficiency hypothesis

Charting Technical Trading Rules and the Lottery of Technical Analysis: Empirical Evidence from Foreign Exchange Market

We use trend-following, trend continuation and trend reversal pattern recognition techniques to apply technical charting rules to trading seven major currency pairs for the period of 1999 through early 2007. Our results suggest that the persistent popularity of technical analysis among practicing traders may be the result of a “lottery” wherein most of the participants end up with zero profits. However, the rest of the participants are much more likely to end up winning rather than losing. In this way, the popularity of technical trading rules may co-exist with the validity of market efficiency hypothesis

Forecasting the New York stock exchange composite index with past price and interest rate on condition of volume spike

We identify trading volume spikes through use of the template matching technique from statistical pattern recognition. For those trading days meeting the condition signifying volume spike recognition, application of linear regression models the future change in price using historical price and prime interest rate values. Also, we train a nonlinear neural network model and use it as a basis for simulated trading, which includes consideration of transaction costs and cash dividends. We illustrate and test with New York Stock Exchange Composite Index data for the period from 1981 to 1999. Results are positive, robust, systematic, economically significant, and informative as to the role of trading volume in the stock market mechanism. (C) 2004 Elsevier Ltd. All fights reserved