be performed on entire exchanges over decades of historic data in very short amounts of time. The use of computers does have its drawbacks, being limited to algorithms that a computer can perform. Several trading strategies rely on human interpretation, and are unsuitable for computer processing. Only technical indicators which are entirely algorithmic can be programmed for computerized automated backtesting. Combination with other market forecast methods John Murphy states that the principal sources of information available to technicians are price, volume and open interest. Other data, such as indicators and sentiment analysis, are considered secondary. However, many technical analysts reach outside pure technical analysis, combining other market forecast methods with their technical work. One advocate for this approach is John Bollinger, who coined the term rational analysis in the middle 1980s for the intersection of technical analysis and fundamental analysis. Another such approach, fusion analysis, overlays fundamental analysis with technical, in an attempt to improve portfolio manager performance. Technical analysis is also often combined with quantitative analysis and economics. For example, neural networks may be used to help identify intermarket relationships. Investor and newsletter polls, and magazine cover sentiment indicators, are also used by technical analysts. Empirical evidence Whether technical analysis actually works is a matter of controversy. Methods vary greatly, and different technical analysts can sometimes make contradictory predictions from the same data. Many investors claim that they experience positive returns, but academic appraisals often find that it has little predictive power. Of 95 modern studies, 56 concluded that technical analysis had positive results, although data-snooping bias and other problems make the analysis difficult. Nonlinear prediction using neural networks occasionally produces statistically significant prediction results. A Federal Reserve working paper regarding support and resistance levels in short-term foreign exchange rates "offers strong evidence that the levels help to predict intraday trend interruptions", although the "predictive power" of those levels was "found to vary across the exchange rates and firms examined". Technical trading strategies were found to be effective in the Chinese marketplace by a recent study that states, "Finally, we find significant positive returns on buy trades generated by the contrarian version of the moving-average crossover rule, the channel breakout rule, and the Bollinger band trading rule, after accounting for transaction costs of 0.50%." An influential 1992 study by Brock et al. which appeared to find support for technical trading rules was tested for data snooping and other problems