![]() ![]() Naved and Srivastava (2015) investigated the profitability of moving averages trading strategy in the Indian stock market. Several robustness checks re-examining the data from the Dow Jones index. asset classes and LeBaron (1999) by implementing Similar findings were found by Bauer and Dahlquist (2001) by measuring monthly, quarterly, and annual market-timing strategies for six major U.S. The study finds that the results in Brock et al appear to be robust against data-snooping, but the superior performance of the moving strategy was not significant after performing out-of-sample The forecast abilities of simple trading rules documented in the study have later been both been supported and questioned by several studies. (2) markets are efficient and the predictable variation can be explained by time-varying equilibrium returns. (1) market inefficiency in which prices takes swings from their fundamental values and The study claims that stock returns are predictable and suggested two competing explanations (1992) presents compelling evidence of the simple moving average strategy outperforming the market by using data samples from the Dow Jones index. In an early widely discusses study Brock et al. Trading rules based on historical data such as the moving average strategy hasīeen a widely discussed topic in the field of finance since the introduction of the concept of efficient capital markets. The basic principle in technical analysis involves identifying trends and using them to generate forecast signals. In this literature review we will discuss an overview of some of the studies on the performance of the trading strategies. ![]() Parameters based on preprogrammed instructions.Īlgorithmic trading provides a more systematic approach to active trading than methods based on trader intuition or instinct Most algo-trading today is High Frequency Trading (HFT), which attempts to capitalize on placing a large number of orders at rapid speeds across multiple markets and multiple decision ![]()
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