Worden Stochastics Definition
The Worden Stochastics indicates the percentile rank that a recently closed price obtains in comparison to other closing prices over a specific period of time. Traders consider using this indicator for knowing if a specific security needs to be overbought or oversold.
A Little More on What is Worden Stochastics
Peter Worden devised the Worden Stochastics indicator with a view to identify a new trading range more efficiently as compared to conventional stochastics that records low, high and closing prices. The Worden Stochastics indicator considers assigning ranks for avoiding over-weighting in outlier intervals. This ultimately offers a more reliable representation of the trading range.
One can use the following equation for calculating the Worden Stochastics: ( 100 / n – 1 )( Rank ). Here, n represents the total number of closing values present in the range, Rank refers to the position that the closing price has on a list which is organized in increasing order in terms of value.
All stochastics indicators such as Worden Stochastics determine the position of the close in relation to the range over a specific time interval. Traders consider using such readings for knowing if a specific security has the potential to be traded at oversold or overbought levels. Also, traders may seek bullish or bearish variations between the price of the security and stochastic levels, especially if the break through appears through the central point of the indicator.
This Worden Stochastics indicator uses 1 to 5 as default settings producing three final Disney buy and sell cycles for a period of 4 months. Though it tends to make a reverse turn at the oversold position in April, but the price keeps to cut sideways to reduce in slow price action. The indicator lowers down in the initial phase of May, recording a double bottom reversal that converts to a rally wave that prolongs for around three weeks. A crossover in the mid of May calls for an exclusive sell cycle that is exactly verified by price retracting to experiment with new support close to 105.
The indicator reaches a high in the initial phase of June and price reacts in response to a rally thrust that records a brand new high. The subsequent crossover takes place on a reversal day that offers a pullback later in June. However, it becomes impossible for the bullish crossover to push the price upward until the indicator arrives at the overbought position when the pattern gains strength, creating a sideways oscillation generally found in the securities that are in trend. The advance finishes in the early phase of August, leading to a dip that further reduces Worden Stochastics with the overbought pattern that happens for the very first time since the start of July. The price reacts with a decline in 5 days.
References for “Worden Stochastics”
Academic research for “Worden Stochastics”
Applications of Hilbert–Huang transform to non‐stationary financial time series analysis, Huang, N. E., Wu, M. L., Qu, W., Long, S. R., & Shen, S. S. (2003). Applications of Hilbert–Huang transform to non‐stationary financial time series analysis. Applied stochastic models in business and industry, 19(3), 245-268.
Applications in dynamic stochastic general equilibrium macroeconomics, Stevens, A. (2013). Applications in dynamic stochastic general equilibrium macroeconomics (Doctoral dissertation, Ghent University).
Stochastic adjustment models of labour demand, Pfann, G. A. (1989). Stochastic adjustment models of labour demand (Doctoral dissertation, Maastricht University).
MEASURING HERD BEHAVIOR IN FINANCIAL MARKETS, LINDERS, D. MEASURING HERD BEHAVIOR IN FINANCIAL MARKETS