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.
Time Series Momentum in the US Stock Market: Empirical Evidence and Theoretical Implications Valeriy Zakamulin and Javier GinerWorking paper, University of Agder and University of La LagunaA version of this paper can be found here [...]
Trend-following strategies are a lot like stock-picking strategies -- there are endless approaches and varying levels of complexity. In this short piece, we explore the decision related to implementing basic trend-following strategies on either a [...]
Trend-following is something I've struggled with for years -- always felt like voodoo magic and data-mining. That said, I finally came around to appreciating the practice after a ton of research replication efforts, independent research. [...]
In our final blog post, that finishes the trend-following series, we briefly review the results of the forward-tests of the profitability of various trend following rules in different financial markets: stocks, bonds, currencies, and commodities. [...]
The Standard and Poor's (S&P) 500 index is a value-weighted stock index based on the market capitalizations of 500 large companies in the US. This index was introduced in 1957 and intended to be a [...]
The difficulty in testing the profitability of trend-following rules stems from the fact that the procedure of testing involves either a single- or multi-variable optimization. Specifically, any trading rule considered in Part 3 has at [...]
We consider an investor and a financial market that consists of only two assets: one risky asset and one safe (or risk-fee) asset. An example of a risky asset is an investable stock market index. [...]
In our context, a technical trading indicator can be considered as a combination of a specific technical trading rule with a particular moving average of prices. In two preceding blog posts we showed that there [...]
A trend following strategy is based on switching between a financial asset and cash depending on whether the asset prices trend upward or downward. Specifically, when the strategy identifies that prices trend upward (downward), it [...]
In this post we aim to give an overview of some specific types of moving averages. Specifically, we cover "ordinary" moving averages and mention some examples of exotic moving averages.
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.