BMO is a notable component of the Canadian TSX 60. In July 2020, the stock was close to the 50 mark, with the Jiseki trending downward. By September 2020, Jiseki’s drop below 20 hinted at a significant low point for the stock. Remarkably, over the next two years, BMO’s stock reversed its fortunes, escalating to a high above 150 by March 2022.
Jiseki, known for its oscillatory nature, is adept at identifying both bullish and bearish trends, demonstrating a complete cycle from a low to a high, from value to growth. This cycle was clearly observed in BMO’s case, as its Jiseki fell below the 80 mark in July 2023, signaling an onset of negative momentum. Since that dip, BMO has seen a period of stagnation and decline.
The ongoing negative trend in Jiseki suggests that BMO is still navigating through challenges. To gain more insights on Jiseki and its future projections, subscribing to our newsletter is recommended.
Jiseki
“Jiseki” is a Japanese word, which means evidence. Every component in a group (benchmark, mandate, sector) gets a multi durational (weeks, months, quarters) probability scores from near ~ 0 to 1. The scores measure and map the component’s performance across statistical factors (returns, volatility, autocorrelation etc.) as it oscillates between 0 to 1 signifying statistical value and statistical growth, respectively. The scores allow interpretation and anticipation of the component’s behavior as it shifts from non-normality (persistence) to normality (reversion) while interacting with other components in the group, in time.
Machine Learning
The Jiseki framework assumes markets as a dynamic complex system operating as a Markov chain, where components move up or down the chain when they cross or fail at a certain threshold level of probability.
Markov Chain
Markov chain allows for mapping several dominant transition styles which have universal (common) and unique characteristics connected to market regime, asset class, region, sectors, group size, risk preference, themes, factors etc. Support Vector Machines are trained and used to anticipate probabilities which lead to better selections, styles, and insights both on the long and short side.
Disclaimer: This is an educational post and is not for trading or investing purpose. Stock selection is known to underperform the benchmark. And past performance can not guarantee future returns.