Trend-Following Filters – Part 5

By |February 15th, 2022|Research Insights, Trend Following, Trend-Following Course, Guest Posts|

There are two general types of Kalman filter models: steady-state and adaptive. A steady-state filter assumes that the statistics of the process under consideration are constant over time, resulting in fixed, time-invariant filter gains. The gains of an adaptive filter, on the other hand, are able to adjust to processes that have time-varying dynamics, such as financial time series which typically display volatility and non-stationarity.

Trend-Following with Valeriy Zakamulin: Moving Average Basics (Part 1)

By |July 14th, 2017|Trend Following, Trend-Following Course, Introduction Course, Guest Posts, Investor Education|

One of the basic principles of technical analysis is that ``prices move in trends". Traders firmly believe that these trends can be identified in a timely manner and used to generate profits and limit losses. Consequently, trend following is the most widespread market timing strategy; it tries to jump on a trend and ride it. Specifically, when stock prices are trending upward (downward), it's time to buy (sell) the stock. Even though trend following is very simple in concept, its practical realization is complicated. One of the major difficulties is that stock prices fluctuate wildly due to imbalances between supply and demand and due to constant arrival of new information about company fundamentals. These up-and-down fluctuations make it hard to identify turning points in a trend. Moving averages are used to ``smooth" the stock price in order to highlight the underlying trend.

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