Читать книгу Reductive-Investment Analysis - Fuad Akhundov - Страница 5

Module 1

The algorithm of the Reductive-Investment Analysis consists of a sequence of clearly aligned Linear Regression Channels, their modelling relative to the current trend. The modelling process starts with the identification of price extremes. For a bearish trend the counting is started from the high price to the low price, from A to B (Fig. 2), while the bullish trend, on the contrary – from the minimum to maximum. Price extremes are the starting points to fix vertical baselines. Vertical baselines are the stationary levels to which the connection points of the Linear Regression Channel are attached (Fig. 3). First of all, the stationary Linear Regression Channel is fixed to the baselines, the minimum of the median line of which (D point, Fig. 4) possesses the function of the point relative to which the breakout line is drawn. Further constructions of the regression channels happen in the process of price consolidation of the asset in question. According to the market laws, after a significant price movement from extreme to extreme, temporary consolidation is sure to occur. This is a state where the prices of stock assets do not have a clearly defined trend and move in a narrow price range due to the fact that the supply and demand for a particular stock asset in the market are approximately equal. In the process of such price consolidation, the next Linear Regression Channel is used, sliding with the price and is intended for visual monitoring and identification of possible regression deviations (Fig. 4). With the usual price dynamics, the median line of the Linear Regression Channel moves evenly with the price, simultaneously updating current extremes with it. But it sometimes happens that during consolidation periods, after significant unidirectional price movements from extremum to extremum, the lateral correction, with the oscillation dynamics different from most cases, separates the price direction and the median line of the Linear Regression Channel (Fig. 4). In such cases, a financial tool is taken for development, designed to identify a suitable investment environment by visual modelling and tracking the general view of price movements. Further, when during prolonged consolidation the median connection point of the sliding Linear Regression Channel reaches the right baseline, it is fixed in this position for further analysis of the current situation. If, with such a fixation, the pole of the median regression line “L” of the sliding LRC (Fig. 4), deviating from the price directivity, breaks through the Breakout Line and the Extremum Line, then a fact occurs signalling a certain deviation from the ordinary norm. Such a deviation is a consequence of the fact that unidirectional trading in the financial tool under research has reached a certain standard where market saturation occurred, or some uncertainty appeared among market participants, which may turn prices in the opposite direction. Due to the fact, that the Breakout Line and the Extremum Line are broken through by the “L” pole of the median line of the sliding LRC (Fig. 4), the “L” level becomes a historical reminder with a further corresponding conjuncture of the monitored object necessary for subjection. After identifying a non-standard situation and fixing a sliding LRC with a clear price divergence from the regression model, further monitoring of the current prices relative to the next sliding-indication LRC is continued (Fig. 5). The need for the next sliding LRC is a clear demonstration of the current situation on the road to achieving an investment-friendly event. This event is favourable for investments when the prices of the Orienting line (Fig. 6, 7) correspond to the range of 75%-85% of the backward level relative to the trend under research, with a corresponding regression model (Fig. 8). This Orienting line is drawn relative to the stationary LRC, the “W” point of the Orienting point, which is determined by the crossing of the Channel Border by the stationary LRC and the Baseline (Fig. 6, 7). The importance of this backward distance lies in the fact that it is at such amplitude that the properties of the regression models are revealed that clearly indicate any changes in the general trend of the observed financial tool. This is necessary to minimize the risks, as well as for a timely and adequate response to force majeure. The following regression construction carries with it the purpose of a visual indication of the above circumstances and changes (Fig.9). For this, the calculated Linear Regression Channel is fixed, the connection points of it are attached to the vertical baselines from right to left – first the “R” point, then the median connection point “N” (Fig. 9). This model of LRC is necessary to draw the Reference Line (Fig. 9), which is determined relative to the connection point “R” of the calculated Linear Regression Channel and carries with it the role of an indication level. With the non-standard angular directivity of the calculated LRC, in the direction opposite to the trend direction, the Reference line is fixed relative to the “T” point (Fig. 10). The Reference line clearly shows the area of location of the “K” pole of the trend line of the indication LRC (Fig. 5), when the prices of the Orienting line and 75% -85%, favourable for investments, reach the range level. If the “K” pole is located in the same area as the “L” market checklist (Fig. 4) and with the same vectorial orientation, then this is one of the confirmations of the favourableness of the event for investment actions (Fig. 5). Such a state visually reveals a discrepancy in the current trend, relative to regression models in comparison with previous models at the same prices. This discrepancy indicates changes in the interests of the market participants, according to the traded financial tool, thereby signalling the maturing of a favourable environment for investment. Also, the connection point “S”